esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Experimental Results and Discussions  
The findings obtained from the experimental work are clearly presented and thoroughly discussed in this section. Figure 15 shows the measurement results for the three-phase input voltages and current based on the prototype of EV fast charging. According to Figure 15a,b, it can be seen that the voltage and current waveforms are shifted by 120\({}^{\circ}\) and the natural aspect of the waveforms is sinusoidal. Figure 15c shows the three-phase voltage waveforms of AC fast charging (first mode charging). This figure presents three voltage waveforms; PWM reference voltage (\(\mathbf{v_{sa}}\)) of phase A, output voltage (\(\mathbf{v_{oa}}\)) of phase A for the SPWM inverter before LC filter, and output voltage (\(\mathbf{v_{oab}}\)) of phase to phase (phase A to phase B) for the SPWM inverter before the LC filter.  
Figure 15: Measurement results of the three-phase input voltages and current: (**a**) input three-phase voltages, (**b**) input three-phase currents (**c**) three-phase voltage waveforms of AC fast charging.  
According to Figure 16a, the input AC current of phase A works in the same phase with the input AC voltage to ensure the unity power factor of the charger. Figure 16b shows the measurement result for the three-phase input currents (phase C, phase B, and phase A) of fast charging. It can be seen that the current is shifted by 120\({}^{\circ}\) sequence of three-phase and the natural aspect of the waves has no distortion.  
It can be observed in Figure 17, the input voltage and current waveforms for phase A and C exhibit sinusoidal wave shape. Moreover, the output DC voltage is observed to be continuously maintained at a fixed value with a constant DC output voltage.  
Figure 16: The measurements waveforms of three-phase input voltages along with input currents: (**a**) input three-phase voltage with input current of phase (A), (**b**) input current phase (A), input current (B), and current phase (C).  
Figure 17: The current and voltage measurements waveforms of DC fast charging.  
Figure 18 shows the THD of phase A input current for the EV fast charger, obtained under medium power test. From the findings, the THD of input current is effectively maintained below 5 %, which complies with IEEE standard 519-2014.
# Oral Microbiota in Patients with Alzheimer's Disease: A Systematic Review
## 4 Discussion
### Oral Microbiota  
Key findings regarding \(\alpha\)-diversity were contradictory and therefore inconclusive: two studies found higher \(\alpha\)-diversity, two--no difference and two--lower alpha diversity in the salivary microbiota of AD patients compared to healthy controls. The studies that did not demonstrate differences in alpha diversity between AD patients and controls likely reflect the exploratory and descriptive nature of their research, as these studies lacked sufficient power to detect a difference [23; 24]. The sequencing methods used in this review varied, with ten studies analyzing the whole microbiome and three studies focusing on specific species, which is the equivalent of 'finding only what you are looking for' and may lead to an underestimation of the differences. The use of targeted methods may have resulted in the omission of oral microbiota, which could be influential or significant. This makes it difficult to effectively compare the results of these studies. Different sampling methods may also influence the results, as different sites in the oral cavity harbor different microbiota. If sample sites were the same, it would be easier to draw conclusions about differences between the groups. In this review, some articles only analyzed the saliva of the included patients, and there are studies that assessed supra- or subgingival dental plaque, buccal or tongue mucosal swabs instead of or besides saliva. It is of great importance to know the oral health of the included patients, as the dynamic (im)balance of the oral microbiota is strongly influenced by lifestyle and diet [32; 33]. Finally, not all the included articles mentioned the oral health status of their patients. Without knowing which important confounders, such as the number of remaining teeth or periodontal status, are influencing the data, it is difficult to conclude that the results regarding the oral microbiota are unbiased.
## 2 Materials and Methods
### Second Phase--Implementation of Deep Learning Models and Algorithms
#### 2.3.2 Xception Model  
The second model involved the Xception, introduced by Google in 2017, which is a deep CNN renowned for its innovative approach to feature learning [42]. It employs depth-wise separable convolutions, which decompose standard convolutions into two parts: depth-wise convolutions, performed independently on each channel of an input, and pointwise convolutions (1 \(\times\) 1 convolutions), which project the output channels into a new channel space. This architecture significantly reduces the number of parameters while maintaining high performance [43]. Building on this architecture, the fine-tuned Xception model is adapted for a 7-class mineral classification task. The original top fully connected layers were removed, and a custom classification head was added, which included a flatten layer, followed by four dense layers. The pre-trained convolutional layers of Xception, initially trained on ImageNet, are frozen to preserve their robust feature extraction capabilities, while training is focused solely on the custom top layers.
# User Identity Linkage across Social Networks by Heterogeneous Graph Attention Network Modeling
## 5 Experiments
### Validation of Advancement  
In this subsection, we use the Facebook-Twitter dataset to validate the advancement of UIL-HGAN. To facilitate the comparison with the existing user identity linkage methods, we use usernames and friend relationships in the dataset. In the experiment, we compared the methods as follows:  
* SVR [15]: A supervised learning method is proposed to extract the longest common string, the longest common subsequence, the Jenson Shannon distance, the editing distance, and others as features of username for user identity linkage. We use support vector regression (SVR) to calculate the _hit-precision_;
* PALE [7]: A supervised user identity linkage method of structural information, that uses a network embedding method to represent nodes in a low dimension;
* UIL-HGAN(P): An UIL-HGAN method using only usernames;
* UIL-HGAN(S): An UIL-HGAN method using only friend relationships.  
For different user identity linkage methods, we set the training ratio to 20%, 40%, 60%, and 80%, respectively, and calculate _hit@1, hit@50, hit@100, hit@500_, and _hit@1000_ separately. The experimental results are shown in Table 2. The _hit-precision_ jitter is shown of different training ratios in the same method. As the _hit@1_ precision directly reflects the performance of user identity linkage, to intuitively  
Figure 5: Performance of different feature combinations with an 80% training ratio on the OSCHINA-Gitee dataset.  
is 0.0740. When the training ratio is 80%, the _hit@1_ precision of PALE is 0.1370, while the _hit@1_ precision of UIL-HGAN(S) reached 0.1500. It shows that in the aspect of friendly relationships, our proposed method UIL-HGAN(S) has a better performance of user identity linkage than PALE.  
Both UIL-HGAN(P) and SVR use only usernames for user identity linkage. In Figure 6, no matter what the training ratio was set to, the _hit@1_ precision curves are all above SVR, which is more evident than UIL-HGAN(S) and PALE. It shows that in regards to usernames, our proposed method UIL-HGAN(P) had a better performance of user identity linkage than SVR.  
The comparisons among the above methods have proved the advancement of our proposed method unilaterally. UIL-HGAN uses both usernames and friendly relationships in the experiments. In Figure 6, UIL-HGAN had a better performances of user identity linkage than UIL-HGAN(P) and UIL-HGAN(S), which validates the advancement of our proposed method.  
In this subsection, we use the Facebook-Twitter dataset to validate the advancement of UIL-HGAN. In the experiment, we found that different features have different convergence rates. It usually takes a longer training time to achieve better performances to aggregate multiple types of features for user identity linkage.
# A Novel Evolutionary Algorithm for Designing Robust Analog Filters
## 3 Methods: A Robust Analog Filter Design Using Evolutionary Algorithms  
### Analog Filter Design using Bond Graphs and GP  
In this work, we used bond graph modeling [44] as the representation scheme for analaog filter design. A bond graph is a graphical representation of a physical dynamic system, which allows the conversion of the system into a state-space representation. This capability of seamlessly incorporating multiple domains makes our results widely applicable in the evolution/design of physical systems. Here, we briefly introduce the bond graph and show how GP can be used to evolve analog filters based on bond graph representation with details explained in our previous work [2; 45; 46].
## 1 Introduction
[PERSON] used discrete element software to simulate and analyze the height of the seed discharge tube and found that it significantly impacts seed discharge performance [18]. The optimal seed discharge performance depended on the operating speed of the machine and the height of the seed discharge tube. [PERSON] conducted a study on the influence of air velocity and material flow rate on distribution accuracy. The study also investigated the effects of outlet closure, different outlet pipe lengths, seal tightness of the distributing head, and angular position of the distributing head [19]. [PERSON] applied a PID controller and model-based neural network PID control system for the control of unmanned agricultural vehicles [20]. [PERSON] introduced the modification of an angle sensor at the pivot point of the drill plow, providing sensor feedback to the control system. The control system delivers a constant plow depth through an electric hydraulic actuator, thereby minimizing low-frequency variations in plow depth. Through using the developed sensor system and control system, it is possible to significantly reduce low-frequency variations in plow depth [21]. In general, research by scholars focuses on optimizing the original seed arrangement device structure and adjusting the seed arrangement tube to increase sowing depth consistency and grain spacing uniformity. However, in actual sowing operations, the seed arrangement tube's outlet position varies significantly due to external factors, reducing the sowing monomer's placement consistency, ultimately leading to reduced corn yield. To address the issue of imprecise plant and row spacing during corn sowing in the middle of a field, this study proposes a dynamic compensation system for corn sowing positions. The experimental setup includes a corn sowing machine operating unit, a sowing variable measurement and control system unit, a terminal control system, and the dynamic compensation system. Stability analyses of the longitudinal and transverse grain spacing were conducted through bench testing to verify the proposed system's stability.
# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### CNN and GNN Based Approaches
#### 3.2.3 Comments on the GNN/CNN Based Community Detection Schemes
**Convolutional networks are integrated with undirected graph models:** Such graphical models are the Markov Random Fields [48; 49]. The convolutional networks generally do not consider community properties and thus the embeddings they construct are not community oriented. On the other hand, the undirected graph models, do not take into account the node information and the model learning requires complex computations. In this regard, the convolutional networks and graphical models like MRF are complementary and form a good combination for CNN/GNN based community detection. 7. **Overlapping and complexity analysis:** Most of the strategies do not consider the fact that communities may overlap; an exception is [57]. Moreover, many strategies do not provide comprehensive complexity analysis, as can be seen in Table 2. For those who do, we can observe linear complexity in terms of the number of nodes, edges, and samples being used.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 4 Implementation Strategy
### TUN Interface Setup  
A TUN interface is used to bypass the kernel's L3 networking stack, redirecting IP packets to the userspace, where LwIP and the DTN Module process them. This approach enables customized processing and flexible handling of IP traffic, which is essential for implementing DTN-specific features. The interface is created using the ip tantap utility, a Linux tool designed to facilitate communication between the kernel and userspace. For this implementation, the TUN interface, named dtn0, must be initialized in TUN mode, which is specifically designed for L3 packet handling. After its creation, the interface must be configured to ensure it operates correctly and directs all relevant traffic through the TUN path. Once initialized, an appropriate IP address must be assigned to the interface, and consequently it must be brought into an active state to allow communication. This ensures that the interface is fully integrated into the networking stack of the system. To achieve the desired behavior, it is necessary to configure the routing rules so that all traffic is forwarded to the TUN interface. By setting the TUN interface as the default route, all inbound traffic is redirected through it, ensuring proper packet interception and processing by LwIP and the DTN Module.
## 1 Introduction
Furthermore, as frequency increases, the impact of the phase lag in the parallel inductive compensation structure gradually diminishes. Therefore, this structure is only suitable for low-frequency applications. Consequently, the challenge arises with increasing bandwidth to maintain high linearity, low insertion loss, compact area, and minimal amplitude and phase error concurrently. In this paper, we propose two innovative compensation structures: a series inductive compensation structure (SICS) designed to compensate for high frequency attenuation values and a small bit compensation structure (SBCS) for large attenuation bits. Additionally, this study adopts a simplified T-type structure and low-pass compensation structure, which not only reduces the insertion loss but also greatly improves the matching performance. Building upon the aforementioned innovations, the proposed 6-bit DSA shows ultra-low insertion loss, excellent attenuation accuracy, high power capacity, and commendable matching performance. This paper is structured as follows:  
In Section 2, we give an in-depth analysis of the conventional switch T-/\(\pi\) structure, deriving optimal resistance values for each attenuation topology to avoid undesired impedance mismatch. In Section 3, we explain the limitations of conventional digital attenuator design. In Section 4, we propose a series inductive compensation structure and a small bit compensation structure to effectively extend the operating bandwidth of the attenuator and reduce the additional phase shift. In Section 5, we propose two methods to reduce the insertion loss, which reduce the insertion loss by 50%. In Section 6, we cascade the six attenuation bits to maximize performance over the entire bandwidth (from 2 GHz to 22 GHz). In Section 7, we present the measurement results of the chip and compare with other recently published broadband digital attenuators. The measurement results show that the DSA proposed in this paper can achieve ultra-wideband operation from 2 GHz to 22 GHz with high amplitude tuning accuracy (i.e., \(<\)0.18 dB RMS amplitude error) and low phase error (i.e., \(<\)7\({}^{\circ}\) RMS phase error). Therefore, the proposed DSA can meet the requirements of multi-band phased array systems.
## 2 Materials and Methods
### The Multi-Body Model
#### 2.1.3 Generation of the Markers' Coordinates  
In order to collect the coordinates in the GCS, a transform sensor was included in the model for each marker. This component measures time-dependent relationships, as the position, between two frames. For each marker, the respective sensor measures the position of the marker frame relative to the GCS frame, which is fixed. In this way, the global coordinates of each marker could be assessed, similarly to what is performed during a common motion capture acquisition.  
A fixed step size equal to 1 ms was selected to run the model, corresponding to a native sampling frequency of marker's data equal to 1000 Hz. All the input for the model (DoFs signals) was provided at the same sampling frequency to ensure consistency. Commercial motion capture systems typically work at much lower sampling rate due to limitations in cameras' refresh rate or simply to limit the amount of data collected for long-lasting observations. Therefore, to achieve a realistic representation of the dataset, the original marker's coordinates produced by the model were downsampled. The default target frequency \(f_{\text{s}}\) was set to 100 Hz, even though the model enables any arbitrary selection of this parameter.  
Finally, to simulate the effect of measurement noise from the instrumentation, as well as the artifacts produced by skin sliding and soft tissue, the downsampled coordinates were altered by additive white Gaussian noise. The simplified approach considered was the same adopted by [PERSON] et al. [26], which consists of two different sources for the noise signals that can be added to each marker trajectory. In particular, white Gaussian noise is primarily added to each coordinate to simulate random accuracy errors related to the instrumentation and to marker cluster non-rigid deformation. Then, a single white Gaussian noise signal is generated and added to all the markers included in the same cluster. This second source of (common) noise simulates those accuracy errors related to skin sliding that should affect the positioning of each marker within the cluster in the same way (rigid transformations, [15]). This approach is rather a simplified one, since soft tissue artifacts, in particular, have quite different dynamics and depend on the motor task considered [14; 16]. However, this method can be considered sufficient to achieve a more realistic representation of the dataset. To define the magnitude of marker noise, the signal-to-noise ratios (SNRs) for the common (SNR\({}_{\text{cmn}}\)) and the random (SNR\({}_{\text{md}}\)) sources were included as configurable parameters in the model and can be selected arbitrarily. By default, they are both set at 100 dB. An example of the corresponding noise signals is presented in Figure 2. Figure 1d shows an example of noisy trajectories of shank and foot markers in the GCS, at the end of the generation process.  
Figure 2: Random (red) and common (black) noise signals for different signal-to-noise ratios.
# The Application of Intelligent Data Models for Dementia Classification
## 4 Discussion
(2021) [49] investigated the possibility of shortening the CRD clinical assessment method to develop an electronic CRD (eCDR) to improve accessibility, reduce evaluation time, and automate the scoring process. The authors utilized the item response theory (IRT) to assess the items of the CDR, and develop an IRT automatic CDR scoring model. The results demonstrated that the IRT model with a short CDR version is able to achieve medically accepted classification rate. [PERSON] et al. (2022a; 2022b; 2022c) [50; 51; 52] investigated elements related to cognitive and functional activities of dementia using real data subjects of the ADNI project. The authors primary objective is to identify functional and cognitive indicators that correlate with one type of dementia progression, which is AD. In [PERSON] et al. (2022a), a number of assessment methods were compared and analyzed using real data to finds out the cognitive domains that overlap in certain cognitive items using the DSM-5 criteria framework related to dementia. Then, the authors improved their initial approach by developing a computational intelligence process to detect functional indicators of AD advancement. The computational data process was evaluated using classification and feature selection methods on ADNI's data subjects by focusing on the elements that belong to the Functional Activity Questionnaire assessment (FAQ) method. Results showed that there are some functional elements that associate with the disease advancement. Lastly, [PERSON] et al. (2022c) [50] evaluated cognitive elements related to ADAS-13 cognitive assessment method (ADAS-Cog) using machine learning (classification algorithms). Results against the cognitive data of ADNI (ADAS-Cog sheet) revealed that the classification models derived can be used for the progression of AD screening, and outperform models derived from functional elements (ADNI-FAQ data sheet).
## 2 Materials and Methods  
### Study Design  
In this prospective study, patients requiring implant surgery were consecutively recruited from December 2020 to September 2022. The trial protocol was registered on ClinicalTrials.gov (NCT05670340), developed under the Declaration of Helsinki on medical research involving human subjects, as revised in 2013 and authorized by the Bioethical Committee of the University of Salamanca (Registration number 473/2020). A sample size calculation determined a subsample size of 31 implants to achieve a power of 80% and a level of significance of 5% (two-sided) for detecting a true difference in means between implant groups of 5 ISQ units, assuming a pooled standard deviation of 7 ISQ units for the day of surgery and expected means between 70 and 75 ISQ among distinct implant subgroups.  
The patients included in this study were partially or completely edentulous with healed bone crests. The exclusion criteria comprised patients with previous chemotherapy or radiotherapy histories, patients with any medical conditions (metabolic bone disorder and/or uncontrolled diabetes), and patients receiving bisphosphonates treatment. Similarly, to provide standardization of bone maturation, patients who had a recent tooth extraction at the implant sites (<3 months) or those treated with bone augmentation procedures were also excluded [25].  
A preoperative CBCT scan was performed on all the patients, which was initially used for implant planning and evaluation of the bone quality at the expected implant site. Three types of implants with two different macro-designs were used. The Nobel Active(r) implant (Nobel Biocare AB, Gothenburg, Sweden) is a conical implant with a reverse neck, micro-threads in the coronal portion, a variable thread design (changing from a V-thread in the coronal portion to buttress threads in the apical portion), and a 1.2 mm thread pitch at the implant apex and body. The Nobel Parallel(r) implant (Nobel Biocare AB, Gothenburg, Sweden) is cylindrical with a conical apex and a V-thread design. It has a thread pitch of 0.6 mm for implants with diameters of 3.75 mm and 4.3 mm and 0.8 mm for implants with a 5 mm diameter, having a more significant number of threads. Finally, the 3i \(\mathrm{T}\mathrm{3}^{\mathrm{\textregistered}}\) implant (ZimVie, Westminster, CO, USA) is a conical implant with no micro-threads in the most coronal portion, a V-thread design, and a thread pitch of 0.8 mm. The implants were distributed randomly among the patients using closed envelopes as the randomization method.