esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
It is well known that the introduction of large-sized rare-earth elements (REE) (with ionic radius \(\geq\) 0.99 A) leads to the stabilization of the Rh phase, which is probably due to the creation of the most favorable (dimensional) conditions for the existence of BiFeO\({}_{3}\), which is known [5] as a boundary position in the perovskite family.  
In the solid solutions under consideration, the Rh phase, characteristic of BiFeO\({}_{3}\), coexists with the emerging R phase. The observed, apparently, is due to the presence of a large amount of Bi- and Fe-containing impurities, usually accompanying the formation of BiFeO\({}_{3}\), and the ballast phases of the non-perovskite structure with the participation of REE, the number of which increases as the ionic radius of the REE decreases, as is clearly seen in Figure 2 with microphotographs of the grain structures of the studied objects. Accumulation of such phases (\"gray\" grains) leads to loosening of the microstructure, thickening of the boundaries of crystallites, and deformation of the habitus of grains of the main phase.  
To describe the process of low-temperature dielectric relaxation, the curves ln\(\tau\)(1/\(T\)) (\(\tau\)--relaxation time from Cole-Cole plot) and ln\(\sigma_{s}\)(1/\(T\)) (\(\sigma_{s}\)--reach-through conductivity) were constructed (Figure 4c,f,i). All the obtained dependences satisfy the Arrhenius law with the activation energy, \(E_{\rm a}\), in the range of 0.5-0.7 eV (see Table 1), which is characteristic of Maxwell-Wagner relaxation [9] process associated with accumulation of free charges on the interface of components in spatially inhomogeneous media against the background of interlayer, interphase and intraphase rearrangements, inter alia, by the accumulation of charge in the near-electrode layer. It is clearly seen that in the case of each object the values of \(E_{\rm a}\) are almost identical. One can suggest that dielectric relaxation is caused by the accumulation of free charges, which is consistent with the Maxwell-Wagner model. This assumption is also confirmed by a sharp increase in the dielectric constant and dielectric loss above the temperatures at which the described relaxation processes take place.  
The reason for its development is the natural-composite structure of BiFeO\({}_{3}\) and BiFeO\({}_{3}\) with REE, which is formed on the basis of at least four Bi-, Fe-containing compounds (Bi\({}_{2}\)O\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), Bi\({}_{25}\)FeO\({}_{40}\), Bi\({}_{2}\)Fe\({}_{4}\)O\({}_{9}\)) that practically always accompany the formation of BiFeO\({}_{3}\) remaining in it (in different amounts) in the form of ballast phases and undergoing a series of bifurcations in the above-mentioned temperature ranges.  
Figure 5: Temperature dependencies of the specific heat, \(C_{\rm p}\), (**a**) and thermal diffusivity, \(\chi\), (**b**) of studied Bi\({}_{1-x}\)Er\({}_{x}\)FeO\({}_{3}\) ceramic samples.
To reduce the number of repeated tokens in text generation when using encoder-decoder models, a repetition reduction module (RRM) [35] is proposed to supervise the training procedure by capturing the consistency of a sentence sample between the encoding and decoding sides. In this study, a contrastive learning penalized cross-entropy with diversity contrastive search (CLpCEwDCS) decoding framework is proposed. To improve the consistency of sentence embeddings and to relieve the anisotropy issue, CL is integrated into the fine-tuning stage and a novel objective function is formed as contrastive learning penalized cross-entropy (CLpCE). Moreover, in the decoding stage, a diversity contrastive search (DCS) decoding method is designed to balance the diversity and quality of report generation. For mitigating degenerative behaviors, the core idea of the DCS decoding method is different from the FECS method [34]. FECS promotes the diversity by augmenting a faithfulness reward term into the CS framework, while DCS determines the outcome via the estimation of the maximum token repetition ratio (maxTRR) of candidate outputs. Specifically, the proposed metric maxTRR estimates the token repetitions in the token space before the text generation, while the measure of word-, phrase-, and the sentence-level consecutive repetitions [36] or the subsentence-level consecutive repetition [35] is for performance evaluation after the text generation. Overall, the contributions of this study can be summarized as follows:  
1. An objective function CLpCE is designed for balancing both unsupervised and supervised learning in the model fine-tuning stage to enhance the consistency of feature representation of sentence embeddings. 2. A novel decoding method DCS is developed to improve the representation diversity and to relieve anisotropic distributions of token generation with maintained quality of text summarization. 3. A supplementary metric named the maximum of token repetition ratio (maxTRR) is implemented which estimates the token repetition and determines the outcome of text generation. 4. The effectiveness of the proposed CLpCEwDSC decoding framework is verified, and competitive performance and better diversity are observed on the DRG task. The remainder of this paper is organized as follows: Section 2 presents the relevant techniques of GPT-2 and contrastive learning of sentence embeddings. The data collection, the proposed framework, experiment design, implementation details and parameter settings are shown in Section 3. We then report the DRG accuracy and diversity and the effect of the diversity control in Section 4. After that, we discuss the results and some limitations of this work in Section 5, and conclude this work and future directions in Section 6.
## 6 Fabrication and Experimental Verification

Table 4: Comparison between the proposed Antenna and other Antennas (Dual CP) (Frequency unit: GHz). challenging which might be overcome through the double tape (conducting tape for feed) and glue. A photograph of the fabricated prototype and random pictures of the antenna under test in the anechoic chamber have been portrayed in Figure 11.  
Figure 12 depicts the predicted and measured bore-sight gains, demonstrating that a sufficient gain of over 5.95 dBic was achieved over the achievable circular polarization spectrum. The antenna emits CP radiation in two operating bands, with measured realized gains ranging from 5.12 to 5.65 dBic and 2.40 to 5.95 dBic, respectively. Figure 13 depicts the predicted and measured radiation patterns for two CP modes. In the boresight direction (\(\theta=0^{\circ}\)), the measured RHCP field strength is roughly 15 dB greater than LHCP at 3.47 GHz, as shown in Figure 13. (a). This shows that in the bottom band, the proposed DRA allows for anticlockwise CP wave rotation. Meanwhile, as shown in Figure 13, the measured LHCP field intensity at 4.41 GHz is about 18 dB higher than that of RHCP; (b). This shows that the upper band rotates in a clockwise direction. As a result, the dual-band CP antenna shown here generates directed radiation with low cross-polarizations that are stable. Figure 11: _Cont._  
Figure 10: Simulated and Fabricated results of S\({}_{11}\) and the axial ratio of proposed RDRA. Figure 11: Fabricated Antenna and pics from the chamber. (**a**) Simple RDRA, (**b**) single strip, (**c**) cross-shape feed, (**d**) top view of proposed RDRA with floating parasitic patch, (**e–h**) AUT in the chamber, and (**i**) VNA. Figure 12: Simulated and measured gain over both bands.
###### Abstract  
In this study, a laser remelting process was applied to the FeCrCoNiTiAl\({}_{0.6}\) high entropy alloy coating in order to improve the density and the surface quality of the coating. The coating was fabricated by high-velocity-oxygen-fuel (HVOF) technology. The microstructure and phase composition of the coating were investigated by scanning electron microscopy (SEM), energy dispersive X-ray spectrometer (EDS), X-ray diffractometer (XRD) and confocal scanning laser microscope (CSLM). Moreover, the wear behavior of the coating was evaluated by use of a ball-on-disc test. The coating was denser after laser remelting treatment by eliminating the previous lamellar structure. The microstructure of the laser-remelted coating exhibits two body-centered cubic (BCC) phases, which is different from the HVOF coating. In addition, aluminum oxide formed during laser remelting. Different from the wear mechanism of the HVOF coating, which comprised abrasion and fatigue, the major wear of the laser remelted coating was abrasion.  
high entropy alloy; coating; laser remelting; microstructure; friction behavior +
Footnote †: journal: Applied Sciences
# A Deep Learning Architecture For 3D Mapping Urban Landscapes
## 3 Network Architecture  
The model proposed to infer a complete 3D shape of a landscape and the objects present in the terrain from a 2D aerial images sequence is shown in Figure 1. It consists of an autoencoder configuration, with the main parts being the Encoder, Bottleneck, and Decoder, which are described in detail below.  
The Encoder has been configured as a Residual Network (ResNet) and is composed of four Residual Convolutional Blocks. This configuration allows obtaining dense feature maps from the input image sequence. At the output of the Encoder, we obtain data correspondences to be able to generate point clouds with the next stage.  
On the other hand, and unlike a classic autoencoder architecture, in this proposal the decoder is based on a Generative Adversarial Network (GAN) architecture. Which is composed of a Generator network and a Discriminator network, capable of generating a point cloud from the input image sequence and the correspondences generated in the previous stage. This stage is called GAN-Decoder.
# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 1 Introduction
### State of the Art
PM and privacy gaps: In PM, to maintain the performance of data analytics, the event logs are kept un-anonymized. This privacy lapse allows the adversary to take advantage of the readily available PII of the data subjects. For this reason, many healthcare providers are reluctant to share sensitive event logs with third-party data processors outside the premises of their organization. In this regard, the _Privacy-Preserving Process Mining (PPPM)_: The proposed Privacy-Preserving Process Mining (PPPM) framework seeks further privacy-preservation by adding noise to pseudonymized healthcare dataset [43]. For PPPM, the metadata with a detailed description of added noise type, quantity, and purpose must be shared with a Trusted Third Party (TTP) [41; 43; 52; 53; 54]. Differential Privacy (DP): New techniques and tools are under consideration to maximize data utility while intrinsically integrating privacy in healthcare analytics. DP is well acclaimed for striking a fair balance between privacy preservation and data utility across multiple domains [55; 56; 57; 58; 59; 60; 61]. DP is achieved by proportionately adding noise to the event log. The noise is added to obscure the reidentification of data subjects while maintaining the accuracy/correctness of data analytics [55; 56]. In PPPM, the protection models based on the Differential Privacy notion provide means to thwart potential privacy leakages in event logs [41]. Noise Addition and PUT in Healthcare Metadata and Process Mining: Concerning PUT, the healthcare providers avoid noise-addition and other such data-alteration methods to ascertain overall healthcare performance [62]. For example, noise addition (for privacy preservation of the datasets) is avoided in the clinical practice-trials database to maintain data utility). Similarly, in Process Mining (PM), disproportionately added noise to datasets can hamper the usefulness of the original datasets (i.e., data utility) [43] causing PUT. The PUT dilemma motivated us to use real-world (healthcare) datasets for the evaluation of the (publicly available) ProM tool's [63] noise-adding plugins. We evaluate the plugins to precisely judge them for their data utility using PM conformance checking indicators. The conformance checking indicators include fitness, precision, and complexity. The privacy-preserving third-party data processing evaluation allowed finding the influence of PUT from the perspective of promtool end-user using publicly available, real-world healthcare event logs. Considering the importance of privacy assurance and the fears it entails, it is vital to evaluate the current situation as a priority. Only then can improved set standards guarantee privacy protection for all stakeholders with standardized indicators and their respective metrics. Furthermore, value modeling bridges the gap between IT and organizational undertakings. The conceptual modeling value frameworks also highlight the financial aspect of mutual undertakings in the Dutch healthcare landscape.
###### Abstract  
The aim of this study was to develop an optimized physical activity classifier for real-time wearable systems with the focus on reducing the requirements on device power consumption and memory buffer. Classification parameters evaluated in this study were the sampling frequency of the acceleration signal, window length of the classification fragment, and the number of classification features, found with different feature selection methods. For parameter evaluation, a decision tree classifier was created based on the acceleration signals recorded during tests, where 25 healthy test subjects performed various physical activities. Overall average F1-score achieved in this study was about 0.90. Similar F1-scores were achieved with the evaluated window lengths of 5 s (0.92 \(\pm\) 0.02) and 3 s (0.91 \(\pm\) 0.02), while classification performance with 1 s were lower (0.87 \(\pm\) 0.02). Tested sampling frequencies of 50 Hz, 25 Hz, and 13 Hz had similar results with most classified activity types, with an exception of outdoor cycling, where differences were significant. Using forward sequential feature selection enabled the decreasing of the number of features from initial 110 features to about 12 features without lowering the classification performance. The results of this study have been used for developing more efficient real-time physical activity classifiers.  
accelerometer; activity classification; activity trackers; machine learning; wearable systems +
Footnote †: journal: Applied Sciences
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 8 Discussion of Limitations
### Reliability Barriers  
The use of privileged Daemonsets is paramount in any reconfiguration tactic that involves modifying worker nodes of a K8s cluster.  
While several mainstream hosted K8s vendors all advocate the use of Daemonsets for modifying the software that runs on worker nodes [45; 46], they also warn that not all possible modifications are supported and some of them by even lead to unhealthy nodes. Therefore, they recommend extensive testing after the modification. From a risk management, this is line with the shared responsibility model that postulates that the security of the worker nodes is the responsibility of the customer. Only the security of the control plane is the responsibility of the hosted Kubernetes vendor.  
Currently, our controller monitors the configuration states of each member cluster by querying their API server. However, this approach is not reliable enough in determining  
Figure 15: Reconfiguration time for each feature in a newly created cluster.  
whether the desired features are correctly working. The configuration states queried from the API server might not match the actual configurations. For example, though the CNI network plugins are installed on the cluster, the cluster might still use the Kubenet network plugin for pod networking. The open-source distribution of K8s has an end-to-end testing suite that can run conformance tests on clusters to determine if a feature is actually supported. However, currently, the coverage of optional features in the testing suite is low [35].  
Our autonomic controller keeps monitoring the cluster when previously activated features are not properly configured anymore. When nodes are autopgraded, autorepaired or autoscaled, for example, all modifications to worker nodes are lost. This is where our autonomic controller shines, as it will trigger the necessary reconfigurations that correspond with the set of currently desired feature configurations again.
# Optimization of the Position and Stiffness of Passive Walking Assistance Devices
###### Abstract  
Walking is a fundamental movement in daily life; however, many factors affect walking that may reduce the mobility of the people. Walking assistance devices can help with gaining mobility back for people who suffer from walking problems. In the present study, a computational method to determine the location and stiffness of the assistive walking systems was developed. The human walking model was created by using nine rigid bodies and eight revolute joints connecting them in the sagittal plane. The walking assistance system was considered as a tension spring with both ends attached to the human walking model. A coordinate system was defined along the distal-proximal direction of the human body. The position of the walking assistance system was determined by using four design variables, and the optimal position of the assistive walking system to reduce the total positive joint energy was found around the hip joint at a walking speed of 1.3 m/s. Hip joint moment and power were significantly affected by the walking assistance system, and the total positive joint energy was reduced by 8.8%. Because walking speed significantly affects walking kinematics and kinetics, the effect of walking speed on the optimal walking assistance device was investigated. The position of the device was kept the same, and the optimal stiffness and free length of the spring were found at different walking speeds. Two different cases were considered: a speed-specific design in which stiffness characteristics were separately optimized for each speed and a general design in which stiffness characteristics were optimized by considering all walking speeds. It was found that, in both cases, hip joint moment and power significantly reduced, and the speed-specific design produced a slightly larger reduction in total joint energy. The performance of the walking assistance systems in both cases were found to be higher at faster walking speeds.  
exoskeletons; walking assistance; exosuit; optimization; joint moment; joint power Article
# The Optimization of UAV-Assisted Downlink Transmission Based on RSMA
## 3 Mathematic Modeling
### The Downlink RSMA System's Modeling  
The downlink RSMA system model studied in this paper is shown in Figure 1. Different groups of users use different communication channels, and users within the same group communicate using the RSMA mechanism, where users need to parse the public messages sent by the base station as well as the private messages sent to themselves.  
The rate optimization problem for downlink RSMA systems can be described as Equation (1), where \(nu_{\text{down}}(i)\) is the number of users in the group \(i\); \(v_{\text{down}}(i,j)\) is the downlink transmission rate of user \(j\) in group \(i\); \(pos_{u}=(pos_{u}x,pos_{u}y,pos_{u}.z)\) is the location of the UAV base station; \(x_{\text{min}}\), \(y_{\text{min}}\), \(z_{\text{min}}\), \(x_{\text{max}}\), \(y_{\text{max}}\) and \(z_{\text{max}}\) are UAV position restrictions; \(\alpha(i,j)\) is the rate proportion allocated to user j in the public message of the user group \(i\); \(p_{c}(i)\) is the public message downlink transmission power of the \(i\)-th group;  
Figure 1: Downlink RSMA network.  
\(B_{\text{down}}=\{B_{\text{down}}(i),B_{\text{down}}(2),...,B_{\text{down}}(nc_{ \text{down}})\}\) is the bandwidth allocation for each user group; \(B_{da}\) is the total bandwidth of the system; \(P_{\text{down}\_\text{aloc}}\) is the power allocation for each user group and user member; \(P_{\text{BS}}\) is the power limit of the base station; and \(ve_{\text{down}}(i,j)\) is the downlink rate requirement of the corresponding user.  
\[\left\{\begin{array}{ll}\arg&\max_{pos_{u},B_{\text{down}},R,P_{\text{down }\_\text{aloc}}}\sum_{i=1}^{nc_{\text{down}}}\sum_{j=1}^{nt_{\text{down}}(i)}v _{\text{down}}(i,j)\\ s.t.&x_{\min}\leq pos_{u}.x\leq x_{\max},y_{\min}\leq pos_{u}.y\leq y_{\max},z_{ \min}\leq pos_{u}.z\leq z_{\max}\\ &\sum_{i=1}^{nc_{\text{down}}}B_{\text{down}}(i)=B_{da},B_{\text{down}}(i)\geq 0,1\leq i\leq nc_{\text{down}}\\ &\sum_{j=1}^{nt_{\text{down}}(i)}\alpha(i,j)=1,1\leq i\leq nc_{\text{down}} \\ &\alpha(i,j)\geq 0,1\leq i\leq nc_{\text{down}},1\leq j\leq nu_{\text{down}}(i) \\ &p_{c}(i)+\sum_{j=1}^{nt_{\text{down}}(i)}p_{p}(i,j)=P_{\text{down}}(i)\\ &\sum_{i=1}^{nc_{\text{down}}}P_{\text{down}}(i)\leq P_{\text{BS}}\\ &v_{\text{down}}(i,j)\geq v_{\text{down}}(i,j),1\leq i\leq nc_{\text{down}},1 \leq j\leq nu_{\text{down}}(i)\end{array}\right. \tag{1}\]
# Multi-Party Controlled Semi-Quantum Dialogue Protocol Based on Hyperentangled Bell States
## 3 Security Analysis
### The Entangle-Measure Attack  
The entangle-measure attack refers to the scenario where [PERSON] uses a unitary operation \(\hat{E}\) to entangle an auxiliary particle \(\left|\varepsilon\right\rangle_{e}\), which she has prepared, with the intercepted sequence of \(S_{B}\) particles. Subsequently, when [PERSON] completes his encoding and transmits the particles to [PERSON], [PERSON] intercepts them again and applies another unitary operation \(\hat{F}\). Finally, by measuring her auxiliary particle \(\left|\varepsilon\right\rangle_{e}\), [PERSON] attempts to infer useful information. \[\hat{E}= \begin{pmatrix}\alpha_{1}&\beta_{2}\\ \beta_{1}&\alpha_{2}\end{pmatrix} \bigotimes \begin{pmatrix}\alpha_{1^{\prime} }&\beta_{2^{\prime}}\\ \beta_{1^{\prime}}&\alpha_{2^{\prime}}\end{pmatrix}
# Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network
## 4 Example Analysis
### Calculation of Posteriori Probability of Root Nodes  
Based on Formula (10), the fuzzy posteriori probability can be calculated and only indicates the occurrence probability of a fault. For instance, when comparing \(\overline{P}(x_{1}=1|T=0.5)\), \(\overline{P}(x_{2}=1|T=0.5)\), and \(\overline{P}(x_{3}=1|T=0.5)\), the common factor \(\overline{P}(T=0.5)\) can be eliminated, so fault probability is more readable. To clearly indicate fault probability, the defuzzification is conducted on fuzzy probability by using the centroid method. The defuzzified fault probability is displayed in Table 5. The calculation of posteriori probability is mainly used in fault maintenance of root nodes when faults occur on leaf nodes, so there is no need to calculate probability of leaf nodes in normal state.  
As shown in the Table 5, \(P(x_{1}=0.5|T=0.5)>P(x_{2}=0.5|T=0.5)>P(x_{3}=0.5|T=0.5)\). If fault of leaf node occurs at 0.5, the probability of root node \(x_{1}\) with fault occurring at 0.5 being the maximum, followed by nodes \(x_{2}\) and \(x_{3}\). If fault 1 occurs in root nodes, fault 1 of each module is eliminated, so \(P(x_{2}=0.5|T=1)>P(x_{1}=0.5|T=1)>P(x_{3}=0.5|T=1)\). Fault at 0.5 should be looked up from root node \(x_{2}\), and then \(x_{1}\) and \(x_{3}\). In the example, in order to simplify calculation process, the simple known condition is set for fault 1 in the CPT, so only \(p(x_{i}=1|T=0.5)=0\) and \(p(x_{i}=1|T=1)=1\) can be deduced. That is, two conclusions--i.e., minimum and maximum fault probability can only be made--while more delicate differences of them cannot be specifically distinguished. As CPT data become more complete, more detailed results can be obtained.