esa-sceva/satcom-chunk-collection huggingface dataset:
# BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization
## 4 Experiments
### Baseline Models  
To evaluate the effectiveness of BART-IT, we compare it against the following strong baselines for _abstractive_ summarization:  
* **IT5**[9] is a state-of-the-art sequence-to-sequence model that relies on the same architecture proposed by T5 [22] but is trained on the Italian language. This model is trained on the same dataset used for the pre-training of BART-IT and is fine-tuned on the same summarization datasets used for its evaluation. The model is available in three different sizes: _small_, _base_, and _large_. We use the _base_ version of the model since it is the most similar in terms of the number of parameters compared to the proposed model (i.e., 220 million parameters);
* **mBART**[10] is a multilingual sequence-to-sequence model that uses the same architecture of the original BART model. It is trained on a multilingual corpus of 25 languages. By construction, the model size for the base model is more than four times larger than the model size of BART-IT (i.e., 610 million parameters). Even in this case, the model is fine-tuned on the same summarization datasets used for the evaluation of BART-IT;
* **mT5**[11] is a multilingual sequence-to-sequence model that uses the same architecture of T5 [22] and is trained on a multilingual corpus of 101 languages. The model is available in five different sizes: _small_, _base_, _large_, _xlarge_, and _xxlarge_. We use the _base_ version of the model that has 390 million parameters (e.g., more than 2.5 times larger than the model size of BART-IT). Similar to the previous models, the model is fine-tuned on the same datasets used for the evaluation of BART-IT.  
The summarization models are also compared against well-established _extractive_ summarization baselines to evaluate the effectiveness of the abstractive models in generating summaries that are more fluent and coherent than the extractive summaries.  
* **Lead-K** is a baseline method that extracts the first \(k\) sentences of the input document. In our experiments, we set \(k\) to 2;
* **LexRank**[38] is an established summarization method that extracts the most important sentences from the input document by first modeling the input document as a graph and then computing the PageRank score [39] of each sentence. The similarity between two sentences is computed using the IDF-weighted cosine similarity between the TF-IDF vectors of the sentences;
* **TextRank**[40] is a baseline method that, similar to LexRank, extracts the most important sentences of the input document by modeling the input document as a graph and computing the PageRank score [39] of each sentence. The pairwise sentence similarity is computed by exploiting the word-level overlap between each pair of sentences.  
All the models are evaluated using both the ROUGE and BERTScore metrics to evaluate the effectiveness of the models in generating fluent and coherent summaries.
# Bitcoin as a Safe Haven during COVID-19 Disease
## 3 Data and Methods
### Results
#### 3.2.2 DCC Model Results: Dynamic Conditional Correlations
Additionally, the maximum values of the correlation computed in the 16 August +14, 20 October +14, and 14 January +14 _event windows_ (0.1262, 0.1456, and 0.1474, respectively) are slightly lower than the maximum value computed in the _July-March 4_ time window (0.2078). All this agrees with the results of the regression model, which gives indications of safe haven for Bitcoin in the same _event windows_ against the downward movement of the SSE A Share Index, in the three _event windows_ defined around 16 August 2020 and 20 October 2020 dates, and in the first two _event windows_ defined around 14 January 2021 date (the coefficients of the _retSS:COVID_ variable, in the intervals just mentioned, are all negative). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & **SS** & **EURN** & **IPAC** & **DX** & **GC** & **DAX** & **GSPC** & **FTSE** & **N225** \\ \hline \multirow{3}{*}{_July-March 4_} & min & \(-\)0.0203 & 0.0525 & \(-\)0.0020 & \(-\)0.2559 & 0.0871 & 0.0248 & 0.0157 & 0.0033 & \(-\)0.0656 \\  & median & 0.1194 & 0.1428 & 0.1737 & \(-\)0.1552 & 0.2408 & 0.1747 & 0.1548 & 0.1369 & 0.0273 \\  & max & 0.2078 & 0.2674 & 0.2177 & \(-\)0.1131 & 0.2903 & 0.2465 & 0.2379 & 0.1972 & 0.0680 \\ \hline \multirow{3}{*}{5 March +14} & min & 0.1296 & 0.1543 & 0.1897 & \(-\)0.2105 & 0.2190 & 0.1842 & 0.1685 & 0.1380 & 0.0494 \\  & median & 0.2271 & 0.2704 & 0.2884 & \(-\)0.1785 & 0.2295 & 0.2935 & 0.2511 & 0.2548 & 0.1512 \\  & max & 0.2854 & 0.4040 & 0.4634 & \(-\)0.0463 & 0.4127 & 0.4100 & 0.3662 & 0.4093 & 0.1988 \\ \hline \multirow{3}{*}{16 August +14} & min & 0.0841 & 0.1104 & 0.1698 & \(-\)0.2213 & 0.2723 & 0.1581 & 0.1498 & 0.0996 & 0.0161 \\  & median & 0.1191 & 0.1316 & 0.1859 & \(-\)0.1904 & 0.2802 & 0.1785 & 0.1582 & 0.1331 & 0.0266 \\  & max & 0.1262 & 0.1474 & 0.1931 & \(-\)0.1802 & 0.3017 & 0.1879 & 0.1657 & 0.1518 & 0.0327 \\ \hline \multirow{3}{*}{20 October +14} & min & 0.1136 & 0.1378 & 0.1996 & \(-\)0.2240 & 0.2716 & 0.1607 & 0.1499 & 0.0982 & 0.0406 \\  & median & 0.1228 & 0.1550 & 0.2129 & \(-\)0.2094 & 0.2768 & 0.1812 & 0.1690 & 0.1309 & 0.0503 \\  & max & 0.1456 & 0.1824 & 0.2260 & \(-\)0.1983 & 0.2885 & 0.1951 & 0.1787 & 0.1494 & 0.0643 \\ \hline \multirow{3}{*}{12 November +14} & min & 0.1353 & 0.1380 & 0.2056 & \(-\)0.2307 & 0.2045 & 0.1794 & 0.1545 & 0.1410 & 0.0507 \\  & median & 0.1407 & 0.1482 & 0.2173 & \(-\)0.2089 & 0.2767 & 0.1912 & 0.1720 & 0.1469 & 0.0706 \\  & max & 0.1579 & 0.1670 & 0.2470 & \(-\)0.1895 & 0.3056 & 0.2106 & 0.2014 & 0.1560 & 0.0777 \\ \hline \multirow{3}{*}{14 January +14} & min & \(-\)0.0298 & 0.1179 & 0.1949 & \(-\)0.1678 & 0.1850 & 0.2015 & 0.1534 & 0.2003 & 0.0394 \\  & median & 0.0314 & 0.1546 & 0.2105 & \(-\)0.1313 & 0.2246 & 0.2263 & 0.1606 & 0.2260 & 0.0533 \\ \cline{1-1}  & max & 0.1474 & 0.1649 & 0.2339 & \(-\)0.1110 & 0.2396 & 0.2794 & 0.1995 & 0.2824 & 0.0900 \\ \hline \end{tabular}
\end{table} 
Table 10: Statistics of dynamic conditional correlations between the pairs Bitcoin and stock indexes.
## 3 3-D Deformable Object Models
### Curved blood vessel  
A curved section in a blood vessel tree is represented as a two-connected-cylinder model as shown in Figure 2(b). The first cylinder corresponds to the parent vessel (indicated by \"1\" in the figure) that is nearer to a heart in respect of its blood flow, and the second its child vessel (\"2\"). The radius of the \(i\)-th cylinder (\(i=1,2\)) is denoted by \(r_{i}^{B_{c}}\), and its zenith and azimuth angles are denoted by \(\theta_{i}^{B_{c}}\) and \(\phi_{i}^{B_{c}}\), respectively. The connection position of the two cylinders is denoted by \(\mathbf{x}^{B_{c}}=(x^{B_{c}},y^{B_{c}},z^{B_{c}})\). The X-ray attenuation of the model and that of its surrounding area are denoted by \(\alpha_{m}^{B_{c}}\) and \(\alpha_{a}^{B_{c}}\), respectively. From these parameters, two additional parameters are calculated: a difference in section area between the cylinders, \(\delta^{B_{c}}\), and an angle between the cylinders, \(\psi^{B_{c}}\). Because the parameters \(r_{1}^{B}\), \(\delta^{B_{c}}\) and \(\psi^{B_{c}}\) are more essential to describe a curved blood vessel, the model is denoted by \(o^{B_{c}}(r_{1}^{B},\delta^{B_{c}},\psi^{B_{c}};\mathbf{x}^{B_{c}},r_{2}^{B_{c}},\)\(\theta_{1}^{B_{c}},\phi_{1}^{B_{c}},\theta_{2}^{B_{c}},\)\(\phi_{2}^{B_{c}},\alpha_{m}^{B_{c}},\alpha_{a}^{B_{c}})\).  
The probability of appearance of the curved blood vessel model is defined by  
\[p\left(o^{B_{c}}(r_{1}^{B},\delta^{B_{c}},\psi^{B_{c}};\cdots)\right) \tag{2}\] \[= P^{B_{c}}\cdot g(r_{1}^{B};\mu_{r1}^{B}(\mathbf{x}^{B_{c}}),\sigma_{r 1}^{B_{c}})\cdot\] \[g(\delta^{B_{c}};\mu_{\delta}^{B_{c}},\sigma_{\delta}^{B_{c}}) \cdot g(\psi^{B_{c}};\mu_{\psi}^{B_{c}},\sigma_{\psi}^{B_{c}})\]  
where \(P^{B_{c}}\) is the probability of the set of all possible curved sections in blood vessel trees.  
In Equation (2), the mean radius of blood vessels \(\mu_{r1}^{B}(\mathbf{x}^{B_{c}})\) is defined as being the function of the position \(\mathbf{x}^{B_{c}}\) so as to represent a relation between the radius and position in a lung. Generally, blood vessels are thick in the central area of a lung, and they become thinner as they approach the peripheral area. The relation is formulated by  
\[\mu_{r1}^{B}(\mathbf{x}^{B_{c}})=\kappa_{1}^{B}exp\left(-\kappa_{2}^{B}\cdot|\mathbf{ x}^{B_{c}}-\mathbf{c}^{L}|\right) \tag{3}\]  
where \(\mathbf{c}^{L}\) is the central position of the boundary box that circumscribes a lung, \(|\mathbf{x}-\mathbf{c}|\) is the distance between \(\mathbf{x}\) and \(\mathbf{c}\). The coefficient values \(\kappa_{1}^{B}\) and \(\kappa_{2}^{B}\) are constant.  
In our previous study [30], we investigated statistical relations between the radii and positions of blood vessels in lung regions from population data, and obtained the 3-D distribution of the blood vessel radii. From several functions, the exponential function represented in Equation (3) is selected as the best-fit one to the distribution, and the coefficient values \(\kappa_{1}^{B}\) and \(\kappa_{2}^{B}\) are determined from the fitting result.
# Precise Orbit Determination of FY-3C with Calibration of Orbit Biases in BeiDou GEO Satellites
###### Abstract  
The emerging BeiDou navigation satellite system has contributed to global precise positioning and has recently moved toward space-borne applications. However, the contribution of BeiDou on LEO orbit determination applications is limited by the poor precision of the GEO satellite orbit and clock products. Current researches suggest that BeiDou GEO satellites should not be included in LEO precise orbit determination. Based on analyzing the characteristics of errors existing in BeiDou GEO orbit products, we propose a feasible method to mitigate the offsets in BeiDou GEO orbit errors by in-flight calibration of the systematic daily constant biases in the along-track and cross-track of BeiDou GEO satellites. The proposed method is investigated and validated using one entire month of onboard BDS data from the Chinese FY-3C satellite. The average daily RMS compared with the GPS-derived orbit indicates that our method achieves 6.2 cm three-dimensional precision. When compared to the solutions that disregard the GEO orbit errors scheme and roughly exclude the GEO scheme, the FY-3C orbit precision has been improved by 89.1% and 20.2%, respectively. The average daily RMS values of phase residuals are about one centimeter for solutions that exclude GEO and that estimate systematic biases in GEO orbits. The calibrated orbits of GEO with the decimeter level in along-track and cross-track can be reconstructed by correcting the orbit biases estimated in the FY-3C precise orbit determination. Statistics of the FY-3C orbit quality, observation residuals, and precision of the recovered GEO orbits demonstrate that calibration of daily orbit biases in GEO can improve the precision of LEO orbit determination and enhance the reliability of the solution.  
precise orbit determination; FY-3C; orbit biases; BeiDou; GEO 10382; 3390/rs10030382 19 January 2018; Accepted: 26 February 2018; Published: 1 March 2018
###### Abstract  
An assessment of the stability and performance of current controllers with harmonic compensators is presented for an inductive-capacitive-inductive (LCL)-filtered grid-connected inverter under distorted weak grid conditions. By using two typical current control schemes which are the direct current controller with the capacitor current-based active damping and integral-resonant state feedback current controller, the closed-loop system stability and current control performance are investigated in the presence of both uncertain grid impedance and distorted grid. Even though the controller stability has been investigated under weak grid in several studies, the stability assessment of the entire current control scheme, including the harmonic resonant controllers, still needs a further comprehensive investigation. The system stability is analyzed by obtaining the movement of the closed-loop poles in the discrete-time domain when the grid impedance varies. To fully study the impact of distorted weak grid condition on the LCL filters, three LCL filter parameter sets giving the resonance frequency in different frequency bands are chosen for the purpose of evaluating the system robustness and grid-injected current quality. In order to support the presented theoretical analyses, comprehensive simulation and experimental results based on 32-bit DSP TMS320F28335 to control 2 kVA grid-connected inverter are presented in terms of grid current quality and control stability in the environment of both uncertain grid impedance and distorted grid.  
Keywords:distorted weak grid; grid-connected inverter; grid impedance variation; harmonic compensator; LCL filter; stability assessment +
Footnote †: journal: Applied Sciences
# Gamification in IT Service Management: A Systematic Mapping Study
## 4 Results
### RQ3. How Has Gamification Been Implemented in ITSM Processes?
#### Game Components  
In terms of game components Table 13 shows that references [P02,P04,P07,P12] are the studies that incorporated more game components in their gamification proposals (around seven or eight game components each). Then, references [P03,P08,P09,P10,P14] mentioned four or five game components each. The rest of the studies did not adopt more than three game components. Figure 7 summarizes the amount of papers that made use of each game component. Points were the most cited component. Around 92% of primary studies (13 papers) mentioned using them in their strategies. The users of the gamified systems obtain points when they perform any of their activities or tasks. For example, in reference [P08], support groups agents received points when they performed incident management activities such as to record a new incident, to resolve the incident within the agreed times, to escalate the incidents to the next support level or to close the incident record, among others. Badges, Levels and Leaderboards were the second most-frequently used game components. Between 46% and 54% of the studies (six/seven papers) implemented one or several of these game components in their gamification proposals. The authors of these papers proposed that users obtain Badges when they meet certain objectives. For example, in reference [P08], the agents of support groups got badges when the percentage of incidents that they resolved correctly improved by a target value and the percentage of incidents solved within the established times was equal or greater than an objective value. On the other hand, the use of Levels will enable to drive the behavior of the users through the gamified system, taking into account their progress. In reference [P02], for example, the purpose of the different levels was to learn conducting process model simulations, to perform process model simulations with an initial process configuration and to conduct simulations by varying the initial process configuration. Lastly, the adoption of Leaderboards in a gamified system would allow users to compare their progress and performance with those of other users. The rest of the game components outlined in Table 13 were mentioned in very few studies.  
Figure 6: Number of primary studies by game mechanics.  
During the analysis of the studies to answer this RQ, we collected the game elements used to gamify ITSM processes. We noticed that not all the studies indicated clearly the approach followed to select the gamification elements and argued broadly the foundation behind using them. Likewise, many papers lack details about the manner in which the game elements were provided and how they were implemented. The articles that discussed more clearly the implementation of game elements in ITSM processes are [P02,P04,P07,P08,P12]. The other studies only mentioned the game elements or explained their use in very little detail.
## 5 Concluding Remarks
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \multicolumn{1}{c}{**County**} & \multicolumn{6}{c}{**Background Indicators**} & \multicolumn{6}{c}{**Safety Performance Indicators**} \\ \hline
**County** & **ISO** & _N\(\_\)pers_ & _GDP_ & _Pop\(\_\)den_ & _Pop\(\_\)urb_ & _P\(\_\)ale_ & _belt\(\_\)fout_ & _belt\(\_\)fear_ & _prot\(\_\)oc_ & _prot\(\_\)pd_ & _Renewal_ & _Car\_age_ \\ \hline Austria & AT & 546 & 136 & 105 & 0.577 & 0.984 & 0.93 & 0.88 & 0.90 & 0.59 & 0.065 & 0.91 \\ Belgium & BE & 497 & 126 & 372 & 0.979 & 0.951 & 0.92 & 0.86 & 0.89 & 0.60 & 0.090 & 0.480 \\ Switzerland & CH & 535 & 137 & 207 & 0.737 & 0.964 & 0.93 & 0.76 & 0.90 & 0.60 & 0.073 & 0.297 \\ Czech Republic & CZ & 485 & 54 & 137 & 0.735 & 0.900 & 0.95 & 0.98 & 0.90 & 0.61 & 0.045 & 0.236 \\ Germany & DE & 548 & 130 & 229 & 0.772 & 0.960 & 0.98 & 0.98 & 0.91 & 0.60 & 0.071 & 0.337 \\ Denmark & DK & 403 & 163 & 132 & 0.875 & 0.851 & 0.96 & 0.85 & 0.87 & 0.57 & 0.080 & 0.412 \\ Estonia & EE & 514 & 54 & 30 & 0.684 & 0.991 & 0.98 & 0.82 & 0.90 & 0.60 & 0.031 & 0.136 \\ Spain & ES & 481 & 81 & 93 & 0.796 & 0.983 & 0.84 & 0.84 & 0.89 & 0.59 & 0.049 & 0.259 \\ Finland & FI & 590 & 132 & 18 & 0.852 & 0.990 & 0.94 & 0.88 & 0.91 & 0.62 & 0.034 & 0.197 \\ France & FR & 484 & 114 & 105 & 0.797 & 0.971 & 0.98 & 0.81 & 0.87 & 0.58 & 0.058 & 0.339 \\ Hungary & HU & 325 & 38 & 106 & 0.705 & 0.888 & 0.83 & 0.56 & 0.90 & 0.58 & 0.063 & 0.108 \\ Ireland & IE & 436 & 161 & 69 & 0.625 & 0.937 & 0.94 & 0.81 & 0.91 & 0.60 & 0.063 & 0.434 \\ Italy & IT & 621 & 93 & 201 & 0.696 & 0.975 & 0.62 & 0.15 & 0.88 & 0.58 & 0.049 & 0.323 \\ Lithuania & LT & 431 & 44 & 46 & 0.672 & 0.82 & 0.96 & 0.33 & 0.91 & 0.61 & 0.106 & 0.046 \\ The Netherlands & NL & 477 & 140 & 503 & 0.902 & 0.971 & 0.94 & 0.83 & 0.89 & 0.60 & 0.055 & 0.298 \\ Poland & PL & 546 & 39 & 124 & 0.603 & 0.959 & 0.96 & 0.76 & 0.90 & 0.59 & 0.055 & 0.098 \\ Portugal & PT & 457 & 60 & 112 & 0.635 & 0.966 & 0.95 & 0.77 & 0.89 & 0.61 & 0.048 & 0.205 \\ Romania & RO & 261 & 28 & 86 & 0.539 & 0.920 & 0.81 & 0.45 & 0.88 & 0.56 & 0.064 & 0.154 \\ Sweden & SE & 474 & 158 & 24 & 0.866 & 0.990 & 0.98 & 0.93 & 0.91 & 0.61 & 0.078 & 0.343 \\ Slovakia & SK & 375 & 50 & 111 & 0.664 & 0.988 & 0.83 & 0.44 & 0.90 & 0.60 & 0.008 & 0.228 \\ United Kingdom & UK & 463 & 138 & 269 & 0.826 & 0.890 & 0.98 & 0.91 & 0.90 & 0.59 & 0.086 & 0.356 \\ \hline \end{tabular} Note: Values with grey background color are imputed values.  
\end{table} 
Table 2: Final dataset for 21 European countries (year 2015).
## 2 System and Channel Model
### Atmospheric Turbulence Model
The latter gives an early indication that while it has been proved in [14; 15] that THz radiation is less vulnerable to atmospheric turbulence-induced scintillations than IR radiation, the atmospheric turbulence effect should not be neglected for the design of modern and future THz links which are expected to cover longer propagation distances.
# BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links +
## 4 Simulation Results  
The performance for the TWR-FSO PNC system with iterative decoding using the E2E BICM-ID code is performed over atmospheric turbulence fading channels ranging from weak to strong. Furthermore, an evaluation of the link performance is investigated by ExIT charts, taking into account the influence of channel impairments such as path loss, atmospheric turbulence fading and pointing errors. The investigation of the E2E coded TWR-FSO PNC system with iterative decoders depends on the mutual information between the transmitter and the receiver. Thus, the convergence behaviour of the proposed detectors of TWR network from the unknown received signal is evaluated.
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 3 AntOR-Based Protocol
### Data Structures
#### 3.1.2 Neighbor Table  
This data structure contains the information that each node has of the one-hop neighbors with its corresponding listening time. The neighbors' table maintained by the node \(i\) is a vector with an entry for each one of its neighbors. Each entry corresponds to the information that the node \(i\) has in the presence of the neighbor node \(j\), as well as a time value that indicates when it was the last heard from, that is that \(i\) received a message from \(j\). This structure is utilized, as its name suggests, to indicate the presence of the neighbors and to detect possible link failures.  
Table 3 represents the generic table from the neighbors of AntOR. In this structure, every local node has a list of one-hop neighbors with the following information: a neighbor identifier Id \(Neigh_{k}\) and the last time value Time \(Neigh_{k}\), associated with the notification message from vicinity (Hello), which the neighboring node sent.
# Ballasted Track Status Evaluation Based on Apparent Track Stiffness Index
## 4 Track Status Evaluation Based on the Track Stiffness Index
### Track Status Evaluation Standard Verification  
Based on an additional field survey, the track status evaluation standard based on the track stiffness index was verified. The site where the verification was performed was a section that had never been field tested before. The section had two earthwork structures and one bridge. For each section, Table 8 lists the measured apparent stiffness result from the field test, track measurement data, TQI, and cumulative tamping length derived from the maintenance data. Next, the track status was classified based on the data. As can be seen from the data in the table, the track status of the earthwork based on the track stiffness index and that based on TQI were consistent. However, the track status of the bridge based on the track stiffness index was worse. As seen earlier, this difference is attributed to the fact that the bridges had a significantly smaller correlation between the track stiffness index and TQI than did the earthwork or tunnels. Based on the comparison of the track status based on the track stiffness index and cumulative tamping length, it was found that the bridges showed consistency; however, the earthwork showed consistency at one location only. This discrepancy occurred because the correlation between the track stiffness index and cumulative tamping length was not significantly high. Therefore, it is thought that if additional data, including additional field survey sections, can be accumulated, the accuracy of the track status evaluation can be improved.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Railway Track** \\ **Maintenance Guidelines** \\ **Management Stage** \\ \end{tabular} } & \multirow{2}{*}{**TQI**} & \multicolumn{3}{c}{**Stiffness Index**} & \multicolumn{3}{c}{**Cumulative Tamping Length**} & \multirow{2}{*}{**Status**} \\  & & & **(kN/mm)** & & & **(m/m/year)** & & \\ \cline{1-1} \cline
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 4 Materials and Methods
### Overview of Proposed Scheme  
The workflow of the proposed two-factor screen unlock scheme is illustrated in Figure 2. During the screen unlocking process, the user draws the unlock pattern on the screen of the device while the sEMG signals of the user are simultaneously recorded. The pattern drawn on the screen is first checked regarding whether it is the same as the one set by the authorized user or not. The access is denied if it is the wrong pattern. Otherwise, the recorded sEMG signals will further be used to verify the user. The verification is performed by extracting the time domain features from the recorded sEMG signals first. The features are then used as the input of a one-class classifier to classify the user as legitimate or not. Note that the one-class classifier is trained only using the data from the authorized user.