esa-sceva/satcom-chunk-collection huggingface dataset:
# Short Text Event Coreference Resolution Based on Context Prediction
## 4 Experimental Setup
### Evaluation Metrics  
The accuracy of ECR models can be evaluated from two perspectives. One is to use precision (P), recall (R), and F1 Score in a simple binary classification model. The other is to evaluate the algorithm performance based on the standard coreference chain or event set. As we view event pair coreference resolution as a binary classification task, we use precision, recall, and F1 score to evaluate ECR models. We first compute the confusion matrix of classification results. It includes four values according to the predicted class and actual class; the true positive rate (TP), which describes the consistency of positive examples; the true negative rate (TN), which describes the consistency of negative examples; the false positive rate (FP), which describes that the positive examples are incorrectly labeled as the negative examples; and the false negative rate (FN), which describes that the negative examples are incorrectly labeled as the positive examples. Then, to keep our description as self-contained as possible, we present these indexes briefly.  
Precision measures the prediction performance, which is the percentage of the positive examples among the predicted results. Its specific formula is as follows,  
\[Precision=\frac{TP}{TP+FP}. \tag{6}\]  
Recall measures the prediction performance, which is the percentage of the positive examples correctly predicted among all predicated positive examples. Its specific formula is as follows,  
\[Recall=\frac{TP}{TP+FN}. \tag{7}\]  
Note that the indexes of recall and sensitivity are equal in the formula.  
\(F_{1}\) score is generally defined in mathematics to reconcile the results of precision and recall. Its specific formula is as follows,  
\[F_{1}-Score=2\cdot\frac{precision\cdot recall}{precision+recall}. \tag{8}\]
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
###### Abstract  
We propose an amplitude shift keying-type asymmetric quantum communication (AQC) system that uses an entangled state. As a first step toward development of this system, we evaluated and considered the communication performance of the proposed receiver when applied to the AQC system using a two-mode squeezed vacuum state (TSVS), the maximum quasi-Bell state, and the non-maximum quasi-Bell state, along with an asymmetric classical communication (ACC) system using the coherent state. Specifically, we derived an analytical expression for the error probability of the AQC system using the quasi-Bell state. Comparison of the error probabilities of the ACC system and the AQC systems when using the TSVS and the quasi-Bell state shows that the AQC system using the quasi-Bell state offers a clear performance advantage under specific conditions. Additionally, it was clarified that there are cases where the universal lower bound on the error probability for the AQC system was almost achieved when using the quasi-Bell state, unlike the case in which the TSVS was used.  
entanglement; quasi-Bell state; asymmetric communication system; error performance 202224708. [[https://doi.org/10.3390/e24050708](https://doi.org/10.3390/e24050708)]([https://doi.org/10.3390/e24050708](https://doi.org/10.3390/e24050708))
###### Abstract  
In this paper, we investigate jamming attacks in the physical layer against cooperative communications networks, where a jammer tries to block the data communication between the source and destination. An energy-constrained relay is able to assist the source to forward the data to the destination even when the jammer tries to block the direct link. Due to a limited-capacity battery of the relay, a non-radio frequency energy harvester equipped in the relay helps to prolong its operation. We propose a scheme based on a partially observable Markov decision process (POMDP) to find the optimal action for the source such that we can maximize the achievable throughput of cooperative communications networks. Under this scheme, the source dynamically selects the appropriate action mode for its transmission in order to obtain maximum throughput under the jamming attack. Simulation results verify that the proposed scheme is superior to the Myopic scheme where only current throughput is taken into account for making decisions.  
cooperative communications network; energy-constrained relay; POMDP; jamming attack; physical layer +
Footnote †: journal: Applied Physics Letters
# A New SORM Method for Structural Reliability with Hybrid Uncertain Variables
## 5 Conclusions  
This paper proposes a unified reliability framework to address the reliability of a complex system in the presence of both stochastic and interval variables. On the aggregation of different uncertain variables, a novel SORM method, UPSORM, is proposed. By introducing a novel polar transformation technique, a multivariate limit-state function is reformulated as a generalized bivariate limit-state function in the polar coordinate. For this purpose, we set the gradient direction of the limit-state function as the polar coordinate. In this manner, the computational cost is significantly reduced as the original n-dimensional reliability problem is simplified to a two-dimensional problem.  
The proposed method was evaluated through a numerical case study. The upper and lower bounds of reliability were estimated. To further validate its practical feasibility, a real engineering case was employed as an application example. The proposed method was compared with the classical MC algorithm and the state-of-the-art method, showing that the proposed method provides comparable results with lower computational costs. In particular, the proposed method outperformed the compared methods for cases with strong nonlinearity.  
Conceptualization, P.W.; methodology, P.W., and L.Y.; validation, P.W. and L.L.; formal analysis, N.Z.; writing--original draft preparation, P.W.; writing--review and editing, L.Y., and L.L.; visualization, P.W. and D.W.; project administration, P.W., L.L., and D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 52005032, the Aeronautical Science Foundation of China under Grant 2018 ZC74001, the Fundamental Research Funds for the Central Universities of China under Grant FRF-TP-20-008A2 and National Key R&D Program of China (No. 2017 YFF0209401) and the Key Laboratory of Quality Infrastructure Efficacy Research Funding, project KF20180401.  
The authors would like to thank [PERSON] from Beihang University for their guidance and the materials and data used for the numerical examples. The authors would like to thank all the reviewers who participated in the review during the preparation of this manuscript.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 Signal Processing and Results of an In-Field Test  
Several in-flight data collection campaigns have been executed in order to test the performance of the prototype in different configurations. The aim of the test campaigns was to demonstrate the capability of the prototype to provide the GNSS measurements necessary to implement a soil moisture retrieval algorithm, such as one of those mentioned in the previous section. In this section, we first review the signal processing principles at the basis of our project (Section 5.1), then some results of the reflectometry measurement campaign are shown (Section 5.2).
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 6 Evaluation
### Results and Discussion
#### 6.2.3 Impact of PQC Algorithms on Performance in the File Transfer Use Case
At 10 KB, as shown in Figure 8b, AES-256's RTT decreased to 0.0063 s, while the PQC standalone protocol algorithms maintained similar trends as with the 1 KB file size, except for a few algorithm pairs that showed slightly higher latencies. The PQC standalone protocols achieved higher RTTs for BIKE-L5-F512 (0.017 s), BIKE-L3-F1024 (0.014 s), BIKE-L5-F1024, HQC-256-F512 (0.016 s), HQC-192-F1024 (0.013 s), and HQC-256-F1024 (0.012 s) compared to the same algorithm pairs in hybrid mode and AES-256. Conversely, it was observed that the remaining algorithm pairs in hybrid mode showed the opposite trend, with slightly higher RTTs (HQC-128-F512 (0.007 s), HQC-192-F512 (0.008 s), HQC-128-F1024 (0.007 s), and K512-F1024 (0.011 s)) than those of PQC standalone mode. This showcases the suitability of migrating applications to PQC algorithms while highlighting the processing bottlenecks associated with certain PQC algorithms. Lastly, Figure 8c shows that for 100 KB files, AES-256 achieved 0.0048 s, outperforming many PQC standalone methods. BIKE-L5-F512 required 0.0117 s, 2.4 times slower, and HQC-256-F512 reached 0.015 s, 3.1 times slower. Exceptions included HQC-128-F512 (0.0028 s) and K768-F1024 (0.0039 s), which were 1.7 times and 1.2 times faster than AES-256, respectively. Notably, in hybrid mode, K768-F1024 incurred a latency of 0.0275 s for 100 KB files, 5.7 times slower than AES-256, highlighting the compounded overhead of hybrid key exchanges and ciphertext processing. These results emphasize that while PQC algorithms may exhibit competitive throughput due to larger ciphertexts, their RTT performance is more directly tied to computational complexity and protocol design, exposing trade-offs between post-quantum security and real-time application suitability.
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 4 Some Studies of Fitts' Law in HCI Scenes
### Groups with Poor Interaction
#### 4.5.3 Visually Impaired Groups  
While graphical user interfaces (GUIs) have long been a common way for people to engage with information devices, people who are fully or partially visually impaired may find it difficult to use the spatial arrangement of GUIs [84]. For visually impaired users, navigating graphical interfaces can be time-consuming; nonetheless, the application of Fitts' law has proven useful in anticipating their motor patterns. [PERSON] law was applied to predict visually impaired people's performance in a study by [PERSON] et al., even though their mean time to movement (MT) was longer than the usual duration for quick aim motions [97]. It has been demonstrated that providing abrupt bump feedback as the mouse pointer crosses target borders helps participants more effectively than altering friction on the target surface to precisely localize and contact smaller targets [98,99]. Expanding upon these findings, Fitts EVAL is an experimental framework designed by [PERSON] et al. to investigate shape-specific accessibility on vibrating touchscreens [100]. Blind users improved their pointing operations by varying parameters such as target size, angle of attack, and distance [101,102]. The findings showed that [PERSON] law is still a reliable predictive model for blind users of vibrating touchscreens, especially when differences in target size and distance are incorporated.  
The cited work creatively applied Fitts' law in a non-visual setting by carefully modifying several parameters to incorporate haptic input and reference points by experimental results. This study emphasizes the value of adding tactile features to improve usability for those with visual impairments in addition to discussing the practical implementation of Fitts' law in settings with limited visual signals. Through the optimization of Fitts' law's parameters to conform to tactile feedback systems, this research significantly contributes to the advancement of visually handicapped individuals' access to visual information in modern computerized environments. Moreover, the discoveries drawn from these studies provide an important direction for the creation of interfaces that are specially designed to accommodate the requirements of people with visual impairments, thereby advancing inclusivity and usability in contemporary technology environments.
## Notes  
1. For the sake of notational simplicity, the derivation is carried out considering a single packet transmission act, i.e., we do not use any index to indicate the specific packet.
2. Note that, for a fixed symbol duration (i.e., transmitting rate), a power variation is in a one-to-one correspondence with an energy variation.
3. Note that the internal iterations in each component subdecoder refer to (i) the iterations between the variable nodes and the check nodes in the presence of LDPC coding and the SP algorithm or (ii) the turbo iterations between convolutional decoders in the presence of turbo coding and the BCJR algorithm (at each convolutional decoder).
4. Since the a priori probabilities need to be evaluated for the systematic bits, in this case \(\
u_{i}^{(k)}=x_{i}^{(k)}\) and, therefore, \(\{P(y_{i}^{(1)},y_{i}^{(2)},\ldots,y_{i}^{(n)})\}=\{P(2x_{i}^{(1)}-1,2x_{i}^{ (2)}-1,\ldots,2x_{i}^{(n)}-1)\}\). The joint PMF of \(\{y_{i}^{(k)}\}_{k=1}^{n}\) can then be obtained directly from (1). Note that equation (24) is an approximation since, heuristically, the first probability in the summation at the right-hand side is obtained from the reliability values generated by the other decoder, whereas the second probability is a priori.
5. Note that the number of internal iterations is fixed with SCCCing, whereas it can vary in the LDPC coded case. Note also that the number \(n_{\rm it}^{\rm ext}\) of external iterations between the component decoders differs between the cases with LDPC codes and SCCC. This is due to the different convergence characteristics of the iterative decoders associated with these channels codes.  
6. Note that the exact statistics of the residual multiple access interference should be better investigated. This goes beyond the scope of this paper. However, the Gaussian approximation allows to have useful insights on the impact of the multiple access interference on the proposed feedback power control strategies.  
(c) 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license ([[http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/)]([http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/))).
# Generating Activity-Based Mobility Plans from Trip-Based Models and Mobility Surveys
## 4 Results and Validation
### Spatiotemporal Mobility Behavior
In this context, it is defined by Equation (17):  
\[\text{SMAPE}=\frac{|\widetilde{n}_{v,\text{ MITO}}-\widetilde{n}_{v,\text{ Simulation}}|}{\widetilde{n}_{v,\text{ MITO}}+\widetilde{n}_{v,\text{ Simulation}}} \tag{17}\]  
Herein, \(\widetilde{n}_{v,\text{ MITO}}\) and \(\widetilde{n}_{v,\text{ Simulation}}\) denote the share of visits \(\widetilde{n}_{v}=n_{v}/N_{v}\) per TAZ for a certain activity type. The third row of Figure 15 depicts the error distribution, conforming a first visual comparison of row one and two. The TAZs for which neither of the data sets counted any visits were left blank in this statistic. The vast majority of the remaining zones exhibited SMAPE values in the single-digit percentages, effectively validating a realistic distribution of activities by our approach. A more aggregated view on the same matter is shown in the fourth row of the figure. Here, the distribution of \(n_{v}\) is summarized for both MITO and the simulation. Additionally, the distribution of the absolute errors \(|\widetilde{n}_{v,\text{ MITO}}-\widetilde{n}_{v,\text{ Simulation}}|\) is depicted. The maximum number of visits per single zone \(m\) as well as the maximum absolute difference are also indicated. Please note that for comparability, all absolute numbers in the fourth row concerning MITO were scaled to match the simulation (i.e., they were multiplied by \(N_{v,\text{ Simulation}}/N_{v,\text{ MITO}}\)). These figures support the conclusion that activities were successfully located by our approach, and because of small absolute differences, qualitatively similar visit distributions were found in comparison with the reference data set as well as similar maximum numbers of visits per individual zone. Figure 15: The heatmaps geographically compare activity locations of MITO and our simulation. The two rows from below validate whether the distribution of visits per TAZ is realistic.
## 4 Data and Methodology  
On 29 December 2020, a strong earthquake M\({}_{w}\)6.2 hit near Strasnik village in the NW Croatia [63]. The tremor occurred at a depth of around six kilometers, with intensity at the epicenter of VIII EMS scale, occurring on the intersection of longitudinal NW-SE right-lateral and transverse NE-SW left-lateral faults along the transitional contact zone of the Dinarides and the Pannonian Basin [54]. All significant institutions for earthquake detection registered and processed the data of the earthquake. In a recent paper [54], Figure 4 shows the focal parameters of the earthquake determined by the INGV [64] and the USGS that describe it as a result of shallow strike-slip faulting within the Eurasia Plate [65]. In order to map the event of this earthquake and its effects on the ground displacement in the area affected by the earthquake, we analyzed the multiple spatio-temporal Sentinel-1 satellite C-SAR (C-band synthetic aperture radar) images and the continuous s GNSS data of the nearest reference station.  
Figure 3: The graph shows earthquakes with magnitudes over M\({}_{w}\)1.5, in the period between 28 December 2020 and 1 February 2021. The data is acquired from EMSC [58]. Silent seismic activity over the year prior to the main foreshock is shown on the upper inset.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 3 Materials and Methods
### Evaluation Metrics
#### 3.3.1 Confusion Matrix  
A confusion matrix is a tabular representation of actual values against predicted values for each class in the classification problem. It shows how many true and false predictions a model has produced for each class.  
The confusion matrix's four types of entries are the following:  
* True Positives (TP)
* False Positives (FP)
* True Negatives (TN)
* False Negatives (FN)  
The number of true positives (TP) reflects the number of instances of a class that were correctly predicted, whereas the number of false positives (FP) represents the number of instances that were incorrectly predicted as belonging to that class. Similarly, true negatives (TN) are examples that were properly projected to be outside of that class, whereas false negatives (FN) are instances that were wrongly predicted to be outside of that class. We can compute other evaluation metrics by using entries from the confusion matrix.
## 3 Result and Discussion of the Simulation Study
### The Resistance to System Disturbance of BPNN Self-Tuning PID Controller  
Lots of process conditions can upset the diethyl oxalate hydrogenation and FTS reactor system [37,38], including fluctuation in flow rate, the feed ratio, and the failure of recycling. Those will lead to hotspot shifts and heat rises and falls. In this study, the heat power was regulated from time to time as a simulation of system disturbance; the control quality and adaptive ability remain to be further seen in Figure 8, which demonstrated a tracking response to furnace power of BPNN self-tuned controller when the drum pressure setpoint was 1.55 MPa. Obvious deviations could be observed between the measured and setting pressures of the drum each time system turbulence arose. BPNN self-tuning parameters led to smooth control, short response time in the transient process, and little error, which moreover highlighted great adaptability. A case of FTS product distribution of TOS (time on stream) = 667-908 h over an iron-based catalyst is shown in Figure 9 when the BPNN self-tuning PID controller is employed in the fixed-bed reactor of this paper. It can be observed that the catalyst activity and product selectivity are particularly stable after the reaction lasts for a long time. Other than this case, the BPNN self-tuning PID controller is also effectively applied to several research works of colleagues, such as the evaluation of the Mn-Fe/ZrO\({}_{2}\) FTS catalyst [39] and Ni catalysts for the hydrogenation of diethyl oxalate [40].  
Figure 8: BPNN self-tuning PID controller real-time curves when altering heater power.  
Figure 9: The product distribution of FTS using BPNN self-tuning PID controllers.
## 4 Discussion
### The Typical Failure Mode of DTT and BST Specimens  
In theory, the failure patterns of isotropic rocks are consistent. However, the failure modes of anisotropic rocks are influenced by bedding and foliation structures, resulting in more complex failure behaviors. Based on the test results presented in this study, the typical failure modes of BST and DTT specimens for layered Longmaxi shale are summarized, as illustrated in Figures 11-13. Figure 12 depicts five representative failure modes observed in layered shales, as summarized by [PERSON] et al. [14].  
For BST specimens, three typical failure modes are identified based on the experimental results: \"Type A\" denotes tensile failure along the bedding plane, \"Type B\" denotes arc-shaped tensile-shear mixed failure, and \"Type C\" denotes tensile failure across the bedding plane. \"Type A\" and \"Type C\" are primarily observed in specimens with loading angles of \(\beta\) = 0\({}^{\circ}\) and \(\beta\) = 90\({}^{\circ}\), respectively, while the failure mode at other loading angles can generally be approximated as \"Type B\". These three representative failure modes align with a subset of the failure modes summarized by [PERSON] et al. [14], demonstrating the precision and applicability of the five typical failure modes for layered shales.  
Figure 11: Typical failure modes of BST specimen of layered Longmaxi shale, where the letter relates to (A) Tensile failure along the bedding plane, (B) Arc-shaped tensile–shear mixed failure, and (C) Tensile failure across the bedding plane.  
Figure 12: Typical failure modes of BST specimen of layered shale [14], where the number relates to (1) Tensile failure across the weakness planes, (2) Tensile failure along the weakness planes, (3) Shear failure across the weakness planes, (4) Shear failure along the weakness planes, and (5) Mixed failure.  
For DTT specimens, four typical failure modes are identified based on the experimental results: \"Type A\" denotes tensile failure within the rock matrix, as observed in specimens with \(\beta=60\)\({}^{\circ}\) and \(\beta=90\)\({}^{\circ}\). \"Type B\" denotes slipping failure along the bedding plane, exemplified by \(\beta=45\)\({}^{\circ}\). \"Type C\" denotes tensile-slipping mixed failure, as seen in specimens with \(\beta=15\)\({}^{\circ}\) and \(\beta=30\)\({}^{\circ}\). \"Type D\" describes a more complex tensile-shear failure, observed in specimens with \(\beta=0\)\({}^{\circ}\) and \(\beta=75\)\({}^{\circ}\). Although the geometry of the DTT specimens used in this study differs from that of conventional DTT specimens, a review of traditional failure modes for DTT specimens of layered rocks [18,19] reveals that the fundamental characteristics of the failure modes are consistent with those summarized in this study.  
In conclusion, this study summarizes the failure modes for both DTT and BST specimens. Compared to previous studies, it is evident that the influence of bedding structure on failure modes is particularly significant. This effect is a primary reason for the discrepancy between the measured tensile strength and the actual tensile strength of the rock.