esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Synopsis of KDN Paradigm
### Detailed KDN Architecture
#### 3.2.5 Application Layer  
Application developers can use this layer as a base to convey their needs to the underpinning physical network. Additionally, it enables network managers to centrally set network settings guidelines that are better matched with general business goals and objectives (intents), with the application function being divorced from hardware, and specify network policies unique to applications. Moreover, application guidelines may be continuously modified in KDN depending on information about the network's functioning, which enhances the delivery of services [231].  
The application layer separates the service function from the physical components in order to centrally define the desired intentions and regulations. Within the application layer, there are essentially two sub-layers: the objective/policy definition sub-layer and the objective/policy update sub-layer, as shown in Figure 7.  
The objective/policy creation sub-layer's main function is to use network supervisors to establish guidelines and goals. Following their definition, objectives and policies can be continually modified by the objective/policy update sub-layer, utilizing the rules and knowledge-based perception obtained using the cognitive layer, the set of settings obtained from the administration layer, and the network's layout and analytics obtained using the control layer. As a result, when the network's condition changes, application principles and intentions may be dynamically changed. There are programming frameworks such as Procera [211], Nettle [232], Frenetic [233], Kinetic [234], etc. that are built on top of common programming languages such as Python, Haskell, etc. for achieving the previously mentioned policy definition and updating tasks.  
Common examples of general KDN applications include traffic optimization, network administration, and security. Moreover, the application layer uses a northbound interface and an abstraction layer of network services to interface with other layers except the data layer.  
Table 2 depicts a synopsis of protocols/models/languages for achieving different functions in each layer of KDN.
## 4 Results and Analysis  
### Characteristics of Spatial Distribution of GPP, ET, and WUE  
Figure 2a shows the spatial distribution characteristics of GPP, ET, and WUE in the Li River Basin, and obviously, their spatial distributions are different. The annual average value of GPP was 1655.02 gC.m\({}^{-2}\), and the spatial distribution of GPP showed a decreasing  
and then increasing trend from the north to the south; among them, the high values were predominantly concentrated in the southeastern of the study area, except for a few distributed in the northern part of the basin, and the low values were mostly distributed in the central part of the basin, except for a few distributed in the southern part of the basin in Pingle.  
Figure 2b shows the spatial distribution of ET values, the multi-year average value of ET was 908.25 mm, and the high values were widely and evenly distributed, while the low values were predominantly distributed in the central and western parts of the basin. The ET change trend had a certain similarity with that of GPP, and it also decreased and then increased from the south to the north, but after passing the middle of the basin, the ET values in the south part grew more rapidly.  
The average value of WUE in the basin in the past 20 years was 1.8253 gC\(\cdot\) mm\({}^{-1}\cdot\)m\({}^{-2}\); Figure 2c further shows that the spatial distribution of WUE was quite different from that of GPP and ET, and its overall distribution was characterized by high values in the south and east parts and low values in the central part. The high values were concentrated in Pingle on the south side of the basin, with a few distributed in the northern part of the basin. The low values transitioned from the central part to the southeast part, but they were predominantly concentrated in the middle part around the bordering areas of Guilin and Lingui. Regarding GPP, combined with the WUE formula, the increase in ET or the decrease in GPP led to the decrease in WUE.
## 4 Method for Implementing a 4 \(\times\) 4 Correlation Matrix
### Arrangement of Virtual Point Sources in the Implementation Model  
To realize a 4 \(\times\) 4 correlation matrix, the arrangement of virtual point sources that generate the initial phase in the implementation model in order to achieve the desired BS correlation is examined. It should be noted that the implementation model used to realize the BS correlation is a physical model that obtains the 4 \(\times\) 4 correlation matrix using a zeroth-order Bessel function of the first kind that expresses the relationship between the spatial correlation and the antenna separation in [PERSON] model. In this paper, the two-point source model depicted in Figure 3[21] is extended to a four-point source model.  
Figure 6 shows the relationship between the channel capacity of a 2 \(\times\) 2 MIMO system calculated using [PERSON]'s theorem and the spatial correlation [32]. The signal-to-noise ratio (SNR) is set to 30 dB. The analytical results in Figure 6 show that the impact of the change in spatial correlation on the MIMO channel capacity is very large in high-correlation situations.  
Figure 5: Arrangement of the BS antennas.  
Figure 6: Channel capacity vs. spatial correlation.  
The maximum distance \(d_{\text{CD}}\) is not utilized when determining the coordinates of the four virtual point sources. This is because there may not always be a geometrical arrangement of the four virtual point sources that realizes all the distances between the virtual point sources; therefore, the shorter distances between virtual point sources, that is, a higher BS correlation, are given priority to establish the coordinates of the virtual point sources. As a result, the arrangement of the virtual point sources may satisfy all the distances required to enable the desired 4 \(\times\) 4 correlation matrix. Accordingly, the arrangement of the virtual point sources, shown in Figure 8a, satisfies all the distances between the virtual point sources, resulting in the desired 4 \(\times\) 4 correlation matrix.  
The initial phase matrix for each BS antenna is created by calculating the geometric phase difference for the designed implementation model, as shown in Figure 8a, with reference to the initial phase matrix of BS #1. Therefore, the coordinates of the virtual point source for BS #1 is translated to the origin, as presented in Figure 8b. Using the above method, the arrangement of the virtual point sources to complete the 4 \(\times\) 4 correlation matrix, which is dependent on the arrangement of the BS antennas, the direction of the incident wave, and the angular spread of the incident wave, can be determined.
# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 4 Experiments  
### Self-Made Dataset  
The performance of the algorithm is affected by the quality of the dataset in the task of detecting overboard behavior. To the best of the authors' knowledge, publicly available datasets of crew overboard behavior are very scarce. We collected surveillance videos related to drowning accidents through various methods, and obtained 10 video clips, including people falling into the water from boats and lakeside and riverside railings. We organized experimenters to simulate crew overboard behavior, and generated our own overboard behavior dataset based on accident surveillance videos and simulation experiment data.  
In the simulation experiment, the height of the railings varies within the interval of [0.5, 1.5] meters. The positioning of cameras in the experiment is set at approximately 2 to 2.5 m from the ground, while the distance from the railing is varied within the range of [5, 10] meters. The falling behavior in the experiment includes both frontal and lateral falling in order to simulate the real crew overboard behavior. The simulation experiment obtains 1445 video clips, including 714 drowning behavior clips and 731 normal behavior clips, each lasting 2 to 7 s.  
Figure 5 shows our dataset, which includes combinations of different railings (types and heights) and shooting positions (distance, height, angle). The dataset has a total of 1455 video clips, with 724 clips documenting overboard behavior and 731 clips documenting normal behavior. We analyze the behavioral characteristics in the dataset and conclude that (1) the information captured by the camera mainly consists of the back and side views of crew members, (2) personnel information is affected by various occlusions and there are few extractable features, and (3) there are some differences in the behavioral characteristics of members in different scenes.
# A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
## 2 Methods
### Pre-Trained DNN Models for Plant Disease Identification
#### 2.2.2 ResNet  
Deep residual networks (ResNet) were proposed by [PERSON] et al. in [19], which have shown compelling performance and good convergence behaviors. The ResNet architecture accepts a \(224\times 224\) image as input. It consists of a stack of residual blocks, which are feed-forward neural networks with shortcuts (or skip connections). Shortcuts are connections skipping over some layers which are used to deal with the problem of vanishing-gradients as the network goes deeper. The ResNet architecture considered in this paper is ResNet-101.
## 2 Materials and Methods
### Analytics  
The gas composition was measured using a gas chromatograph (GC) (CTC Analytics model HXT Pal) equipped with a thermal conductivity detector (TCD) that determines the composition of the gases in terms of H\({}_{2}\), CO\({}_{2}\), O\({}_{2}\), N\({}_{2}\) and CH\({}_{4}\). Samples taken from the digesters were used for the measurement of dissolved oxygen (Hach, HQ40d-two camel digit multimeter, Hach Lange Spain, Barcelona, Spain), pH (pH-Meter BASIC 20+, Crison, Hach Lange Spain, Barcelona, Spain) and REDOX potential (pH-Meter, pH 91; Wissenschaftlich Technische Werkstatten, WTW), and every three days gas samples were obtained and analysed as described above. Data on the volume of gas produced were also collected. Since the feed and sample extraction are performed through the same port in the reactors, the first 100 mL were systematically discarded to avoid the possibility of entraining substrate residues. In addition, analyses of the extracted samples were performed to know the evolution of the VSs during operation. Finally, samples were retained for analysis of  
Figure 1: Schematic representation of the digesters, from left to right (R1, R2 and R3). R1 includes PLA fillers, R2 is an external bioelectrochemical cell and R3 is a normal digester as a control.  
volatile fatty acids (VFAs). VFAs were analysed using a GC (Bruker 450-GC, Billerica, MA, USA) with a flame ionisation detector (FID).  
Microbial communities were analysed by extractions at the end of the experiment from the digest in the case of R1 and R3 and from the electrode in the case of R2. The analysis was carried out by high-throughput sequencing of massive 16S rRNA gene libraries. Bacteria and total archaea were analysed. Genomic DNA was extracted with a DNeasy PowerSoil kit (Qiagen) according to the manufacturer's instructions. All polymerase chain reactions (PCR) were carried out on a Mastercycler (Eppendorf, Hamburg, Germany), and the product size of PCR samples was checked on a 1% agarose gel and quantified using NanoDrop 1000 (Thermo Scientific, Waltham, MA, USA). The whole DNA extract was used for high-throughput sequencing of massive 16S rRNA gene-based libraries with 16S rRNA gene-based primers for Bacteria 314F (5\({}^{\prime}\)-CCTACGGGAGGCAGCCAGCCAGCG-3\({}^{\prime}\)) and 518R (5\({}^{\prime}\)-ATTACCACCACCGCGGGGCTGGCTTGGCTTG-3\({}^{\prime}\)) and for Archaea 349F (5\({}^{\prime}\)-GYGCASCAGKCGMGAWW-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GGACTACVSGGGTATCTAAT-3\({}^{\prime}\)). Illumina sequencing was performed by Novogene (Cambridge, UK) using a HiSeq 2500 PE250 platform [32].
# Tunable and Switchable Erbium-Doped Fiber Laser Using a Multimode-Fiber Based Filter
###### Abstract  
We demonstrated a compact tunable and switchable single/dual-wavelength erbium-doped fiber laser. The fiber laser can be tuned and switched from single-wavelength to dual-wavelength oscillation by using our recently proposed tunable comb filter. The comb filter consists of a section of multimode fiber (MMF) coiled into a polarization controller and two sections of single mode fibers (SMFs) to form a \(\mathrm{SMF}/\mathrm{MMF}/\mathrm{SMF}\) structure, serving as a simple tunable all-fiber Mach-Zehnder interferometer. Due to the insertion of the MMF-based polarization controller (PC), an additional phase shift is introduced from the difference of the birefringence intensity in different dominant modes, which can be used to tune the fiber laser. In the experiment, by properly adjusting the PC, a tuning range of 9.3 nm can be achieved for the single-wavelength operation. Moreover, dual-wavelength operation with different free-spectral-ranges can be obtained. The tunable and switchable fiber lasers are of great importance for their applications in optical testing, optical fiber sensing, and signal processing.  
fiber laser; laser tuning; switchable; tunable optical filter +
Footnote †: journal: Applied Physics Letters
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 5 Conclusions  
In-field high-throughput plant phenotyping is essential to characterize crop populations accurately and efficiently. This study reports the development of CBM, a low-cost integrated sensor system with LiDAR to measure crop biomass and height in the field. Additionally, CBM can operate as an IoT device to enable remote and direct uploading of the captured data on a cloud platform for further processing and analysis. The CBM sensor was deployed on a wheat field trial to evaluate the accuracy and operational efficacy. The study achieved significant capabilities by developing a unique integrated sensor system that was assembled from widely available hardware resources and using open-source software packages. The system is promising for crop monitoring and estimating growth rate for the evaluation of genotypes. Overall, the CBM sensor produced a reliable performance in collecting phenotypic data in the field. The sensor provides researchers access to an affordable high-throughput digital technology with a complete end-end pipeline.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/bios12010016/s1](https://www.mdpi.com/article/10.3390/bios12010016/s1)]([https://www.mdpi.com/article/10.3390/bios12010016/s1](https://www.mdpi.com/article/10.3390/bios12010016/s1)), Table S1: List of primary parameters for the 3D printer. Table S2: Wheat genotypes used in the field trial.  
B.P.B. conceived the experiment, conducted the experiments, data analysis, and writing of the original draft. S.K. performed investigation, resources, writing, and supervision. S.K. and G.S. conducted project administration, funding acquisition, and manuscript review and editing. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We thank [PERSON] and [PERSON] for their help in managing the field trial and collecting the manual measurement of biomass harvest.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Results and Discussion
### Outlook of Naphtha, Plastic Products, and By-Products
#### 3.2.1 Plastic Products  
The plastic resin production was approximately 10,500.00 thousand ton in 2019. According to the Japan Plastic Industry Federation [40], plastic resin production has remained at the same level since 2009. This means that the domestic demand for plastic products remains high, and consumption has not been reduced. In future, the production volume should be reduced by reducing the use of disposable plastics. The global plastics market is growing steadily, with an annual growth rate of 3.4% for the projected period 2021-2028 [41]. High-performance materials such as conductive plastics are used in various high-tech products, and the demand might remain at a certain level in the future from the perspective of product weight reduction and energy saving of electronic equipment.  
Figure 4 shows the outlook of the supply and demand volume of plastic resin production. The figures in Table 2 were used for the supply transition. Trends in demand were estimated assuming that the annual growth rate (3.4%) indicated above would continue until 2050. Plastic resin production is expected to reduce to approximately 7511.00 thousand ton by 2030 and approximately 4084.00 thousand ton by 2050. Conversely, the plastic resin production from petroleum refineries in 2019 was approximately 10,110.00 thousand ton from Figure 3. If the demand volume is changed at the aforementioned annual growth rate, approximately 15,168.00 thousand ton of resin production from petroleum refineries is expected to be required by 2030. In 2050, 29,602.00 thousand ton is expected to be needed. This result indicates that a large output gap might occur in the plastic resin. Japan currently relies on imports for more than half of its demand for naphtha. Covering all shortfalls of naphtha and plastic resins by imports is difficult under the international commitment to fossil fuel reduction.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **2019** & **2020** & **2030** & **2040** & **2050** \\ \hline Petroleum treatment & & & & & \\ (thousand kL) & 177,048.00 & 171,737.00 & 126,643.00 & 93,389.00 & 68,868.00 \\ Plastic resin production & & & & & \\ (thousand ton) & & & & & \\ Hydrogen production & 14.02 & 13.60 & 10.03 & 7.40 & 5.45 \\ Hydrogen production \({}^{\star}\) & & & & & \\ (billion Nm\({}^{3}\)) & & & & & \\ Dry ice production & 157.00 & 153.00 & 113.00 & 83.00 & 61.00 \\ (thousand ton) & & & & & \\ Sulfur production & & & & & \\ (thousand ton) & & & & & \\ \hline \hline \end{tabular} \({}^{\star}\)Maximum hydrogen production when the hydrogen production plant is operated.  
\end{table}
Table 2: Outlook of petroleum treatment, naphtha, plastic products, and by-products.  
Figure 4: Outlook of supply and demand volume of plastic resin production.
# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
###### Abstract  
This study evaluates the efficacy of six artificial learning (AL) models--nonlinear autoregressive (NAR), long short-term memory (LSTM), adaptive neuro-fuzzy inference system (ANFIS), gated recurrent unit (GRU), multilayer perceptron (MLP), and CNN-RNN hybrid networks--for time-series data for failure prediction in aerospace assembly lines. The data consist of 45,654 records of configurations of failure. The models are trained to predict failures and assessed via error metrics (RMSE, MAE, MAPE), residual analysis, variance analysis, and computational efficiency. The results indicate that NAR and MLP models, respectively, achieve the lowest residuals (clustered near zero) and minimal variance, demonstrating robust calibration and stability. MLP exhibits strong accuracy (MAE = 2.122, MAPE = 0.876%, RMSE = 1.418, and ME = 1.145) but higher residual variability, while LSTM and CNN-RNN show sensitivity to data noise and computational inefficiency. ANFIS balances interpretability and performance but requires extensive training iterations. The study underscores NAR as optimal for precision-critical aerospace applications, where error minimisation and generalisability are paramount. However, the reliance on a single failure-related variable \"configuration\" and exclusion of exogenous factors may constrain holistic failure prediction. These findings advance predictive maintenance strategies in high-stakes manufacturing environments with future work integrating multivariable datasets and domain-specific constraints.  
Article  
1
## 2 Materials and Method
### Testing the Composite Hyroscopic Material
#### 2.1.4 Adsorption/Desorption Isotherm  
The adsorption/desorption isotherm is the basis of any dynamic analysis of moisture transfer and represents an important means of assessing the hygroscopic properties of materials. The isothermal adsorption curve is based on the moisture content of the test material according to ISO 12571 [31]. The experimental climate chamber is equipped with an oven. The temperature of the oven is controlled by an electric heater wire and the relative humidity is controlled by different saturated salt solutions. The specimen size is 100 mm \(\times\) 100 mm \(\times\) 20 mm. The specimens were dried at 105 \({}^{\circ}\)C to constant weight after being cured under standard conditions. Before each test, the cured specimens are placed in the climatic chamber for a test interval of not less than 24 h until the mass change of two consecutive measurements is less than 0.1%.  
After drying, the specimens are wrapped in plastic, cooled to room temperature and weighed. The specimens are then placed in the air above saturated salt solutions in 1-L glass jars until equilibrium is reached. The mass is weighed, and the specimens are placed in a jar with a different salt and the process is repeated with increments of about 10% RH up to 97% RH for the adsorption process. In the experiments, the temperature of isothermal adsorption is set as 20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, respectively. The hygroscopic properties of specimens are tested respectively in six kinds of humidity conditions. The equilibrium moisture content (\(u\)) at a given relative humidity is calculated as:  
\[u=\frac{m-m_{0}}{m_{0}} \tag{1}\]  
where \(m\) is the final mass of a specimen after equilibrium is attained between the specimen and the air in the jar at a particular relative humidity, in g; \(m_{0}\) is the mass of the dry specimen, in g.