esa-sceva/satcom-chunk-collection huggingface dataset:
# Structure of All-Digital Frequency Synthesiser for IoT and IoV Applications
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Technology** & **\(\mathbf{F_{\text{min}}}\)**, **GHz** & **\(\mathbf{F_{\text{max}}}\)**, **GHz** & **\(\mathbf{F_{\text{off}}}\)**, **MHz** & **\(\mathbf{F_{\text{cr}}}\)**, **GHz** & \(\mathbf{\mathcal{L}[F_{\text{eff}}]}\)**, **dBc/Hz** & **\(\mathbf{P}\)**, **mW** \\ \hline \([36]\) & 28 nm & 1.48 & 2.13 & 1 & 2.09 & \(-108.0\) & 11.6 \\ \([37]\) & 40 nm & 0.10 & 3.00 & 1 & 1.00 & \(-94.0\) & 9.1 \\ \([16]\) & 65 nm & 4.90 & 6.90 & 1 & 5.37 & \(-114.2\) & 22.0 \\ \([38]\) & 65 nm & 4.70 & 6.10 & 1 & 3.87 & \(-114.9\) & 42.6 \\ \([39]\) & 65 nm & 2.80 & 3.50 & 1 & 3.50 & \(-126.0\) & 15.6 \\ \([40]\) & 90 nm & 39.13 & 42.21 & 1 & 40.00 & \(-103.9\) & 46.0 \\ \([41]\) & 0.13 \(\upmu\)m & 2.10 & 3.50 & 1 & 2.40 & \(-102.5\) & 12.0 \\ This Work & 65 nm–0.18 \(\upmu\)m & 4.30 & 5.44 & 1 & 5.44 & \(-116.4\) & 32.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of calculated parameters of frequency synthesisers to other works. Tools for Intelligent Transport Systems\" project. The APC was funded by Vilnius Gediminas technical university Faculty of Electronics. **Acknowledgments:** The authors would like to thank the Research Council of Lithuania and the project \"Design and Research of Internet of Things (IoT) Framework Model and Tools for Intelligent Transport Systems\" team for support. **Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### P2 MP Flexible Optical Transceivers
In the ONU signal demodulation module, a conventional single-tap equalizer is first used to realize subcarrier equalization. The multi-channel de-aggregation operations illustrated in Figure 2b are performed to separate the upstream and direct inter-ONU communication channels for a specific ONU. Detailed descriptions of this multi-channel de-aggregation technique can be found in [24]. Finally, the transmitted upstream or direct inter-ONU communication data can be decoded. The proposed concurrent direct inter-ONU and upstream communications offer the following three salient features: (1) for each ONU, its upstream channel count and/or direct inter-ONU communication channel count can be changed independently and adaptively according to end-users' requirements with reduced channel interferences [24]; (2) for each ONU, by dynamically reconfiguring the transceiver DSPs only, dynamic channel allocations are achievable without considerably compromising the channel performance, as verified in Section 3.4; and (3) according to upstream and direct inter-ONU communication traffic loads, dynamic radio frequency allocations/variations can also be made for each ONU by adaptively adjusting the adopted digital up-sampling factors and orthogonal digital filter parameters [24]. Because the wavelengths for the upstream/inter-ONU communications are redirected and broadcasted back to all ONUs, the downstream communications can use different wavelengths for reducing interferences and adopt various transmission techniques (conventional orthogonal frequency-division multiplexing/pulse-amplitude modulation techniques and/or P2 MP flexible transceiver techniques used in this paper).
# Trend Prediction of Event Popularity from Microblogs
## 4 Experiment
### Evaluation Metrics  
Aiming to evaluate the prediction accuracy of the proposed model, this paper performs model evaluations in terms of different metrics. More specifically, the evaluation metrics are defined as follows:  
(1) Precision \[P_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}}\] (21) \[P_{micro}=\frac{\sum\limits_{i=1}^{n}TP_{i}}{\sum\limits_{i=1}^{n}\left( TP_{i}+FP_{i}\right)}\] (22) \[P_{weight}=\sum\limits_{i=1}^{n}\left(P_{i}\cdot w_{i}\right)\] (23)  
(2) Recall \[R_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}}\] (24) \[R_{micro}=\frac{\sum\limits_{i=1}^{n}TP_{i}}{\sum\limits_{i=1}^{n}\left( TP_{i}+FN_{i}\right)}\] (25) \[R_{weight}=\sum\limits_{i=1}^{n}\left(R_{i}\cdot w_{i}\right)\] (26)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Time Span**} & \multicolumn{2}{c}{**Event**} \\ \hline Dataset 1 & 14 July 2017–3 November 2017 & CCTV news microblog refutes ten incidents of genetically modified food \\ \hline Dataset 2 & 4 January 2019–23 May 2019 &
\begin{tabular}{c} The famous blogger posts a blog: the harm of genetically modified food \\ will be written into the textbook of colleges \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: The details of event datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multicolumn{5}{c}{**Popularity Factors**} \\ \cline{2-5}  & **\#Post Number** & **\#Forwarding** & **\#Commenting** & **\#Likes** \\ \hline
14 July 2017 & 21 & 75 & 5 & 20 \\
15 July 2017 & 7 & 32 & 0 & 50 \\
16 July 2017 & 7 & 73 & 5 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The details of sample data in dataset 1.  
(3) F1-score  
\[F_{i}=2\frac{P_{i}\cdot R_{i}}{P_{i}+R_{i}} \tag{27}\]  
\[F_{micro}=2\frac{P_{micro}\cdot R_{micro}}{P_{micro}+R_{micro}} \tag{28}\]  
\[F_{wight}=\sum_{i=1}^{n}\left(F_{i}\cdot w_{i}\right) \tag{29}\]  
In the paper, _TP_ represents the number of samples that are labeled as positive samples and also classified as positive samples; _FP_ refers to the number of samples labeled as negative samples but classified as positive samples; _FN_ is the number of samples that are labeled as positive samples and classified as negative samples. However, this paper mainly focuses on the prediction accuracy of the popularity trend of microblog events. Therefore, this paper chooses specific three metrics that pay more attention to the accuracy to evaluate the prediction model, which are \(P_{micro}\), \(P_{weight}\) and \(F_{wight}\).
## 4 Experiment and Results
### System Setup
After the phase tracking is completed, the phase difference is calculated based on the NCO index of each channel, and the phase difference is converted into the azimuth and pitch axis angle in combination with DWS. The calculation result of this angle is then compared with the known deflection amount of FSM, thereby achieving a high-precision measurement of the angular deflection amount of the signal laser beam by the system.
# Optimizing Automated Detection of Cross-Recessed Screws in Laptops Using a Neural Network
## 1 Introduction
### Related Work in Vision Detection
[51] proposed a method called \"Active Screw Detection\" in which a camera attached to a robotic arm is moved around to align with the normal vector of the screw pointing out of a plane to determine the best image of a screw. The images are fed to a YOLOX-s network, and this method was able to obtain a mAP of 95.92%. The objective of this study was to experiment with various operational parameters within a robotic system specifically designed for screw removal. The goal was to identify the optimal settings for screw detection. The parameters under investigation encompass mechanical collection aspects, such as the system's velocity and the number of passes it makes over the laptop, as well as the system's lighting conditions. Additionally, variations in the network size parameters of the YOLO-v5 neural network were explored. While there have been several works on screw detection [42; 43; 44; 45; 46; 47; 48; 49], we found no reported work that analyzes how different screw characteristics impact the detection of these screws. Thus, a unique contribution of this work, and a motivation for this study, is the analysis of how different screw characteristics, such as their location, their color contrast with the laptop, their depth, or the presence of a taper in the screw hole, impact the overall detection rate of the system. The remainder of this paper is organized as follows. The next section discusses the robotic setup, the software methods used to train the neural networks, and the experimental approaches used to perform the trials. This is followed by Section 3, which presents the results obtained, and Section 4, which is a discussion of the results. Finally, Section 5 provides concluding remarks.
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
## 1 Introduction
This challenge is most frequently met when unsupervised techniques are used for SITS analysis. In these cases, a method to determine the optimal number of evolution classes is usually used [16]. A query-by-example retrieval method would alleviate this issue by associating semantic meaning to each query that a user addresses to the retrieval system. In this regard, we hereby present a query-by-example retrieval method whose goal is to separate spatio-temporal evolutions that are similar to a given query from non-similar ones. After the user performs a specific query, the retrieval method consists of two main steps. Firstly, the system performs the computation of distance measures between the query and the rest of spatio-temporal evolutions. Secondly, an optimal threshold is determined through the Expectation-Maximization technique in order to obtain a binary map displaying similar patterns with respect to the given query. The rest of the paper is structured as follows. The two-step retrieval method is presented in Section 2. Sections 3 and 4 present the experiments conducted over several SITS datasets, discuss the results obtained for different application scenarios, and compare the proposed method to other SITS analysis methods. Finally, the last section concludes the paper.
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
## 2 Sensing WASH
### Household Water Use  
Several products are available commercially to monitor residual chlorine, an indication of water treatment practices and water safety. These include pocket colorimeters from Hach, Analyticon DKK-TOA, Omega, and other vendors.  
The use of household water filters has also been measured in some studies. In Rwanda, researchers distributed household water filters and collected data on product compliance using: (1) monthly surveys and direct observations by community health workers and environmental health officers; and (2) sensor-equipped filters. The adoption rate interpreted by the sensors varied from the reporting: 96.5% of households reported using the intervention filter regularly, while the sensors interpreted no more than 90.2%. Also, the sensor-collected data estimated approximately 36% less water volume per day than survey estimates [45].  
Expanding on this study, a cluster randomized controlled trial was later conducted in Rwanda among 170 households (70 blinded to the presence of the sensor, 100 open) to test whether awareness of an electronic monitor would result in a difference in weekly use of household water filters and improved cookstoves over a four-week surveillance period. A 63% increase in the number of uses of the water filter per week between the groups was observed in week 1 (an average of 4.4 times in the open group, and 2.83 times in the blind group), declining in week 4 to an insignificant 55% difference (of 2.82 uses in the open and 1.93 in the blind). Use of the water filters decreased in both groups over the four-week installation periods. This study suggests behavioral monitoring should attempt to account for reactivity to awareness of electronic monitors that persists for weeks or more [46].
# A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery
## 2 Materials  
In this study, the experimental imagery was acquired by the Zhuhai-1 hyperspectral satellite constellation (OHS-A, B, C, D) launched on 26 April 2018. The camera onboard the Zhuhai-1 hyperspectral satellite is spliced by three CMOS image sensors. The splicing method for the focal surface is illustrated in Figure 1. Figure 2 displays the spectral composition of each CMOS image sensor. Each CMOS image sensor is composed of \(5056\times 2968\) pixels, the size of each pixel is 4.25 \(\upmu\)m, and the spectral range is 400-1000 nm. Each CMOS image sensor divides the 400-1000 nm spectrum into 32 spectral bands on average with a gradient filter. Each spectral band occupies about 92 lines on the sensor (2968 divided by 32). Only eight rows of pixels are used in each spectral band for eight-level integral imaging due to the low resolution and signal-to-noise ratio (Figure 2). The main parameters for the OHS and payload are displayed in Table 1. Figure 3 shows the spectral response functions of each band of OHS hyperspectral sensors.  
Figure 1: Focal plane arrangement for the hyperspectral camera.  
Three hyperspectral datasets with different topographies and roll angles were selected to evaluate the registration accuracy. The first dataset covers the southwestern part of Arizona, USA. The area consists of a large mountain basin with plain areas. The second dataset covers the central part of Chongzuo City, Guangxi Zhuang Autonomous Region, China. This area is crossed by the Zuojiang River and its tributaries and features both alluvial plains and hilly areas. The third dataset covers the northern part of Turpan City, Xinjiang Uygur Autonomous Region, China. The image shows the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameters** & **Index** \\ \hline \multirow{4}{*}{Satellite} & Total satellite mass & 67 kg \\  & Orbit height & 500 km \\  & Orbit inclination angle & 98\({}^{\circ}\) \\  & Regression cycle & 2.5 days \\ \hline \multirow{4}{*}{Payload} & Detector size & 4.25 μm \\  & Field of view (FOV) & 20.5\({}^{\circ}\) \\  & Spectral range & 400–1000 nm \\  & Quantitative level & 12 bits \\ \cline{1-1}  & Band number & 32 \\ \cline{1-1}  & Signal Noise Ratio (SNR) & \(\geq\)300 dB \\ \cline{1-1}  & Ground sample distance & 10 m \\ \cline{1-1}  & Spectral resolution & 2.5 nm \\ \cline{1-1}  & Ground swath & 150 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orbita hyperspectral satellite (OHS) and payload parameters.  
Figure 3: Spectral response functions of OHS hyperspectral bands.  
Figure 2: CMOS sensor spectrum distribution.  
majestic Bogda Mountains, as well as the oasis plain belt that lies below sea level. The size of each image was \(5056\times 5056\) pixels. The experiment dataset parameters are listed in Table 2.
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Fresh Properties
#### 3.1.2 Initial and Final Setting Time  
Both the initial and final setting times increased as the replacement level of MGWP rose, as shown in Figure 4. For the MGWP30 mix, the initial setting time was 200 min, and the final setting time was 316 min, representing a 29.0% and 31.7% increase, respectively, compared to the control mix. The increased setting time is attributed to the presence of silicon oxide in MGWP, which has a retarding effect on the cement paste. Silicon oxide contributes to delaying the setting time by initiating a pozzolanic reaction that affects the hydration of the cement, particularly by lowering the C3S content in the mix [39]. Similar findings have been reported in other studies [40,41], while contrasting results were observed in research involving marble as a cement replacement [42]. Both ES 1177 [17] and ASTM C150 [16] recommend a minimum initial setting time of 45 min and a maximum final setting time of 600 and 375 min, respectively, which were comfortably met by all the mixes.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test Category** & **Property** & **Standard** & **Examined Sample** & **Curing Ages\({}^{\prime}\)** \\ \hline \multirow{3}{*}{Microstructure} & TGA/DTA & ASTM E1131 & MGWP0, MGWP10, and & \multirow{3}{*}{7 and 28 days} \\  & FT-IR & ASTM E1252-98 & MGWP30 & \\  & SEM & ASTM C1723-16 & MGWP0, MGWP10, MGWP15, & \\  & & and MGWP30 & & \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 3: Consistency of binder with various replacement levels of MGWP.
## 9 Effect of Parameters on Performance of Cementitious Self-Healing Agents
Furthermore, an enhanced crack sealing capacity was developed when the cracks were exposed to water [96]. Upon water exposure, such hydrophilic products melted and re-crystallized within the formed cracks due to the presence of crystalline admixtures. The internal cracks of the sample were plugged-in as calcium carbonate was produced and the supply of Ca\({}^{2+}\) was facilitated by the incorporation of the expansive additives and crystalline admixtures into the concrete. Exposure to a humid environment could also enhance the precipitation of calcium because the additives mixed into the concrete had higher pH [99]. The incorporation of multiple polymer precursors into the concrete could facilitate the sealing of the generated larger and wider cracks in the sample. The mechanical properties of the concrete were enhanced due to the superior binding of mortar and polymers facilitated by the elastic properties of polyurethane [105]. The effects of microcapsules (containing triethylborane and methyl methacrylate monomer) insertion as healing agents into the concrete and their ability to heal cracks were studied [20]. After 28 days of curing, the value of CS of the concrete was increased up to 45.8%. As a result of swelling, the polymer particles become larger, leading to a reduction in the micro-cracks within the concrete due the presence of SAP. A high pH environment in the concrete caused the reduced swelling of the SAP [103]. The cellulose fibers-assisted autonomous healing mechanism on cracks in the concrete was examined [111]. A reduction in the CS value of the concrete occurred due to the use of cellulose fibers. The mix ratio, cracked surface, roughness, and interaction of the fibers with the concrete were among the influential factors that were found to be responsible for effective healing [108]. Autonomous healing capability of cracks were improved in the ultra-high performance hybrid fiber-reinforced (UHFR) cementitious concrete [107]. The incorporation of bacteria and polypropylene into concrete was found to be less effective than PVA-based concrete in terms of healing efficacy [110]. The sealing of crack width was enhanced through the incorporation of micro-fibers and SPA into the concrete [47]. The autonomous healing capability and flexural performance of the concrete were also enhanced upon the incorporation of steel fibers into the concrete when subjected cryogenic temperatures [109].
# Privacy-Preserving Overgrid: Secure Data Collection for the Smart Grid
## 2 Background and Related Work
### Privacy-Preserving Schemes  
Several solutions exist to perform privacy-preserving data mining [16]. Thus far, two main approaches have been proposed: modifying the data before transferring it to the data aggregator (in such a way that the total results are not compromised) or relying on groups of users that must work together to provide aggregate results. Data modifications can lead to mining errors if random noise is used [17] or may become complex when using homomorphic data encryption [18]. Instead, in PP-Overgrid, we employ the second approach based on Multi-Party Computation (MPC)protocols [19]. These algorithms were developed for aggregating information collected from multiple users, without disclosing any information of the single users. Usually, multi-party computing architectures rely on multiple (independent) nodes that deal with randomized versions of the user information provided by the input peers. Several data distribution solutions between nodes exist, based on logical [20] or arithmetic circuits [21] or linear secret sharing [22]. In particular, linear secret sharing mechanisms e.g., Sharemind [23], SEPIA [24], and P4P [25], have been proven to scale well with the increase in the number of users.  
PP-Overgrid is based on scalable MPC techniques, exploiting secure functions that can be expressed as iterative data sums. While previous works such as [25] and [26] assume that nodes never leave the network, in PP-Overgrid, the network topology may be unstable during the execution of the data mining operation.
## 6 Performance Evaluation
### Sensitivity to MPMF's Factors of \(W_{0}\), \(\alpha\), \(L\), and \(\epsilon\)
This is because with a larger \(L\) value, more pending messages in the finite buffer capacity could be replaced frequently, thereby causing higher transmission overhead, as shown in Figures 7b and 8b. Figure 6: Results by MPMF with different values of \(\alpha\) and \(\epsilon\) under NCCU trace (TTL = 5 h and buffer size = 10 MB). Figure 7: Results by MPMF with different values of \(L\) and TTL under TVCM trace (buffer size = 10 MB). In summary, the above findings point out that the relay node selected by the MPMF with a smaller \(\epsilon\) and \(W_{0}\) can facilitate the message distribution at the higher delivery rate and lower overhead ratio, as displayed in Figures 3-6. Therefore, in order to strike a better performance, it is beneficial to take \(\epsilon=50\), \(W_{0}=0.6\), and \(\alpha=0.5\) in TVCM and \(\epsilon=60\), \(W_{0}=0.5\), and \(\alpha=0.6\) in NCCU. With the performance results against the quantity of \(L\), as shown in Figures 7 and 8, it is unnecessary to maximize the \(L\) value, but a moderate value is applicable to sustain the transmission overhead ratio while the delivery rate is maintained. Thus, \(L=10\) will be used in the following experimental cases.
## 2 The Effects of Temperature on UGW
Due to the  
Figure 5: Phase delay at different temperatures for UTF. Figure 2:0 Phase delay at different temperatures for NTF. absence of a reference point at the temperature transition junction, reading the signal phase at that point is challenging. Nevertheless, it can be identified by the intersection of the extrapolated fitted lines from either side, which aligns well with the simulated setup. Therefore, temperature variations significantly impact the UGW signal phase, showing a linear relationship. Consequently, in practical applications, the NTF in pipelines can cause some signals to lead while others lag, introducing interference in UGW monitoring. This highlights the need for an advanced temperature compensation algorithm to overcome these complex temperature variations.
###### Abstract  
Since 2008, annual outbreaks of green tides in the Yellow Sea have had severe impacts on tourism, fisheries, water sports, and marine ecology, necessitating effective interception and removal measures. Satellite remote sensing has emerged as a promising tool for monitoring large-scale green tides due to its wide coverage and instantaneous imaging capabilities. Additionally, drift prediction techniques can forecast the location of future green tides based on remote sensing monitoring information. This monitoring and prediction information is crucial for developing an effective plan to intercept and remove green tides. One key aspect of this monitoring information is the green tide distribution envelope, which can be generated automatically and quickly using buffer analysis methods. However, this method produces a large number of envelope vertices, resulting in significant computational burden during prediction calculations. To address this issue, this paper proposes a simplification method based on azimuth difference and side length (SM-ADSL). Compared to the isometric and Douglas-Peucker methods with the same simplification rate, SM-ADSL exhibits better performance in preserving shape and area. The simplified distribution envelope can shorten prediction times and enhance the efficiency of emergency decision-making for green tide disasters.  
green tide; satellite remote sensing; distribution envelope; buffer analysis; simplification method +
Footnote †: journal: Remote Sens.  
Article
## 5 Results and Discussion
### Performance Comparison under Different Workloads  
This subsection presents performance comparisons of the considered networks under different workloads for E2E delay, average throughput, and PDR.