esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion  
The most challenging part of figuring out our methodology, which applies automation and orchestration and implements it via code, was twofold. There were inherent issues related to how we envision the method to work, and then there is the actual code that implements it.  
When we started developing this methodology, the goal was clear--we needed the repeatability of hundreds of evaluations across multiple CNIs in various configurations across different power profiles, frame sizes, and interfaces. The first challenge was related to CNI deployment, which has proven to be much more complex than we initially thought. The order in which we deploy packages is significant, as it always is with anything related to Kubernetes. Configuring some of these deployment procedures to work was very challenging. Good examples are Cilium in GENEVE configuration and Calico, which require a lot of evaluation and error handling so that their corresponding deployment processes work reliably. That meant we also needed to write Ansible code well and document it to debug it more easily.  
We deliberately went very deep into various scenarios related to the Ethernet frame sizes, as these are either excellent approximations for specific applications or represent how a physical network would be configured to a specific use case. For example, suppose we are using an HPC application that deals with object recognition from a live video feed. In that case, this scenario includes a large set of streamed serial data, which will lend nicely to a larger Ethernet frame and incoming package sizes. It should mean better throughput as less data will be spent on headers, but an even more significant benefit will be that we will need to spend less computing power on package framing. We can influence this efficiency by using TCP/UDP offloading on a network interface level, but that feature is only sometimes available in all situations. This will also mean we must spend more time configuring end-to-end Ethernet frame sizes, as we do not want mismatched frame sizes leading to performance degradation. This was the only way to avoid numerous issues that we might have with our network configuration, like:  
* Fragmentation or dropping;
* Frame size mismatching;
* Performance degradation;
* Path MTU discovery problems.  
Regarding implementation, the most extensive set of issues was related to Netplan's unpredictable behavior on Ubuntu 24.04, especially in more advanced network configurations such as link aggregated scenarios. On top of these issues associated with Netplan configuration for aggregation scenarios, performance could have been improved. We are not proponents of using such configurations at all, as there is a much simpler method to increase bandwidth without a lot of additional configurations (faster network cards, cabling, and switches), but we still made a set of playbooks in case people still want to do this kind of configuration.
# Dependence Representation Learning with Convolutional Neural Networks and 2D Histograms
## 3 Case Studies
### Correlation Regression
#### 3.2.1 Background: Correlations  
Pearson's correlation (pCor): pCor is the best known and most widely used correlation metric. It measures linear dependence and has a range of \([-1,+1]\), where \(+1\) is a perfect positive linear correlation and \(-1\) is a perfect negative linear correlation. A pCor of zero means that the features are not linearly correlated, but does not mean that the variables are independent. Note that pCor cannot capture any type of nonlinear dependence.  
Distance correlation (dCor): in contrast to pCor, dCor can be used to analyze nonlinear relationships, but cannot measure the direction of the correlation. The dCor takes values between \([0,1]\) and it is equal to zero if and only if the two random variables are independent. As dCor tends to 1, the linearity of the relationship increases. One disadvantage of dCor is its computational complexity, which is \(O(n^{2})\). A fast computation algorithm with \(O(n\log n)\) complexity was proposed to overcome this disadvantage [29].
## 2 Data Quality Parameters and Related Work in OSM
### Semantic Accuracy
(2010) have presented the methodology to quantitatively measure the content bias in contributed data and maintenance efforts by the users to provide a deeper analysis of data. They have found that no content bias exists between two segments of users (power users and crowd users) for contribution, whereas significant geographic bias variation can occur with changes in culture. [PERSON] et al. (2010) have revealed that maintenance efforts of GI vary country-wise in terms of the spread of maintenance efforts, data, users involved, places of involvement and the motive that triggers users. The third category to assess the semantic information is context-centric. It relies on the law of geography as discussed by [PERSON] (1999) that \"All things are related, but nearby things are more related than distant things\". For example, an off-ramp is required to depart from a freeway at a small angle in the direction of traffic flow, and freeways have strict limits for the radius of curvature based on design speed ([PERSON] et al., 2010; [PERSON] et al., 2011). Further, the LinkedGeoData ontology represents information in the form of tree structures with objects being connected by relationships (e.g., 'is_a') ([PERSON] et al., 2010; [PERSON] et al., 2011; [PERSON] et al., 2011). OSMonto, an ontology-based tool, was designed for use in a navigation web service by [PERSON] et al. (2010) for providing the context-centric assessment. [PERSON] et al. (2010) have developed a methodology on the semantic assessment of prohibition signs in OSM data. Indeed, context-centric assessment rules are essential to provide an automated triage of a very large number of updates and corrections by contributors. To summarize, researchers have used various established assessment methods by comparing OSM with authoritative datasets. However, very few studies have reported on the fitness-of-use of the dataset in developing countries ([PERSON] et al., 2011; [PERSON] et al., 2011; [PERSON] et al., 2011; [PERSON] et al., 2011; [PERSON] et al., 2011) using established quality indicators. The established methods are unsuitable to assess OSM data quality in the case of the non-availability of authoritative data ([PERSON] et al., 2012). Hence, assessment through intrinsic quality indicators would certainly encourage researchers to have a deeper understanding of datasets. The recent developments of OSM have witnessed intrinsic quality indicators to assess the data using history files ([PERSON] et al., 2010; [PERSON] et al., 2011; [PERSON] et al., 2011). Three frameworks have been developed on these lines by [PERSON] et al. (2010), [PERSON] and [PERSON] (2010) and [PERSON] and [PERSON] (2010). However, these frameworks suffer from various issues that pose a challenge before the researchers extend existing tools and identify more indicators to assess datasets. This study offers an extension to the processing toolbox of the QGIS framework for sufficiently assessing the data quality based on the intrinsic parameters by providing easy-to-use workflow models.
# Chemical Migration from Wine Contact Materials
## 5 Wine Migration Studies on Chemical Contaminants
### Analytical Methods
#### 5.2.4 Gravimetric Analysis  
One study focusing on the cork-wine interaction and in particular on the migration of non-volatile compounds, in addition to liquid absorption, performed migration experiments using two types of cork stoppers, natural and 1 + 1 technical (with an agglomerate cork body and a washer of natural corkwood glued on both ends) [84]. Bottles of 375 mL capacity were filled with 12% \(v/v\) aqueous alcohol as wine simulant and were stored laid down at 16 \({}^{\circ}\)C and relative humidity of 55% \(\pm\) 15% for 3, 6, 12, and 24 months. Overall migration was measured by eliminating the wine simulant in a rotary evaporator and drying the residue at 103 \({}^{\circ}\)C. Overall migration was the weight of the dry residue per stopper. The maximum values obtained were 20.20 and 6.18 mg/stopper for 1 + 1 technical and natural cork stoppers, respectively. In addition, absorption and overall migration were assessed by univariate variance analysis using a complete factorial model and cork stopper type, surface treatment, and contact time as fixed factors. The results of the statistical analysis demonstrated that the contact time, the type of cork stopper, and the surface treatment of the stopper affect the cork-liquid interaction phenomenon. In more detail, regarding overall migration, technical cork stoppers (1 + 1) produced higher values from six months on compared to the natural cork stoppers. In addition, it was shown that surface treatment produced a relatively small increase in overall migration, around 2 mg/stopper, an amount that seemed to remain constant and independent of the type of cork stopper and contact time [84].
# _M\({}^{2}\)Tames_: Interaction and Semantic Context Enhanced Pedestrian Trajectory Prediction
## 2 Related Work  
### Interaction Modeling  
Agent-to-agent interactions: In the field of trajectory prediction, the application of artificial neural networks leads to significant advancements. Social-LSTM [1], as a pioneering work, employs LSTM networks to model pedestrian dynamics and captures interactions among individuals through social pooling. SR-LSTM [3] further quantifies these interactions using a state refinement module's weighted mechanism. Social-GAN [2] introduces generative adversarial networks, offering diverse possibilities for trajectory prediction tasks for the first time. [PERSON] [8] integrates attention mechanisms to merge scene and social information for trajectory prediction. Trajectron++ [9] proposes a modular, graph-structured recurrent model to predict the future trajectory distribution of multiple pedestrians. Conditional Generative Neural System (CGNS) [10] combines the advantages of conditional latent space learning and variable dispersion minimization to learn the future trajectory distribution space. PECNet [11], employing an endpoint-conditioned encoder, simulates human trajectories and generates socially compliant trajectories through social spatio-temporal graphs. Despite these advances, these models still face challenges in handling complex social interactions.  
_12_, proposed in this study and inspired by the work of [12], comprehensively considers agents' historical trajectories and the motion patterns of nearby agents. It learns interaction types through multi-scale feature processing and effectively reduces the complexity and prediction error in pedestrian trajectory forecasting.  
Agent-to-environment interactions: Early trajectory prediction research relies on physical models and statistical techniques, such as the social force model [13; 14]. However, these models often neglect environmental semantic information, especially in complex urban settings. With the advancement of deep learning, particularly the application of Convolutional Neural Networks (CNNs), in image recognition and scene parsing, new trajectory prediction models begin to incorporate environmental semantics. For instance, [PERSON] [8] integrates attention mechanisms to merge scene and social information for trajectory prediction. Social Ways [15] uses generative adversarial networks to learn the distribution of pedestrian trajectories, emphasizing the generation of realistic trajectories while considering interactions among agents and between agents and the environment. SHENet [16] introduces a cross-modal interaction module for modeling the interactions between individual past trajectories and their surroundings. This study utilizes refined semantic segmentation through the modified U-Net architecture to learn spatio-temporal context information, aiding the model in better understanding interactions between agents and the environment.
# Strain-Softening Analyses of a Circular Bore under the Influence of Axial Stress
## 4 Numerical Examples
### Discussions of the Numerical Analysis  
In last subsection, a set of parameters was used to study the influence of the out-of-plane stress on bore stability. The plastic radius and the distribution of the stresses and displacements were presented and compared for \(q=30\) and 60 MPa.  
Figures 8 and 13 indicate that the axial in situ stress affects displacement, plastic radius and type of plastic zone in the rock mass surrounding the bore in strain-softening analysis. For the same '\(\eta\)\", a comparison of Figures 11, 12, 16 and 17 show that the displacement and plastic radius are larger when \(q=60\) than when \(q=30\) MPa.  
Different evolutionary laws of the dilation angle were considered in the strain-softening analysis, and the calculation results were presented in Table 3. In the first case, the dilation angle (\(\psi\)) and the internal friction angle (\(\varphi\)) were equal and had the same evolution law in the post-failure stage, and the displacements at the bore wall were the largest. When the dilation angle was half of the internal friction angle and both evolved in a similar manner, the displacements at the bore wall were much smaller than the displacements in case 1.Case 3 showed displacements obtained by means of the parameters in Table 1 and were the smallest.  
When \(q=30\) MPa, the displacement in case 1 was about 4 times that of case 2, and about 6 times that of case 3. When the out-of-plane stress increased to \(q=60\) MPa, the displacement in case 1 was about 5 times that of case 2, and about 8 times that of case 3. The differences were obvious for various evolution laws of the dilation angle. Moreover, the ascending out-of-plane stress made the difference increase.  
Normally, most engineering rock masses display neither in an elastic-perfectly plastic nor in an EBP way. They behave in a strain-softening manner as shown in Figure 5. During the strain-softening process, a progressive loss of strength occurs and the parameters (cohesion \(c\), internal friction angle \(\varphi\) and dilation angle \(\psi\)) will vary from their peak values to residual ones. The EBP model overestimates the deformation and the plastic zone of the geomaterials. However, the EP model underestimates the deformation and the plastic region of geomaterials. If the strain-softening analysis is carried out, the numerical results are more appropriate for practical use. When deep bores are excavated in hard rock masses, high geostress occurs. In this situation, the influence of axial stress cannot be ignored when the problem is treated as a normal plane strain.
###### Abstract  
Background: In most software projects, new members must comprehend the features already implemented since they are usually assigned during the project period. They often read software documents (e.g., flowcharts and data models), but such documents tend not to be updated after they are created. Herein we focus on tickets issued because they are created as a project evolves and include the latest information of the implemented features. Aim: The purpose of this paper is to clarify the way of helping new members understand the implemented features of a project by using tickets. Methodology: We propose a novel method to categorize tickets by clustering and visualizing the characteristics of each category via heatmapping and principal component analysis (PCA). Our method estimates the number of categories and categorizes issue tickets (tickets) automatically. Moreover, it has two visualizations. Ticket lifetime visualization shows the time series change to review tickets quickly, while ticket feature visualization shows the relationships among ticket categories and keywords of ticket categories using heatmapping and PCA. Results: To evaluate the effectiveness of our method, we implemented a case study. Specifically, we applied our method to an industrial software development project and interviewed the project members and external experts. Furthermore, we conducted an experiment to clarify the effectiveness of our method compared with a non-tool-assist method by letting subjects comprehend the target project, which is the same as that of the case study. These studies confirm our method supports experts' and subjects' comprehension of the project and its features by examining the ticket category lifetimes and keywords. Implication: Newcomers during project onboarding can utilize tickets to comprehend implemented features effectively if the tickets are appropriately structured and visualized. Conclusions: The original contribution of this paper is the proposal of the project feature comprehension method by visualizing the multi-dimensional nature of requirements in an organized and structured way based on available tickets and the result of its application to the industrial project.  
software development projects; software comprehension; issue tickets; text mining; machine learning +
Footnote †: journal: Applied sciences
###### Abstract  
In vehicular ad hoc networks, trajectory-based message delivery is a message forwarding strategy that utilizes the vehicle's preferred driving routes information to deliver messages to the moving vehicles with the help of roadside units. For the purpose of supporting trajectory-based message delivery to a moving vehicle, the driving locations of the vehicle need to be shared with message senders. However, from a security perspective, vehicle users do not want their driving locations to be exposed to others except their desired senders for location privacy preservation. Therefore, in this paper, we propose a secure location-sharing system to allow a vehicle user (or driver) to share his/her driving trajectory information with roadside units authorized by the user. To design the proposed system, we put a central service manager which maintains vehicle trajectory data and acts as a broker between vehicles and roadside units to share the trajectory data on the cloud. Nevertheless, we make the trajectory data be hidden from not only unauthorized entities but also the service manager by taking advantage of a proxy re-encryption scheme. Hence, a vehicle can control that only the roadside units designated by the vehicle can access the trajectory data of the vehicle.  
VANETs; location sharing; authentication; privacy preservation; trajectory-based message delivery +
Footnote †: journal:  
_Article_  
Self-Controllable Secure Location Sharing  
for Trajectory-Based Message Delivery on  
Cloud-Assisted VANETs
# A New, Fast Pseudo-Random Pattern Generator for Advanced Logic Built-In Self-Test Structures
## 3 Classical Method to Design LBIST Circuits with the STUMPS Architecture
### Phase Shifts between Binary Sequences at Outputs of T-type Flip-Flops Incorporated into DT-LFSRs  
This section is dedicated to investigating phase shifts between bitstreams at the outputs of D- and T-type flip-flops in the DT-LFSR type ring registers. Let us consider an \(n\)-bit register with the D\({}^{\text{\tiny{T}}}\)T\({}^{n-k}\) structure and associated with the primitive polynomial \(p(x)=1+x^{k}\cdot(1+x)^{n-k}\). Let us also denote the phase shift introduced by D-type and T-type flip-flops present in that register as \(\Phi_{\text{D}}\) and \(\Phi_{\text{T}}\), respectively. The analysis carried out in [38] makes it possible to find out that \(\Phi_{\text{D}}=1\), while the phase shift \(\Phi_{\text{T}}\) can be calculated from the following congruence:  
\[(n-k)\times\ \Phi_{\text{T}}+k\ \equiv\ 0(mod\ 2^{n}-1), \tag{3}\]  
Please note that the phase shift \(\Phi_{\text{D}}\) is identical for all D-type flip-flops incorporated into the DT-LFSR structure; likewise, the phase shift \(\Phi_{\text{T}}\) is the same for all T-type flip-flops within that register. In addition, the congruence (3) indicates that \(\Phi_{\text{T}}\
eq 1\) for every \(n>1\). According to Corollary 2 in [53] (p. 20), when g.c.d. \((n-k,2^{n}-1)=1\), the \(\Phi_{\text{T}}\) solution of the Equation (3) always exists, and the solution can be found by means of \(O\Big{(}\log^{3}(2^{n}-1)\Big{)}\) binary operations. In practice, for the example when \(n=100\) and \(k=63\), the \(\Phi_{\text{T}}\) solution of the Equation (3) was found in less than 4 s with the use of a calculator available online [61]. The \(n\) and \(k\) parameters provided in that example correspond to the only D\({}^{63}\)T\({}^{37}\) structure of a DT-LFSR register with the length of 100 bits that is associated with a primitive polynomial.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 3 Analysis of the Current Situation and Limitations of Top-Down Monitoring Methods
### Gaussian Plume Model
Nevertheless, the low resolution of carbon satellite data and the impact of environmental noise (e.g., clouds and precipitation) continue to present challenges in terms of identifying biomass burning emission sources and accurately determining atmospheric background concentrations. For instance, sensors such as OCO-2 and GOSAT have low resolutions that prevent them from capturing smaller farmland fires and transient fire points with low emission intensities, resulting in an underestimation of emissions.
# Quantum Key Distribution Networks: Challenges and Future Research Issues in Security
###### Abstract  
A quantum key distribution (QKD) network is proposed to allow QKD protocols to be the infrastructure of the Internet for distributing unconditional security keys instead of existing public-key cryptography based on computationally complex mathematical problems. Numerous countries and research institutes have invested enormous resources to execute correlation studies on QKD networks. Thus, in this study, we surveyed existing QKD network studies and practical field experiments to summarize the research results (e.g., type and architecture of QKD networks, key generating rate, maximum communication distance, and routing protocol). Furthermore, we highlight the three challenges and future research issues in the security of QKD networks and then provide some feasible resolution strategies for these challenges.  
quantum key distribution; quantum key distribution network; unconditional security