esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The manuscript represents a novel square tooth-enabled superstrate metamaterial loaded microstrip patch antenna for the multiple frequency band operation. The proposed tooth-based metamaterial antenna provides better gain and directivity. Four antenna structures are numerically investigated for the different geometry of the patch and tooth. These proposed structures are simulated, fabricated, measured, and compared for the frequency range of 3 GHz to 9 GHz. The electrical equivalent model of the split-ring resonator is also analyzed in the manuscript. The comparative analysis of all of the proposed structures has been carried out, in terms of several bands, reflectance response, VSWR, gain and bandwidth. The results are compared with previously published works. The effects are simulated using a high-frequency structure simulator tool with the finite element method. The measured and fabricated results are compared for verification purposes. The proposed structure provides seven bands of operation and 8.57 dB of gain. It is observed that the proposed design offers the multiple frequency band operation with a good gain. The proposed tooth-based metamaterial antenna suits applications, such as the surveillance radar, satellite communication, weather monitoring and many other wireless devices.  
microstrip patch antenna; metamaterial; advanced; multiband; directivity; gain +
Footnote †: journal: _Micromachines_
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 3 Model Framework
### Local Transfer Mechanism of Edge Model
#### 3.2.3 Edge Inference and State Update  
After the local migration model is fused, the fused model is used for the inference of the edge side four-view satellite images (\(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), \(270^{\circ}\)) and then sent to the cloud  
\begin{table}
\begin{tabular}{c c c} \hline
**Module Name** & **Effect** & **Representative Shape** \\ \hline extractor & Feature extraction (ResNet) & Multi-level convolution and residual blocks \\ rpm & Region proposal & Candidate box classification and position regression \\ head.classifier & Feature encoding & Three bottleneck blocks \\ head.cls\_loc.weight & Position regression head weight & (4 \(\times\) 12, 2048) \\ head.cls\_loc.bias & Position regression head bias & (4 \(\times\) 12, 1) \\ head.score.weight & Class score output head weights & (12, 2048) \\ head.score.bias & Category score bias & (12, 1) \\ \hline \end{tabular}
\end{table}
Table 3: The overall architecture of the edge model (Faster R-CNN).  
again for fusion detection results to update the migrated \(acc_{c}^{edge}\) and \(acc_{c}^{cloud}\) as the state input item of the next round of the reinforcement learning module.
## 4 Results
### Limitations of This Study  
However, there are a few limitations associated with analyzing Twitter data. For instance, users often use non-standard text, incorrect English, multilingual content, and emojis to express their opinions, which makes it incredibly challenging for NLP to classify some of the tweets and calculate the sentiment accurately. Also, users use sarcasm to express their views, which is very complex for the program to understand. There are also spelling mistakes or incomplete sentences, which makes it difficult for the model to understand the underlying sentiment. It is essential to preprocess the data to ensure that these algorithms provide valuable information. However, computers may not be able to distinguish between similar words such as \"doctor\" and \"physician\" or identify that \"meal\" and \"meals\" convey the same information. Additionally, spelling errors may create further complications because a minor modification in a word can make the computer perceive it as entirely different. These constraints are solved by using stemming or lemmatizing to reduce words to their base form, utilizing NLP libraries to correct spelling errors, manually analyzing comments to identify context-specific words, and replacing them with common words. Preprocessing patient-derived data may be more complex because respondents may need more healthcare literacy, make spelling mistakes, or use texting language outside the NLP library used for processing. In addition, sometimes tweets contain phrases that need to convey the words' literal meaning, making it difficult for the program to understand. For instance, users who use the phrase \"baby fever\" usually express the longing that some people experience that relates to their desire to have a child. Additionally, isolating unique tweets from millions of tweets is challenging as there are several occasions of retweeting or quoting another person's tweet. However, the computer program interprets the literal meaning of the phrase, so the program fails to extract the proper sentiment from tweets.  
Future research should compare the evaluation score between the more advanced topic modeling approaches such as BERTopic, NFM, and Top2 Vec and find out which one is performing better in certain cases. Additionally, studies should further explore different approaches to find the optimal number of topics, such as perplexity with better quality topics. A temporal analysis of topic distribution to demonstrate how it evolved over time would be a significant contribution and also very beneficial to the healthcare policymakers.  
Nevertheless, despite all these limitations, the model produces an initial insight that gives policymakers a way to capture patient experience to improve healthcare systems and understand system-level concerns for the under-represented communities.  
Figure 18: Tweets regarding maternal healthcare inequality.
## 5 Results and Analysis
### The Peak Overpressure Factor of Different Ground  
It is assumed that the peak overpressure of gauges in Model 1 to Model 3 is \(P_{\text{ground}}\), and the peak overpressure of gauge in Model 4 is \(P_{\text{air}}\), then \"peak overpressure factor\" is defined in this paper, which is \(\alpha=P_{\text{ground}}/P_{\text{air}}\). It reflects the enhancement effect for peak overpressure of different grounds.  
As shown in Figure 11(a), for rigid ground, when \(x\) is small, \(\alpha\) at different heights varies greatly. The lower the height, the greater the \(\alpha\) is, and the maximum reaches 3.8. With the increase of \(x\), \(\alpha\) of different heights gradually approaches, and eventually approaches 2. As shown in Figure 11(b),\(c\), for the concrete and soil ground, with the increase of height, \(\alpha\) increases successively from 1, the lower the  
Figure 11: Distribution of peak overpressure of gauges at different height produced by explosion on different ground.  
height, the greater the rise will be, and then starts to stabilize at a specific value. The maximum \(\alpha\) with a height of 6 m is 2.1, and the minimum \(\alpha\) with a height of 10 m is 1.43.  
It can be seen from Figure 12a-c that, for rigid ground, the enhancement effect of peak overpressure with different height is roughly the same, and the peak overpressure factor is about 2. However, the concrete ground and soil ground have different effects on the peak overpressure factors at different heights. Among them, peak overpressure factor of 6m height is the largest, reaching 2.1, while the factor of 10 m height is the smallest, which is 1.43.
## 3 Energy and Information Beamforming
### Strongest-Path Beamforming Scheme  
In this subsection, we present a two-step heuristic approach to obtain a low-complexity beamforming scheme. In the first step, we design the energy and information beamformers jointly to maximize the average power of useful signal (i.e., the numerator of (13)), without considering the power of MUI (i.e., the first term on the denominator of (13)). In the second step, we propose an MUI-aware sensor scheduling scheme to mitigate the MUI under the beamforming scheme obtained in the first step.
## 2 Role of AI in Dentistry
### Oral Cancer and Oral Pathogens  
Early detection and diagnosis of oral cancer are critical in improving patient prognosis and survival rates. Oral cancer, particularly head and neck squamous cell carcinoma (HNSCC), is a serious health concern with high mortality rates, primarily due to late-stage diagnosis. However, recent advancements in convolutional neural networks (CNNs) have shown great potential in aiding the early detection of oral cancer through automated image analysis. CNNs can analyze clinical images, such as those obtained from intraoral cameras or histopathological slides, to identify malignant lesions with a high degree of accuracy. Studies have demonstrated that CNNs can achieve a specificity of 78-81.8 percent and an accuracy of 80-83.3 percent, which are comparable to, and sometimes even exceed, the diagnostic performance of experienced specialists. This level of accuracy is crucial in reducing the number of false positives and false negatives, thereby ensuring that patients receive timely and appropriate treatment. Moreover, CNNs are not only effective in detecting cancerous lesions but also in identifying oral pathogens that contribute to the development of oral cancer. Certain bacteria and viruses, such as human papillomavirus (HPV), are known to be associated with the onset of oral cancers. By integrating pathogen detection with lesion analysis, CNNs can provide a comprehensive diagnostic tool that addresses both the direct and indirect factors contributing to oral cancer. This integrated approach has the potential to significantly enhance early detection efforts, ultimately improving patient outcomes and reducing the burden of oral cancer on healthcare systems.
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 2 Related Work
### Emerging Flow Matching Models  
A novel generative paradigm--flow matching--was recently introduced in image synthesis [35] and has gained attention for its efficiency and robustness. Conceptually related to diffusion models, flow matching solves ordinary differential equations (ODEs) to map input noise to output features, enabling faster generation and potentially improved stability without iterative sampling. Unlike diffusion processes that rely on stepwise denoising, flow matching enables the linear evolution of features along a predefined  
Figure 1: General architecture of VC models.  
trajectory, significantly improving inference speed and reducing computational overhead. Its applicability to multimodal generation--including audio and text--has opened up new opportunities for efficient, high-fidelity speech synthesis.  
Recently, flow matching has emerged as a promising alternative to diffusion models in speech generation due to its improved efficiency and generative quality. Meta AI introduced Voicebox [36], the first non-autoregressive speech synthesis system that leverages flow matching to enable robust and controllable generation across multiple tasks. Building on this, VoiceFlow [37] proposes an acoustic model that replaces diffusion sampling with a rectified flow matching algorithm to synthesize mel spectrograms efficiently, achieving superior quality with significantly fewer sampling steps. In the domain of singing voice synthesis, TechSinger [38] employs flow matching to enable multilingual synthesis with fine-grained control over vocal techniques via natural language prompts, enabling increased expressiveness and realism in generated singing. To address the stability and control limitations of flow-based models, PitchFlow [39] incorporates speaker scoring and pitch guidance, improving both pitch controllability and timbre consistency during generation. Most recently, StableVC [40] extended these advancements to zero-shot voice conversion by combining dual-attention mechanisms with conditional flow matching, allowing independent control of timbre and style from unseen speakers while achieving high-quality conversion at real-time speeds.  
In contrast, our MPFM-VC integrates flow matching into a cascaded VC framework with several novel components: a multi-dimensional perception network that fuses diverse acoustic features (pitch, energy, prosody, and voiceprint), a content perturbation strategy to improve robustness under domain shifts, and a voiceprint adversarial module to enforce clean speaker-content disentanglement. These innovations support high-quality and robust voice conversion in both speech and singing scenarios. Comparative evaluations suggest that MPFM-VC offers performance improvements over prior flow-based approaches in multiple subjective and objective metrics.
## 2 Theory of Virtual-Spring-Force Algorithms
### Original Virtual-Spring-Force Algorithm
The results suggested that each damping condition, with its unique characteristic, could play to its strengths in different deployment stages in real applications. Therefore, in this paper, damping coefficient \(\gamma\) is not discussed. We then followed their work and alsoused a dynamic damping coefficient. In time steps 0-300, \(\gamma=1.08\times\gamma_{critical}\), and after 300 time steps \(\gamma=\gamma_{critical}\). Centripetal force is defined as  
\[\vec{F}_{i}^{cor}(t)=-F_{centri}\times\vec{x}_{i}(t), \tag{5}\]  
which is only an external auxiliary force acting on each sensor node. With centripetal force, sensors move closer to the region center and always return to the sensor networks. The \(F_{centri}\) constant must be much smaller than spring force so that it does not affect the main virtual algorithm. Once a wireless sensor network is deployed to the final hexagon topology, this force is released in the algorithm. A second-order leapfrog scheme was utilized for the time discretization in Equation (1), as follows:  
\[\vec{r}_{i+1} = \vec{r}_{i}+\vec{v}_{i}dt+\vec{a}_{i}(dt)^{2}/2 \tag{6}\] \[\vec{v}_{i+1} = \vec{v}_{i}+(\vec{a}_{i}+\vec{a}_{i+1})dt/2 \tag{7}\]  
where \(\vec{v}\) and \(\vec{a}\) are particle velocity and acceleration at each step, respectively. After obtaining all the position information of neighbors, the sensor node can calculate \(r\) and \(v\) using Equations (1) and (2). For completeness, the pseudocode of the leapfrog schema solution for VFA-SF within the wireless sensor network is shown in Algorithm 1. To prevent the description from becoming cumbersome, we omitted details that could either be inferred or implemented using standard techniques. ```
Event:node \(p\)'s timer expires at time step \(t_{i}\) begin  Exchange node positions \(\vec{r}_{i+1}\leftarrow\vec{r}_{i}+\vec{v}_{i}\times\Delta t+\frac{1}{2} \times\vec{a}_{i}(\Delta t)^{2}\) compute \(\vec{a}_{i+1}\) using Equations (1) and (2) \(\vec{v}_{i+1}\leftarrow\vec{v}_{i}+\frac{1}{2}\times(\vec{a}_{i}+\vec{a}_{i+ 1})\times\Delta t\) set timer to \(t_{i+1}\gets t_{i}+\Delta t\) replace \(\vec{v}_{i}\), \(\vec{r}_{i}\) with \(\vec{v}_{i+1}\), \(\vec{r}_{i+1}\) end
```  
**Algorithm 1**Distributed Algorithm for Leapfrog in Virtual Spring Force (VFA-SF) in Wireless Sensor Networks.
###### Abstract  
Titania (TiO2) is a key material used as an electron transport in dye-sensitized and halide perovskite solar cells due to its intrinsic n-type conductivity, visible transparency, low-toxicity, and abundance. Moreover, it exhibits pronounced photocatalytic properties in the ultra-violet part of the solar spectrum. However, its wide bandgap (around 3.2 eV) reduces its photocatalytic activity in the visible wavelengths' region and electron transport ability. One of the most efficient strategies to simultaneously decrease its bandgap value and increase its n-type conductivity is doping with appropriate elements. Here, we have investigated using the density functional theory (DFT), as well as the influence of chromium (Cr), molybdenum (Mo), and tungsten (W) doping on the structural, electronic, and optical properties of TiO2. We find that doping with group 6 elements positively impacts the above-mentioned properties and should be considered an appropriate method for photocatalytic applications. In addition to the pronounced reduction in the bandgap values, we also predict the formation of energy states inside the forbidden gap, in all the cases. These states are highly desirable for photocatalytic applications as they induce low energy transitions, thus increasing the oxide's absorption within the visible. Still, they can be detrimental to solar cells' performance, as they constitute trap sites for photogenerated charge carriers.  
a 2021 202  
properties [26; 27]. Many doping strategies have been adopted, such as nitrogen (N) and niobium (Nb) doping, which resulted in the enhanced photocatalytic activity of TiO\({}_{2}\)[35; 36]. Herein, we investigate the chromium (Cr), molybdenum (Mo), and tungsten (W) (group 6 of the periodic table) doping of anatase TiO\({}_{2}\) using the density functional theory (DFT). We focus on the structural, electronic, and optical properties of anatase TiO\({}_{2}\) before and after doping with the group 6 elements and we provide evidence that these structures can be applicable to photocatalytic applications and devices. Furthermore, we compare our results with already published experimental data and theoretical results referring to other dopants. To fully understand the structural and electronic properties changes, we considered the density of states (DOS) of the energetically favorable sites and the partial DOS (PDOS) for the orbital contribution. To our knowledge, the orbitals hybridization of the interstitial and substitutional doping cases, has not been systematically discussed before. Finally, we calculated the absorption, refractive index, and dielectric constants for these dopants. In this work, we made a complete investigation both for the substitutional and the interstitial doping cases, to serve as a roadmap to the applications of TiO\({}_{2}\) to photocatalysis and photoelectrical devices.
## 3 Application Protocol for Wireless Intelligent Sensor Networks
### Protocol Description
#### 3.2.2 Protocol Services  
This protocol is agent oriented because its services are produced and consumed by the agents running inside the sensor, the neighbors, or the base station. These services were divided among three classes: sensor management, FRBS support and KB distribution, and alarm handling.  
Sensor management services allow agents to send or receive configuration parameters or commands to control the sensor. Services that cover the second class can set up a new KB in a sensor or modify it with few transmissions. The third class allows agents to set up alarms and notifications that will be activated when some parameters exceed established thresholds. The services are explained in further detail in the following paragraphs.  
Sensor management services:  
1. Get resource: With this service, any parameter of an agent or test probe can be requested.
2. Set resource: This service is usually activated by a base station. It changes the value of any agent parameters, such as time to be idle and times to check values from a probe. The behavior of the agents is controlled through this service.  
FRBS support and KB distribution services:  
1. Set KB: A new KB is sent to the sensor and stored for future use.
2. Get KB: The sensor can send the KB to other neighbors or the base station to refresh their data.
3. Get FRBS Result: Obtain the result of the FRBS.
4. Set Rule: Each fuzzy rule can be accessed independently to adapt the FRBS to the environment.  
Alarm services:  
1. Set Alarm: Establish a new alarm condition with the action to accomplish and the threshold for the monitored parameter.
2. Alarm: An alarm beacon is broadcasted to inform the base station and the neighbors about the alarm condition in the sensor.
###### Abstract  
Correctly quantifying total losses of irrigation in a center pivot system is important for improving application management and efficiency (Ea). The equations usually used to estimate Ea in sprinkler irrigation systems do not consider certain aspects, such as height of sprinklers relative to crop height, leaf interception (LI) of tall-growing crops or partial residue retention (PRR). The aim of this study was to incorporate these components into a new Ea equation adapted to the center pivot system. The trials were conducted in corn grown under no tillage in Cordoba, Argentina. To determine the distribution uniformity (DUpa), 96 catch cans were arranged at a spacing of 3 m, and the sprinklers with similar discharge flow from a center pivot of five towers (27.8 ha) were grouped together. Four irrigation depths (40, 24, 12 and 6 mm) were evaluated at different phenological stages, as well as the control condition without crop. Twenty-eight measurements were taken, and DUpa was statistically compared with respect to the different depths applied and phenological stages as well as the impact on yield. For the 11 grouped segments, with irrigation intensity between 5.7 and 77.4 mm h\({}^{-1}\), DUpa for the control condition ranged from very good to excellent (85 to 90%) but decreased significantly with crop growth. Neither the different intensities nor the irrigation depths influenced DUpa up to V\({}_{10}\), when it decreased significantly for the 6 mm depth. The spacing between sprinklers had an effect on DUpa and crop yield, decreasing from 18 to 14 ton ha\({}^{-1}\) with the largest spacing (5 m). PRR and LI were statistically adjusted, and a revised equation of application efficiency was obtained.  
Keywords:uniformity of distribution; water retention in residues; stemflow; throughfall; corn
## Instrument Description
### Control and Recording System
#### 2.5.1 Instrument Controller  
The instrument controller is the fundamental subsystem that makes it possible to achieve a sustained data recording rate of 320 MB/s (2.56 Gb/s) with exact time-tagging information for each recorded sample. Thus, we will now provide a detailed description on how this performance is achieved.  
The core of the instrument controller is the main control unit responsible for the synchronization of all other blocks. The temporal coordination of the different activities is completed by two nested loops that are continuously repeated: a major cycle with a one-minute duration consists of 60 minor cycles of one second each. The instrument has three operation modes: _(i) automatic_: During the first second of every UTC minute, the instrument performs calibration measurements, and during  
Figure 4: Block diagram of the control and recording system of SPIR.  
the remaining 59 s, it pursues antenna measurements; _(ii) calibration_: The instrument performs only calibration measurements; and _(iii) antenna_: The instrument only performs antenna measurements.  
The one-second cycles start with the 1 PPS mark delivered by the GPS receiver, and its objective is to perform a regular set of actions to program and monitor the status of all subsystems of the SPIR, in addition to collecting the raw data. The sequence of actions during the one-second cycle controlled by the Control Unit is (see Figure 5):  
1. Read and save the data to the ancillary data and status memory in the following order: 1. [label=()]
2. Offset measurements, offset PWM programming values and DC compensation steering mode.