esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
## 3 Knowledge Conflict Resolution Model
### Constrained on Time Confliction Amony Facts  
We mainly considered three types of time disjoint, temporal precedence, and time mutual exclusion. These three types can handle most of the time-constrained knowledge conflicts.  
The time disjoint limit is to limit the time interval between any two relationships, while avoiding the existence of two facts with the same head entity and relationship as well as the overlaps of different tail entities, as shown in condition (3):  
\[R(e_{i},\,e_{j},t_{1})\wedge\,R(e_{i},e_{k},t_{2})\wedge(e_{j}\
eq e_{k}) \to disjoint(t_{1},t_{2}), \tag{3}\]  
where \(e_{j}\) and \(e_{k}\) are not the same entity, and the times of \(t_{1}\) and \(t_{2}\) cannot overlap. For example, in a marriage relationship, a person cannot maintain a marital relationship with two persons at the same time.  
The temporal precedence restriction ensures the order in which some pairs of relationships occur. The end time of relationship \(R_{1}\) is earlier than the start time of \(R_{2}\) and the temporal precedence constraints can be expressed by condition (4):  
\[R_{1}(e_{i},\,e_{j},\,t_{1})\wedge R_{2}(e_{i},\,e_{k},\,t_{2}) \to before(t_{1},\,t_{2}). \tag{4}\]  
For example, one obvious constraint is that a person's date of birth should precede the time they joined the work. The knowledge graph conflict problem often appears when the query on knowledgegraph is invoked, so the time-constrained confliction will be resolved on the query time with knowledge graph. For example, there are two relationships below, the first is \"\(<\)[PERSON], retirement from, NSA,1.0, [2014, now]\(>\)\", the second one is \"\(<\)[PERSON], served as, the 1 st Commander of the United States Cyber Command, 1.0, [2010, 2015]\(>\)\", there is a time-constrained confliction between [2014, now] and [2010, 2015], which will appear when an [PERSON]'s position query after 2014 is invoked.  
The mutually exclusive relationship of facts means that a relationship \(R\) cannot appear at the same time, which can be described as condition (5):  
\[R(e_{i},\ e_{j},\ t)\wedge R(e_{i},\ e_{k},\ t)\wedge(e_{j}\
eq e_{k})\to false. \tag{5}\]  
For example, a student cannot attend elementary school in two different cities at the same time. In order to solve the problem of time conflicts in the process of knowledge graph evolution, we use semantic embedding, path embedding, and time embedding methods based on the recurrent neural network to resolve the knowledge conflicts generated during the evolution of knowledge graphs and predict the relationship between entities. Therefore, the knowledge graph can change dynamically.
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 4 Performance Evaluation
### Average Inter-Visiting Time Comparison  
We evaluated the average inter-visiting time of reconnoitered area in order to check whether UAVs scout the area uniformly or not. Figure 8 shows the average inter-visiting times of three models for 2 and 3 h. As shown in Figure 8a, the proposed model shows 18.5% and 9.4% lower inter-visiting time than the Random Waypoint model and the Pheromone Repel models, respectively. The proposed model shows 34% and 15.1% less than the two other models in 3-h simulations. In addition, the proposed model in 3-h simulation shows less inter-visiting time than that of 2-h simulation, which implies the proposed model makes UAVs scan the area evenly in the steady state. On the contrary, the Random Waypoint model has higher inter-visiting time in the case of the 3-h simulation because UAVs fly mostly in the center area.  
We also plot the visited numbers of the unit region (1 km \(\times\) 1 km) in Figure 9a in order to analyze the uniformity of coverage. Let us note that the coverage of the Random Waypoint model is distributed in center of simulation area in Figure 2a, and the Pheromone Repel model has high coverage on a particular region in Figure 2b. However, the proposed model shows uniform search compared to the other two models.  
Figure 9b shows the distribution of regions according to the number of visits. The maximum number of visits in unit region is 10, while the two other models show 14 and 1, as shown in Figure 3. To analyze the skewness of visits, we plot the number of regions per the number visits with the connected lines in Figure 10. We also draw the mean, median, and mode (most frequent value) of the three distributions. As shown in Figure 10, the proposed model is close to the Gaussian distribution so that mean, median, and mode are almost same in the center. On the contrary, the two other models show left-skewed distributions, which indicates much imbalance among regions in terms of number of visits.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **The Mobility Model** & **The 80\% Reached Time (s)** & **The 90\% Reached Time (s)** \\ \hline \multirow{3}{*}{**7200 s**} & Random Waypoint & 3908 & - \\  & Pheromone Repel & 2775 & 4194 \\  & Random Zone 15 \(\times\) 15 & 2276 & 3173 \\ \hline \multirow{3}{*}{**10,800 s**} & Random Waypoint & 3872 & 6472 \\  & Pheromone Repel & 2817 & 4208 \\ \cline{1-1}  & Random Zone 15 \(\times\) 15 & 2249 & 3122 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The 80%- and 90%-coverage reaching time of each model.  
Figure 7: The coverage rate results: (**a**) 7200 s; and (**b**) 10,800 s.
## 4 Discussion
There was little difference in the ROM between all groups when a rotation moment was applied, but the ROM did increase for the bending motion in groups b and c. When the disc was augmented with PU or hydrogel, the bending motion was still greater, but to a lesser degree. In an in vitro study, [PERSON] et al. [34] reported little change in the rotational ROM after conservative disccetomy or aggressive disccetomy, but also did not record multi-axial translations under an axial rotation moment. [PERSON] found that the rotational ROM only increased after total disccetomy, which is supported by [PERSON] et al. [35], who used finite element analysis to show that the range of axial rotation similarly increasedafter total disccotomy. The results for rotational ROM in this current study were similar to [PERSON] et al. [34] but had an additional increased bending motion after aggressive disccotomy. In the lateral bending condition, groups b, c, d, and f showed a higher bending ROM and greater extension than the intact model. Filling the cavity in the nucleus with PU acted to reduce bending and extension movements and stabilize the spine. In contrast, using hydrogel only offered a slight improvement in bending and extension control. Previous studies have indicated that the softer nature of hydrogel fillings mean that the augmented disc cannot support as much load, limiting the stability of the spinal segment [36]. There are some limitations to this study. First, the material characteristics of the entire model were assumed to be isotropic and homogenous, and the effects of muscle contraction were not considered. This is not truly representative of the physiological state of the human spine. Second, only the L3-L4 spinal segment was simulated, so the effect of adjacent vertebrae was not captured. Also, the disccotomy type and properties of the material filler were simplified in each group. This study is limited by the finite element models not being capable of evaluating the time-dependent behavior of the materials, but instead the models were built to accurately estimate reactions to multi-axial motions.
# Cooperative Jamming-Based Physical-Layer Group Secret and Private Key Generation
## 4 Performance Analysis
### The SK/PK Upper Bound  
To derive the SK/PK upper bound, we streamline the DMS model depicted in Figure 2. Assume that [PERSON] and [PERSON] are colluding and can exchange all of their respective observed information. Thus, [PERSON] and [PERSON] can be considered to be one node, defined as node CB. The enhanced DMS model with respect to group SK/PK generation is shown in Figure 3.  
In the formulated enhanced DMS model for SK generation, the observations of the relay and [PERSON] are known by either [PERSON] or [PERSON]. First, we consider the scenario where [PERSON] has access to both the observations of the relay and [PERSON], as illustrated in Figure 3a. As a result, the DMS model presented in Figure 3 reduces to a standard point-to-point DMS model. In this model, [PERSON], [PERSON], and [PERSON], respectively, have access to \(\tilde{Y}_{A}\), the combined set of \(\left(\tilde{Y}_{B},\tilde{Y}_{C},\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}, \tilde{Y}_{E}\right)\) and \(\tilde{Y}_{E}\). According to [26], the SK capacity is upper bounded by \(I\big{(}\tilde{Y}_{A};\tilde{Y}_{B},\tilde{Y}_{C},\tilde{Y}_{R}^{(1)},\tilde {Y}_{R}^{(2)},\tilde{Y}_{E}|\tilde{Y}_{E}\big{)}=I\left(\tilde{Y}_{A};\tilde{Y }_{B},\tilde{Y}_{C},\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}\right)\). Second, assuming that the  
Figure 3: Group SK and PK generation in the enhanced DMS models. (**a**) SK. (**b**) PK.  
observations of the relay and Eve are known by [PERSON], the SK capacity is upper bounded by \(I\left(\tilde{Y}_{A},\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)},\tilde{Y}_{E};\tilde{ Y}_{B},\tilde{Y}_{C}|\tilde{Y}_{E}\right)=I\left(\tilde{Y}_{A},\tilde{Y}_{R}^{(1)}, \tilde{Y}_{R}^{(2)};\tilde{Y}_{B},\tilde{Y}_{C}\right)\).  
For the enhanced DMS model for PK generation, assume that the relay and Eve are colluding, and thus the relay and Eve can be considered to be one node, defined as node ER. Additionally, assuming [PERSON] has access to the observations of the relay and Eve, as depicted in Figure 3b, the subsequent observations made by [PERSON], CB, and ER are \(\tilde{Y}_{A}\), \(\left(\tilde{Y}_{B},\tilde{Y}_{C},\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}, \tilde{Y}_{E}\right)\) and \(\left(\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)},\tilde{Y}_{E}\right)\), respectively. Therefore, according to [26], the upper bound on PK capacity is \(I\left(\tilde{Y}_{A};\tilde{Y}_{B},\tilde{Y}_{C},\tilde{Y}_{R}^{(1)},\tilde{Y }_{R}^{(2)},\tilde{Y}_{E}\right|\tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}, \tilde{Y}_{E}\right)=I\left(\tilde{Y}_{A};\tilde{Y}_{B},\tilde{Y}_{C}\right| \tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}\left)\).  
According to the above analysis, the upper bounds on SK and PK capacities can be given by  
\[C_{s}^{U} =\min\left\{I\left(\tilde{Y}_{A};\tilde{Y}_{B},\tilde{Y}_{C}, \tilde{Y}_{R}^{(1)},\tilde{Y}_{R}^{(2)}\right),I\left(\tilde{Y}_{A},\tilde{Y }_{R}^{(1)},\tilde{Y}_{R}^{(2)};\tilde{Y}_{B},\tilde{Y}_{C}\right)\right\} \tag{24}\] \[C_{p}^{U} =I\left(\tilde{Y}_{A};\tilde{Y}_{B},\tilde{Y}_{C}\right|\tilde{Y} _{R}^{(1)},\tilde{Y}_{R}^{(2)}\left) \tag{25}\]
## 5 Results  
### Clustering Efficiency  
In Section 3.2, the local data on each client is passed through a shared VAE model to extract meaningful latent representations. These representations are then used as inputs to an unsupervised clustering algorithm to group clients based on data similarity. To evaluate the effectiveness of this clustering process, Figure 6 presents the clustering results obtained using the affinity propagation algorithm, visualized in two-dimensional space via Principal Component Analysis (PCA) for dimensionality reduction.  
As shown in Figure 6, clients are clearly separated into four clusters, reflecting the original division of the dataset into four zones, as described in Table 2. Furthermore, the final clustering outcome perfectly corresponds to the actual distribution of each client's local private data. Clients whose data belonged to a specific zone are grouped accurately into the same cluster without any prior knowledge of the number of clusters or DGA attack types.  
Figure 6: PCA visualization of client’s clustering results using latent vectors from the VAE model, based on the affinity propagation algorithm. Each point in the visualization space represents a client.  
This result demonstrates that the proposed client clustering method based on VAE latent vectors achieves high accuracy in reflecting the underlying data characteristics in a fully unsupervised manner. The approach is particularly practical for real-world scenarios, enabling IDS nodes to detect and adapt to previously _unseen DGA attacks_ without the need for labeled data or predefined DGA family information.
# A Study on the Development of Tourist Support System Using ICT and Psychological Effects
## 4 History of the Development of a Sightseeing Support Application for Nikko  
As explained in the previous section, we have been developing a sightseeing support application for inbound travelers since 2014 in the Nikko area. We first investigated what is the most attractive aspect of travel in Nikko [41]. Then, we tried to increase the expectation and satisfaction of travel. Nikko is one of the world heritage sites in Japan. There is the Toshogu Shrine [42] that is a gorgeous grave of [PERSON], the first Shogun of the Tokugawa Era. However, now, Nikko is not so famous for foreigners. Research by travel agency displayed that Nikko is not listed in the top 30 locations where foreigners would like to visit [43].  
Figure 2: The relation between a cognitive model of our application and original CogInfoCom model//Extension to introduce intra-cognitive infocommunications.  
In the Nikko area, tourists who walk from the Nikko Station to the Toshogu Shrine are mainly inbound tourists. They require support from smartphone applications since they are not familiar with the Nikko area. We used BLE beacons instead of GPS as GPS has a high battery consumption and is thus not suitable for sightseeing [44]. The outline of the information delivery using BLE beacons and a smartphone is described in Figure 3.  
Figure 4 shows the photos of BLE beacon used in Nikko along the road from Nikko Station to Toshogu Shrine. Our research is separated into three parts, as described in Figure 5. In the following subsections, we explain the outline of each phase.
## 2 Materials and Methods
### Characterization  
The appearance and elemental composition of the synthesized particles were evaluated using the in-lens secondary electron detector of a field emission scanning electron microscope (SEM, Zeiss LEO 1530) equipped with an energy-dispersive X-ray spectroscopy system (EDS, Oxford AZtec). In preparation for analysis, the particles were dispersed in alcohol by ultrasound, pipetted onto sample stubs, and sputter coated with a thin Au/Pd conductive layer. Imaging was conducted using an acceleration voltage of 2 keV and a working distance of approximately 3 mm, whereas EDS data was collected using an acceleration voltage of 6 keV and a working distance of 8 mm. Crystallinity of the particles was evaluated on powder samples by X-ray diffraction (XRD, Bruker D8 ADVANCE), using CuK\(\alpha\)-radiation and scanning 20 from 10 to 60\({}^{\circ}\) with a step size of 0.0125\({}^{\circ}\). Particle size distribution was evaluated using dynamic light scattering (DLS, Malvern Zetasizer Nano) after dispersing a fine amount of particles by ultrasound in ethanol. The Brunauer-Emmett-Teller (BET) particle-specific surface area was measured using N\({}_{2}\) adsorption (Micrometrics TriStar II). In addition to the EDS measurements, the content of Ca, Mg, and P in the particles was quantified using inductively coupled plasma optical emission spectroscopy (ICP-OES, Perkin Elmer Avio 200) after dissolution in 5% HNO\({}_{3}\). The instrument was calibrated with stock standard solutions (Perkin Elmer) and corresponding blanks. Fluoride concentration was additionally determined using a calibrated fluoride ion selective electrode coupled to a benchtop meter (Hanna Instruments HI 4110, HI 5522-02), measuring in total ionic strength adjustment buffer (TISAB II) after dissolving the particles in diluted HCl. Fluoride release from the particles was analyzed by adding 50 mg of particles to 100 mL of TISAB II under stirring and measuring the fluoride concentration in solution after 1, 5, 10, and 30 min.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 4 Discussion
Overall, they found their system performed above baseline across all measures. The authors of [59] perform an iterative design of an explanation system for recurrent neural networks (RNN) including machine learning researchers and clinicians. They use feedback from clinicians while designing the algorithm. They evaluate it using a mixture of techniques including quantitative, qualitative, and case-studies. To the best of our knowledge, there are no studies such as this for explanation techniques. There is a study that interviews a cohort of clinicians to gain insight on what they consider to be explainable deep learning. The authors of [86] interviewed 10 clinicians belonging to two groups--Intensive Care Unit and Emergency Department. The goal of this study was to shed light on what clinicians look for in explainable machine learning and developing a metric to measure it. The authors interviewed each clinician for 45-60 min. They proposed the clinician's notions of explainability to determine what each clinician understood by the term and what he/she expected from ML models in their specific clinical setting. They designed an hypothetical scenario regarding the use of an explainable deep learning system to gain insights into what the clinicians looked for in explainable systems. The authors found that the clinicians viewed explainability as a means of justifying their clinical decision-making (for instance, to patients and colleagues) in the context of the model's prediction. The clinicians desired to understand the clinically relevant model features that align with current evidence-based medical practice. The implemented system/model needs to provide clinicians with information about the context within which the model operates and promote awareness of situations where the model may fall short (e.g., model did not use specific history or did not have information around certain aspect of a patient). Models that lacked accuracy were deemed suitable as long as an explanation of the prediction was provided. The authors state a quote that stood out was \"the variables that have derived the decision of the model\" and mentioned it was brought up by 3 ICU, 1 ED clinicians which is also identified in. The authors report that the clinical thought process for acting on predictions of any assistive tool appears to consist of two primary steps following presentation of the model's prediction: (i) understanding, and (ii) rationalizing the predictions. The clinicians stated they wanted to see both global and local explanations. Clinicians found finding similar training examples to be useful is certain applications such as diagnosis but not as much in other cases. The clinicians reported one of the most sought after features was a measure for uncertainty. 'Patient trajectories that are influential in driving model predictions' was reported to be an important aspect of model explanation. These studies provide direction for future algorithmic development as well as a call for more research regarding the clinical performance of current explanation techniques.
# Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment
## 3 Method
### Markov Decision Process
#### 3.1.2 Action Space  
The choice of action space significantly impacts the robustness and performance of the learned policy. A common approach is to work in the joint space of the robot [46]. However, in this approach, each action requires approximating the Jacobian of each pose. In addition, erroneous Jacobian modeling may prevent an optimal transfer of learning to reality. Instead, working in task space allows sending the pose commands in Cartesian space to the robot's internal controller, which uses the internally encoded Jacobian. Thus, specifying actions in task space can improve robustness and even accelerate the learning rate by improving sample efficiency [47]. Moreover, learning a policy in task space can ease its transferability to different robots with a varying number of joints, as long as the policy respects the joint boundaries of each robot while working in Cartesian space. Therefore, the action space \(A\) is defined as:  
\[A=[\Delta x,\Delta y], \tag{3}\]  
where \(\Delta x\) and \(\Delta y\) are translational and rotational motions along the Cartesian \(x\) and \(y\) axes, respectively.
# RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
## 3 FDWA Algorithm  
The RRT* algorithm is able to perform global path planning in an already established map model environment or with static obstacles. However, in the real environment, there are many uncertainties, such as unknown obstacles. If global path planning alone is used, the robot will easily collide with obstacles in such unknown environments. Therefore, in order to achieve the dynamic obstacle avoidance capability of a mobile robot, this paper incorporates a local path planning algorithm--the FDWA--for local dynamic obstacle avoidance.
###### Abstract  
Flood hazards have increasingly threatened the East Kazakhstan region in recent decades due to climate change and growing anthropogenic pressures, leading to more frequent and severe flooding events. This article considers an approach to modeling and forecasting river runoff using the example of the small Kurchum River in the East Kazakhstan region. The main objective of this study was to evaluate the numerical performance of the flood hazard model by comparing simulated flood extents with observed flood data. Two types of data were used as initial data: topographic data (digital elevation models and topographic maps) and hydrological data, including streamflow time series from stream gauges (hourly time steps) and lateral inflows along the river course. Spatially distributed rainfall forcing was not applied. To build the model, we used the software packages of HEC-RAS version 5.0.5 and MIKE version 11. Using retrospective data for 3 years (2019-2021), modeling was performed, the calculated boundaries of possible flooding were obtained, and the highest risk zones were identified. A dynamic map of depth changes in the river system is presented, showing the process of flood wave propagation, the dynamics of depth changes, and the expansion of the flood zone. Temporal flood inundation mapping and performance metrics were evaluated for each individual flood event (2019, 2020, and 2021). The simulation outcomes closely correlate with actual flood events. The assessment showed that the model data coincide with the real ones by 91.89% (2019), 89.09% (2020), and 95.91% (2021). The obtained results allow for a clarification of potential flood zones and can be used in planning measures to reduce flood risks. This study demonstrates the importance of an integrated approach to modeling, combining various software packages and data sources.  
monitoring system; modeling; flooding; databases; geoinformation system; hydrological information; space images; digital map; HEC-RAS; MIKE +
Footnote †: journal: Applied sciences