esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 4 Experimental Results and Analysis
### Experiments on CWRU Datasets
#### 4.1.2 Result Analysis of CWRU Datasets
The reason why the proposed method can reach a higher diagnosis with less number of features can be explained as follow. First, in terms of relevance, not only the relevance among features but also relevance among features and fault categories are taken into account. It can eliminate redundant features based on relevance among features and fault categories, and eliminate irrelevant features based on relevance among features. Second, an intersection operation is used, which deletes redundant and weak relevance features in two subsets, avoiding complex computation. In summary, a conclusion can be drawn that the proposed feature selection method performs better in both reducing feature dimensions and achieving higher diagnosis accuracy, compared to the other 3 feature selection methods on the datasets of CWRU.
## 4 Discussion  
### Effect of Runex Japonicus Houtt  
Extract and Nepodin on Plasma and Hepatic Lipid Profiles  
Obesity can induce insulin resistance, thereby inducing hyperglycemia, leading to the progress to T2 DM [2; 3; 4; 11]. The HFD-fed mice showed significant increases in body weight and adipose tissue weight as well as increases in plasma TG, TC, FFA, HDL-C, nonHDL-C, and apo B. And plasma apo A-I and apo A-I/apo B ratio were decreased in the HFD group compared to the ND group. Although the NE and RU supplements did not change body weight and adipose tissue weights, the NE supplement significantly decreased plasma TC and FFA. The RU supplement markedly reduced plasma TG, FFA, nonHDL-C, and apo B levels. In addition, the RU supplement significantly increased HTR, apo A-I, and apo A-I/apo B ratio. The RU supplement also tended to elevate HDL-C level compared to HFD mice. Previous studies have suggested that an elevated HTR combined with TG is associated with high coronary heart disease (CHD) risk [12; 13]. Moreover, the apo B/apo A-I ratio was also markedly linked with the risk of CHD, even when the levels of other lipids were within normal ranges, and the apo B/apo A-I ratio was a superior predictor of CHD risk compared to the HTR [14; 15; 16; 17]. Thus, the results of the present work indicate that RU has the potential to regulate plasma lipid profiles, which is linked to reduced dyslipidemia in HFD-fed obese mice.  
Figure 3: Effect of NE and RU supplementation for 16 weeks on fasting blood glucose (**A**), glucose tolerance (**B**), area under the curve (AUC) (**C**), plasma glucose (**D**) and insulin (**E**) levels, HOMA-IR (**F**), c-peptide level (**G**), and pancreatic immunohistochemistry analysis (200\(\times\) magnification) (**H**) in C57 BL/6] mice fed high-fat diet. Data are mean \(\pm\) S.E. Significant differences between HFD versus ND are indicated; * \(p<\) 0.05, ** \(p<\) 0.01, *** \(p<\) 0.001. \({}^{\text{ab}}\) Means not sharing a common superscript are significantly different among the high-fat diet fed groups at \(p<\) 0.05. HOMA-IR, homeostatic model assessment-insulin resistance; C-peptide, connecting peptide.  
In general, a reduction of body fat mass and an improvement of dyslipidemia are highly correlated with improved hepatic steatosis [18]. Neither the NE nor the RU supplement altered body fat mass, although the NE supplement improved hepatic and plasma lipid profile as well as hepatic steatosis. The RU supplement changed plasma lipid compositions more favorably than the NE supplement, and improved dyslipidemia in diet-induced obese mice.
## 4 Simulation Results  
In order to verify the feasibility of the proposed formation controller, which is briefly named VS-ESO-MPC below, serious simulation experiments--including numerical simulations and physical simulations for the multi-small spherical robot system--were conducted by using professional software.
# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 4 Results and Discussion  
### Experimental Results of Different Models Under Missing Labels and Incomplete Features  
This experiment was designed to evaluate the robustness and generalization capabilities of various models under conditions of label sparsity and feature incompleteness. Special attention was paid to assessing whether the proposed multi-view SSL method could effectively model latent semantic structures in scenarios characterized by high missing rates and weak supervision. By controlling the proportion of missing features and the visibility of labels in the input data, the performance of several model types (traditionallinear models, tree-based models, tabular deep learning models, self-supervised models, and pretrained architectures) was compared on a classification task. The metrics used for evaluation included accuracy, F1-score, AUC, and normalized mean squared error (MSE).  
As shown in Table 3 and Figures 4 and 5, under conditions of high feature missingness and low label availability, shallow models such as logistic regression exhibited the weakest performance, with the lowest values across all metrics. Models based on self-supervision and pretraining generally outperformed the others, with our proposed method achieving the best results on all four metrics. From a structural and learning mechanism perspective, the limited performance of shallow models such as logistic regression and XGBoost can be attributed to their reliance on complete feature inputs and strong supervision signals, which hinders their ability to capture complex nonlinear dependencies under sparsity. Models such as TabNet and TabTransformer leverage deep architectures and attention mechanisms to capture feature interactions in tabular data, but still require sufficient supervision to achieve semantic aggregation. As a self-supervised method designed for structured data, VIME improves performance through masked reconstruction and contrastive learning. BERT, although originally developed for natural language processing, demonstrates strong contextual and feature modeling capabilities when adapted to tabular tasks. Notably, the proposed method further integrates multi-view partitioning, consistency-based contrastive learning
## 2 Papers in the Special Issue  
This Special Issue includes five papers: the first one is a review paper, and the other four include results of novel research work.  
The review paper [1] presents the two main V2X technologies: dedicated short-range communications (DSRC) and cellular vehicle-to-everything (C-V2X); their core parameters, shortcomings and limitations; and explores the need for integration of IoT-based technologies into modern ITS solutions.  
The analysis of technologies shortcomings would provide solutions to cover every possible situation in road scenarios. 5G technology promises several advantages, but it could have some operation failures which could isolate the vehicle from the rest of the infrastructure. So, a solution is required which can improve communication reliability. A solution with a short/middle range is proposed in [2].  
For platooning and highly congested scenarios, Ref. [3] proposes a novel channel access scheme, Distributed Triggered Access, for distributed V2V Basic Safety Message dissemination. The extensive simulation results corroborate that the scheme improves the transmission success rate.  
An important issue in vehicular communications is security, and much work is in progress to guarantee this essential property. More specifically, in vehicle networking, unicast communications between vehicles must be encrypted, so ref. [4] propose a method of negotiation-free encryption for securing vehicular unicasting communications to improve the efficiency of vehicle ad hoc network transmissions.  
Finally, Ref. [5] proposes a V2X communication system integrating the probabilistic shaping bitloading multi-input-multi-output visible light communication and the millimeter wave radar.  
**Funding:** This work received no external funding.  
**Acknowledgments:** Thanks are due to all authors for their valuable collaboration and contributions to this Special Issue. All papers presented to the call passed a rigorous refereeing process as full manuscripts. The accepted papers underwent final revision and approval for publication in a second/third round of reviewing. Gratitude is owed to the international team of reviewers for their diligence in assessing the papers and their thoughtful and constructive criticism with a great effort and dedication of their time.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Half-Elliptical Resonator Lowpass Filter with a Wide Stopband for Low Band 5G Communication Systems
## 2 LPF Design Process
### Attenuator Resonator Design  
In the previous two types of resonators, two transmission zeros were produced at low frequencies. In order to produce a TZ at higher frequencies, an attenuator needs to be used. In this design, a square-shaped attenuator is used. This square-shaped attenuator can create a TZ at a frequency of 11 GHz. The attenuator layout and its frequency response are depicted in Figure 9a,b.
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 2 Methods and Results
### Radiation Performance Analysis
#### 2.2.2 Surface Current Density  
The influence of the structure modifications on the antenna surface current density is presented. The impact of the antenna structure modifications on the current distribution is shown in Figures 8-11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency** & **Relative Permitivity (\(\varepsilon\)r)** & **Bulk Conductivity (S/m)** \\ \hline
402.5 MHz & 61.2155 & 0.8779 \\
2.45 GHz & 57.201 & 1.968 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dielectric properties of the pancreas [18].  
Figure 4: Reflection coefficient after antenna structure modifications in the MedRadio band in the pancreas model.  
Figure 5: Reflection coefficient after antenna structure modifications in the ISM band in the pancreas model.  
Figure 6: Calculated impedance of the antenna in the MedRadio band in the pancreas model.  
Figure 7: Calculated impedance of the antenna in the ISM band in the pancreas model.  
Figure 11: Surface current distribution of the final antenna design at (**a**) 402.5 MHz and at (**b**) 2.45 GHz.  
Figure 8: Surface current distribution without elements upon the patch at (**a**) 402.5 MHz and at (**b**) 2.45 GHz.  
Figure 10: Surface current distribution with the shorting pin and one of the U-slots at (**a**) 402.5 MHz and at (**b**) 2.45 GHz.  
Figure 9: Impact of the shorting pin on the surface current distribution at (**a**) 402.5 MHz and at (**b**) 2.45 GHz.  
At 402.5 MHz, the surface current was mostly concentrated near the feed. Adding the shorting pin, the surface current was directed towards the pin, as shown in Figure 9a. This explains the resonance that occurred with the pin insertion, noted before. Adding the U-shaped slots distributed the surface current around the perimeter of the antenna surface, as shown in Figures 10a and 11a, and shifted the resonance to the left. As a result of these changes, the antenna tuned in the MedRadio band. In the ISM band, the shorting pin did not much affect the surface current's course.  
It was observed that the current distribution was concentrated in the middle of the surface, as shown in Figure 9b. Changes occurred in the surface current distribution due to the U-slots, as displayed in Figures 10b and 11b, which made the antenna resonance shift to the left and tune at the ISM band. In the ISM band, the current distribution was more affected by the U-slots than by the pin, compared with the MedRadio band.
## 3 The Proposed Method
### Modification Strategies
#### 3.3.2 Section Area of the Cables  
The second modification is made on the section area of the cables. Instead of identifying areas for each cable as a variable, 3 to 5 adjacent cables are grouped and share the same section area. However, there are still too many area variables that hinder the optimization efficiency. Therefore, an iterative method is adopted in this paper to estimate each cable area with a certain designated safety factor and calculate the cable force changes caused by the modification.  
The steps are as follows:  
1. Calculate objective cable stress \(\sigma_{obj}=\sigma_{s}/\eta\), where \(\sigma_{s}\) is the design strength of cable material and \(\eta\) is the designated safety factor (\(\eta=3\) in this paper).
2. Designate initial section area \(A_{i0}\) for each cable. Initialize \(\Delta A_{i}^{(k)}=0\).
3. Get the cable force determined with the cable forces predictor \(q_{i}\).
4. Calculate the cable stress \(\sigma_{id}^{(k)}\) under dead load according to \(\sigma_{id}^{(k)}=q_{i}/\left(A_{i0}+\Delta A_{i}^{(k)}\right)\).
5. Impose load cases, launch analysis and combine the results. Get the cable stress response \(\sigma_{il}^{(k)}\) under live load.
6. Calculate \(\Delta A_{i}^{(k+1)}\) with: \(\left(A_{i0}+\Delta A_{i}^{(k+1)}\right)\cdot\sigma_{obj}=A_{i0}\cdot\left( \sigma_{id}^{(k)}+\sigma_{il}^{(k)}\right)+\Delta A_{i}^{(k+1)}\cdot l_{i} \cdot\gamma\), where \(l_{i}\) is the length of the cable and \(\gamma\) is the volumetric weight of the cable material.
7. Designate \(A_{i0}+\Delta A_{i}^{(k+1)}\) for each cable area.  
Check convergence of \(\Delta A_{i}^{(k+1)}\). If convergence is achieved, perform the last load case analysis; otherwise, return to step 4 and update \(\Delta A_{i}^{(k)}\) with \(\Delta A_{i}^{(k+1)}\).  
The cable areas and stresses are supposed to converge after only a few iterations (two times in our later example). This method benefits the optimization by cutting off the design variables of cable section areas, thus reducing the complexity of the optimization problem.  
It is worth pointing out that the presented method causes a slight disturbance to the prediction results due to the redistribution effect of stiffness change. This disturbance is closely connected with the initial section area \(A_{i0}\). The closer \(A_{i0}\) is to its convergent value \(A_{i0}+\Delta A_{i}^{(k)}\), the less the disturbance and the more desirable the corresponding completion state will be.
## 2 Plasma
### Benefits of Cold Atmospheric Pressure Plasma  
Nowadays, cold atmospheric pressure plasma is considered more advantageous over low-pressure (<100 Pa) plasma for industrial applications, due to its technological and economic advantages [19]. First of all, CAPP is able to generate stable plasma at atmospheric pressure, i.e., there is no vacuum and the reactor is frequently open [6]. The application thus requires lower investment and operational cost compared to low-pressure plasma systems due to the absence of costly time-, space- and energy-consuming vacuum systems [20]. Moreover, the simplest CAPP reactors use only ambient air as working gas, which is converted into plasma (i.e., gas supply is not required) [6]. Thus, CAPP systems are easy to handle with excellent scalability and industrial applicability (integrable in existing process lines, capable of continuous surface modification, etc.) [11]. Another advantage of cold plasma is the ability to treat thermally labile samples (soft and organic materials, polymers) without any surface damage because the substrate temperature remains close to room temperature (in general < 50 \({}^{\circ}\)C) [15,21]. The treatment time is relatively short (from seconds to minutes) [9,15]. The efficiency of CAPP treatment depends on the reactor configuration (electrode arrangement, distance from the substrate surface) and the operating parameters of the device (gas composition, flow rate, power, temperature, process duration, etc.) [22,23,24].
## 2 Vehicle Model Analysis and Simplification
### Battery Pack Model  
A simplified battery model, shown in Figure 3, contains an internal resistor, an open circuit voltage (OCV) source connected in series topology to characterize the battery dynamic and static performance. The simplified model has been widely adopted in developing the energy management strategy without influencing the model precision [2]. The detailed battery parameters are listed in Table 2. It can be found that the capacity is 37 Ah and the nominal voltage is 259.2 V.  
Figure 3: The battery internal resistance and the open circuit voltage (OCV) curve with respect to state of charge (SOC).  
The OCV and the internal resistance with respect to the battery SOC are shown in Figure 3. It can be observed that the OCV ranges from 216 V to 288 V. According to the acquired parameters and Figure 4 [40; 41], the battery power \(P_{bat}\) can be calculated,  
\[P_{bat}=U_{ocv}i-i^{2}R_{0} \tag{13}\]  
where \(U_{ocv}\) and \(R_{0}\) state the battery OCV and internal resistance, respectively. Now, the battery current \(i\) and SOC can be calculated,  
\[i=\frac{U_{ocv}-\sqrt{U_{ocv}^{2}-4R_{0}P_{bat}}}{2R_{0}} \tag{14}\]  
\[SOC(t+1)=SOC(t)-\frac{i\times\Delta t}{C_{bat}} \tag{15}\]  
where \(SOC_{0}\) denotes the initial battery SOC, \(\Delta t\) and \(C_{bat}\) are the time interval and the battery capacity, respectively. It is noteworthy that, the above-mentioned specifications regarding battery and gearbox are derived from an existing vehicle model in the simulation software Autonomie.
## 4 Our Approach
### Decoder Framework
#### 4.2.2 Twice-Handshaking Decoding  
Usually, ORE can be converted into multiple triples extraction. When the subject and predicate are related, and the predicate is related to another object, one ORE triple (subject, predicate, object) can be extracted. We call this twice-handshaking decoding; thus, the subject shakes hands with the predicate once, and the predicate shakes hands with the object again. In general, for less complex statements, twice-handshaking decoding is enough to combine the correct triples. However, considering extreme cases, we give examples and solutions for three-times-handshaking decoding.
# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 4 Experimental Results
### Datasets Description  
The Olympic Sports dataset [53] consists of RGB sequences only. It contains sports videos from YouTube in which athletes perform a total of 16 activities: high-jump, long-jump, triple-jump, pole-vault, basketball, bowling, tennis-serve, platform, discus, hammer, javelin, shot put, spring board, snatch, clean jerk, and vault. A total of 783 videos are available. However, only 10 of the activities are human-object interactions which involves humans performing an activity on or using a certain object. Therefore, only 480 videos are used. Moreover, this dataset poses the challenge of illumination variation and motion blur.  
The MSR Daily Activity 3D dataset [54] consists of both depth and RGB sequences. It was recorded using a Kinect sensor at Microsoft Research Redmond. Ten different subjects perform a total of sixteen daily activities: drink, eat, read book, call cellphone, write on a paper, use laptop, use vacuum cleaner, cheer up, sit still, toss paper, play game, lay down on sofa, walk, play guitar, stand up, sit down. Each subject performs the same action twice, once while standing and then while sitting. Hence, a total of 320 videos are available. However, only 10 of the activities are human-object interactions which involves humans performing an activity on or using a certain object. Therefore, only 200 videos are used. Moreover, this dataset poses the challenge of very high intra-class variation.  
The D3D HOI dataset [55] consists of RGB sequences. The authors used human-object relations to improve the 3D reconstruction process of the human poses and objects. However, in this article, the dataset is only used to classify human-object interactions. It includes eight classes, named after the objects involved in the interactions: refrigerator, storage furniture, trashcan, washing machine, microwave, dishwasher, laptop, and oven. Each subject opens and closes all eight objects, once while standing and then while sitting. Hence, a total of 256 videos are available. Moreover, this dataset poses the challenge of viewpoint variation. The respective action classes for each dataset are provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial No.** & **Olympic Sports** & **MSR Daily Activity 3D** & **D3D-HOI** \\ \hline
1 & basketball & drink & refrigerator \\
2 & bowling & eat & storage furniture \\
3 & tennis-serve & read book & trashcan \\
4 & platform & write on a paper & washing machine \\
5 & discus & use laptop & microwave \\
6 & hammer & play game & dishwasher \\
7 & javelin & call cellphone & laptop \\
8 & shot put & use vacuum cleaner & oven \\
9 & spring board & play guitar & - \\
10 & clean jerk & lay on a sofa & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Human–object interactions included in all three datasets.
# Energy Saving Potential of PCMs in Buildings under Future Climate Conditions
## 2 Background
### Relationship between Climate Change and Energy Consumption  
The climate is the main determinant of energy demand. Changes in the climate will alter the demand and demand patterns of energy use in buildings [26]. There is agreement that a hotter climate will increase energy demand for air conditioning [27; 28]. As shown by [PERSON] using existing buildings data in the USA, buildings in moderate climates will see increasing energy demand for cooling, whereas buildings in cold zones will reduce their energy consumption for heating systems because of warmer winters [29]. A study by [PERSON] et al. examined the effects of climate on comfort hours and energy consumption in buildings using data from five locations around the USA. The one-year results showed that the number of hours requiring heating increased, while the need for cooling decreased, along with number of the global comfort hours [30]. Another study in Spain included various climates and data from existing buildings from representative cities in Spain. It used the degree day method and concluded that cooling demand will rise between 10.7% and 29.6%, although heating demand has decreased 30-36% in some cities [31]. A study in Chile considered the optimization of annual energy demand in office buildings under the influence of climate change using the nine climatic zones that represent the country's main inhabited areas in 2020, 2050, and 2080. That research clarifies how future climate scenarios will affect the energy demand for different types of office buildings in Chile and how their shape and enclosure can be optimized [28]. A study in the Guangdong province in southern China found that cooling and heating demands are increasing significantly because of global warming. The average increase in surface temperature has reached 0.38 \({}^{\circ}\)C per decade, the highest rate in 50 years [32]. Several previous studies have shown that climate change directly influences a building's external conditions, which affects energy demand. Therefore, increasing the energy efficiency of buildings is an effective strategy for reducing energy consumption and mitigating greenhouse gas emissions. PCMs represent one main measure for adapting to climate changes in a building's design and operational stages.