esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The application of antagonist yeasts, as biological control agents (BCAs) in the production of clean label products, has been at the epicenter of intensive study over the past few years, as a promising alternative to chemical preservatives. Several possible mechanisms have been reported for the biocontrol activity exerted by yeasts, among which are competition for space and nutrients, biofilm formation and secretion of killer toxins, volatile organic compounds (VOCs) and hydrolytic enzymes [1]. The use of _Wickerhammovces anomalous_ strains as antimold agents has received considerable attention due to the antagonistic features of the yeast [2].  
Wickerhamomyces anomalies_ is an ascomycetous yeast, formerly known as _Hansenula anomala_, _Pichia anomala_ and _Saccharomyces anomalous_, the anamorph name of which is _Candida pelliculosa_[3]. Its frequent isolation from a wide variety of ecological niches, ranging from soil, plants and water to dairy and baked products, indicates the highly competitive nature of this yeast species [4; 5]. In addition, the ability of _W. anomalous_ strains to ferment many carbon and nitrogen sources, combined with their capacity to survive in harsh conditions, in terms of temperature, initial pH value, water activity and osmotic pressure, suggest a robust character [6]. Due to the ability of _W. anomalus_ strains to grow in a broad range of temperatures, its implication as an opportunistic pathogen has been claimed and its occasional identification in clinical settings has raised some consideration [7]. However, the European Food Safety Authority (EFSA) addressed these safety concerns, by granting the Qualified Presumption of Safety (QPS) status to _W. anomalus_ for use in food industries [8]. Regarding the antimold activity assessment of _W. anomalus_ strains, several studies have associated their biocontrol activity with VOCs production [9; 10], with ethyl acetate and 2-phenylethanol being reported as the major ones [11; 12].  
Despite the fact that the application of _W. anomalus_ strains for postharvest decay control in fruits and vegetables has extensively been reported [13; 14], only scarce is the literature concerning their effect on the shelf life of foodstuff in general, and particularly bread. Bread spoilage represents a serious concern for food industries, leading to significant economic losses and posing a major threat for human health. The most common molds causing bread spoilage belong to _Penicillium_, _Aspergillus_ and _Cladosporium_ genera [15]. The applicability of _W. anomalus_ strains to control their proliferation has only been marginally assessed [16].  
In our previous study [17], the inhibitory activity of three _W. anomalus_ strains, namely LQC 10346, 10353 and 10360, against the growth of _P. chrysogenum_, was reported and attributed to the production of non-proteinaceous compounds. Thus, the aim of the present study was to assess the effect of substrate, initial pH value and NaCl content on the growth and antimold activity of the three _W. anomalus_ strains, identify the responsible VOCs and to evaluate the effect on the shelf life of experimental breads.
## 3 Data Processing and Analysis for Smartphone Real-Time Kinematic (RTK) Positioning
### Analysis of RTK-Positioning Convergence Time by Segment
Table 8 shows that the cycle-slip rate may not determine the number of candidates directly because it is also related to other factors, but a large number of candidates must correspond to a low cycle-slip rate. The pseudo-range residuals are approximately 5 m, and there is no obvious correlation with the fixing rate. However, the pseudo-range accuracy is the main factor influencing the main convergence speed [31], but it can be superseded by other factors such as the cycle slips. The effect of the cycle-slip rate and pseudo-range accuracy on convergence is reported in Section 5. Nevertheless, we can conclude that higher-quality GPS and Galileo observations increase the number of candidate ambiguities, accelerate convergence, and increase the fixing rate.
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 4 Detecting Radiation Frequency
### Data Collection  
Since a HackRF One SDR device was used for capturing EM radiation, the possible EM radiation frequency range to consider can span from 10 Mz to 6 GHz. However,  
Figure 1: Illustration of the proposed attack model and experimental setup. Malware on an infected target computer manipulates the network communication, resulting in deliberate EMR patterns used to transmit a covert signal to a nearby receiver.  
it was empirically observed that the frequencies closer to the lowest edge of HackRF's sensitive range are often affected by the device's internal noise itself. Therefore, it was decided to consider 30 MHz as the starting frequency in the suspecting frequency range to scan through. Similarly, it is important to decide the upper bound of the suspecting frequency range. Although it could have been set to 6 GHz, i.e., HackRF's maximum sensitive frequency, it was practically impossible to consider such a large frequency range. This is due to the storage and computational overhead posed by the data produced by the HackRF device. Due to this reason, it was decided to consider frequencies only up to 220 MHz in the experimental procedure. As a result, this work considered the frequency range from 30 MHz to 220 MHz in the experiments to identify an information-leaking EM radiation signal.  
The EMR data collection thread of the attacker's computer controls the SDR hardware to scan the frequency range from 30 MHz to 220 MHz, with 1 MHz steps. This results in 160 frequency channels to monitor, e.g., 30 MHz, 31 MHz, 32 MHz,..., 218 MHz, 219 MHz, and 220 MHz. The procedure in collecting data was to transmit different network traffic patterns through the Ethernet cable while capturing the EM radiation coming from each of the aforementioned frequency channels. For each frequency channel, six EM trace files and six network packet trace files were recorded, respectively, per each network packet transmission pattern. While a broad range of potential network packet patterns can be effective in producing unique and detectable EMR patterns, the following limited set of network packet patterns were used during the experimentation due to simplicity and ease of performing experiments:  
* No packets (Medium is idle)--P0;
* 50 UDP packets with non-zero payload--P1;
* 50 UDP packets with all zero payload--P2;
* 50 TCP packets with non-zero payload--P3;
* 50 TCP packets with all zero payload--P4;
* 50 IP packets with all zero payload--P5.
# Learning Motion Constraint-Based Spatio-Temporal Networks for Infrared Dim Target Detections
## 1 Introduction
The contributions made in this paper are summarized below:  
* This paper introduces a learning spatio-temporal constraint module based on Conv-LSTM cells. Experiments show that the model could be applied in long-range infrared target detection. * This paper fuses multiscale features along the temporal dimension, which makes the detection more efficient and robust. * This paper applies a state-aware module to achieve dynamic switching between local searching and global re-detection. * Real datasets are employed to evaluate detection speeds and the robustness of the model. The remainder of this paper is organized as follows. The proposed model and its analysis are demonstrated in Section 2. The performance of the model is evaluated and discussed in Section 3. We provide conclusions in Section 4. Figure 1: The processing flow of the proposed method. It consists of two parts: training phase and inference phase. In the training phase, the spatio-temporal module (STM) firstly learns the motion constraint and the deep feature module (DFM) and secondly fuses the multiscale feature to achieve robust detections. In the inference phase, the state-aware module (STAM) is added to face emergencies during real-time detections.
# Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes
## 2 Materials and Methods
### Asymptotic Feature Pyramid Network  
The core idea of AFPN is to gradually integrate the bottom, top, and high-level features into the object detection process by introducing an asymptotic feature fusion strategy. This gradual fusion helps to reduce the semantic gap between features at different levels, improve the feature fusion effect, and enable the detection model to better adapt to the semantic information at different levels.  
AFPN fuses the bottom-level features by introducing a gradual fusion of the bottom-level features, first fusing the bottom-level features, followed by the deep-level features, and finally integrating the top-level features. This hierarchical fusion helps to better utilize different levels of semantic information and improve the detection performance.  
Adaptive Spatial Fusion Mechanism (ASFF) is introduced, which introduces changing spatial weights in the multi-level feature fusion process to strengthen the importance of key levels while suppressing the influence of contradictory information from different objects. This helps to improve the detection performance and is especially effective in dealing with contradictory information.  
AFPN adopts the idea of asymptotic fusion, which makes the features of different levels approach gradually in the fusion process and reduces the semantic gap between them. The gradual integration of the underlying features improves the effect of feature fusion, making the model more capable of understanding and utilizing information at different levels.  
As shown in Figure 2, it is the network structure of AFPN, it can be seen that after extracting the features from the Backbone, the features are fed into the AFPN for processing, and it obtains the features of different layers for fusion, which is one of the main ideas of it, and also feeds the results into the detection header for prediction.  
Asymptotic Feature Pyramid Network (AFPN) helps in direct feature fusion across non-adjacent layers, thus preventing loss or degradation of feature information during transmission and interaction.
## 3 Results and Discussion
### Cement Paste Analysis
#### 3.1.3 Heat of Cement Hydration  
As can be seen in Figure 4, compared with the blank group, the addition of water-reducing agents NF, H, Z, and J to the cement has a retarding effect, which extends the induction period of cement hydration. In particular, the longer induction period of cement hydration in the presence of water-reducing agents H, Z, and J demonstrates that polycarboxylic acid superplasticizers, due to the long side chains and a strong steric hindrance effect, have a more significant retarding impact than the naphthalene-based water-reducing agent. The molecular structure of polycarboxylic acid superplasticizers contains functional groups, such as ether and carboxyl groups, adsorbed on the surface of cement particles, which prevent water molecules from approaching cement due to shielding and hinder the cement hydration reactions. Therefore, polycarboxylic acid water-reducing agents have a substantial retarding effect on cement hydration. On the other hand, the naphthalene-based water-reducing agent (NF) only contains sulfonic acid groups with shorter chains and a weaker steric hindrance effect, hence it cannot retard cement hydration as good as polycarboxylic acid water-reducing agents.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group ID**} & \multirow{2}{*}{\
\begin{tabular}{c} **Water-Reducing** \\ **Agent (g)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Water** \\ **Consumption** \\ **(g)** \\ \end{tabular} } & \multicolumn{3}{c}{**Cement Slurry Flow**} \\ \cline{3-5}  & & & **Initial Cement** & **Fluidity of** & **Fluidity of** \\  & & **(g)** & **Slurry Flow** & **Cement Paste** & **Cement Paste** \\  & & & **(mm)** & **after 1 h (mm)** & **after 2 h (mm)** \\ \hline B (Blank) & 0.0 & 115 & No flow, dry & No flow, dry & No flow, dry \\ H & 3.0 & 115 & No flow & 230 & 255 \\ Z & 3.0 & 115 & 280 & 255 & 240 \\ NF & 3.9 & 115 & 160 & No flow & No flow \\ J & 3.0 & 115 & 280 & 280 & 270 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effects of functional polycarboxylic acid superplasticizers on the flow performance of the cement paste.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group ID**} & \multirow{2}{*}{\
\begin{tabular}{c} **Water-Reducing** \\ **Agent (g)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Water** \\ **Consumption (g)** \\ \end{tabular} } & \multicolumn{3}{c}{**Compressive Strength**} \\ \cline{3-5}  & & & **1 Day** & **7 Days** & **28 Days** \\ \hline B (Blank) & 0 & 115 & 51.5 & 63.0 & 75.5 \\ H & 3.0 & 115 & 67.1 & 75.6 & 87.5 \\ Z & 3.0 & 115 & 76.8 & 89.0 & 95.8 \\ NF & 3.9 & 115 & 69.6 & 73.4 & 90.6 \\ J & 3.0 & 115 & 74.6 & 86.1 & 97.9 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of functional polycarboxylic acid superplasticizers on the compressive strength of the cement paste.
## 3 Results and Discussion  
### Simulation  
AirSim is an unmanned automatic control simulation platform developed by Microsoft. The entire system is based on Unreal Engine 4, with a highly restored physics engine and realistic visual scenes. AirSim supports both software-in-the-loop and hardware-in-the-loop simulation, with sensors such as lidar, camera, IMU, GPS, and more to control drones not only with python and C++ code, but also directly via remote control. Compared with Matlab, Gazebo, FlightGear, and other simulation platforms, AirSim has advantages such as strong 3D rendering, flexible control methods, fine model textures, and strong scalability.  
The simulation system, shown in Figure 7, mainly includes the AirSim simulation platform, Pixhawk4 flight controller and UAV ground control station. AirSim is housed in Unreal Engine 4. As the core and hub of the system, it is connected to the Pixhawk4 flight controller through serial ports, to the ground station through UDP, and communicates with each other using the MAVLink protocol. The Pixhawk4 flight controller has PX4 firmware built in. PX4 provides a UAV control model that sends control commands to the UAV fuselage model in AirSim, enabling the UAV to follow algorithm instructions. UAV ground control station can directly control the UAV's flight and can also set up missions that allow the UAV to fly autonomously. The entire simulation process can obtain information such as flight trajectory, visual image and position status of UAV from the ground station and Unreal Engine 4.  
Figure 6: (a) Subspace path fitting result; (b) 3D detection path.  
The energy budget determines the total flight time of the UAV. For simplicity, we use the flight time to represent energy consumption in simulations and evaluate the algorithm's performance by measuring total flight time under different sampling coverage in the same region. Table 3 shows the comparison information of the lawnmower path planner (LMP), the auxiliary coarse cell-based hexagonal grid coverage (HGC) sampling planner [29] and the path planner (HGTB) in this paper. Because LMP and HGC are both single-plane covering algorithms, the algorithm in this paper only considers the energy consumption of one subspace region. The lawnmower path can be regarded as a square-based unit division, so the L of the lawnmower path in the table is the side length of the square unit. It can be seen from the table that under the same coverage, HGTB takes less time, which means that with the same energy budget, HGTB can achieve longer flight sampling and obtain spatial sampling data with higher coverage.
## 3 Results and Discussion
### Isolation and Characterization of Compounds by NMR
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{n}^{\circ}\) & \(\mathbf{\xi_{13C}}\)**Type** & \(\mathbf{\delta_{1H}}\) **(J in Hz)** & **COSY \({}^{1}\)H-\({}^{1}\)H** & **HMBC \({}^{2-3}\)J\({}_{\mathbf{1H-13C}}\)** & **NOESY \({}^{1}\)H-\({}^{1}\)H** \\ \hline
1 & 34.7, CH\({}_{2}\) & 1.68, m & 1, 2 & 2, 3, 10 & 25 \\
2 & 30.2, CH\({}_{2}\) & 2.12, m & 1, 2 & 1, 3, 10 & \\
3 & 179.2, C & & & & \\
4 & 26.5, CH & 1.92, m & 23, 24 & 6, 10, 23, 24 & 23, 24 \\
5 & 48.6, CH & 1.05, brd (10.5, 3.3) & 6 & 6, 10, 23, 24 & 9, 23 \\
6 & 19.3, CH\({}_{2}\) & 1.38, m & 5 & 7 & 23, 24 \\
7 & 34.5, CH\({}_{2}\) & 1.38, m & & 6, 26 & 26, 27 \\
8 & 41.7, C & & & & \\
9 & 42.2, CH & 1.56, dd (12.7, 2.9) & 11a, 11b & 8, 10, 11, 26 & 5, 11b, 27 \\
10 & 41.4, C & & & & \\
11a & 22.7, CH\({}_{2}\) & 1.25 dd (13.2, 4.3) & 9, 11b, 12b & 9 & 25, 26 \\
11b & 22.7, CH\({}_{2}\) & 1.44 m & 11a, 12a & 9 & 9 \\
12a & 10.8 dd (13.2, 4.6) & 11b, 12b, 13 & 13 & 19 \\
12b & 26.8, CH\({}_{2}\) & 1.72 m & 11a, 12a & 13 & 19 \\
13 & 39.8, CH & 2.33 td (12.3, 3.6) & 12a, 12b, 18 & 12, 14, 18, 27 & 19, 26 \\
14 & 44.1, C & & & & \\
15a & 30.9, CH\({}_{2}\) & 1.52 td (13.5, 3.5) & 15a & 14, 16, 27 & 16b, 26 \\
16a & 14.3 m & 16b & 17, 28 & 18 \\
16b & 33.3, CH\({}_{2}\) & 2.24 dt (12.9, 3.2) & 15a, 16a & 14, 15, 17, 18, 28 & 15b, 22b \\
17 & 57.5, C & & & & \\
18 & 50.4, CH & 1.65, t (11.2) & 13, 19 & 13, 17, 19, 20, 28 & 16a, 22a, 27, 30 \\
19 & 48.5, CH & 3.03, td (11.2, 5.0) & 18, 21a, 21b & 13, 18, 20, 21, 29, 30 & 12b, 13, 21b, 29b \\
20 & 151.8, C & & & & \\
21a & 31.8, CH\({}_{2}\) & 1.94, m & 19 & 17, 18 & 19 \\
22a & 1.44, m & & & 28 & 18 \\
22b & 38.2, CH\({}_{2}\) & 1.90, m & & 17 & 16b \\
23 & 25.2, CH\({}_{3}\) & 0.92, d (6.8) & 4 & 4, 5, 24 & 4, 5, 6, 24 \\
24 & 19.3, CH\({}_{3}\) & 0.81, d (6.8) & 4 & 4, 5, 23 & 4, 6, 23 \\
25 & 20.3, CH\({}_{3}\) & 0.82, s & & 1, 5, 9, 10 & 1, 11a, 26 \\
26 & 16.6, CH\({}_{3}\) & 0.98, s & & 7, 8, 9, 14 & 7, 11a, 13, 15b, 25 \\
27 & 15.1, CH\({}_{3}\) & 1.02, s & & 8, 13, 14, 15 & 7, 9, 18 \\
28 & 180.0, C & & & & \\
29a & 110.2, CH\({}_{2}\) & 4.60, m & 29b, 30 & 19, 20, 21, 30 & 30 \\
29b & 110.2, CH\({}_{2}\) & 4.72, m & 29a, 30 & 19, 20, 21, 30 & 19, 30 \\
30 & 19.5, CH\({}_{3}\) & 1.70, s & 29a, 29b & 19, 20, 29 & 18, 29a, 29b \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \({}^{1}\)H (600 MHz) and \({}^{13}\)C NMR (150 MHz) data of compound **1** in MeOD at 300 K. Diastereotopic protons are marked with letters. Figure 5: Structure of compound **1** annotated with key HMBC, COSY, and NOESY correlations.
# Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension
## 5 Conclusions  
In this work a proof-of-concept damage diagnosis strategy that can be deployed online and during the WT service has been stated. This main contribution of the paper is accomplished by using only the vibration-response accelerometer signals instead of the standard approach based on guided waves. Furthermore, the methodology is based on machine learning techniques. In this regard, the second main contribution of this work is to introduce the FD as a suitable feature to detect and classify different damage scenarios inspired by the physical insight that the different fractal structures of the accelerometer signals should be capable to discriminate different types of damage. Three supervised machine learning classifiers have been studied and optimized for the specific problem. Finally, the proposed methodology has been validated in an experimental laboratory test bed where for the best selected model (Gaussian SVM with box constraint \(C=50\) and kernel scale \(\gamma=1\)) all the studied measures (average accuracy, average precision, average recall, average F1 score and average specificity) have attained values higher than 97%. These results encourage future work in this area of research to develop further this proof-of-concept. More tests including changing the damage location and taking into account and dealing with variable environmental operating conditions, including waves, will be the focus of future work.  
All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Performance measures (percentage) comparison among the different classifiers.  
**Funding:** This research has been partially funded by the Spanish Agencia Estatal de Investigacion (AEI)-Ministerio de Economia, Industria y Competitividad (MINECO), and the Fondo Europeo de Desarrollo Regional (FEDER) through the research project DPI2017-82930-C2-1-R; and by the Generalitat de Catalunya through the research project 2017 SGR 388.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 3 U-Net Model and Network Structure Improvement
### CBAM Attention Mechanism  
The attention mechanism lets the model learn and pay attention to the important information; it can let the model focus on the important region or feature in the image and then improve the accuracy and precision of the segmentation model [17]. U-Net for image segmentation requires simultaneous enhancement of channel dependencies and spatial location accuracy. CBAM, consisting of the channel attention module and spatial attention module, automatically identifies key channels and spatial locations in feature maps, assigning importance weights. This dual mechanism perfectly matches U-Net's needs: the channel attention filters critical feature channels, while the spatial attention pinpoints vital regions, complementing U-Net's encoder-decoder structure. Compared with other modules, SE focuses solely on channels, ignoring the spatial context indispensable for segmentation; BAM, though a bidirectional channel, introduces higher computational complexity [18]. CBAM's lightweight design achieves an optimal balance, enhancing U-Net's performance with minimal extra overhead--making it more suitable for U-Net than modules that neglect spatial aspects or impose excessive computational costs (BAM) [19]. The CBAM module consists of the channel attention module and the spatial attention module. The structure is shown in Figure 4. The CBAM attention mechanism can automatically identify and assign importance weights to the key channels and spatial locations in the feature map and improve the learning ability of the entire U-Net model on spatial and channel attention so as to enhance the feature representation ability and overall performance of the U-Net model. The formula of the CBAM attention mechanism is as follows.  
\[x^{\prime}=M_{c}(x)\bigotimes x \tag{5}\]  
\[\tilde{x}^{\prime}=M_{S}(x^{\prime})\bigotimes x^{\prime} \tag{6}\]  
where x is the input feature map, \(M_{c}\) and \(M_{s}\) are the channel attention module and spatial attention module, respectively, \(X^{\prime}\) denotes two-by-two weighting (which is the feature map optimized by the channel attention mechanism), and \(\tilde{X}^{\prime}\) is the feature map optimized by the CBAM attention mechanism.  
To address limitations in feature fusion and boundary segmentation, the improved U-Net integrates the Convolutional Block Attention Module (CBAM) and depth-separable convolutions. The CBAM is embedded before the decoder skip connections to enhance spatial-channel feature weighting, while depth-separable convolutions replace traditional kernels to reduce computational complexity by 25% without compromising detail retention. These architectural adjustments facilitate more efficient information flow the between encoder and decoder stages, improving the model's sensitivity to fine-grained disease features such as leaf texture and lesion boundaries.