esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Signal Preprocessing
### Interference Filtering  
Interference filtering is responsible for removing the interference scattering points reflected from unwanted objects in the FoV. Reflections from a noisy background, such as reflections from walls, must be removed, as well as reflections from the clutter of static (non-moving) objects, such as trees. Because drone targets are continuously moving, reflections from drone targets are combined with such inferences, causing significant issues in the clustering, tracking, and recognition processes of drones. The interference is filtered by applying a constant false alarm rate (CFAR) [40] and the moving target indication (MTI) [41].
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 7 Conclusions and Future work
This work was supported by the Ericsson--BME 5G joint research and cooperation project, partly funded by the National Research, Development and Innovation Office, Hungary with project number 2018-1.3.1-VKE-2018-00005. The data used to support the findings of this study are available from the corresponding author upon request. This research is supported by Tempus Public Foundation, Stipendium Hungaricum Scholarship Programme and High Speed Networks Lab, Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics. The authors declare no conflict of interest. Samples of the compounds are available from the authors. The following abbreviations are used in this manuscript: \begin{tabular}{l l} \(BS\) & Base station. \\ \(BW\) & Bandwidth of system. \\ \(UEs\) & Users/Terminals. \\ \(v\in(1,2,\ldots,V)\) & The number of sub-channel. \\ \(u\in(1,2,\ldots,U)\) & The number of user. \\ \(P_{BS}\) & The power at base station. \\ \(B_{v}\) & Bandwidth of the sub-channel \(v\). \\ \(P_{u,v}\) & The power of the user at the sub-channel \(v\). \\ \(x_{v}\) & The signal sent by BS. \\ \(s_{i}\) & The modulated symbol of the \(i\)th user. \\ \(y_{j,v}\) & The received signal of the \(j\)th user at the sub-channel \(v\). \\ \(h_{j,v}\) & The coefficient of the sub-channel \(v\) from \(BS\) to the \(j\)th user. \\ \(w_{j,v}\) & Additive white Gaussian noise with mean of 0 and variance of \(\sigma^{2}\). \\ \(P_{T}\) & The base station transmit power. \\ \(\alpha_{k}\) & is the power allocation factor for the \(UE_{K}\) power allocation factor. \\ \(n_{uk}\) & represents the received Gaussian white noise. \\ \(h_{bu}\) & indicates the channel from the base station to \(UE_{K}\). \\ \(W\) & is the transmission bandwidth. \\ \(J\) & is the fairness index. \\ \(I^{\prime}\) & is the target fairness index in the network. \\ \(EE\) & energy efficiency. \\ \(P_{T}\) & is the power consumed by the signal transmission. \\ \(P_{s}\) & is the power consumed due to other hardware and signal processing operations. \\ \end{tabular}
# Morphological Changes in H1299 Human Lung Cancer Cells Following W-Band Millimeter-Wave Irradiation
## 4 Discussion
However, irradiation within an IPD range of 0-5 mW/cm\({}^{2}\) does not generate cellular stress or thermal effects [32], concomitant with a stable temperature range of 24-26 \({}^{\circ}\)C measured during MMW exposure at 0.2 mW/cm\({}^{2}\) in our experiments. These indicate that both the short- and long-term morphological effects reported here arise as a treatment-specific non-stress response corresponding directly to the duration of treatment. This study demonstrates that W-band MMW exposure at a minimal power density of 0.2 mW/cm\({}^{2}\) is applicable to efficiently target H1299 cancer cells without harming healthy epithelial cells, which tolerate irradiation at 1 mW/cm\({}^{2}\)[38] without developing effects of cytotoxicity. Further, the estimates of thecellular energy dosage presented here allow our findings to be effectively calibrated to patient-specific clinical adaptations of MMW therapy for controlling tumor metastasis. Reports of morphological heterogeneity [23], as well as our results, demonstrate that cancer cells maintain generally constant sizes throughout their lifetime. In addition, the cells, being highly dynamic, can either grow or shrink in size in response to changes of the specific conditions via robust and adaptable control mechanisms. Illustratively, alterations of the cell morphologies in response to the chemical cancer drug paclitaxel have been observed with similar results [39]. Cancer cell tumorigenicity is associated with cell softening and a decrease in cell stiffness arising from cytoskeletal restructuring [40]. Integral membrane proteins mediate this transition of normal cells into cancer cells [41]. Changes in cell shape and size following MMW irradiation in the present study were specific and irreversible, directly corresponding to changes of the cell circularity and Feret's diameter. MMW irradiation affects cell growth [8; 9; 10] by changing the organelle structure and cell membrane permeability [11; 12; 13]. Such interactions lead to the activation and inhibition of signal transduction mechanisms due to MMWs interacting with DNA, RNA, and proteins [14]. Increased membrane stiffness resulting in apoptosis of cells has been shown to correspond to changes in prostate cancer cells responding to anti-neoplastic treatment [42]. These suggest the accelerated cellular senescence (ACS) effect [43] occurring in an energy (dose)-dependent manner observed in this study arise from the non-thermal low power density MMW exposure affecting H1299 membrane fluidity. The biochemical properties of oncogenic heterogeneity [23] are accountable for the altered physical properties of cancer cells [44], allowing for distinct electromagnetic interactions [45] as compared to normal cells. Exposure to 35 GHz irradiation activates the apoptotic caspase pathway [46] in A375 melanoma cells in vitro and is likely to be the mechanism behind the ACS effect reported here involving W-band MMW treatment.
## 8 Further Discussion
Furthermore, a geo-enabled model-based framework was proposed that could relate collected distress data with real-time location and diverse material attributes and environmental factors, substantially improving the accuracy and effectiveness of maintenance plans, which would ultimately result in a more sustainable transportation infrastructure system. From the authors' point of view, a futuristic groundbreaking infrastructure-management system is expected to leverage AI capabilities, IoT sensors, blockchain technology for data security, and automated inspection tools to generate predictive and preventive maintenance models. By training the AI models using historical data including several design, construction, and environmental components, the lifecycle performance model of a constructed transportation system can be projected. This predictive maintenance model allows agencies to use both real-time and projected data to enhance their forecasting accuracy. The AI-based asset-management system should provide precise recognition of initial pavement cracking locations before their occurrence as well as critical insights into the primary factors contributing to their initiation, aiding in timely preventive maintenance interventions. Additionally, an advanced AI-based AMS should have the capabilities to interact with various stakeholders, such as engineers, managers, operators, contractors, and official agencies, through multiple mediums, such as dashboards, reports, alerts, and online portals, to provide them with relevant and crucial real-time insights. However, the true potential of AI-based approaches is yet to be fully realized as performing these tasks even on a small scale is still either exceptionally resourceful or time-consuming.
## 4 Results and Discussion
### Origin of Overpressure  
Previous studies have extensively investigated the causes of overpressure, which can be broadly categorized into five types: disequilibrium compaction, hydrocarbon generation from organic matter, overpressure transmission, tectonic compression, and clay mineral transformation [11; 13; 40]. However, since the research area is situated within the stable zone of the Sichuan Basin craton and is minimally affected by tectonic stress, this study does not account for pressure formation attributed to tectonic compression.
## 3 Results and Analysis
### Statistical Analysis of Results
#### 3.3.1 T30 and EDT  
Table 4 shows the correlations between the measurement and the simulations with ODEON and CATT-Acoustics of the temporal parameters T30 and EDT. In the case of T30, both simulations show a moderate correlation with the measurement (ODEON 0.534 and CATT-Acoustics 0.576), while there is a very high correlation between both simulations (0.870). For EDT, the ODEON simulation shows a moderate correlation with the measurements (0.544), while the correlation between the CATT-Acoustic simulation and the measurement is low (0.285). On the other hand, the simulations between both pieces of software have a moderate correlation (0.519).  
In Figure 10, the dispersion by frequencies of T30 and EDT is observed as a function of the location of the source at the measurement point 1. For parameter T30, the measurement results are those that represent a greater dispersion, while simulation results with ODEON turn out to be the most stable. Furthermore, a decrease in dispersion can be observed in medium and high frequencies both in the measurement and in both simulations. In the case of EDT, simulation results with CATT-Acoustics represent the greatest dispersion, while simulation results with ODEON remain the most stable.  
Table 5 shows the reliability statistic and the interclass correlation for T30 and EDT. The internal consistency of T30 is good (\(0.8<\alpha\leq 0.9\)), while that of EDT is questionable (\(0.6<\alpha\leq 0.7\)). On the other hand, T30 presents an interclass correlation coefficient with a substantial degree of agreement (\(0.6<ICC\leq 0.7\)), while the degree of agreement for EDT is regular (\(0.2<ICC\leq 0.4\)).
## 1 Introduction
Additionally, kinetic models are less fault-tolerant, and the inaccurate estimation of certain values can result in the failure of the entire kinetic model. With the advancement of deep learning technology, an increasing number of researchers and scholars are employing deep learning for tracking and prediction in various domains, including drones [19; 20; 21], unmanned vehicles [22; 23; 24], ships [25; 26], and aircraft [27; 28; 29]. Deep learning endeavors to emulate the data processing mechanisms of the human brain, acquiring feature representations of data through multilayer neural network architectures [30]. With its capacity to discern and represent intricate nonlinear relationships, deep learning proves to be particularly adept at trajectory tracking and prediction tasks. These tasks often involve trajectory data characterized by complex spatiotemporal dependencies that traditional methods struggle to capture. Deep learning, on the other hand, excels in modeling the dynamic properties inherent in trajectories. This paper introduces a novel neural network architecture termed adaptive optimization depth BiLSTM (AWOA-DBiLSTM) for trajectory tracking and path prediction of AUVs. The proposed model demonstrates notable success in AUV trajectory prediction. Moreover, the model extracts both the position information and physical attitude of the AUV as distinct features, incorporating a gating mechanism between the two layers of BiLSTM to filter the data features effectively. Finally, the model addresses the deficiency of poor generalization observed in traditional dynamics models. The proposed model achieves AUV prediction and optimizes neural network parameters using an enhanced optimization algorithm by learning the intricate mapping relationship between inputs and outputs. The experimental results showcase the model's significant contribution to AUV trajectory tracking and prediction. The rest of this paper is organized as follows: The Section 2 introduces the modeling principles of several classical neural networks and their limitations and proposes a new neural network model (AWOA-DBiLSTM). The Section 3 presents the experiments conducted, analyzes the results, and verifies the model's applicability by employing it in AUV prediction in diverse regions. This paper's summary and future directions are discussed in the fourth Section 4.
# To Cache or Not to Cache
## 2 Background and Motivation  
Popular storage caching algorithms such as LRU, LFU, LIRS, ARC, and S3 FIFO [16] attempt to cache items considered important based on different metrics such as recency, frequency, and reuse distance. However, they are all datapath caching algorithms and therefore always make cache updates on a cache miss. When these datapath cache replacement algorithms are used on _non-datapath caches_, such as host-side (flash or persistent memory) SSD caches, the amount of cache updates incurred wears out the cache at an alarming rate. Selective cache updates, on the other hand, allow non-datapath caching algorithms to improve the lifetime of cache devices, and improve cache hit rates by avoiding cache replacement in cases wherein the evicted item is considered more valuable than the inserted item.
## 2 Resistance Capacity and Performance Degradation Analysis
### Corrosion Damage Model for Reinforcing Bars Embedded in Concrete  
Damage to the materials comprising the cross-sectional area of the bridge structure directly affects the resistance capacity of the bridge. The effective area of the steel reinforcement decreases when the steel is corroded by chloride penetration and carbonation. In this study, the decrease in the effective cross-section by corrosion of the reinforced steel of a floor plate and girder was considered. [PERSON] and [PERSON] [11] presented an equation for calculating the effective area based on the form of the corrosion (Equation (14)). The corrosion depth (\(p(t)\)) and current corrosion density (\(i_{\text{corr}}\)) required to calculate the effective area (\(A_{r}(t)\)) according to the service period were determined by using Equations (15) and (16):  
\[A_{r}(t)=\left\{\begin{array}{l}\frac{\pi D_{0}^{2}}{4}-A_{1}-A_{2},\ p(t) \leq\frac{\sqrt{2}}{2}D_{0}\\ A_{1}-A_{2},\ \frac{\sqrt{2}}{2}D_{0}\leq p(t)\leq D_{0}\end{array}\right., \tag{14a}\] \[a=2p(t)\sqrt{1-|\frac{p(t)}{D_{0}}|^{2}},\] (14b) \[A_{1}=\tfrac{1}{2}\bigg{[}\uptheta_{1}\big{(}\tfrac{D_{0}}{2} \big{)}^{2}-a\bigg{|}\tfrac{D_{0}}{2}-\tfrac{p(t)^{2}}{D_{0}}\bigg{|}\bigg{]},\] (14c) \[A_{2}=\tfrac{1}{2}\bigg{[}\uptheta_{2}p(t)^{2}-a\tfrac{p(t)^{2}}{D_{0}}\bigg{]},\] (15) \[p(t)=0.0116(t-t_{i})i_{\text{corr}}R,\] (16) \[i_{\text{corr}}=\frac{T_{k}H_{r}(w/c)}{d_{c}},\]  
where \(A_{1}\) and \(A_{2}\) are the effective area factors, \(R\) is the penetration ratio between the maximum and average penetration, \(t_{i}\) (year) is the time of corrosion initiation, \(i_{\text{corr}}\) (\(\upmu\)A/cm\({}^{2}\)) is the corrosion current density, \(H_{r}\) (\(\%\)) is the relative humidity, \(T_{k}\) (K) is the absolute temperature, \(w/c\) is the water-cement ratio, and \(d_{c}\) (mm) is the effective depth.
# A Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
## 3 Proposed Framework
### Feature Extraction and Training Unit
#### 3.5.2 Difference Extraction of Angle Features  
In order to accurately describe changes in expression and mitigate misjudgment caused by deflection and flip actions, the angle feature extraction scheme aims to minimize their influence. To achieve this objective, Figure 7 illustrates the designed process for angle feature extraction in this paper.  
In Figure 7, the \"Determine the reference frame\" step yields certain correction parameters, which are subsequently utilized for flip correction in the feature extraction process, as depicted in step 1 below. And in Figure 7, The \"feature extraction\" step must execute the following specific operations:  
1. Flip correction: Use Formulas (16) and (17) to correct the deflection and flip of the angle of this frame, and obtain \(A^{\prime}_{i}=0.5\times\left(A^{\text{UD}}_{i}+A^{\text{LR}}_{i}\right),i=1,2,\cdots,20\).
2. Feature calculation: Calculate the expression difference between the two frames before and after; then, the feature \(F_{j,i}\) is calculated by: \[F_{j,i}=A^{\prime}_{j,i}-A^{\prime}_{j-1,i},\ i=1,2,\cdots,20,\ j=1,2,\cdots,n-1\] (19) \(n\) in Formula (19) is the total number of frames that can detect expressions.  
Following the above feature extraction process, we obtain the original input data needed for the experiment from the video. The subsequent step involves partitioning the original feature data into training units.
## 3 Results
### Noise Criterion, dB (C) and dB (A)  
In this study, the noise criterion (NC), dB (C), and dB (A) data of the eight hospitals were obtained by calculating the audio data (according to ANSI/ASA S12.2-2019). The results are shown in Table 5. The obtained data related to noisiness were subsequently analyzed to determine their correlation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Band (Hz)** & **Correlation** \\ \hline
125 & \(-\)0.21 \\
250 & \(-\)0.32 \\
500 & \(-\)0.52 \\
1000 & \(-\)0.72 \\
2000 & \(-\)0.74 \\
4000 & \(-\)0.71 \\ \hline \hline \end{tabular} Notes: In this study, Microsoft 365 Excel 2209 software was used for analysis.  
\end{table}
Table 4: T30 frequency band (125 Hz–4 kHz) correlation analysis.  
Figure 4: Reverberation time (T30) of the 8 hospital lobbies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **NC** & **Correlation** & **dB (C)** & **Correlation** & **dB (A)** & **Correlation** \\ \hline Hospital A & 56 & & 63.8 & & 61.4 & \\ Hospital B & 51 & & 61 & & 55.6 & \\ Hospital C & 54 & & 65.1 & & 57.3 & \\ Hospital D & 55 & & 64.2 & & 59.3 & \\ Hospital E & 59 & 0.298 & 69.2 & 0.270 & 0.368 \\ Hospital F & 64 & & 69.6 & & 66.9 & \\ Hospital G & 59 & & 67.6 & & 63.5 & \\ Hospital H & 60 & & 67.4 & & 63.3 & \\ \hline \hline \end{tabular} Notes: In this study, Microsoft 365 Excel 2209 spreadsheet software was used for analysis.  
\end{table}
Table 5: NC (noise criterion), dB (C), and dB (A) correlation analysis.  
According to the analysis results, it is evident that there is no correlation between NC, dB (C), dB (A), and noise.  
This study referred to ISO 15666 [35] and decided to use decibels dB as the parameter basis for subsequent analysis.
## 2 Materials and Methods
### Construction of Cotton Boll Detection Dataset
#### 2.1.2 Data Preprocessing and Enhancement  
To prevent model overfitting due to a small dataset, image data augmentation techniques were utilized, including adjusting brightness, adding noise, applying CutOut [23], rotating, cropping, translating, and mirroring the images (see Figure 2). These methods doubled the dataset size. First, adjusting brightness increases the contrast of images, thus making the features of objects more pronounced, which is crucial for object detection. Second, adding noise increases the complexity of images, thereby enhancing the model's robustness against noisy data. CutOut represents an effective data augmentation method that involves randomly obscuring parts of an image, thus training the model to handle incomplete information. Additionally, geometric transformations such as rotation, cropping, translation, and mirroring help the model to adapt to various object poses and scenes. For instance, rotating or cropping the images can simulate different positions and angles of objects as they might appear in real-world scenarios. Finally, the dataset, which comprised 1821 images including both original and augmented images, was organized for the object detection algorithm. These images were divided into a training set, validation set, and test set in ratios of 7:1:2, comprising 1274, 182, and 365 images, respectively. Original images were used as the test dataset to demonstrate the performance of the model in this study. The specific distribution of the data set is shown in Table 1.  
We analyzed the labels of the dataset, the specific analysis results are shown in Figure 3, finding a total of 9894 labels. Cotton bolls were classified based on their size relative to the original image area: those less than or equal to 2% as small targets, those greater than 2% but less than or equal to 5% as medium targets, and those greater than 5% as large targets. In our dataset, there are 6212 small target cotton bolls, 2671 medium target cotton bolls, and 1011 large target cotton bolls. Small target cotton bolls comprised about 62.8% of the total, medium target cotton bolls comprised about 27%, while large target cotton bolls accounted for approximately 10.2% of the total. A few large bolls were found in the upper part of the cotton plant or near the main stem, although the large target represents only one-tenth of the size of the original image; thus, the number of small and medium bolls constitutes the majority.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Weather Situation** & **Number of Original Samples** &
\begin{tabular}{c} **The Number of Samples after** \\ **Image Amplification** \\ \end{tabular} \\ \hline Sunny & 147 & 441 \\ Cloudy & 401 & 1203 \\ Rainy & 59 & 177 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cotton image dataset.  
Figure 1: Examples of cotton boll images: (**a**) 1100–12:00 (sunny), (**b**) 11:00–12:00 (cloudy), (**c**) 11:00–12:00 (rainy), (**d**) 13:00–14:00 (sunny), (**e**) 13:00–14:00 (cloudy), (**f**) 13:00–14:00 (rainy).
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Path Traversal  
Path traversal in graph theory involves exploring a graph by following paths with many practical applications such as optimizing routes for delivery, public transit, or flight paths on transportation networks. There are three traditional problems in path traversal, i.e., _Depth-first search, shortest distance queries, reachability queries._