esa-sceva/satcom-chunk-collection huggingface dataset:
_Appl. Sci._**2015**, \(5\), 209-221; doi:10.3390/app5030209  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
Self-Assembly of High Density of Triangular Silver Nanoplate Films Promoted by 3-Aminopropyltrimethoxysilane  
**[PERSON] 1,*, [PERSON] 2,*, [PERSON] 1 and [PERSON] 1**  
1 Institute of Microengineering and Nanoelectronics (IMEN),  
Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia;  
E-Mails: [EMAIL_ADDRESS] (M.M.S.); [EMAIL_ADDRESS] (A.A.U.)  
2 School of Chemical and Physical Sciences and Flinders Centre for Nanoscale Science and Technology, Flinders University, Bedford Park, Adelaide South Australia 5001, Australia  
* Authors to whom correspondence should be addressed; E-Mails: [PERSON]DDRESS] (N.A.B.); [EMAIL_ADDRESS] (J.G.S.); Tel.: +60-3-8911-8548 (N.A.B.); +61-8-8201-2005 (J.G.S.); Fax: +60-3-8925-0439 (N.A.B.); +61-8-8201-2905 (J.G.S.).  
Academic Editor: [PERSON]  
_Received: 29 May 2015 / Accepted: 16 July 2015 / Published: 30 July 2015_  
**Abstract:** In this work, we studied the structure of synthesized triangular silver nanoplates in solution and the growth of the nanoplates on a silicon surface using 3-aminopropyltrimethoxysilane (APTMS) as a coupling agent. The triangular-shaped colloidal silver nanoplates were simply synthesized by a direct chemical reduction approach. We studied the three characteristic peaks of the unique optical absorbance of triangular silver nanoplates and subsequently measured an average edge length of \(26\pm 1\) nm. The nanoplate thickness was determined to be \(7\pm 2\) nm from transmission electron microscopy images. Depositing the nanoplates on a silicon surface was carried out to determine the coverage of triangular nanoplates obtained when adhesion was promoted by a coupling agent. The APTMS film assisted the attachment of the nanoplates to the silicon surface and the coverage of the nanoplates increased with increasing deposition time. The triangular silver nanoplate thin film was a monolayer and a high coverage (near complete) was obtained after eight hours of exposure to the nanoplate solution. The silver film formed was shown to be a good surface-enhanced Raman scattering (SERS) substrate as it gave an enormous Raman enhancement for bisphenol A (BPA).  
**Keywords:** 3-aminopropyltrimethoxysilane (APTMS); high density surface; self assembly; triangular silver nanoplate thin film
## 2 Materials and Methods
### The Problem: FS Interferences on Low Orbit Satellite Downlinks  
The X band is one of the frequency bands (8.025-8.400 GHz) regulated for satellite-to-ground data transmission applications in EESS [30]. Though, fixed service (FS) links that work within the same frequencies with beamwidth and maximum Effective Isotropic Radiated Power (EIRP) interfere at determined azimuth and elevation angles on the satellite receiving antenna. Figure 4 illustrates the interference scenario with the satellite receiver system.  
The ITU Report [31] provides EESS downlink recommendations for system evaluation. The emission limit should not exceed the specified values, according to Table 1, and additional issues can be found in the Standard of CCSDS, 2021 [32].  
This interference scenario occurs at INPE's Cuiaba Earth station damaging CBERS4 and AMAZONIA1 satellite images of Amazon region. There, the EESS mission of the CBERS-4 (China-Brazil Earth Resources Satellite-4) satellite, launched in 2014, contains four cameras: PAN5/10, IRS, MUX, and WFI. The function of the wide field imager (WFI), with a resolution of 64 m and a coverage range (swath) of 866 km, is to monitor the Amazon rainforest. Figure 5 shows a frame (set of scenes) from the CBERS-4 (C-4) WFI image catalog covering twenty-six consecutive days. To examine how interference disrupts the WFI satellite images, we observe how the INPE image catalog is affected. In Figure 5a, we can see that the ideal framing of the image forms an almost perfect circular contour.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Frequency Band**} & \multirow{2}{*}{**Service**} & \multicolumn{2}{c}{**Limit in dB (W/m2) for Angle of Arrival (s) above the Horizontal Plane**} & \multirow{2}{*}{
\begin{tabular}{c} **Reference Bandwidth** \\ \end{tabular} } \\  & & & & \\ \hline \multirow{2}{*}{8025–8500 MHz} & Earth Exploration & 0–5\({}^{\circ}\) & 5–25\({}^{\circ}\) & 25–90\({}^{\circ}\) & 4 kHz \\  & Satellite & \(-150\) & \(-150\) + 0.5(\(8-5\)) & \(-140\) & 4 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: EESS emission limit recommended by ITU.  
Figure 4: LEO satellite downlink scenario.  
In Figure 5b, we can observe the harmful effects on the set of images with the lack of coverage points in certain locations in Brazil or South America, regions in northwest azimuths. These effects occur due to the existence of a FS link near INPE's ERG in that geographic angular region.
# Machine Failure Prediction Using Survival Analysis
## 4 Materials and Methods
### Dataset Description  
The dataset used in this study [27] was provided by [PERSON] from the School of Engineering-Technology and Life at Hochschule fur Technik und Wirtschaft Berlin, Germany. The dataset comprises 10,000 rows, each containing 14 columns of features, including a unique identifier, product ID indicating the quality variant, and variant-specific serial number. Additional features include air temperature, process temperature, rotational speed, torque, tool wear, and a machine failure label. The product ID denotes the quality variant as either low, medium, or high. The air temperature and process temperature in a manufacturing process are generated using separate random walk processes with different standard deviations. The rotational speed is calculated from a power value with added noise, and torque values follow a normal distribution with no negative values. The quality variants add different amounts of tool wear to the process. The machine failure label indicates whether the machine has failed due to one of five failure modes, each with specific conditions, but it is not disclosed to the machine learning algorithm which failure mode has caused the failure.  
The right-censored items in the dataset, meaning the machines that did not fail during its composure, are presented in Table 1. The percentage of the censored machines was a high 96.61%, which declares an imbalance in the dataset. The high proportion of right-censored instances in a survival dataset can have a significant impact on the performance of prediction mechanisms such as RSF, due to the potential lack of information about the failure times of non-censored instances. As the RSF model is designed to handle right-censored data, it can still provide reasonable estimates of the survival function and hazard rate. However, the imbalance in the dataset can result in a biased model that is more accurate for censored instances, which can lead to inaccurate predictions for non-censored instances. This limitation can have important implications for decision making based on the model's predictions, especially if the non-censored instances represent high-value assets.
# Flowering Index Intelligent Detection of Spray Rose Cut Flowers Using an Improved YOLOv5s Model
## 2 Materials and Methods  
### Image Acquisition  
This study focused on two varieties of spray rose cut flowers--Orange Bubbles and Yellow Bubbles--within the orange and yellow color series. The images were collected at the Jinhai Flower Base in Tonghai County, Yunnan Province, which is the primary rose cultivation area in Yunnan. To enhance the diversity of the image sample set, spray rose cut flowers were photographed using both a Canon EOS 600D (Canon Inc., Tokyo, Japan) digital camera and a HUAWEI Mate 40 smartphone (Huawei Technologies Co., Ltd., Shenzhen, China). A total of 2145 images of roses with varying degrees of flower openness were selected to construct the dataset for network training and testing.  
The degree of flower openness serves as a crucial grading indicator for cut flowers and provides the most immediate visual impression for consumers. The national standard categorizes rose flower openness into five distinct levels, each accompanied by a qualitative description (Table 1).  
Due to the lack of quantitative evaluation indicators, the grading of flower openness in spray roses currently relies on manual identification. Figure 1 shows the manually labeled results corresponding to the different flowering index, denoted as Fi-j, where \(j\) = 1, 2, 3, 4, 5. Furthermore, since the two types of spray rose cut flowers studied were primarily solid outside the province and required a longer transportation time, their flowering indices predominantly fall within levels 1 to 3. Consequently, among the images collected for both color series, 59.4% of samples exhibited flowering indices at levels 1 and 2; conversely, level 5 accounted for only 8.1%. The specific quantity and distribution of the dataset are shown in Table 2.  
All rose flower samples in the dataset were meticulously labeled for flowering index according to the standards outlined in Table 1 and illustrated in Figure 1, utilizing Labeling software (labelImg 1.8.4). The labels are designated as O-Fi-j and Y-Fi-j, where j = 1, 2, 3, 4, 5. The capital letters O and Y represent the orange and yellow color series of spray roses, respectively. Data augmentation techniques, including Cutout, Add Noise, Change Light, Translate, Rotate, and Mirror, were then applied to increase the dataset size by eight times (see Figure 2). Finally, the image dataset was randomly divided into three groups: training set, validation set, and test set, in a 7:2:1 ratio.
# Leveraging User Comments for Recommendation in E-Commerce
## 5 Experimental Results
### Experiment 6--Impact of User Comments  
We show the effectiveness of using user comments for clustering products and users in CFUCC.  
* Method 1. This is the method that CFUCC adopts. That is, it applies Word2 Vec on user comments to get item vectors from which user vectors are obtained, and then clusters products and users based on the item and user vectors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CFUCC** & **SCC** & **ICRRS** & **CFCB** & **BiFu** \\ \hline No & MAE & **0.996** & 1.168 & 1.419 & 1.235 & 1.202 \\ imputation & RMSE & **0.709** & 0.835 & 0.998 & 0.830 & 0.837 \\ Imputation & MAE & **0.996** & 1.028 & 3.398 & 3.325 & 3.305 \\ by least & RMSE & **0.709** & 0.746 & 3.222 & 3.139 & 3.120 \\ Imputation & MAE & **0.996** & 1.514 & 1.630 & 1.610 & 1.635 \\ by mid & RMSE & **0.709** & 1.381 & 1.485 & 1.464 & 1.568 \\ Imputation & MAE & **0.996** & 1.176 & 1.418 & 1.174 & 1.218 \\ by mean & RMSE & **0.709** & 0.842 & 0.998 & 0.796 & 0.804 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Impact of imputation on performance for different methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method 1** & **Method 2** & **Method 3** \\ \hline MAE & **0.709** & 1.526 & 1.530 \\ RMSE & **0.996** & 1.723 & 1.742 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparisons among different methods of generating user vectors.  
* Method 2. User comments are not used. Products and users are clustered directly based on the rating scores provided in the given user-item rating matrix.  
Table 9 shows the performance comparison of these methods for the Amazon Digital Music dataset. Note that both methods use identical values for the parameters involved in the recommendation process.  
As can be seen, Method 1, which we adopt in CFUCC, performs better in terms of both accuracy and efficiency. Method 2 uses the user-item rating matrix for clustering. This causes the sparsity problem and makes the recommended results less accurate. Clearly, both the MAE and RMSE of Method 2 are worse than those of Method 1. Secondly, Method 2 clusters users directly. Since there are 3568 products in Amazon Digital Music dataset, each pattern is a 3568-dimensional vector to be considered in the clustering process. In Method 1, by PCA and Word2 Vec, each pattern is only a vector of size less than 100. Therefore, users can be clustered faster by Method 1, and recommendation can be done more efficiently.
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
###### Abstract  
Preprocessing Synthetic Aperture Radar (SAR) data is a crucial initial stage in leveraging SAR data for remote sensing applications. Terrain correction, both radiometric and geometric, and the detection of layover/shadow areas hold significant importance when SAR data are collected over mountainous regions. This study aims at investigating the impact of the Digital Elevation Model (DEM) used for terrain correction (radiometric and geometric) and for mapping layover/shadow areas on windthrow detection using COSMO SkyMed SAR images. The terrain correction was done using a radiometric and geometric terrain correction algorithm. Specifically, we evaluated five different DEMs: (i-ii) a digital terrain model and a digital surface model derived from airborne LiDAR flights; (iii) the ALOS Global Digital Surface Model; (iv) the Copernicus global DEM; and (v) the Shuttle Radar Topography Mission (SRTM) DEM. All five DEMs were resampled at 2 m and 30 m pixel spacing, obtaining a total of 10 DEMs. The terrain-corrected COSMO SkyMed SAR images were employed for windthrow detection in a forested area in the north of Italy. The findings revealed significant variations in windthrow detection across the ten corrections. The detailed LiDAR-derived terrain model (i.e., DTM at 2 m pixel spacing) emerged as the optimal choice for both pixel spacings considered.  
SAR; windthrow detection; preprocessing; digital elevation model; digital surface model; COSMO SkyMed 2024 16 2024 1
# Sugar Beet Profitability in Lubelskie Province, Poland
## 2 Methodology
#### Forecasting Using ARIMA Models  
ARIMA (autoregressive integrated moving average) models are used to model and forecast processes using time series. ARIMA modeling has been used to study autocorrelation (dependence between subsequent observations relative to previous values) [42; 43]. There are three terms in the model: AR, I, and MA. They respectively mean the following: AR-autoregressive model, where the current value of the series depends on its past values; I (integrated)-trend removal; and MA-moving average. The ARIMA(p, d, q) model has 3 parameters:  
p--order of the autoregressive part (how many past values to consider),  
d--degree of differencing,  
q--order of the moving average.  
The following ARIMA models were used for the calculations:  
\[\text{ARIMA}(1,\;1,\;1):\;(1-\phi_{1}B)(1-B)y_{t}=(1+\theta_{1}B)\epsilon_{t} \tag{7}\]  
\[\text{ARIMA}(1,\;1,\;2):\;(1-\phi_{1}B)(1-B)y_{t}=\Big{(}1+\theta_{1}B+\theta _{2}B^{2}\Big{)}\epsilon_{t} \tag{8}\]  
\[\text{ARIMA}(2,\;1,\;2):\;\Big{(}1-\phi_{1}B-\phi_{2}B^{2}\Big{)}(1-B)y_{t}= \Big{(}1+\theta_{1}B+\theta_{2}B^{2}\Big{)}\epsilon_{t} \tag{9}\]  
where  
\(B\)--delay operator,  
\(By(t)=y_{t-1}\), \(B^{2}y(t)=y_{t-2}\),  
\(\phi_{1}\), \(\phi_{2}\)--autoregression coefficient (AR),  
\(\theta_{1}\), \(\theta_{2}\)--moving average coefficients (MA),  
\(\varepsilon_{t}\)--white noise,  
\(y_{t}\)--observed time variable.  
The dynamic properties of the system, modeled using ARIMA series, can be represented and expressed through a discrete or continuous transfer function (Laplace function).  
\[G(z)=\frac{X(z)}{Y(z)}\text{ or }G(s)=\frac{X(s)}{Y(s)} \tag{10}\]  
where z--complex variable (z-plane/Z-Domain), s--complex variable (s-plane/Laplace Domain).  
This model enables the assessment of process stability. The model is stable if all poles of the transfer function \(G(z)\) lie inside the unit circle: \(|z_{i}|<1\).
# Capacity and Delay Analysis for Large Social-Aware Mobile Ad Hoc Wireless Networks
## 1 Introduction  
There will be trillions of devices in 5G and beyond 5G networks [1; 2]. These devices are connected to the base station, then massive amounts of data will be generated. If these devices are maintained in a decentralized way, exchanging data over multi-hops (paths consisting of multiple devices), an ad hoc network is formed.  
A decentralized structure that all participating devices are responsible for exchange information by themselves, makes wireless ad hoc networks useful [3]. Usually there is no previously established network infrastructure in scenarios, such as emergency services and disaster recovery. These characteristics make wireless ad hoc networks suitable for such applications where a centralized structure could be invalid.  
Wireless ad hoc networks have a better scalability in contrast to centralized networks. They can be easily extended with more devices at any position in the network region. However, the mobile devices connected by radio, in which case the topology could be rapidly changed. Unpredictable mobility of nodes introduces more routing problems [4].  
In this paper, we provide the scaling lows of social-aware mobile ad-hoc wireless networks. The sections are organized as follows. We introduce some related works in Section 2. In Section 3, preliminaries (such as the mobility model, social model, and throughput definitions) are introduced. In Section 4, the upper bound throughput of each node is studied. In Section 5, a multi-hop relay and slots allocation scheduling scheme is proposed, and the achieved throughput of each node is given.  
In Section 6, we investigate the network delay by queueing theory. In Section 7, we discuss and analyze the results.
# Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers
## 2 Background
### Decoding for Space-Time Block Codes  
Maximum likelihood (ML) detection calculates the Euclidean distance among the received signal matrix \(\mathbf{R}\) and the product of all possible transmitted signal vectors by the channel matrix \(\mathbf{H}\). Considering \(\mathbb{A}\), the set of constellation symbols of the transmitted signal, and \(M_{S}\), the number of transmitted symbols per MIMO block, ML detection determines the estimation of the conveyed signal vector \(\mathbf{s}\) as [11]  
\[\widehat{\mathbf{s}}=\underset{\mathbf{s}\in\mathbb{A}^{M_{S}}}{\operatorname{ argmin}}\big{\|}\mathbf{R}-\mathbf{H}^{T}\mathbf{X}\big{\|}^{2}. \tag{5}\]  
As in maximum a posteriori (MAP) detection, ML detection achieves the optimal performance when all transmitted vectors are equally probable. However, the number of ML computation metrics is \(A^{M_{S}}\), where \(A\) is the modulation order. Thus, the ML complexity increases exponentially with the modulation order or the number of transmit symbols, or both [44; 54; 60]. Although this method has a high computational complexity, the ML decoding is used as a benchmark due to its optimal performance [11].  
For orthogonal coding schemes, the ML metric can be simplified, decoding symbol by symbol [54]. Via this simplification, it is possible to circumvent the issue of exponential computational complexity. However, even with this simplification, the computational complexity can be considerably high. In QOSTBC, the ML metric can be also simplified, but the computational complexity remains higher than the orthogonal case, because QOSTBC is decoded in pairs of symbols [11].
## 1 Introduction
Similarly, [PERSON], [PERSON] and [PERSON] [20] studied the relationship between the hole depth parameters of laser drilling and the intensity of plasma luminescence emitted during drilling. The above two methods focus on hole depth measurement during laser drilling, which are not suitable for depth measurement in other processes such as mechanical drilling. The above methods are mainly aimed at the spatial position identification of holes, quality inspection of hole shape, or hole depth measurement for laser drilling. However, existing research on the depth measurement of through holes is lacking. Existing methods can only measure the depth of blind holes using laser measurements or machine vision, and it is impossible to measure the depth of through holes, especially in the case where the depth/diameter ratio is large. At present, there are no reports on methods and instruments for on-machine measurement of through-hole depth in the field of automatic drilling and riveting. In view of the above problems, this paper carried out research on the on-machine measurement of through-hole depth. A new measurement method is proposed, a corresponding laser light path and novel measuring device with optical sensor and grid-capacitance sensor is designed, and its prototype is fabricated. The remainder of this paper is organized as follows: In Section 2, the optical path design, device design, and the measuring method design of the hole depth on-machine optical measuring equipment are described in detail. Section 3 shows the experimental testing process and discusses data processing and analysis results. Finally, this paper draws conclusions in Section 4 and looks forward to follow-up research.
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Depth-Based Layering MAC (DL MAC)
#### 4.2.3. Preamble-MAC (P-MAC)  
P-MAC is a hybrid protocol that combines a frequency domain MAC protocol with a slotted multiple access collision avoidance (Slotted-MACA) protocol. The inaccuracies caused by the loss of time synchronization can be solved with this protocol [95]. The default distance level information, which is a file containing evaluated and accumulated knowledge about the channel status and changes collected over periodic monitoring of the undersea environment, is used by P-MAC to operate dynamically and adaptively [96].
## 5 System Equilibrium Analysis under Arbitrary Transmission Limit
### Equilibrium Analysis for S-ALOHA System
#### 5.2.2 Equilibrium Analysis in the Non-Saturation State for \(Q\leq 8\)  
Let we define function \(h(s)\) as follows:  
\[h(s):=\frac{1-\left(1-e^{-s}\right)^{Q}}{e^{-s}} \tag{41}\]  
Obviously, the solutions for Equation (35) are the cross points of line s/\(\rho\) and curve \(h(s)\). Function \(h(s)\) has exactly one convex piece on (0,\(s_{0}\)) and one concave piece on (\(s_{0}\),+\(\infty\)) for \(Q\geq 1\). Especially for \(Q\leq 8\), there is only a single solution for Equation (35) on (0,+\(\infty\)) (the proof is not a contribution of this paper, readers may refer to [44] for more details). Apparently, s/\(\rho_{th}\) has a cross point with \(h(s)\) at point s = 1. Therefore, as shown in Figure 3, there must be precisely a single equilibrium point on (0,1) if \(\rho<\rho_{th}\). On the other hand, there must be precisely a single solution for Equation (35) on (1,+\(\infty\)) if \(\rho>\rho_{th}\), which means that the system is in a saturation state.