esa-sceva/satcom-chunk-collection huggingface dataset:
# Electric-Field-Coupled Resonator Antenna for 5G Applications
## 5 Results and Discussion  
A prototype of the proposed antenna (Antenna 4 in Figure 7d) is fabricated and measured to validate the above analysis. The simulated results are in good agreement with measured results, where a slight variation is observed. It is actually due to the cable loss and fabrication tolerance. In addition, the cable used in the Satimo StarLab measurement system is very long. The reflection coefficient of the prototype is measured using a PNA network analyzer N5227A (10 MHz to 67 GHz), whereas the radiation patterns and gain are measured using the Satimo StarLab antenna pattern measurement system. The photography of the fabricated prototype is presented in Figure 12. The measurement setup of PNA and Satimo Star LAB are shown in Figure 13a,b. The simulated and measured reflection coefficient and realized gain of the prototype are shown in Figure 14a and b, respectively. The measured reflection coefficient for \(\mid\)S11\(\mid\)\(<\) -10 dB is from 3.4 GHz to 4.2 GHz, and the maximum gain of 1.16 dBi is realized.  
Figure 11: Equivalent circuit of the proposed antenna (Antenna 4 in Figure 7d). Here, Cse1 and Cse2 = The capacitance between the edges of the capacitive gap and the left arm. Cs = The capacitance due to the gap of L6. Ca = The capacitance be-tween the two arms on the right side. Cp1 and Cp2 = The capacitances between the parasitic blocks and the patch.Cg1 and Cg2 = The coupling capacitances between the patch and parasitic blocks with the ground.  
Figure 10: Surface current distribution of (a) Antenna 3 and (b) Antenna 4 at 3.6 GHz and 3.8 GHz, respectively.  
pattern is omnidirectional, and both patterns are stable. However, the measured cross-polarization levels in both planes are less than \(-12\) dB. The simulated 3d gain plots at three different frequencies are shown in Figure 16.  
A comparison between the proposed antenna with some published referenced research is listed in Table 2. All the research works except reference [5] use substrate materials of high dielectric constants, which will cause high dielectric loss. They are slightly smaller or equal in electrical length to the proposed state-of-the-art. However, all of them have the problem of low bandwidth at the low-frequency band except references [3; 9] and the presented work. However, reference [9] shows a huge difference between simulated and measured data. Only reference [5], which uses substrate material of low dielectric constant, is not more compact than the proposed one, and the bandwidth is also poor. So, the presented antenna is superior to all other research works listed in the table in terms of bandwidth and dielectric loss.  
Figure 16: Simulated 3d gain at (**a**) 3.4 GHz, (**b**) 3.9 GHz, and (**c**) 4.2 GHz.  
Figure 15: Simulated and measured radiation patterns of the proposed antenna. (**a**) YZ-plane (elevation plane), (**b**) XY-plane (horizontal plane).
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
###### Abstract
In addition, new functional components that had not been identified in previous works are included in the proposed architecture in this paper regarding mobility, RAN management and optimization, RAN NDT exploitation and NDT management. Moreover, the designed multi-component architecture allows a flexible customization of the RAN NDT to be used in different applications and use cases. Second, given the relevance and benefits of using NDT for training RL solutions for the RAN, this NDT use case is addressed in detail in the paper and different functionalities are proposed for supporting the NDT configuration and the training of RL algorithms on it. These functionalities are described in the context of the O-RAN Alliance ML workflow [16]. To the authors' best knowledge, none of the previous works has proposed the integration of a RAN NDT within the O-RAN framework, nor have the functional requirements been discussed for conducting the training on an NDT. So, this paper presents an illustrative example of the proposed framework for training that consists of the implementation of an NDT for the RAN to conduct the training of a specific RL solution for capacity sharing. The implemented NDT is built following the proposed NDT architecture in the paper. Results on the training of the capacity sharing solution on the implemented RAN NDT are provided, showing the usefulness of the NDT for the RAN as a platform to successfully train RL solutions. The rest of the paper is organized as follows. Section 2 presents the architecture of the NDT for the RAN. Then, Section 3 presents a functional model for training RL solutions on an NDT of the RAN. Section 4 presents a specific applicability example of the proposed model illustrated with some results. Finally, conclusions and future work are summarized in Section 5.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 5 Results and Discussion
### Response Time and QoS-Node Content Store Size  
A lower content response time is a desirable characteristic in every network scenario, and more so in networks implementing QoS privileges. A lower response time provides a  
Figure 6: Content store size vs. hit rate for overall traffic. (**a**) Content store size vs. hit rate (overall traffic) for \(|M|\) = **500. (b)** Content store size vs. hit rate (overall traffic) for \(|M|\) = **1000.**better user experience and signifies lower traffic congestion and better resource utilization. Response times corresponding to the QoS-node content store size for QoS traffic among overall traffic is provided in Section 5.4.1. Response times corresponding to QoS-node content store size for overall traffic is provided in Section 5.4.2. It may be observed from both the comparisons that QoS traffic received better treatment in the network through the QLPCC strategy.
## 2 Materials and Methods
### Signal Processing  
The superposition of the light reflected by the sample and the light reflected from the reference arm results in an interferogram, which is detected with the spectrometer. To be able to read depth information from the raw signal, a fast Fourier transformation (FFT) is performed on the measured spectrum by means of interpolation after conversion into wavenumber units. The height information in the generated A-Scan is not given by the absolute intensity information of the backscattered light, meaning that a stable signal can still be detected even in the case of larger fluctuations in the process that can influence the proportion of backscattered light. Figure 6a shows the interference spectrum, which is a combination of the Gaussian shaped intensity spectrum of the light source and the overlaying modulation signal. Figure 6b shows the corresponding A-Scan. In the case of highly reflective metal surfaces, the height of the peak can be well extracted from the background noise and provides the absolute distance to the sample surface. With the help of the scanning by the galvo mirrors used and the relative movement of the process head to the weld bead, a volume scan, the geometric profile, is created. In order to eliminate any remaining signal noise, a dark reference is carried out before the measurement process. For this purpose, the sample arm is blocked, and the resulting signal is subtracted from the actual signal during the measurement process. The A-Scans recorded during the process are additionally filtered with a quality threshold value in order to reduce the further noise.  
Figure 5: The optical set-up shows the optical paths of the processing and scanning beam from their corresponding collimators to being focused on the substrate. The blue image planes show the spot diagrams of the focused processing beam and the circular scan pattern of the focused measurement beam in the melt pool.
## 3 Results
### Results of CT Scan  
Due to the use of critical air pressure for the impact tests in this experiment, the granite samples did not produce a large number of fragments under the combined effects of confining pressure and impact load. To better understand the changes in the internal fissure of the rock samples after impact, a set of no-damaged rock samples was selected. These samples were subjected to impact testing under different confining pressures and then underwent industrial CT scanning. Three-dimensional reconstruction techniques were applied to process the scanned CT images, and the reconstructed 3D models were analyzed to examine the internal changes.  
During the CT scanning process, each test rock sample was scanned to produce 1000 slices, with the first slice corresponding to the end of the sample subjected to the incidence bar, and the slices numbered sequentially. The CT scan results of both the untreated rock samples and a group of no-damage samples to impact load under different confining pressures are shown in Figure 7.  
Figure 7a shows that granite, being a dense rock, contains very few large pores or microcracks internally. In particular, the rock samples used in this experiment were extracted from depths of several kilometers, where years of evolution have resulted in very few microscopic pores, indicating that the selected samples are highly suitable for testing.  
We compared the CT scan images of the group of no-damage rock samples subjected to impacts under different confining pressures (Figure 7). It is evident that as confining pressure increases, the number, width, and complexity of fissure in the transverse slices of the rock samples decrease. This phenomenon is also clearly observed in the front views of the rock samples. The underlying reason for this is that under impact loads, the confining pressure restricts the transverse deformation of the rock, thereby impeding the expansion of the internal fissure. This effect becomes more pronounced with increasing confining pressure. Additionally, the presence of confining pressure alters the failure mode of the rock. At low confining pressures, the rock primarily experiences tensile-shear mixed failure. However, as confining pressure increases, the failure mode shifts to purely shear failure, a transition that is particularly evident in the front views of the CT images shown in Figure 7.
## 2 Data and Methods
### Data Collection
#### 2.1.3 AmeriFlux Data  
We selected AmeriFlux data to analyze the responses of GPP and satellite SIF to the environment since AmeriFlux provides the latest GPP and meteorological data ([[https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)]([https://ameriflux.lbl.gov/](https://ameriflux.lbl.gov/)), accessed on 22 October 2022). We selected all sites that had GPP and meteorological data after 2018, and chose daily variables including GPP based on the night-time partitioning method, Ta, incoming shortwave radiation (SW), LE, and H, as well as screening out negative GPP values. Finally, we obtained 64 flux sites that had flux data after 2018.
## 3 Results and Discussion
### Reference Results and Computed Mode Shapes
#### 3.1.2 Spectral Decomposition of the Surface-Averaged Velocity Amplitude
The combination of the five underlying discrete natural modes of the five blades yielded a range of high-amplitude values of around \(n\approx 4.2\) in Figure 3. For OP20, this resulted in the peak amplitude \(v_{z}(4.173)\), which is dissolved to \(\vartheta_{z,i}\) in Figure 3(a). The same exact mode shape can be distinguished from the LSV data as predicted from FEM in Figure 3(b). The maximum-amplitude blade (bottom position) is flanked by two counter-flapping blades, and one blade is in-phase, preceding it by two positions. The first natural frequency is visibly a dominant location of vibration, and attention should be paid not to operate the fan at rotation rates for which significant excitation would coincide with \(f_{\text{e,1}}\). Compared to \(n_{\text{e,1}}\), the second natural frequency range measurement was lower in amplitude \(v_{z}(n_{\text{e,2}})\), as shown in Figure 3. The respective predicted mode shape is shown in Figure 4(b). Again, the image refers to one of the five associated distinct frequencies in great proximity, and the blade with maximum attenuation is located at the upper right. The mode bends around a spanwise axis in the blade centerline; the leading edge swings in counter-phase to the trailing edge and maximum values at the blade tips. The remaining four non-dominant blades describe a counter-motion to the blade at the upper right, and the most discernible motion patterns at the two blades oppose it (bottom and left in Figure 4(b)). Figure 4: Computed modal shape of first natural frequency in comparison to measured vibration at associated frequency. In reference to the associated amplitude peak \(v_{z}(n_{\text{e,2}})\) in Figure 3, the relative axial motion \(\vartheta_{z,i}(9.620)\) is displayed in Figure 4(a) for OP20. Again, the mode shape resembles the computation very strongly, and the dominant blade is at the upper right. The single difference between the measured vibration response and computation may be discerned in the motion of the remaining four blades. The two opposing blades do not show a counter-motion to the upper-right blade. Instead, such motion is more strongly found in the blades flanking the dominant blade. This discrepancy between the measurement and computation may be attributed to the differences due to manufacturing and installation, as well as the interaction of presumably more distinct individual natural frequencies for each blade.
# Sentiment Classification Using Convolutional Neural Networks
## 4 Experiment  
### Data  
We used three Movie Review (MR) data from kaggle [4; 5; 6]. The dataset in [4] consisted of two labels, positive and negative, while [5] was composed of three labels of positive, neutral, and negative. Furthermore, the dataset in [6] was composed of five labels of positive, somewhat positive, neutral, somewhat negative, and negative. The positive class of [4], the positive class of [5], and the positive and somewhat positive classes of [6] were merged and labeled as the positive class. The neutral class of [5] and the neutral class of [6] were merged and labeled as the neutral class. Then, the negative class of [4], the negative class of [5], and the somewhat negative and negative classes of [6] were merged and labeled as the negative class. Positive, neutral, and negative were assigned as 1, 0, and 2, respectively. Some 11,800 positive data, 5937 neutral data, and 9698 negative data were used, totaling 27,435. Amazon Customer Review data (CR) [7] were labeled as positive and negative, and the amount of positive data was more than that of negative data. Therefore, we used only 3671 out of 34,062 data to control the positive:negative ratio. Stanford Sentiment Treebank data (SST) [8] were labeled as a value between 0 and 1, so a value of 0.5 or more was relabeled as positive, and a value of less than 0.5 was relabeled as negative.  
Table 1 shows the detailed statistics of the data. \(N\) is the number of text data, Dist (\(+\), \(-\)) is the ratio of positive:negative classes, and aveL/maxL is the average length/maximum length of the text. We divide all the data into three sets (i.e., train, validation, and test) with a ratio of 55:20:25, and \(|V|\) was the dictionary size of each dataset.
## 1 Introduction  
Improvements in implant surface design and surgical technique have led to modifications in the traditional treatment concept of implant loading time and have increased the rate of success.  
The success of immediate-loading implants, in which the force is loaded within one week of the surgery, is determined predominantly by the primary stability and osseointegration during the initial healing period. The initial stability of the installed implant is primarily dominated by the mechanical stability rather than the biological stability [1]. This tendency is reversed as the bone undergoes a phase of formation and remodeling. A temporary decrease in stability is noted during this transition, and overloaded or unfavorable forces applied to the implant during this phase of instability hinder successful osseointegration [2].  
[PERSON] et al. reported a 100% survival rate of single immediate-loading implants on the anterior maxillary region, which can be largely attributed to the strict inclusion and exclusion criteria that prevented failures [3]. Despite the challenges associated with immediate-loading implants, studies reporting success rates close to 100% have included high primary stability with insertion torque more than 30 Ncm as one of the indications for applying immediate loading [4; 5; 6]. The primary stability is an essential factor in planning immediate-loading as the treatment modality [7].  
[PERSON] et al. reported that micro-motion of more than 100 \(\upmu\)m interfered with direct osseointegration, and [PERSON] et al. noted that micro-mobility of more than 150 \(\upmu\)m caused fibrous tissue formation whereas 28 \(\upmu\)m or less did not influence osseointegration in any way. In contrast, [PERSON]'s law states that increases in stress stimulate new bone formation, and decreased stress leads to bone loss. Thus, controlled micro mechanical stress through temporary restorations during the initial healing phase may be a useful stimulant for bone formation [8; 9; 10].  
Provisional restorations fabricated with acrylic or composite materials provide a means of both functional and esthetic rehabilitation for a certain period of time in immediate-loading treatment [11].  
A study using finite element analysis revealed that the distribution of occlusal force loaded on the provisional restoration varies according to the material of the fitness of the restoration, which then delivers the force to the retention screw and the underlying bone tissue [12].  
A customized healing cap simplifies the fabrication of acrylic provisional restorations over the cap while also allowing easy removal from the abutment with appropriate fitting at the margin. Hence, it is used in immediate-loading cases; however, studies of its response to occlusal loading is scarce.  
The purpose of this study was to analyze the load distribution of provisional restorations using a healing cap during immediate-loading implant treatment.
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{l l l l l} \hline
**Dataset** & **Model** & **Overall Acc.** & **OOV Acc.** & **Ambig. Acc.** \\ \hline \multirow{3}{*}{Turkish} & cMD\({}_{mlp}\) & 91.47 & 87.97 & 82.61 \\  & cMD\({}_{plm}\) & 92.44 & 88.45 & 84.58 \\ \hline \multirow{3}{*}{kD1} & cMD\({}_{mlp}\) & 86.58 & 69.35 & 79.47 \\ \cline{2-5}  & cMD\({}_{plm}\) & 92.64 & 85.27 & 88.74 \\ \hline \multirow{3}{*}{kD2} & cMD\({}_{mlp}\) & 83.05 & 80.04 & 74.53 \\ \cline{2-5}  & cMD\({}_{plm}\) & 89.23 & 87.61 & 83.35 \\ \hline \end{tabular}
\end{table} 
Table 4: Accuracy results of MD for Kazakh and Turkish datasets before and after fine-tuning on PLMs. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Dataset** & **Model** & **Category** & **Overall** & **OOV** & **Ambig.** \\ \hline \multirow{6}{*}{Turkish} & \multirow{3}{*}{cMD\({}_{mlp}\)} & root & 98.12 & 95.81 & 96.16 \\ \cline{3-6}  & & POS & 96.26 & 97.82 & 92.38 \\ \cline{2-6}  & & morpheme & 91.90 & 88.18 & 83.46 \\ \cline{2-6}  & & root & 98.26 & 96.13 & 96.45 \\ \cline{2-6}  & cMD\({}_{plm}\) & POS & 96.56 & 97.82 & 92.98 \\ \cline{2-6}  & & morpheme & 92.87 & 88.77 & 85.44 \\ \hline \multirow{6}{*}{kD1} & \multirow{3}{*}{cMD\({}_{mlp}\)} & root & 97.34 & 91.92 & 95.93 \\ \cline{2-6}  & cMD\({}_{mlp}\) & POS & 93.44 & 84.80 & 89.97 \\ \cline{1-1} \cline{2-6}  & & morpheme & 88.80 & 73.87 & 82.88 \\ \cline{1-1} \cline{2-6}  & & root & 98.33 & 96.68 & 97.44 \\ \cline{1-1} \cline{2-6}  & cMD\({}_{plm}\) & POS & 97.09 & 96.19 & 95.55 \\ \cline{1-1} \cline{2-6}  & & morpheme & 94.06 & 86.93 & 90.91 \\ \hline \multirow{6}{*}{kD2} & \multirow{3}{*}{cMD\({}_{mlp}\)} & root & 96.61 & 94.95 & 94.77 \\ \cline{1-1} \cline{2-6}  & cMD\({}_{mlp}\) & POS & 91.54 & 90.12 & 86.93 \\ \cline{1-1} \cline{2-6}  & & morpheme & 84.11 & 80.46 & 75.42 \\ \cline{1-1} \cline{2-6}  & & root & 98.26 & 96.13 & 96.45 \\ \cline{1-1} \cline{2-6}  & cMD\({}_{plm}\) & POS & 96.55 & 97.82 & 92.97 \\ \cline{1-1} \cline{2-6}  & morpheme & 92.87 & 88.77 & 85.44 \\ \hline \end{tabular}
\end{table} 
Table 5: Accuracy results of MD for Kazakh and Turkish datasets for root, POS and morpheme before and after fine-tuning on PLMs. The results show that the proposed model with PLMs trained with contrastive loss improves the model's performance in terms of ambiguous cases and OOV tokens, especially in the context of low-resource languages, including Kazakh, with a small-sized dataset, without the help of any designed features. Table 6 compares the proposed approach with the previous work. Since the datasets are the same as for the previous work, the results are comparable. The results for the HMM and voted perceptron approaches were obtained from [26]. The voted perceptron approach for Turkish was trained with the tool developed by [3]. It can be observed that the cMD\({}_{plm}\) outperforms the other approaches for the Kazakh language and Turkish without using any feature and decoding techniques.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 2 Related Work
[23] proposed a globally optimal online EV charging strategy, which not only improves energy utilization of the whole system but also prevents charging stations from overloading, which may cause a voltage drop in the power distribution system. In [24], a publish-subscribe based event notification framework was proposed to deliver events to vehicles that subscribe to them within the validity periods of both the subscriptions and the events. Nevertheless, none of these strategies considers the content replication in the dynamically provisioned RSUs with limited storage capacity under diverse request demands. Here, we will present a system design for the RSU service in the urban area, and explore the service efficiency under the impact of comprehensive factors after the RSU allocation. Leveraging mobile cloud paradigms, VCC has been proposed recently to change the way of network service provisioning in VANETs. Many works use underutilized vehicle resources to form a cloud by aggregating vehicular computing resources. For example, a distributed D-hop cluster formation algorithm was presented to dynamically form vehicle clouds for efficient data delivery in [25]. [PERSON] et al. [26] proposed a precoded cooperative transmission technique to extract the underlying rich multipath-Doppler-spatial diversity, which is a relay selection scheme for available relaying vehicles. [PERSON] et al. [27] further presented a prefetch based strategy from remote cloud to RSUs for efficient data dissemination in VCC. Different from previous works, we will explore service provisioning in VCC under the impact of a macro view of traffic conditions and request demand variation.