esa-sceva/satcom-chunk-collection huggingface dataset:
# Flooding Message Mitigation of Wireless Content Centric Networking for Last-Mile Smart-Grid +
## 3 Proposed Scheme
### PMPR-Based Aggregated Content (Prefix) Announcement and Content Caching  
In TOP-CCN, a CA packet has two types of prefix information such as content prefix of the sender and the neighbor content prefix list of the sender. When a device receives a CA packet, the content prefix list and the neighbor prefix list in the CA are inserted to FIB with received face information. In addition, 1-hop and 2-hop neighbor tables are updated using NBR-L, NBR-TL, and NBR-PL. Thus, a neighbor who receives a CA packet from its 1-hop or multi-hop neighbors can get the several prefix information of the neighbors of the sender. However, the proactive CA flooding increases network overhead such as packet collision, and congestion. To reduce the number of CA packet flooding, TOP-CCN selects PMPR devices who are allowed to broadcast multi-hop CA messages and can cache the data that is received from the AMI gateway. If the flooding type attribute of the received CA has single-hop value of 0, then CA is not rebroadcasted. Multi-hop CA is generated only by the PMPR who has the largest number of neighbors among 1-hop MPRs. If the MPR device does not receive its neighbors prefix information during \(\O\)t, then the MPR is changed as PMPR to notify neighbor(s) prefix information to the network. Therefore, content prefix information of the entire content provider in the network can be announced by only few multi-hop CA messages generated by PMPR.  
The PMPR selecting algorithm is started by changing the 1-hop and 2-hop table information, and then deals with three cases as shown in Algorithm 1: Content Publisher MPR selection for content caching. The first is the absolute case. If a MPR device cannot find any MPR neighbor in its 1-hop neighbor table, then the MPR device itself becomes a PMPR for broadcasting multi-hop CA packet to the network. The second is the recovery case. If the MPR device cannot find content prefixes of the neighbor(s) from received CA packets during 6 s, then MPR also becomes a PMPR to notify neighbor(s) prefixes. The third is the competition case. When a MPR finds neighbor MPR list from its 1-hop neighbor table, it compares the number of neighbors between MPR and MPR list. If the MPR has the largest number of neighbors among all the neighbors, the MPR device will become the PMPR. Figure 6 shows an example of PMPR selection. In this figure, PMPR set is 4, C, and 5 because they have the largest number of neighbors between each neighbor MPRs. Therefore, only three devices in the network generate multi-hop CA messages. In addition, a multi-hop CA message is only relayed through MPR devices in the network. As a result, network overhead of the proactive content announcement is reduced., because the number of devices that makes the content announcement is also effectively reduced.
# Development and Optimization of a SPME-GC-FID Method for Ethanol Detection
## 1 Introduction
In summary, while significant advancements have been made in the detection of ethanol using SPME-GC-FID, there is still room for improvement in terms of sensitivity, selectivity, matrix effects, and real-time monitoring. The present study aims to evaluate the effectiveness of the SPME-GC-FID technique and to contribute to the field by exploring novel approaches or techniques to enhance the accuracy, precision, and efficiency of ethanol analysis using SPME-GC-FID. Therefore, the main contributions of this paper are:  
1. [label=()]
2. Validation of a SPME injection gas chromatography with flame ionization detection method (GC-FID) using a capillary column for the accurate detection of ethanol, exploring different factors that can influence its sensitivity and reproducibility;
3. Application of the method for the determination of ethanol in fluids with biomedical, clinical, and forensic importance, including water, phosphate-buffered saline (PBS), and artificial sweat;
4. Establishment of a strategy that yields good peak resolution, demonstrating a linear correlation between concentration and peak areas for ethanol in all matrices. The method exhibits inter- and intra-day precisions below 15.5% and 6.5%, respectively, with detection limits below 1.3 mg/L across matrices. Notably, this approach allows for ethanol detection at lower concentrations using a lower temperature and time of extraction, distinguishing it from previous studies. By overcoming the limitations of other detection methods, GC-FID can provide accurate and reliable results for ethanol detection in biological samples in a wide range of applications.
## 2 Numerical Modeling
### Model Validation
#### 2.4.1 A Trial Embankment in Rio de Janeiro  
A trial embankment construction was performed at a test area in the stockyard of ThyssenKrupp Company, located in Rio de Janeiro, Brazil. The height of the test embankment was 5.35 m and the total vertical stress produced was approximately 150 kPa. The piles had a diameter of 0.8 m and a length of 11 m and were arranged in a square with a 2 m pile spacing (see Figure 3). The underground conditions of the test area were explored by means of three borings. The subsoil was found to consist of soft clay, medium sand, stiff clay, and dense sand. The pile tip was situated within the medium sand stratum, classifying it as a rigid floating pile. The detailed parameters can be found in [PERSON] et al. [37].  
Field monitoring of stress and deformation was conducted for 240 days after the construction of the embankment began. Figure 4 compares the settlement variations over time at the pile top and the surface of the subsoil in the center of the embankment. It has been found that the finite element analysis results show good agreement with the field data. At completion of embankment construction (65 days), the field monitoring recorded settlements of 258 mm and 318 mm at the pile top and the subsoil surface, respectively. In the finite element simulation, these values were 225 mm and 309 mm, with a maximum error of less than 15%.  
Figure 5 illustrates the temporal evolution of vertical stress at the pile top and subsoil surface in the center of the embankment. It was found that the finite element analysis overestimated the vertical stress above the pile top and at the soil surface during the embankment construction phase, while it underestimated the vertical stress during the consolidation phase after the completion of embankment construction. Nevertheless, the  
Figure 3: Profile of the embankment and borehole locations in in situ tests.  
finite element calculations captured the realistic stress growth trend, with a maximum error of only 10.6%.
## 2 Related Work
### 3D Hand Pose Estimation from RGB Image/Video  
The ubiquity of RGB cameras has accelerated progress in estimating hand poses from RGB imagery. Hand pose estimation has emerged as a focal area in computer vision, driven by advancements in monocular RGB-based methodologies [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. [PERSON] et al. [15] introduced a neural framework combining pre-trained 3D joint models with detected 2D key points to estimate hand poses from single RGB inputs. [PERSON] et al. [9] proposed a novel architecture that combines a graph convolutional network with the transformer architecture to achieve simultaneous pose estimation and hand mesh reconstruction. To resolve depth uncertainty, [PERSON] et al. [7] leveraged a 2.5D heatmap encoding strategy within a convolutional network, enhancing positional precision through multi-dimensional feature fusion. [PERSON] et al. [11] employed a hierarchical framework utilizing pixel-wise heatmap predictions for 2D joint localization and depth regression. [PERSON] et al. [14] addressed the challenge of data scarcity in 3D hand pose estimation by proposing a weakly supervised framework. Robust pose estimation can be achieved through kinematic constraints without the need for full 3D annotation.  
Related research methods have been extended to 3D hand pose estimation from continuous video data [1; 16; 17; 18]. Temporal continuity in video streams offers enhanced contextual cues compared to static frames, capitalizing on motion patterns and kinematic coherence. [PERSON] et al. [17] introduced a self-supervised framework for inferring 3D hand meshes and joint trajectories from unannotated videos while eliminating dependency on manual 3D supervision. [PERSON] et al. [1] introduced a large-scale first-person RGB-D dataset that addresses a critical gap in first-person movement analysis. Depth sensors and inverse kinematics are also utilized to generate accurate 3D joint annotations, avoiding the reliance on error-prone manual annotations. Research on gesture recognition and fine-grained motion understanding is enhanced. [PERSON] et al. [16] advanced temporal  
Figure 1: Schematic diagram of the framework process. The framework first processes each frame in the input first-person-view RGB video \(\upsilon\) to estimate the hand pose for each frame via HPEM and determine the type of object involved within each frame. Subsequently, the processed frames are passed to the ARM, which aggregates the information from all the frames, captures the temporal dynamics of the hand movements, and ultimately outputs the recognition results of the hand movements.  
modeling via graph convolutional architectures, leveraging spatiotemporal coherence in sequential 2D joint data to jointly estimate body and hand poses.
# Diffusion Model as a Base for Cold Item Recommendation
## 4 Evaluation Environment
### Evaluation Metrics  
We assessed the recommendation performance by employing Precision@k (P@k) [68], Recall@k (R@k) [68], and NDCG@k (N@k) [69].  
P@k represents the fraction of relevant items (the items that the user has interacted with) included in the top k positions of the recommendation list. It is computed using Equation (24).  
\[\text{P@k}=\frac{1}{|U|}\sum_{u\in U}\frac{|L_{u}^{k}\cap I_{u}^{+}|}{|L_{u}^ {k}|} \tag{24}\]  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Training Set**} & \multicolumn{4}{c}{**Evaluation Set**} & \multicolumn{4}{c}{**Test Set**} \\ \cline{2-11}  & **Users** & **Items** & **Clicks** & **Sparsity** & **Users** & **Items** & **Clicks** & **Users** & **Items** & **Clicks** \\ \hline CiteULike & 5551 & 13,584 & 164,210 & 99.78\% & 5551 & 1018 & 13,037 & 5551 & 2378 & 27,739 \\ ML25M & 162,466 & 11,053 & 11,369,010 & 99.37\% & 55,056 & 829 & 519,586 & 161,053 & 1934 & 3,541,109 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset statistics.  
R@k measures the proportion of the interacted items within the top k recommendations relative to the total number of interacted items in the test dataset. It is determined by Equation (25).  
\[\text{R@k}=\frac{1}{|U|}\sum_{u\in U}\frac{|L_{u}^{k}\cap I_{u}^{+}|}{|I_{u}^{+}|} \tag{25}\]  
Here, \(I_{u}^{+}\) refers to the set of items that user \(u\) has interacted with, while \(L_{u}^{k}\) denotes the set of recommended items that appeared within the top k positions of the recommendation list for \(u\).  
N@k considers the interacted items' relevance and ranking position within the top k recommendations. Even if the number of relevant items in the top-k list remains the same, N@k assigns higher scores to items appearing in earlier ranks. It is computed using Equation (26).  
\[\text{N@k}=\frac{DCG@k}{IDCG@k} \tag{26}\]  
\[DCG@k=\frac{1}{|U|}\sum_{u\in U}\sum_{l=1}^{k}\frac{1_{l_{u}^{+}}(l)}{max(1, log2(l))} \tag{27}\]  
Here, \(1_{l_{u}^{+}}(l)\) in Equation (27) represents the indicator function that identifies whether an item appeared in the \(l\)-th position in \(L_{u}^{k}\) is included in \(I_{u}^{+}\) or not. It takes a value of one if the item is included in \(I_{u}^{+}\), and zero otherwise. \(IDCG@k\) (Ideal \(DCG@k\)) corresponds to the maximum possible \(DCG@k\) where all relevant items (\(i\in I_{u}^{+}\)) are ranked consecutively from the top of the recommendation list.
# Improvement of Learning Stability of Generative Adversarial Network Using Variational Learning
## 4 Experimental Results and Discussions
### Improving Learning Stability Using the Proposed Method  
The main reason for the vanishing gradient in the learning process of GAN is that the discriminator is learnt so fast that the performance between the generator and the discriminator are not balanced. Therefore, we first established a strong discriminator that was able to discriminate the fake data generated by the generator based on the learning of the discriminator with 10 epochs to verify whether the proposed method can solve the problem of the vanishing gradient. Subsequently, DCGAN and the proposed VLGAN were learnt again afresh with the use this strong discriminator as the initial value. Figure 4 shows the experimental results for the MNIST and the CelebA datasets. It is confirmed that the gradient of the generator approaches zero (i.e., vanishes) within around 50 iterations, as shown in Figure 4a,c. Conversely, in the case of learning, which was based on the use of VLGAN (Figure 4b,d), the generator could continue to use the gradient received from the VAE decoder, even when the gradient vanished in the loss of GAN owing to the strength of the discriminator. Consequently, the learning of GAN continued and was unaffected by the vanishing gradient problem.
# A Novel Approach for Software Defect prediction Based on the Power Law Function
## 5 Conclusions  
In this study, we proposed a novel approach that adopted characteristics of the power law function for software defect prediction. The kernel of our approach was using the power law function to describe metrics distributions and set the transformation values in the maximal curvature point of each power law function curve as the threshold value for labeling metrics. In our empirical studies, we found that the proposed approach performed significantly better than other commonly used four algorithms across all evaluated twelve terms. The average values of recall and F-measure were improved by over 14.3% and 6.0%, respectively. Furthermore, the proposed approach had a complexity of \(O(2n)\), which was the lowest among the tested five algorithms. Therefore, we demonstrated that our proposed approach is feasible and highly efficient at defect prediction with unlabeled datasets.  
In summary, the proposed approach offers a viable choice for software defect prediction on unlabeled datasets. However, similar to any other method, there are some issues to handle in future work. The precision of the prediction should be analyzed in-depth and improved in the future. At the same time, future work should attempt to use the proposed approach to rank predictions.  
Conceptualization, methodology, writing--original draft preparation and software, J.R. Project administration, funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 2019 JM026). The authors declare no conflict of interest.  
The metrics and their descriptions for dataset Camel1.6 utilized in case study.
# QoE Modeling on Split Features with Distributed Deep Learning
## 4 Effect of Video Content on QoE  
In this section, in order to motivate the use of split learning in QoE, we quantify the effect of individual features on the QoE estimation, in particular the _MOS_ label. We consider an example QoE use case with splitting of the features according to different split indexes. We test and experiment with different feature split assumptions. In the dataset, we hypothesize through domain expertise that the specific features are the ones related to the spatio-temporal content complexity, represented by the _TI_ and _SI_ features. In order to quantify the effect of the video content, in particular, on the QoE modeling, we performed at least 100 experiments to achieve statistical significant comparison of scenarios. In each experiment, we randomly select 70% of data as training set, and 30% as test set. We use the coefficient of determination \(R^{2}\) and mean absolute error (MAE), indicating the mean absolute difference between the true label and the predictions, as accuracy evaluation metrics. 95% confidence interval half-sizes are also obtained. XGBoost model of a Python Scikit-learn [34] implementation was used and the hyper-parameters were set as follows: eta to 0.004, maximum depth to 4, subsample to 0.5, subsample ratio of columns when constructing each tree to 1, and objective function's evaluation metric to root mean squared error.
## 2 Materials and Methods
### Methods  
Although PS-InSAR technology has the drawbacks of intricate data processing and the requirement for a substantial amount of data, it not only addresses the issues associated with lengthy monitoring cycles, slow speed, susceptibility to natural weather conditions, and limited monitoring range encountered by traditional technology but also offers superior advantages in terms of monitoring accuracy and sensitivity compared to InSAR. Prior to PS-InSAR data processing, the image data had to be imported and trimmed using ENVI5.6+ SARScape5.6 software. This technology leverages the interaction between electromagnetic waves emitted by spaceborne radar and stable scattering targets such as buildings and bare rocks [39; 52], ensuring both temporal and spatial stability. By minimizing noise and incoherence effects, PS-InSAR technology effectively addresses challenges associated with lengthy monitoring cycles, slow operational speeds, susceptibility to natural weather conditions, and limited monitoring range inherent in traditional methods.  
The fundamental principle of this technique involves acquiring N interference images from N captured images within the research area, followed by obtaining corresponding differential interference images through DEM difference processing. The phase value of the (N-1)th interferogram element is \(\Delta\varphi=\frac{4\pi}{\lambda}R_{n}\), while the post-difference phase can be expressed as:  
\[\varphi_{n}=\frac{4\pi}{\lambda R_{n}\cdot\sin\theta}B_{\perp}^{n}\delta_{H}+ \frac{4\pi\cdot\cos\theta}{\lambda}h+\varphi_{A}^{n}+\xi^{n} \tag{1}\]  
In the equation, \(R_{n}\) is the sensor-to-ground target slant distance; \(\theta\) is the radar incidence angle; \(\lambda\) is the radar wavelength; \(B_{\perp}^{n}\) is the effective spatial baseline; \(\delta_{H}\) is the elevation correction value; \(h\) is the surface deposition; \(\varphi_{A}^{n}\) is the atmospheric phase; and \(\xi^{n}\) is the noise phase.  
To mitigate atmospheric phase impact, a spatial differentiation can be applied to the interference phases of adjacent Persistent Scatterer (PS) points resulting in distinct phases for PS points I and j denoted as:  
\[\begin{cases}\varphi_{n}^{i}=a^{i}B_{\perp}^{n}\delta_{H}^{i}+b^{i}t^{n}v+ \varphi_{n-res}^{i}\\ \varphi_{n}^{j}=a^{i}B_{\perp}^{n}\delta_{H}^{j}+b^{i}t^{n}v+\varphi_{n-res}^{ j}\end{cases} \tag{2}\]  
Differencing the two equations again gives the PS point phase differential equation:  
\[\Delta\varphi_{n}=aB_{\perp}^{n}\Delta\delta_{H}+b_{1}t^{n}v+\Delta\varphi_{n -res} \tag{3}\]  
Accurate estimation of the difference between subsidence velocity \(\Delta\
u\) and elevation correction value \(\Delta\delta_{H}\) for two adjacent PS points, along with \(\varphi_{n-res}<\pi\), enables determination of complete phase difference value \(\Delta\varphi_{n}\) between these points. The gradient \(\varphi_{n}\) is represented by \(\Delta\varphi_{n}\). Given knowledge of the complete gradient value, unwinding \(\varphi_{n}\) allows for determination of surface deformation. Figure 2 presents a flow chart illustrating the PS-InSAR technology.
# The Uses of a Dual-Band Corrugated Circularly Polarized Horn Antenna for 5G Systems
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Frequency** & **3 dB Beam Width** & **Axial Ratio** & **Peak Gain** & **Fabrication Complexity** \\ \hline \multirow{2}{*}{This work} & 24–30 GHz & \multirow{2}{*}{105/77} & \(<\)4.5 dB & \multirow{2}{*}{6.1/8.7} & \multirow{2}{*}{easy} \\  & 37–40 GHz & & \(<\)5.1 dB & & \\ \hline
[10] & 55–62.5 GHz & 112.37 & NA & 7.32 & easy \\ \hline
[8] & 50–75 GHz & 30–60 & \(<\)3.2 dB & 12.21–12.56 & easy \\ \hline
[17] & 19.6–21.2 GHz & 23 & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{mid} \\  & 29.4–31 GHz & 19 & & & \\ \hline
[9] & 75–110 GHz & 58.3–76 & \(<\)3 dB & 6.7–9.8 & easy \\ \hline
[18] & 1.0–1.7 GHz & 98.6 & N/A & 3.4–2.5 & hard \\ \hline
[19] & 1.19–1.22 GHz & 94/90 & \(<\)3 dB & 6–6.4 & easy \\ \hline
[20] & 10.8–11.3 GHz & N/A & \(<\)3 dB & 19.8 dBic & mid \\ \hline
[21] & 8.45–11.5 GHz & N/A & \(<\)3 dB & N/A & hard \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of antenna performance with previously proposed antennas. Figure 23: Antenna parts breakdown illustration. **Author Contributions:** Conceptualization, C.-K.L., W.-Y.C. and S.-L.C. ; Data curation, C.-K.L., W.-Y.C., P.-Z.R., S.-H.C. and P.-H.H. ; Formal analysis, C.-K.L., W.-Y.C. and P.-H.H. ; Funding acquisition, C.-K.L., W.-Y.C., P.-Z.R., C.-A.C. and L.-H.W. ; Investigation, W.-Y.C. ; Methodology, C.-K.L. and W.-Y.C. ; Project administration, W.-Y.C., S.-H.C., C.-A.C., L.-H.W., P.A.R.A. and S.-L.C. ; Resources, C.-K.L., W.-Y.C., C.-A.C., L.-H.W., P.A.R.A. and S.-L.C. ; Supervision, S.-H.C., C.-A.C., L.-H.W., P.A.R.A. and S.-L.C. ; Visualization, C.-A.C. and S.-L.C. ; Writing--original draft, W.-Y.C. and P.-Z.R. ; Writing--review & editing, C.-A.C., P.A.R.A. and S.-L.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the Ministry of Science and Technology (MOST), Taiwan, under grant numbers of MOST-108-2628-E-033-001-MY3, MOST-110-2218-E-035-007, MOST-110-2622-E-131-002, MOST-109-2622-E-131-001-CC3, MOST-108-2622-E-033-012-CC2, MOST-109-2221-E-131-025, MOST-108-2112-M-213-014, MOST-109-2112-M-213-013, and the National Chip Implementation Center, Taiwan. The authors declare no conflict of interest.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
Therefore, the NaI(Tl)-detector-based seawater in situ \(\gamma\) spectrometer has difficulties in qualitative judgment and quantitative measurement when encountering complex \(\gamma\) spectrum. As concerns progress for applying underwater medium resolution gamma-ray spectrometer in the marine environment, a recent detection system is developed (named GeoMAREA) integrating a 2\({}^{\prime\prime}\)\(\times\) 2\({}^{\prime\prime}\) CeBr\({}_{3}\) crystal [12]. This system is applied effectively for studying groundwater-seawater interaction in submarine groundwater discharge system [13; 14]. Furthermore, the advantage of the HPGe detector is the good energy resolution, which makes it has excellent ability of peak discrimination and radionuclide identification. However, the HPGe system is not very robust. It needs to operate at a low temperature, so it consumes more power compared to low and medium resolution systems and it cannot detect continuously for a long time. Therefore, if there is any detector with high detection efficiency and good energy resolution at room temperature, it would be an optimum system for in situ \(\gamma\)-ray spectrometry and long-term monitoring in seawater. In this regard, an attempt has been made in this study to obtain in-situ gamma spectrometry of liquid discharges by deploying a lanthanum bromide scintillator fixed to the buoy at the outlet of a coastal nuclear power plant.
## 3 Results
### Experimental Considerations
#### 3.3.5 Repeatability of Measurements  
The final experimental issue to be considered is measurement repeatability. Figure 10 shows the mean and standard deviation of five scans of the emitter at 600 GHz in the H-plane. It is seen that the noise in the data is negligible, and the error bars are too small to be distinguishable in the plot.