esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data
#### 2.2.2 Discharge and Sediment Data  
River width changes are directly related to the flow regime; the discharge information was provided free of charge by the _Instituto de Hidrologia_, _Meteorologia y Estudios Ambientales_ (IDEAM; [[http://dhime.ideam.gov.co/atencionciudadano/](http://dhime.ideam.gov.co/atencionciudadano/)]([http://dhime.ideam.gov.co/atencionciudadano/](http://dhime.ideam.gov.co/atencionciudadano/)); accessed on 30 April 2023) and the _Corporacion Autonoma Regional del Valle del Cauca_ (CVC; [[https://www.cvc.gov.co/](https://www.cvc.gov.co/)]([https://www.cvc.gov.co/](https://www.cvc.gov.co/)); accessed on 28 February 2023). Gauging stations were chosen for their location in the main channel and recorded time before and after dam construction (see Table 2). In this table, identifiatory numbers of each station (ID) are the same as in Figure 1. Gauging stations were only selected downstream of the dams because upstream stations do not have information prior to dam construction. The hydrological regime in the influence area of each selected dam was analyzed for selecting the images in the summer or with similar summer discharges, avoiding years under the influence of El Nino-Southern Oscillation (ENSO) phenomena extreme phases (El Nino or La Nina years), in which the river can exhibit abrupt width changes. After dam construction and according to the design criteria, the natural discharge regime of a river can be maintained, decreased, or increased throughout the year [49; 50; 51]. As the spatial information was taken in the summer season, it was necessary to estimate the magnitude of change with statistical tests. We used [PERSON]'s \(t\)-test [52; 53] to evaluate the mean change, and [PERSON]'s [54]\(F\)-test to evaluate the variance change, for the pre- and post-dam periods. Although the literature describes a general tendency due to dam operation (increased discharges during the dry period, medium discharges maintained, and decreased high discharges [55; 56; 51]), the influence of a dam is also observed in annual values. Therefore, minimum, medium, and maximum annual flow time series were built, and the differences (in percentage) between the pre-dam and post-dam periods for all stations were evaluated. The Kolmogorov-Smirnov [57; 58] and Mann-Whitney [59] nonparametric tests were performed to evaluate changes in the distribution before and after dam construction. In Colombia, only a few gauging stations count with a record of suspended sediment transport. Nevertheless, we also included suspended sediment measurements from available reports and theses in our analysis [60; 61; 62; 63; 64]; a summary of the reduction trend in this parameter is shown in Table 3. The gathered data allowed us to establish an approximate
## 3 Materials and Methods
### Differential Privacy  
Differential privacy provides a formal guarantee that the privacy risk remains similar whether or not an individual provides his/her data [19]. It has been successfully applied to frequent sequential pattern mining, but its strict privacy guarantees oftentimes involve considerable information loss. To reduce such information loss, we propose an in-process approach to protect the relation of the patterns to users with differential privacy. Here, we include the definition for edge-differential privacy adapted from [20].  
**Definition 11** (Edge-differential privacy).: _It is said that a randomized function \(\mathcal{A}\) is \(\varepsilon\)-differentially private if for all graphs \(G\) and \(G^{\prime}\) differing on at most one edge and all \(S\subseteq Range(\mathcal{A})\), it holds that:_  
\[Pr[\mathcal{A}(G)\in S]\leq exp(\varepsilon)\times Pr[\mathcal{A}(G^{\prime} )\in S].\]  
Therefore, an adversary will not be able to infer the existence of an edge in the graph by observing the output of the function \(\mathcal{A}\).
# A Focused Event Crawler with Temporal Intent
## 3 Mathematical Proof and Identification of Temporal Intent
### Identification of Temporal Intent by Google Trends  
Temporal intent identification has already been studied in temporal information retrieval with the help of some priori data, such as query logs and time series data derived from Wikipedia [31, 32]. Inspired by these works, we design a temporal intent identification method using some priori data from Google Trends data.  
Google Trends data, which can be obtained from the website [[http://www.google.com/trends/](http://www.google.com/trends/)]([http://www.google.com/trends/](http://www.google.com/trends/)) (accessed on 23 February 2023), represents a normalized portion value of all web searches performed worldwide for user-specified terms relative to the total number of searches conducted over a defined period [33]. The range of the Google Trends data is from 0 to 100 (see Figure 1), and the higher the value, the greater the search volume. It has been widely used in many fields, such as healthcare research [34, 35], unemployment forecasting [36, 37], international financial reporting standards [38], presidential election predictions [39], conservation culturomics [40], and so on. These applications have proved that Google Trends data is highly correlated with an event and its value size indirectly reflects the evolution of the event [41]. That is to say, the larger the value of the Google Trends data is, the more web information is published about the event. For this reason, we select Google Trends data as a priori data to identify the temporal intent of an event.  
As discussed in Section 3.1, the start time and temporal distribution in the event temporal intent play an important role in focused crawlers. Therefore, the identification of start time and temporal distribution using Google Trends data is the sole focus of this paper. The main steps of the method are described as follows.  
First, the start time of an event is identified by the first fluctuation of the Google Trends data from zero to a small value. This is because, before the event, there are few web searches relative to the total number of searches about the event, which keeps its Google Trends data at zero; when the event occurs, more and more web searches are submitted, which makes its Google Trends data suddenly increase from zero. Therefore, when we get the Google Trends data of an event, the time when the non-zero value appears for the first  
Figure 1: Google Trends data about the “Typhoon Haiyan” event.  
time is identified as the start time. For example, Figure 1 shows that the first non-zero value for the event \"Typhoon Haiyan\" appears in November 2013, and thus the time \"November 2013\" is identified as the start time of \"Typhoon Haiyan\".  
Secondly, the temporal distribution of an event is quantized by the value of the Google Trends data. This is because its values change as the event evolves, as mentioned in the previous paragraphs. Therefore, a series of time and Google trend data are used to create quantitative temporal distributions.
# GPU-Accelerated CNN Inference for Onboard DQN-Based Routing in Dynamic LEO Satellite Networks
## 4 Proposed Method
### Integrated ReLU for Minimize 1/O Overheads  
In Figure 2, examining the baseline confirms that convolution and ReLU operations are executed separately. In CNNs, ReLU, an activation function, is applied after the convolutional operation to introduce nonlinearity and make the feature map extracted by the filter more meaningful. The traditional approach treats these two processes as separate operations. However, this method posed challenges during kernel development. It led to unnecessary kernel calls and created a memory bottleneck since the GPU's computation speed is much faster than its memory access speed. Moreover, by performing the operations in separate kernels, we lost opportunities for parallelism, leading to computational inefficiency. To address this issue, we adopted a unified approach by combining convolution and ReLU into a single kernel, as shown in Figure 2, instead of separating the two operations. This unified method optimizes the execution process and significantly reduces unnecessary kernel calls, resulting in more efficient computation.
###### Abstract
[17] argued that there is a need to explore the end-user's perception and foresee the diverse service production from the perspective of the end-user, particularly for social infrastructure PPPs. To summarize, the research question in this study originates from practice, i.e., the social PPP infrastructure, such as hospitals and schools, are normally confronted with major modification works due to the end-user's need and requirements, which could lead to major changes in the contracts. Although there is a KPIs mechanism as the benchmarking tool by which governments measure and monitor the performance or quality of service delivered by private sector against the agreed standards, the satisfaction of the end-user, which is playing a significant role in PPP operations, after a review of normative literature, is always overlooked in the current research mainstream. Therefore, the purpose of this study is to shed light on how the end-user can be better engaged to improve PPPs performance, mainly directed at social PPPs operations. An innovative reliability-based decision support framework (RDSF) in terms of dealing with potential major changes during operations based on an ex-post performance evaluation through the lens of the end-user is developed, which utilizes the probability of uncertainty of end-user sentiments and contractual measurements at particular points in time. It compares data gathered on end-user perceived service quality and the assessment of delivered contractual services as measured by abatement adjustments to period payments. The RDSF proposed in this study is a managerial decision-making tool that can be used in a performance management system (PMS) for public managers. On one hand, it enriches theories in the field of social PPPs performance evaluation and contribute to new knowledge in terms of dealing with the potential major changes during PPPs operations by extending the previous use of reliability analysis [18] in the appraisal of a desalination plant [19], residential buildings [20] and port infrastructure [21], into considering the uncertainties in social PPPs performance. On the other hand, it provides government with a practical tool of social PPP operations that can transparently incorporate the end-user's views into decisions as to whether enhanced services are warranted or not. Application of this framework would assist in maintaining the long-term value for money of social infrastructure PPP agreements. The RDSF is tested using qualitative survey data from the end-user and quantitative cost data on three PPP schools in Australia. In addition, the practical application of the framework over the long term is considered using a synthetic forward projection example. The following sections detail the: establishment of the conceptual framework, the research methodology, testing of the model using three PPP schools and a discussion of the broader applicability of the model.
## 5 Results and Discussion  
We compared the proposed SSMOECHS to centralized protocols including LEACH-C [7], PSO-C [8], and SMOTECP [18] through an implementation on Python 3.6 using relevant libraries, such as Networkx, Numpy, and Matplotlib. The experiment was conducted both in a homogeneous setup, where the initial energy of all nodes was the same, and in a heterogeneous setup, where the initial energy differed among nodes. The experimental results are reported as network topology, network lifetime, consumed energy, and energy efficiency.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### Performance Comparison
#### 5.4.5 End-to-End Delay Analysis  
End-to-end delay directly impacts the responsiveness and quality of service in IoT applications. Figure 9 shows the delay measurements for the proposed framework and compared methods over varying network sizes.  
Figure 8: Energy consumed by [PERSON] et al. [33], [PERSON] et al. [39], and [PERSON] et al. [40] (0).  
Figure 9: End-to-end delay comparison among [PERSON] et al. [33], [PERSON] et al. [39], and [PERSON] et al. [40] (ms).  
At smaller network sizes, i.e., 10-30 nodes, our framework achieves delays of between 1.0 ms and 1.1 ms, which is comparable to or better than all baselines. Notably, at 10 nodes, it is 20% faster than [PERSON] et al. (2019) (1.2 ms) and 60% faster than [PERSON] et al. (2019) (1.5 ms).  
As network size grows to 100 nodes, our framework maintains an average delay of 1.9 ms, which is:  
* 13% lower than [PERSON] et al. (2019) (2.1 ms)
* 14% lower than [PERSON] et al. (2019) (2.2 ms)
* 24% lower than [PERSON] et al. (2019) (2.5 ms)  
The relatively low delay is attributed to our distributed trust evaluation approach, which avoids delays caused by hierarchical or cluster-based leader election protocols. Additionally, IOTA's asynchronous transaction validation allows for trust updates without blocking network traffic.
# Liquid-Crystal-on-Silicon for Augmented Reality Displays
## 4 LCoS for Augmented Reality Displays  
### Augmented Reality Head-Mounted Displays  
So far, LCoS is the dominant solution for augmented reality (AR) head-mounted display (HMD) because of its compact size (<1.5 inches), high optical efficiency (>90%), high fill factor (>90%), and high resolution (4K2K achieved). In 2013, [PERSON] et al. reviewed the HMD optical systems focusing on the combiner designs [85]. Here, we just use Figure 12 to schematically show how an LCoS functions in an AR-HMD optical system (Google Glass's design [86]). A polarizing beam splitter (PBS) is engaged as crossed polarizers for the reflective display system. When the light emitted from an LED source enters the PBS (purple line), the s-polarized light (red line) is reflected away so that only the p-polarized light (blue line) can reach the LCoS panel, indicating that only half of the incident light could be modulated. To enhance the display brightness, polarization conversion optical elements have been developed [87] and can be inserted between the LED source and the PBS together with collimation lenses. Passing through the LCoS, the unmodulated p-polarized light (thin blue line) transmits back to the LED source, while the \(\pi\)-phase retarded s-polarized light (red) gets reflected by the PBS and directed to human eyes by a partially-reflective mirror (PRM) and a focusing mirror (FM). As the ambient light transmits PRM as well (green line), an AR experience with an LCoS-generated display and real world can be achieved.  
Current 3D displays are mainly based on binocular disparity: two eyes receive different images representing slightly different viewing points. However, the mismatch of other focus cues gives rise to visual discomfort [88, 89] and distorted depth [90, 91, 92]. As a promising one-stop solution to provide correct focus cues with aberration correction [93], phase-only LCoS-based AR-HMD has been demonstrated [12, 13, 21]. Different from the intensity modulation configuration, the incident linearly polarized light on phase-only LCoS should be parallel to the LC directors. Therefore,  
Figure 11: The proposed fabrication method of a PSLC LCoS with FFE-opposing features: (**a**) UV transmittance control by a gradient optical density photomask. (**b**) HG cell with inhomogeneous polymerization. (**c**) Spatially variant LC response to an external electric field.  
Figure 10: The proposed fabrication procedure of an LCoS with FFE-opposing features.  
PBS could be eliminated in phase-only AR systems, enabling an eyeglasses size form factor [12]. In contrast to the hardware lightness, the computational burden on phase encoding is heavy. In the past decades, extensive efforts have been devoted to developing high-quality real-time holography algorithms [94; 95; 96; 97; 98]. Though prototypes have been demonstrated [12], the commercialization of high-quality holographic AR-HMD may still need time.
## 4 Results
### Performance of Skeleton Stream  
To show the effectiveness of the skeleton stream which treats skeleton data as heatmaps instead of graphs and uses less attention layers, we compare the fast pathway only to skeleton-based action recognition methods on the NTU RGB+D 60 and NTU RGB+D 120 datasets. Table 2 shows the result. Most skeleton-based methods use the 3D skeleton which contains 25 points collected by Microsoft Kinect v2 sensors. In contrast, RGBSformer takes 2D skeletons estimated by HRNet [27] pretrained on the COCO-keypoint [44], which consits of 17 points. On the NTU RGB+D 60 dataset, our framework outperforms other models by at least 0.9%, which proves the skeleton heatmaps are helpful for human action recognition, and on the NTU RGB+D 120 dataset, RGBSformer also performs well. It is worth noting that compared to the Transformer-based ST-TR-agcn [45], treating the skeleton as heatmaps performs better than treating skeleton as graphs. Figure 8 shows the normalized confusion matrix of the skeleton stream on both datasets. On the NTU RGB+D 60 dataset, the skeleton stream has extracted features to significantly distinguish the classes. However, on the extended dataset, the skeleton stream performs poorly in gesture-related categories, such as \"thumb up\", \"thumb down\", \"make ok sign\", and \"make victory sign\". This is understandable, because the COCO-keypoint does not refine the skeleton joints of the hand, and the skeleton stream can only judge by the direction of the arm swing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Modality** & **FineGym99** & **Kinetics400** \\ \hline I3D [39] & RGB & 63.2 & 71.1 \\ R(2+1)D-TwoStream [40] & RGB, Flow & - & 75.4 \\ TRN [41] & RGB & 68.7 & - \\ TSM [42] & RGB & 70.6 & - \\ TSM Two-stream [42] & RGB, Flow & 81.2 & - \\ RSANet-R50 [38] & RGB & 86.4 & - \\ MARES [43] & RGB,Flow & - & 74.9 \\ TP-ViT [11] & RGB, Skeleton & - & 80.8 \\ STGAT [20] & Skeleton & - & 39.2 \\
**RGB stream** & RGB & 69.3 & 78.8 \\
**skeleton stream** & Skeleton & 83.7 & 39.7 \\
**RGBSformer** & RGB, Skeleton & **86.7** & **80.9** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance of RGBSformer and two single pathways on FineGym99 and Kinetics400 compared to state-of-the-art models. We report the mean-class accuracy for FineGym99 and Top-1 accuracy for Kinetics400. Units are in %.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Method** & **NTU RGB+D 60** & **NTU RGB+D 120** \\ \hline GVFE + AS-GCN & & & \\ with DH-TCN [46] & GCN & 85.3 & 78.3 \\ Mix-Dimension [47] & GCN & 89.7 & 80.5 \\ PGCN-TCA [48] & GCN & 88.0 & - \\ ST-TR-agcn [45] & Transformer & 90.3 & 82.7 \\ EfficientGCN-B0 [49] & GCN & 90.2 & **85.9** \\
**Ours** & Transformer & **91.1** & 85.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of the skeleton stream only compared to the state-of-the-art skeleton-based action recognition methods on NTU RGB+D 60 and NTU RGB+D 120 (cross-subject). Units are in %.
## 5 Conclusions  
In this paper, the static positioning accuracy, rotational positioning accuracy, and dynamic positioning accuracy in an NLOS environment are analyzed. The results show that in a line-of-sight (LOS) environment, the positioning accuracy of the Bluetooth array angle measurement is higher in its coverage range than in NLOS environment. The positioning accuracy of the points within 0.3 m (i.e., the near distance) can be guaranteed, but the maximum positioning error of points at a long distance can reach 0.595 m. The proposed SVD-STKF algorithm can improve the overall positioning accuracy up to 0.03 m, and the maximum value can be modified by 0.08 m. In the NLOS environment, the positioning accuracy decreases sharply, and the positioning error is close to 1 m in severe cases. However, the proposed SVD-STKF algorithm has a good effect. By using the proposed algorithm, the Hausdorff distance is improved by 0.513 m, the positioning results are smoother, there is no random mutation point, and good correction for the points far from the true trajectory is achieved. The comprehensive comparison results show that the proposed positioning algorithm can achieve the positioning accuracy within 1 m in the coverage area in a LOS environment, while in an NLOS environment, the dynamic positioning accuracy of the proposed Bluetooth array angular measurement and positioning system decreases sharply. Thus, by relying only on the positioning accuracy of the proposed system, the high-precision positioning within the coverage area cannot be guaranteed. However, after modifying the SVD-STKF tracking algorithm, the positioning accuracy within 1 m can be achieved for the source rotation situation in a LOS environment and the dynamic situation in an NLOS environment. Therefore, the high-precision positioning in the coverage area can be achieved by using a single base station.  
[PERSON] conceived the idea and designed the experiments with [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] wrote the main manuscript; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] reviewed the paper. All  
Figure 18: Trajectory comparison of the dynamic experiment.  
components of this research were carried out under the supervision of [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program (No. 2016 YFC0803102), The National Key Research and Development Program (No. 2016 YFB0502201), the National Natural Science Foundation of China (Grant Nos. 42030109, 42074012), and The Liaoning Key Research and Development Program (No. 2020 JH2/10100044).  
Not applicable.  
The authors gratefully acknowledge the Chinese Academy of Surveying and Mapping that provides the experimental environment.  
The authors declare no conflict of interest.
###### Abstract  
In this study, the potential of employing SiN\({}_{\rm x}\) (silicon nitride) waveguide platforms to enable the use of liquid-crystal-based phase shifters for on-chip optical modulators was thoroughly investigated using 3D-FDTD (3D finite-difference time-domain) simulations. The entire structure of liquid-crystal-based Mach-Zehnder interferometer (MZI) optical modulators, consisting of multi-mode interferometer splitters, different tapering sections, and liquid-crystal-based phase shifters, was systematically and holistically investigated with a view to developing a compact, wideband, and CMOS-compatible (complementary metal-oxide semiconductor) bias voltage optical modulator with competitive modulation efficiency, good fabrication tolerance, and single-mode operation using the same SiN\({}_{\rm x}\) waveguide layer for the entire device. The trade-off between several important parameters is critically discussed in order to reach a conclusion on the possible optimized parameter sets. Contrary to previous demonstrations, this investigation focused on the potential of achieving such an optical device using the same SiN\({}_{\rm x}\) waveguide layer for the entire device, including both the passive and active regions. Significantly, we show that it is necessary to carefully select the phase shifter length of the LC-based (liquid crystal) MZI optical modulator, as the phase shifter length required to obtain a \(\pi\) phase shift could be as low as a few tens of microns; therefore, a phase shifter length that is too long can contradictorily worsen the optical modulation.  
optical waveguides; electro-optic modulators; liquid crystals; photonic integrated circuits +
Footnote †: journal: Applied sciences  
**Citation**: [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Analysis of SiN, Waveguide-Integrated Liquid Crystal Platform for Wideband Optical Phase Shifters and Modulators. _Appl. Sci._**2024**, 14, 10319. [[https://doi.org/10.3300/app142210319](https://doi.org/10.3300/app142210319)]([https://doi.org/10.3300/app142210319](https://doi.org/10.3300/app142210319))