esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
The Statistic program, version 12.0 (StatSoft, Krakow, Poland), was utilized for statistical evaluation. A one-way ANOVA was conducted, with a significance level of \(p\leq\) 0.05. The Tukey test (\(n\) = 3) was used to assess differences between groups.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Compound** & **RT (min)** & **Calibration Curve** & **R\({}^{2}\)** & **Linear Range** & **LOD** & **LOQ** \\  & & & & & **(mg/mL)** & **(mg/mL)** & **(mg/mL)** \\ \hline
1 & nCGA & 8.5 & A =19,417,474 \({}^{*}\)C – 34,606 & 0.999 & 0.01–1.00 & 0.003 & 0.010 \\ \hline
2 & _p_-Coumaric acid & 12.5 & A = 67,676,230 \({}^{*}\)C – 37,723 & 0.999 & 0.01–1.00 & 0.004 & 0.009 \\ \hline
3 & CGA & 14.5 & A = 22,172,995 \({}^{*}\)C – 59,822 & 0.999 & 0.01–1.00 & 0.003 & 0.009 \\ \hline
4 & Rutin & 33.5 & A = 10,349,788 \({}^{*}\)C – 10,128 & 0.999 & 0.01–1.00 & 0.009 & 0.027 \\ \hline
5 & Hyperoside & 34.9 & A = 15,145,554 \({}^{*}\)C – 139,314 & 0.999 & 0.01–1.00 & 0.006 & 0.017 \\ \hline
6 & Isoquercitrin & 36.1 & A = 22,172,995 \({}^{*}\)C – 59,822 & 0.999 & 0.01–1.00 & 0.006 & 0.0019 \\ \hline \hline \end{tabular} nCGA, neochlorogenic acid; CGA, chlorogenic acid; A, peak area; C, concentration (mg/mL) of the compound.  
\end{table}
Table 2: Concentration range, LOD detection limit, and LOQ qualification limit.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
To elucidate the effect of clone and oak barrel type on the evolution of volatile compounds in these wine samples during the aging process, principal component analysis (PCA) was carried out using all the detected volatiles in young and 12-month aged wines as the variables (Figure 2). Principal components 1 and 2 (PC1 and PC2) accounted for 60.3% and 14.1% of total variability, respectively. An obvious segregation was observed between the young and aged wines, positioned at the right and left side of the plot, respectively (Figure 2a). This indicates that the variation of volatile compounds in these wines was mainly due to aging. As seen from the loading plot in Figure 2b, the young wines were mainly characterized by higher levels of numerous esters, including ethyl decanoate, methyl decanoate, ethyl dodecanoate, isobutyl octanoate, 2-ethyl-1-hexyl acetate, ethyl nonanoate, isopentyl decanoate, ethyl octanoate, etc., while the aged wines were associated with higher levels of volatile phenols (_cis_-isoeugenol, syringol, eugenol, and guaiacool), furanic compounds (furfural, 5-methyl furfural, acetylfuran, and maltol), esters (diethyl succinate, ethyl lactate, isoamyl lactate), _cis_- and _trans_-whiskey lactone, and norisopenoids (vitispirane A, vitispirane B, and TDN). The second major cause of the variation, along PC2 was the clone (Figure 2a). It was also observed that whether before or after aging, the clone 191 and 169 wines tended to cluster into separate groups on the positive and negative y-axis, respectively (Figure 2a). The corresponding loading plot (Figure 2b) showed that clone 191 wines were characterized by higher levels of some alcohols (1-octen-3-ol and (_Z_)-2-hexen-1-ol), esters (hexyl acetate, ethyl acetate, isoamyl acetate, ethyl 2-hexenoate and methyl salicate), acids (decanoic acid and isobutyric acid), _trans_-isoeugenol, and nerol. The clone 169 wines were associated with higher levels of several alcohols (1-hexanol, 1-butanol, 1-pentanol, benzyl alcohol and 3-methyl-1-pentanol), volatile phenols (o-cresol, _m_-cresol, phenol, 4-propylguaiacol, guaiacol, 4-ethylguaiacol, and 4-methylguaiacol), acetovanilone, acetosyringone, benzeneacetaldehyde, and benzaldehyde. Compared to the variation on the volatile profile caused by the aging and clone, the use of different barrels led to a more subtle difference (Figure 2a).
## 8 Expected altimeter performances
### Measurements objectives and principle
#### Ka-band waveform generation from a simulator run over ocean and non ocean surfaces  
Analyzing the behavior of the instrument in ocean and non ocean conditions and in particular over land and water transitions (coastal areas, inland water) is of course of major interest. The first aspect to check is the acquisition duration: the shorter this duration, the faster the instrument can track a useful signal and even get a return signal above very small water areas. The second aspect is of course the range tracking design. On AltiKa, both a classical tracking loop and an open tracking loop based on a coupling with DORIS/DIODE and a DEM are implemented and optimized in order to improve as much as possible the instrument behavior over non ocean areas compared with POSEIDON-2/ Jason-1 whose tracking algorithm was optimized over sea surfaces. In order to characterize this behavior, a simulation tool has been developed: it consists of building altimeter waveforms along a portion of orbit over all types of surfaces by using a DEM (as illustrated on Figure 9) and then running all tracking algorithms on these waveforms. The performance can then be derived from the analysis of the losses of track, the time spent in the acquisition mode, the comparison of useful samples that would be transmitted in telemetry and so on.
# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 3 Remote Metering Life Prediction Method
### Data Collection and Preprocessing
Figure 4: Voltage graphs before and after missing value imputation. Left **(a)** shows original voltage values, and right **(b)** shows imputed voltage values. Figure 5: Graph before and after applying convolutional smoothing. The noise in the graph on the left **(a)** is greatly decreased in the graph on the right **(b)**. For the final step, we applied a machine learning technique, normalization. This method aims to standardize the scales of input features, ensuring equitable significance during model training. To address the scale disparities within our collected data, encompassing discharge capacity, temperature, voltage, and the communication count, we opted for min-max normalization. This process scaled the original data to fall within the range of 0 to 1, as per Equation (5). \[Y(t)=\frac{y(t)-y_{min}}{y_{max}-y_{min}}, \tag{5}\]  
In Equation (5), where t represents time, \(y(t)\) signifies the data before normalization, \(y_{min}\) denotes the minimum value of \(y(t)\), \(y_{max}\) represents the maximum value of \(y(t)\), and \(Y(t)\) is the normalized data.
## Physical Layer Design in Wireless Sensor Networks for  
Fading Mitigation  
**[PERSON] * and [PERSON]**  
ECE Department, The University of Auckland, 38 Princes Street, Auckland 1010, New Zealand;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +64-9238-966; Fax: +64-3737-461.  
_Received: 26 June 2013; in revised form: 12 August 2013 / Accepted: 19 August 2013 / Published: 2 September 2013_  
**Abstract:** This paper presents the theoretical analysis, simulation results and suggests design in digital technology of a physical layer for wireless sensor networks. The proposed design is able to mitigate fading inside communication channel. To mitigate fading the chip interleaving technique is proposed. For the proposed theoretical model of physical layer, a rigorous mathematical analysis is conducted, where all signals are presented and processed in discrete time domain form which is suitable for further direct processing necessary for devices design in digital technology. Three different channels are used to investigate characteristics of the physical layer: additive white Gaussian noise channel (AWGN), AWG noise and flat fading channel and AWG noise and flat fading channel with interleaver and deinterleaver blocks in the receiver and transmitter respectively. Firstly, the mathematical model of communication system representing physical layer is developed based on the discrete time domain signal representation and processing. In the existing theory, these signals and their processing are represented in continuous time form, which is not suitable for direct implementation in digital technology. Secondly, the expressions for the probability of chip, symbol and bit error are derived. Thirdly, the communication system simulators are developed in MATLAB. The simulation results confirmed theoretical findings.  
**Keywords:** wireless sensor networks; correlator receivers; physical layer; fading channel; bit error rate (BER).
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Supervised Learning
#### 4.1.1 Optimization Problems
In [43], the authors compare different supervised ML algorithms to predict data rate (ANN, SVM, random forest). Results show that random forest approach can achieve the lowest prediction error. The error is minimized in the uplink transmission direction (in downlink it is more significant). In [44], a supervised cooperative data rate prediction approach is introduced. This cooperative model reduces average prediction error by 30%. In [45], combination of 2 well-known beamforming schemes (maximum ratio transmission and zero-forcing) is used in a K-user Multiple Input Single Output (MISO) channel. The proposed approach is based on a DNN in which the input nodes take channel vector with transmit power and the output returns the combining factors from transmitter's beamforming. The model achieves a sum rate of 99% when compared with conventional approaches. A K-means clustering model for users in Thz MIMO-NOMA systems is proposed in [46]. Based on whether the user belong to Small Cell Base Stations (SBSs) coverage or Macro Base Station (MBSs) coverage, they are separated into different cluster. The great path spreading path loss and molecular absorption loss are two important challenges in THz systems. So an efficient clustering scheme can both reduce interference and improve the channel quality, resulting in higher throughput and Signal-to-interference-plus-noise ratio (SINR).For the user's clustering an enhanced K-means approach is proposed in the same paper. The channel's correlation parameters of different cluster are examined and the one that maximizes the metric is used to address the issue of fluctuation of clustering centers. The simulation results show the efficiency of the proposed schemes. In [47], a machine learning based predictive DBA algorithm is proposed for the contention of upstream bandwidth and bottleneck latency in Passive Optical Networks (PONs). The proposed algorithm using an ANN at the Central Office (CO) to learn the uplink latency and estimate the bandwidth demand of every units. Using this approach, the CO can allocate the required bandwidth to forthcoming packet bursts without the need to have them wait until the following transmission cycle. The simulation results show that the model is able to achieve a >90% accuracy in predicting the Optical Network's status leading to the improvement of the accuracy of estimating the bandwidth demands of the optical units. Table 5 holds a brief summary of the supervised ML models in Beyond 5G(B5G)/6G optimization problems.
## 2 Overall Structure and Working Principle  
### Overall Design  
In this research, we first made a new design for the whole safflower picking platform, and innovatively added four electric push rods to the four driving legs of the robot as part of the levelling system (as shown in Figure 1, No. 2). In addition, three IMUs were innovatively added as angle measuring tools, so as to ensure the accuracy of the measuring angle. The automatic mobile safflower picking platform levelling mechanism is composed of four independent electric push rods, a stepper motor driver, a controller, a CAN communication module (the model is MCP2515), one 9-degree-of-freedom (DOF) (IMU), two single-degree-of-freedom (DOF) (IMU), one cable displacement sensor, four pressure sensors, and a level gauge. The upper end of the electric push rod was then fixed to the box body of the mobile platform, and the lower end fixed to the wheel support plate through a square steel structure. The driver and electric push rod were connected by bolts. The nine-axis inclination sensor was installed at the center of the picking platform in order to measure the angle of the \(X\)-axis and \(Y\)-axis when the car body is inclined. The two single-axis inclination sensors were then installed vertically on the \(X\)-axis and \(Y\)-axis in order to measure the inclination of the \(X\)-axis or \(Y\)-axis separately. The pull wire displacement sensor was used to sense the telescopic length of the electric push rod. The level gauge was used to detect the levelling accuracy after levelling. The pressure sensor was used to detect the pressure of each outrigger and in order to ascertain whether there was a virtual leg. The tilt angle of the mobile platform is controlled by the expansion and contraction of the electric push rod in order to realize the levelling of the vehicle body. Each part of the levelling system is shown in Figure 1. In this design, the levelling parts are fully used, which greatly improves the accuracy of platform levelling.
# Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism
## 2 Materials and Methods
### Method  
In the task of converting chest X-rays to radiology reports, the entire process can essentially be viewed as a highly specialized machine translation task, with the core goal of translating complex image data into accurate and informative text descriptions. Similar to machine translation tasks, this task typically uses a sequence-to-sequence (seq2 seq) [14] model structure, which has been widely used to process and convert different forms of sequence data. The seq2 seq model architecture originated from the field of machine translation and is based on the encoder-decoder principle. It has two key components: the encoder part is responsible for receiving and processing the input source sequence, that is, parsing and converting the image data into a fixed-length vector representation; the decoder part generates the target language sequence based on the encoder's output vector, that is, the corresponding radiology text report. The model structure proposed in this paper is shown in the Figure 1.  
In practice, encoding transformation of image data usually relies on powerful visual encoding models, such as ResNet-101 [15] and Vision Transformer (ViT) [16], which extract features from input chest X-ray images through deep neural network structures. As a variant of a convolutional neural network (CNN), ResNet-101 has a deep network hierarchy and residual connections, which can effectively capture the hierarchical features in the image. Due to the large distribution difference between abnormal and normal areas in chest X-ray data, traditional visual encoding models may not be sensitive enough when dealing with abnormal areas. To address this problem, we introduce the CBAM (Convolutional Block Attention Module) attention mechanism [14], which dynamically weights the spatial and channel dimensions of the image, allowing the model to pay more attention to abnormal areas in the image, such as lesions or lesion areas, thereby improving the model's recognition ability in abnormal areas.  
To strengthen the association between image and text sequences, we integrate the cross-attention mechanism [17] into the model. This mechanism dynamically adjusts the matching weight between image regions and text descriptions by calculating the correlation between images and text. During the training process, the cross-attention mechanism calculates the correlation between images and text through dot product operations, thereby automatically assigning the importance of image regions when generating text, ensuring that the description in the final report is closely aligned with the image content. The introduction of this mechanism improves the correlation between the text generated by the model and medical images.
## 3 Method
### Model Evaluation  
To evaluate the practicality of the models, we applied the leave-one-year-out prediction to all the models for each test year from 2009 to 2016. We first eliminated the test year and then trained the model to predict the test year with all the remaining years. The leave-one-year-out strategy has been applied in previous studies [8; 10; 27]. Although we cannot capture the real-world future data, the leave-one-year-out strategy ensures that each test year has the same number of training years, making the model performance independent of the amount of training data [53]. We applied 2009-2016 as the test years to ensure that each test year has the same training data without using too much future data. We further used the better-performing models to evaluate the within-season yield prediction. The within-season yield prediction using data from different periods can detect when the models can achieve the optimal prediction.  
Our study was conducted at the district level. Therefore, we selected \(R^{2}\) (coefficients of determination), \(RMSE\) (root mean square error), \(MAPE\) (mean absolute percentage error), and \(d\) ([PERSON]'s index of agreement) [54] between the observed and predicted district-level yield for each prediction year to assess performance.  
\[R^{2}=\left(\frac{\sum_{i=1}^{n}\left(O_{i}-\overline{O}\right)\left(P_{i}- \overline{P}\right)}{\sqrt{\sum_{i=1}^{n}\left(O_{i}-\overline{O}\right)^{2} }\sqrt{\sum_{i=1}^{n}\left(P_{i}-\overline{P}\right)^{2}}}\right)^{2} \tag{9}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(O_{i}-P_{i}\right)^{2}}{n}} \tag{10}\]  
\[MAPE=\frac{100\%}{n}{\sum_{i=1}^{n}\left|\frac{P_{i}-O_{i}}{O_{i}}\right|} \tag{11}\]  
\[\text{d}=1-\frac{\sum\limits_{i=1}^{n}\left|Pi-Oi\right|}{\sum\limits_{i=1}^{ n}\left(\left|Pi-\overline{O}\right|+\left|Oi-\overline{O}\right|\right)} \tag{12}\]  
where \(n\) is the number of samples. \(P_{i}\) and \(O_{i}\) represent the predicted and observed values. \(\overline{P}\) and \(\overline{O}\) represent the mean values of the predicted and observed yields.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Transport Layer
#### 5.4.2 Poisoning Mechanism  
The poisoning mechanism identifies which specific nodes experience communication problems in their path to the GW, so that they can perform subsequent retransmissions. Nodes having problems with their children transmit packets with the poison flag activated. An STA is considered _poisoned_ if, before transmitting an outgoing data packet, one of the following conditions is satisfied:  
* The STA is part of a poisoned path; i.e., it has received one or more packets with the poison flag activated during the current transmission window.
* The STA has not received any data packet from one or more of its children.
* The STA has not received all the expected segments from one or more of its children.  
In Figure 7, node \(N_{3}\) activates its poison flag after not receiving data from its child \(N_{6}\). In its way to the GW, a data packet from \(N_{3}\) poisons its next hop: \(N_{1}\). Therefore, nodes \(N_{6}\), \(N_{3}\), and \(N_{1}\) form a _poisoned path_, as shown in Figure 8.  
Figure 8: State of the network from Figure 7 after the corresponding e2e ACK. Note the _poisoned path_ passing through nodes \(N_{6}\), \(N_{3}\), and \(N_{1}\). Together with the GW, these nodes (colored in red) stay awake during the second transmission window. The rest of nodes (colored in green) go to sleep as they are not involved in the new transmission process.  
Figure 7: Network topology of the multi-hop LPWAN from Figure 6, with a GW and 14 STAs (\(N_{1}-N_{14}\)) deployed in four rings (\(R_{1}-R_{4}\)).
## 3 Proposed Methodology
### Evaluation Metrics  
Assessing the effectiveness of recognition involves employing a confusion matrix, a distinct element that effectively illustrates the efficiency of deep learning models. The mathematical portrayal of a confusion matrix in a multiclass scenario is delineated below. In this representation, the rows denote instances in a predicted class, whereas the columns signify cases in the actual class.  
\[C=\begin{bmatrix}c_{11}&c_{12}&c_{13}&\dots&c_{1n}\\ c_{21}&c_{22}&c_{23}&\dots&c_{2n}\\ c_{31}&c_{32}&c_{33}&\dots&c_{3n}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ c_{n1}&c_{n2}&c_{n3}&\dots&c_{mn}\end{bmatrix}\]  
The components of confusion corresponding to each class are as follows:  
* True positive: \(TP(C_{i})=C_{ii}\);
* False positive: \(FP(C_{i})=\sum_{t=1}^{n}c_{li}-TP(C_{i})\);
* False negative: \(FN(C_{i})=\sum_{t=1}^{T}c_{li}-TP(C_{i})\);
* True negative: \(TN(C_{i})=\sum_{t=1}^{T}\sum_{k=1}^{n}c_{lk}-TP(C_{i})-FP(C_{i})-FN(C_{i})\).  
The mathematical expressions for these assessment metrics are formulated as follows:  
\[Accuracy=\frac{1}{|Class|}\times\sum_{i=1}^{|Class|}\frac{TP_{i}+TN_{i}}{TP_ {i}+FP_{i}+TN_{i}+FN_{i}} \tag{14}\]  
\[Precision=\frac{1}{|Class|}\times\sum_{i=1}^{|Class|}\frac{TP_{i}}{TP_{i}+FP_{ i}} \tag{15}\]  
\[Recall=\frac{1}{|Class|}\times\sum_{i=1}^{|Class|}\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{16}\]  
\[F1\text{-}score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{17}\]  
The datasets employed in this study exhibit an imbalance in their distribution. In tackling the challenge of uneven class representation in the data, we adopted the F1-score as an evaluation metric to identify the optimal parameter combination for the model, as recommended in [51]. As indicated in [52], this metric measures the model's accuracy, particularly in imbalanced dataset scenarios.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 4 Spatiotemporal Data Collection and Processing for Bridge Digital Twin
### The 2D Image Data Collection and Processing
#### 4.1.2 The 2D Image Data Preprocessing  
After image collection, preprocessing was necessary to ensure the data were suitable for accurate detection. Given the variability in UAV flight angles, geometric distortion was corrected using a perspective projection model [32]. This correction ensured that the spatial relationships in the images precisely represented the true geometry of the bridge. The corrected image coordinates \((x^{\prime},y^{\prime})\) are given by Formula (1):  
\[x^{\prime}=\frac{f\cdot x}{z}\ \ \ \ \ y^{\prime}=\frac{f\cdot y}{z} \tag{1}\]  
where \(x^{\prime},\ y^{\prime}\) are the pixel coordinates in the image; \(x,\ y\) represent the real-world coordinates on the bridge surface; \(f\) is the focal length of the camera; and \(z\) is the distance from the camera to the bridge surface.  
Both optical and infrared images underwent alignment to ensure consistency across datasets, enabling the joint analysis of surface and subsurface conditions. After geometric correction, Gaussian filtering was applied to reduce noise [33]. Gaussian filtering enhanced image smoothness while retaining essential details such as crack edges and is defined as in Formula (2):  
\[I^{\prime}(i,j)=\sum_{m=-k}^{k}\sum_{n=-k}^{n}I(i+m,j+n)\cdot G(m,n) \tag{2}\]  
Figure 3: Image data collection of bridge surface defects.  
where \(I(i,j)\) is the intensity of the pixel at coordinates \((i,j)\) in the original image, \(I^{\prime}(i,j)\) is the intensity of the same pixel after applying the filter, \(G(m,n)\) represents the Gaussian kernel, and \(k\) defines the size of the filter window.  
To further improve the visibility of cracks in the optical images, histogram equalization was applied [34]. This method adjusted the intensity distribution of the pixels, improving the contrast and making fine cracks more easily detectable. In the infrared images, thermal contrast enhancement was used to emphasize temperature variations that may indicate structural weaknesses.