esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Network Architecture  
In recent years, deep learning has made remarkable progress in ESC, with CNN [25; 39] playing a central role. Among them, ResNets have been widely adopted for their strong feature extraction capabilities and efficient convergence. However, traditional CNN architectures are inherently limited in capturing global contextual information and long-range temporal dependencies, which can hinder performance in complex, non-stationary acoustic environments typical of urban soundscapes.  
To address these limitations, attention mechanisms have emerged as a powerful solution to enhance model performance in ESC. They dynamically assign weights to different regions of the input, enabling the network to focus on salient time-frequency regions in the spectrogram. This selective attention improves both classification accuracy and robustness in scenarios involving overlapping sources, variable durations, and spectral ambiguity. Originally developed in NLP and computer vision, attention-based architectures have also proven effective in audio signal processing, particularly in urban noise classification, where capturing subtle variations and maintaining temporal awareness are essential. For example, [PERSON] et al. [40] proposed ESResNet, which leverages pretrained vision models for efficient ESC. Similarly, [PERSON] et al. [41] introduced a ResNet-based ESC model combining L-mHP features with an SE-ResNet50 architecture, further validating the role of attention in improving feature discrimination.  
This study introduces ResNet-TF, a residual convolutional architecture enhanced with a TF attention mechanism. The attention module is designed to jointly capture temporal and spectral relevance within the input feature map, allowing the model to selectively amplify important regions across both dimensions. This design improves the model's ability to distinguish structurally similar sound events and enhances its generalization to diverse urban soundscapes. The overall architecture of ResNet-TF is illustrated in Figure 5.
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
## 1 Introduction
In order to solve this problem, an MCSA method based on the sum-of-squares of three-phase current signals is proposed in this paper for IMs' BRB detection, called the SS method. The SS method only needs to calculate the sum-of-squares of the three-phase current signals, which contains the signature frequency components and is less influenced by fundamental frequency and noise. Its computation cost is greatly reduced compared with popular MCSA methods. The remainder of this paper is organized in the following way. Section 2 proposes an improved signal model of motor current signals to explain the modulation effect of BRBs on the current signal. Section 3 introduces the theoretical basis of the SS method and the process using the SS method to detect BRBs. Section 4 validates the effectiveness of the SS method by experimental data, and the computation cost of the SS method is discussed by comparing it with popular MCSA methods.
## 4 Simulation Results
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**End-To-End Delay (Milliseconds)**} \\ \hline \multirow{2}{*}{**Optimization Algorithm**} & \multicolumn{4}{c}{**Number of Nodes**} \\  & **50** & **100** & **250** & **500** \\ \hline PSO & 2.89 & 2.97 & 3.02 & 3.18 \\ GWO & 2.81 & 2.86 & 2.99 & 3.09 \\ FSA & 2.65 & 2.76 & 2.88 & 2.92 \\ GOA & 2.44 & 2.59 & 2.70 & 2.86 \\ MGOA & 2.09 & 2.12 & 2.19 & 2.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance analysis of MGOA-WAVLCN in terms of end-to-end delay. Figure 5: Graphical analysis of MGOA-WAVLCN in terms of routing overhead.
## 2 Location  
The two dams chosen for the study are part of the same hydropower ensemble, owned by Hidroelectrica SA, and are representative for several dams in Romania and across.  
The Gura Apelor dam represents the tallest embankment (rockfill with a clay core) dam in Romania and the second largest dam of this type in Europe, after the Thissavros Dam in Greece [19]. It is located in the Hunedora county (Figure 1) on the south-western edge of the Retezat National Park, approximately 40 km away south from the Hateg town in the same county. The dam (Figure 1--right) is accessible to the public both by car and foot, with the crest of the dam part of the local road DJ685, popular with both locals and tourists hiking the local mountains. Located at the confluence of the Lapusnicu and Ses River, tributaries to the Raul Mare river, the dam was commissioned in 1986.  
The Herculane dam, on the other hand, represents an arch concrete dam located in the Caras-Severin county in the Domogled-Valea Cernei National Park, approximately 10 km away north from the famous touristic spa town, Baille-Herculane (Figure 1). Compared to the Gura Apelor dam, the Herculane dam is not open to the public, even though there is a hotel built next to the hydrotechnical facility, and the dam is accessed only by those working at the facility. The dam, commissioned in 1987, is placed on the Cerna river.
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 3 Methodology
### Hand Gesture Recognition (HGR)
#### 3.3.1 Hand Distance Estimation Module  
In Section 3.2, pitch manipulation is achieved by adjusting the hand's position. Several depth estimation methods exist to determine the distance between an object and an RGB camera in the absence of a dedicated distance sensor. One common approach involves estimating the depth from a single image by measuring the scale change of a target object based on the pinhole camera model. [PERSON] et al. [31] utilized this to estimate the scale variations of objects according to the distance. Similarly, [PERSON] et al. [32] computed the distance between a subject and a pinhole camera by considering the focal length and the size of the horizontal iris diameter.  
For this application, sophisticated and complex approaches are unnecessary. Instead, it suffices to classify the distances into three broad categories, forward, backward, and neutral, providing a simplified representation. Consequently, we also utilize a pinhole camera model and estimate the real-world distance \(d_{w}\) between the camera and the hand by examining the pixel length \(l_{p}\) between two hand joint landmarks. To achieve this, we describe a distance function \(F\) that represents the relationship between \(l_{p}\) and \(d_{w}\) as  
\[F:l_{p}\to d_{w}. \tag{4}\]  
To do this, \(l_{p}\) is expressed as  
\[l_{p}=||\widetilde{v}_{i}-\widetilde{v}_{j}||, \tag{5}\]  
where \(\widetilde{v}_{i}\) and \(\widetilde{v}_{j}\) represent the relative distances with respect to the \(i\)-th and \(j\)-th landmarks, respectively. We use the 5 th and 17 th landmarks. Then, following the pinhole camera model as described in [31], the hand distance can be computed as follows:  
\[d_{w}=F(l_{p})=\frac{f}{l_{p}}L, \tag{6}\]  
where \(f\) denotes the focal length obtained from our camera specifications, and \(L\) represents the real length between the joints corresponding to the landmarks. We approximate this constant value to be 7 cm, the average size of our four participants. Since our goal is to classify the hand's movement direction rather than measure precise distances, we omit the correction for radial distortion as it does not impact the classification accuracy. Finally, we simply establish two thresholds, \(\tau_{1}\) and \(\tau_{2}\), to delineate the distance bands as follows:  
\[\textit{Pitch}=\begin{cases}\text{forward},&d_{w}>\tau_{1}\\ \text{backward},&d_{w}<\tau_{2}\\ \text{neutral},&\textit{else}\end{cases}, \tag{7}\]  
where \(\tau_{1}\) and \(\tau_{2}\) represent user-adaptable values and the condition is \(0<\tau_{2}<\tau_{1}\).
## 1 Introduction
The influence of the initial phase modulation on the phase sensitivity was analyzed theoretically, and the relationship between them in an ideal case was obtained by numerical simulations. An experimental system was built on this basis, and then the initial phase of the sensor was modulated by a Y-branch waveguide, which is an excellent integrated optical device with the functions of a polarizer, a coupler, and two wideband phase modulators. Our work shows the fitting curve of the experimental data is in good agreement with the results of numerical simulation. The results show that the initial phase of the sensor is not \(\pi\) because of the influence of the \(2\times 2\) single-mode fiber coupler, and the greatest phase sensitivity can be acquired, with approximately a nine-fold increase, when the initial phase is modulated close to \(3\pi/2\), whilst decreasing the harmonic interferences dramatically.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 4 Verification and Analysis  
This section aims to verify the discussion in the previous sections. It can be divided into three parts. Based on the SAR imaging system introduced above, Section 4.1 designs an example system and verifies the imaging performance. Section 4.2 verifies the effectiveness of the proposed algorithm with real airborne SAR data. Section 4.3 analyzes the performance variation of the proposed system with the number of subswaths. The details are as follows.
## 3 Data Analysis
\(h_{TS,in}\) derives from an energy balance at the pre-condenser:  
\[h_{TS,in}=h_{\text{res}}-\frac{\dot{m}_{w,PC}\cdot c_{p,w}\cdot\Delta T_{w,PC} }{\dot{m}_{ref}} \tag{6}\]  
where \(h_{\text{res}}\) is the specific enthalpy of the refrigerant at the inlet of the pre-condenser in superheated conditions calculated from the knowledge of temperature and pressure with REFPROP 10 [6], \(\dot{m}_{w,PC}\) is the water mass flow rate at the pre-condenser, \(c_{p,w}\) is the water specific heat, \(\Delta T_{w,PC}\) is the water temperature difference across the pre-condenser, and \(\dot{m}_{ref}\) is the refrigerant mass flow rate. In a similar way, the outlet vapor quality \(x_{out}\) is calculated as:  
\[x_{out}=\frac{h_{TS,out}-h_{L,out}}{h_{V,out}-h_{L,out}} \tag{7}\]  
where \(h_{TS,out}\) is the specific enthalpy of the refrigerant at the outlet at the test section, and \(h_{L,out}\) and \(h_{V,out}\) are the specific enthalpies of saturated liquid and vapor, respectively, calculated with REFPROP 10 [6] from the knowledge of the outlet pressure. \(h_{TS,out}\) derives from an energy balance at the test section:  
\[h_{TS,out}=h_{TS,in}+\frac{q_{TS}}{\dot{m}_{ref}} \tag{8}\]  
The calculated mean, minimum, and maximum uncertainties on the heat transfer coefficient are 4.6%, 1.8%, and 12.5%, respectively, whereas the mean uncertainty on the mean vapor quality is 0.029.
## 5 Line Usage Allocation  
With the line flow traced, the line usage of a particular \(g^{th}\) generator can be decomposed into two elements, the line usage factor (LUF) and the line remnant factor (LRF). LUF can be evaluated by:  
\[PLU\underline{F}_{k}^{g}=\frac{P_{line,k}}{\overline{P}_{line,k}}\times\frac {P_{line,k}^{g}}{\sum\limits_{w\in NG}P_{line,k}^{w}} \tag{33}\]  
\[QLU\underline{F}_{k}^{g}=\frac{Q_{line,k}}{\overline{Q}_{line,k}}\times\frac {Q_{line,k}^{g}}{\sum\limits_{w\in NG}Q_{line,k}^{w}} \tag{34}\]  
where \(\overline{P}_{line,k}\) and \(\overline{Q}_{line,k}\) are the power flow capacities of line k and \(P_{line,k}\) and \(Q_{line,k}\) are net flows obtained from the converged load flow result and are always considered positive. Note that \(P_{line,k}^{g}\) becomes negative when the \(g^{th}\) generator is providing the \"counter flow\" on line \(k\). That is, LUF is not necessarily positive in this study. LRF can be calculated by:  
\[PLRF_{k}^{g}=\frac{\overline{P}_{line,k}-P_{line,k}}{\overline{P}_{line,k}} \times\frac{\left|P_{line,k}^{g}\right|}{\sum\limits_{w\in NG}\left|P_{line,k }^{w}\right|} \tag{35}\]\[QLRF_{k}^{g}=\frac{\overline{Q}_{line,k,}-Q_{line,k}}{\overline{Q}_{line,k}}\times \frac{\left|Q_{line,k}^{g}\right|}{\sum\limits_{w\in NG}\left|Q_{line,k}^{m} \right|} \tag{36}\]  
This is the capacity charge of the \(g^{th}\) generator directly proportional to line usage quantity regardless of the flow direction. The allocated costs of transmission for the \(g^{th}\) generator of active and reactive power are:  
\[\begin{array}{l}P_{AC}^{g}=\sum\limits_{t\in NL}cP_{t}\cdot(PLUF_{t}^{g}+ PLRF_{t}^{g})\\ Q_{AC}^{g}=\sum\limits_{t\in NL}cQ_{t}\cdot(QLUF_{t}^{g}+QLRF_{t}^{g})\end{array} \tag{37}\]  
where NL is the set of all line sections and \(cP_{t}\) and \(cQ_{t}\) are the corresponding cost rates.
## 4 Discussion
On the one hand, CBCT images are not randomly selected, and researchers artificially exclude images containing fillings, crown restorations, or images containing edentulous areas when selecting patients [39; 40]. On the other hand, some studies have considered only anterior teeth and premolars when selecting segmentation targets and have intentionally ignored molar teeth containing complex root conditions [51], or have concealed target selection [37; 43; 47]. All of these factors can lead to an inappropriate increase in segmentation accuracy. In addition, it is unclear whether the researchers were able to maintain objectivity when analyzing the data with a clear understanding of the reference standard. Therefore, the risk of index tests and reference standards cannot be ruled out, and there is subjectivity in the segmentation results obtained by the study, but better research methods and reference standards are lacking. Notably, only two papers have disclosed their model code and part of their datasets. This is common in the academic field, but it highlights the importance of open science and transparent research [35; 38]. On one hand, open-source code and datasets not only enhance the reproducibility and verifiability of research but also strengthen academic communication and collaboration. On the other hand, this practice also faces issues related to information and copyright protection. Furthermore, there are currently no standardized formats or secure data storage and access platforms, making the management and acquisition of shared resources complicated. We believe that this study provides readers with a comprehensive understanding of the emerging applications of artificial intelligence in CBCT tooth segmentation, establishing a theoretical foundation for novel approaches in the diagnosis and treatment of oral diseases. However, it is important to acknowledge that this article still has certain limitations. Only studies written in English were included, and the unavailability of full texts for some relevant articles may have led to potential inaccuracies in the results. Additionally, only studies based on CBCT were included.
# A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals
## 2 System Design and Implementation  
The main job of the system is to perform a serial of functions to correctly monitor the water quality and water consumption for both domestic livestock and wild animals. For example, the system should detect the event that the animal is at the trough, which is about 60 cm away from the motion detector. Furthermore, the system should be able to identify the specific livestock using the RFID ear tag. The system needs to take pictures  
Figure 1: The proposed smart water intake monitoring system with multiple sensors and data collections.  
when any animal visits the drinking station. In addition, the system should be able to measure the amount of water that the animal drinks each time, as well as the temperature and turbidity of water. The data, including water consumption, water quality, timestamp, animal identification (ID), and pictures, should be stored in an SD card. Moreover, the system must trigger a warning signal to indicate if the water level is too low. Above all, the system must be able to withstand harsh environments.
# HalluciNet-_ing_ Spatiotemporal Representations Using a 2D-CNN
## 4 Experiments
### Utility of Hallucination Task in Pretraining  
To determine if the hallucination task positively affects pretraining, we conducted an experiment on the downstream task of dive classification on the MTL-AQA dataset. In Experiment Section 4.2.1 the backbone network was trained on the UCF-101 action classification dataset; however, the hallucination task was not utilized during that pretraining. Table 6 summarizes the results of pretraining with and without the hallucination for dive classification. The use of hallucination during pretraining provided better initialization for both the vanilla 2D-CNN and HalluciNet, which led to improvements in almost every category besides rotation (RT) for HalluciNet. Additionally, HalluciNet training had the best performance for each dive class, indicating its utility both in pretraining network initialization and task-specific training.  
\begin{table}
\begin{tabular}{l l l l l l} \hline  & & & \multicolumn{3}{c}{(**a**)} \\ \hline
**Method** & \(\mathcal{L}_{haltu}\left(\times\mathbf{10^{-3}}\right)\) & **Accu** & **Time/Inf.** \\ \hline HalluciNet(1f) & 3.3 & 68.60 & 3.54 ms \\ HalluciNet(2f) & 3.2 (\(\downarrow\) 3.08\%) & 69.55 & 5.91 ms \\ \hline \multicolumn{5}{c}{(**b**)} \\ \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(\mathcal{L}_{haltu}\left(\times\mathbf{10^{-3}}\right)\)} & \multicolumn{3}{c}{**Accuracies**} \\ \cline{3-6}  & & **P** & **AS** & **RT** & **SS** & **TW** \\ \hline HalluciNet(1f) & 4.2 & 89.24 & 99.72 & 94.62 & 86.69 & 87.54 \\ HalluciNet(2f) & 3.9 (\(\downarrow\) 8.05\%) & **92.35** & 99.72 & **96.32** & **89.90** & **90.08** \\ \hline \end{tabular}
\end{table}
Table 5: (**a**) Single-frame vs. two-frame on UCF-101. (**b**) Single-frame vs. two-frame: MTL-AQA dive classification. \(\mathcal{L}_{haltu}\): lower is better.
## 3 Materials and Methods
### HS-SPME Sampling Conditions  
For the genuine, spiked and commercial EO samples, 2 \(\upmu\)L of the dilution in cyclohexane (5 mg/mL) were introduced in a 20-mL headspace vial, immediately hermetically closed with a PTFE-silicone septa and sampled for 20 min at room temperature (i.e., 25 \({}^{\circ}\)C) by HS-SPME. Stableflex carboxen/divinylbenzene/PDMS (CAR/DVB/PDMS) SPME fibres (2 cm long) from Supelco Co. (Bellafonte, PA, USA) were chosen.  
After sampling, the fibre was automatically removed from the vapor phase (headspace) and introduced into the GC injector to allow the complete thermal desorption of the sampled analytes to occur in the GC column. Blank runs were carried out to verify the absence of carryover effects.  
The fibre performance was periodically checked (every ten analyses) by adopting in-fibre external standardisation and by analysing a standard aqueous solution containing some of the selected markers (5 \(\upmu\)L of a 2-mg mL\({}^{-1}\) solution containing 4-terpineol, linalol and linalol acetate) [24; 25].
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
## 4 Discussion
Although precipitation had increased in recent years, a high drought index continued to restrict moisture accumulation, resulting in weaker vegetation responses to rainfall [55]. Additionally, the relationship between drought index and GPP varied across regions. In southern Xinjiang, prolonged water scarcity significantly suppresses GPP growth. In contrast, northern Xinjiang's mountainous areas and surrounding basins, with lower drought indices and more stable water supplies, show higher GPP values. This highlighted the essential role of adequate water availability in supporting vegetation growth in arid regions [40].
# Contemporary Approaches in Evolving Language Models
## 5 Key Challenges of Language Modeling for Languages with Morphological Structure  
Many NLP research studies focus on popular languages with simple morphological structures, such as English and Chinese. Based on these studies, it is challenging to assess the effectiveness of various methods and their enhancements for other languages, especially for agglutinative languages which have complex morphological structures [94]. This question remains unresolved since the efficiency of these methods for languages of different typologies has not been fully determined.  
Researchers [PERSON] et al. [95] implemented n-gram and neural language models for 50 languages. They found that a model's effectiveness heavily depends on the language's morphological structure. Their results suggest that the complexity of modeling varies for each language, which stems from the diverse morphological makeup of these languages. These languages fall into different linguistic groups, such as agglutinative (mostly Turkic languages), isolating, fusional, and introflexive.  
From this, one can infer that a language's morphological structure influences the complexity of language modeling. There are several aspects related to language modeling for languages with rich morphology:  
* Languages with rich morphological diversity have a multitude of grammatical forms and rules, leading to an increase in word form variations, making modeling and understanding them more challenging. Complex morphology can result in ambiguity in which the same word form can have different grammatical meanings in various contexts, complicating precise interpretation and text generation.
* Different language types (agglutinative, isolating, fusional, etc.) have their unique morphological characteristics. This demands the development of specialized modeling methods that effectively account for these differences. Many languages with rich morphological structures, like several agglutinative languages, are often under-researched and have limited data for model training, impacting the quality of produced language models.
* In some languages, words can be lengthy due to the presence of various morphemes. This poses challenges when processing them with neural models, especially if the model has a limited context length.  
Considering these complexities, research in language modeling should account for the morphological intricacies of various languages and refine methods specifically tailored to their unique requirements.