esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing a Dead Fish Recognition Model Based on an Improved YOLOv5s Model
###### Abstract  
To address the challenges of low detection accuracy and high difficulty in dead fish identification due to water surface reflections, low contrast between targets and the environment, uncertain positions between detection sources and targets, and the impact of water mist blurriness, this paper proposes a dead fish recognition model named YOLO-DWM based on an improved YOLOv5s model. To address the weak feature extraction capabilities of existing convolutional modules for targets, a multi-scale convolutional module (DWMonv) based on depthwise separable convolution is designed, enhancing the detection performance for targets. To further improve accuracy, the EMA mechanism is embedded in the C3 module, enhancing its feature processing capabilities. Additionally, to reduce the model's parameters and FLOPs, a lightweight approach called C3-Light is introduced, which replaces the Conv convolution in the C3 module with a DWConv convolution. A total of 670 images of dead fish in a fish farm were collected prior to the experiment and used to train the model. The experimental results demonstrate that, compared with Faster RCNN, SSD, YOLOV3-tiny, YOLOv5s, YOLOv6n, and YOLOv8n, the YOLO-DWM model exhibits superior performance, with mAP increases of 8.13%, 12.3%, 5.9%, 4.5%, 4.5%, and 2.8% and increases in the F1 score of 17.5%, 7.5%, 2.7%, 2.6%, 3.0%, and 0.4%.  
Keywords:dead fish recognition; YOLO; deep learning; multi-scale convolutional; attention mechanism +
Footnote †: journal: Applied sciences
## 2 Bearing Failure Modes
### Acoustic Analysis  
Acoustic analysis, as an important supplement to vibration analysis methods [31], utilizes high-frequency acoustic signals to detect small defects and early faults. Similar to vibration analysis, acoustic characteristics such as the frequency and amplitude of the emitted sound waves vary depending on the failure mode and fault location of the bearing [32]. The sound wave signal is also highly susceptible to environmental noise interference, but due to its ability to collect high-frequency signals, it can eliminate low-frequency noise interference. The principle of acoustic analysis is shown in Figure 4.  
Commonly used acoustic detection techniques include ultrasonic testing and acoustic emission technology. The ultrasonic bearing detection method consists of emitting ultrasonic waves to the test object, and when it encounters a defect reflection, it detects its reflection signal to achieve diagnosis [33; 34; 35; 36]. Ultrasonic detection has high accuracy, fast speed, and is harmless to the human body and environment, but it is prone to scattering and loss in the air, requiring the use of coupling agents.  
Acoustic emission technology refers to when high-frequency signals emitted by materials during the process of bursting and crack propagation under stress and load are analyzed and detected through computer simulation technology [37; 38; 39; 40; 41; 42; 43; 44]. It can detect the fatigue state and structural health of materials, but it is characterized as a form of destructive testing with low accuracy.  
They are all suitable for detecting small cracks, but according to their characteristics, they are suitable for different detection needs and scenarios.  
Compared to vibration analysis, acoustic detection has certain advantages in sensitivity and detection range, particularly in environments with low background noise. However, acoustic signals are susceptible to interference from environmental noise, requiring effective denoising and filtering. Additionally, acoustic detection equipment is often expensive, and  
Figure 4: Principle of acoustic signal detection: The detection process generally consists of high-frequency signal acquisition, signal preprocessing, feature extraction, and fault diagnosis. First, high-frequency signals generated during operation are collected using acoustic sensors. After amplifying the signals, specific high-frequency components are extracted, and the acoustic signals are processed to extract feature parameters. These feature signals are typically used to identify specific fault patterns.  
the detection accuracy is dependent on experience, necessitating the use of complementary detection methods to enhance diagnostic precision.
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 4 Feudalistic Platooning
### Central Server  
The central server consists of a central data storage that contains information about platoons and their vassalages. Communication with the server is performed using LTE only. Thus, it is only available under coverage. The data storage may be located far off the roads. From the platoon perspective, lords are the only vehicles that interact with the central server. Lords ship data from their own vassalage to the central server. There, shipped data are stored within a data storage per platoon and per vassalage (see Figure 6). Furthermore, lords may order data from vassalages within their platoon or from neighboring platoons.  
_Shipping:_  
Lords collect vehicle data, i.e., CAMs and DENMs, from their own vassalage and send it in terms of a _FeudalMsg_ containing a single _VassalMsg_ (see Section 4.3). Shipped data are periodically refreshed in larger time intervals, compared to CAM dissemination, e.g., every two seconds.  
However, shipped data are not critical for the operation of the platoon, as the transmission of highly critical data from surrounding vehicles is ensured using ad hoc and C-V2X networks (see Section 4.2). Of course, the control policy needs to be selected appropriately: no data from vassalages other than the own and preceding vassalage are required. This ensures two decisive things: first, platoon operation is still possible without cellular coverage. Although cellular networks increase the performance of the platoon management, e.g., by allowing more advanced control policies that may include an arbitrary variety of preceding vehicles, they are not necessary for safe platoon operation. Second, the quality of service of ad hoc networks highly depends on data load. By transmitting non-crucial data over a different network, ad hoc networks are relieved and the overall platoon performance is increased.
## 4 Understanding Centralised Identity Systems
### SAML: Security Assertion Markup Language
#### 4.7.1 SAML: Technical Architecture  
The working mechanism and architecture of SAML involves several steps (see Figure 5):  
1. When a user seeks to access the resources, the SP will initiate an authentication request and redirect it to the IDP with a SAML request object containing the authentication context [37].
2. When the validation is completed, a SAML response is generated and returned to the user. This SAML answer specifies both the claim and the scope of access. The user automatically sends the claim to the service provider (SP), who validates digital signatures and allows access to services [16].
3. Each SAML assertion is digitally signed and the transactions between these parties (user, SP, and IDP) are all carried out over HTTPS, which makes the communication secure and prevents any eavesdropping or man-in-the-middle attacks [37].
4. The SP maintains each session to prevent the misuse of assertions, thereby mitigating the risk of session hijacking [37].  
Figure 5: Sequence diagram of SAML authentication.
## Materials and Methods
### Characterization of TOBC and TOBC/CD Hybrid Nanopaper  
_Degree of oxidation_: was determined by conductometric titration of the introduced carboxyl groups as described elsewhere [3; 42]. In brief 0.1 g of TOBC that was beforehand subjected to freeze-drying (\(-80\)\({}^{\circ}\)C, 30 Pa) was thoroughly disintegrated by continued magnetic stirring (1400 rpm, 15 min) in 52.25 mL of millipore-grade water. After addition of 2.75 mL 0.1 M hydrochloric acid, titration was conducted by adding a total of 5 mL 0.1 M NaOH at an increment of 25 \(\upmu\)L every 30 s. Precise dosing was accomplished using an automated titration apparatus 800 Dosing device connected to an 856 Conductivity Module equipped with an 801 Stirrer (all Metrohm, Herisau, Switzerland). The degree of oxidation as calculated according to Equation (2):  
\[DO_{COOH}=162\cdot(V_{2}-V_{1})\cdot c\cdot[w-36\cdot(V_{2}-V_{1})\cdot c]^{-1} \tag{2}\]  
where \(V_{1}\) and \(V_{2}\) represent the respective initial and final amounts of NaOH (in L), \(c\) the molarity of NaOH (in mol L\({}^{-1}\)), and w the dry weight (g) of the sample.  
_Tensile stress measurement:_ The prepared TOBC and TOBC/CD hybrid nanopapers (cf. above) were cut into stripes (5 mm \(\times\) 50 mm) using a pair of scissors. The stripes were clamped into a universal materials testing machine (Z020, Zwick Roell, Ulm, Germany) to record the respective mechanical response profiles towards tensile stress. The latter was applied at a constant rate of deformation (1 mm min\({}^{-1}\)); free clamping length was about 20 mm. Yield strength (\(R_{P0.2}\)) was defined as the stress at 0.2% plastic deformation.  
_Contact angle (CA) measurements_ on the TOBC reference and TOBC/CD hybrid nanopaper were conducted at room temperature and constant relative humidity of 55% using a CCD camera (Sony 93D, Model XC-77 CE, 2/3 inch CCD, 11 \(\times\) 11 \(\upmu\)m pixel size, Sony City, Minato, Tokyo, Japan) and white background lighting to provide better separation between the samples and the background. The droplets (3 \(\upmu\)L each) were applied by a manual dosage system (1 mL syringe, \(\upmu\)m screw driven dosage). Ten seconds after the droplet had been placed onto the surface of the samples, the water contact angle was measured using the DSA1 drop shape analysis software package (KRUSS Optronic GmbH, Hamburg, Germany).
## 2 Materials and Methods
### LC-MS/MS Analysis of Mycotoxins
Concentrations of individual mycotoxins in the calibration mixtures were in different ranges, for AFLB1 and AFLG1 from 0.002-1 ng mL\({}^{-1}\); for AFLB2 and AFLG2 from 0.0005-0.25 ng mL\({}^{-1}\); for OTA from 0.01-5 ng mL\({}^{-1}\); for FB1, FB2, HT-2, T-2, and ZEA from 0.1-50 ng mL\({}^{-1}\); and for DON, 3-AcDON, 15-AcDON, and NIV from 0.4-200 ng mL\({}^{-1}\). Calibration concentration in solvent corresponded to a 20 times higher value of mycotoxin content in a sample, expressed in \(\upmu\)g kg\({}^{-1}\). Internal standard, 13C17-AB1 (ILS), was added prior to the injection into the LC-MS/MS and was used to control the accuracy of calibration standard and sample extract injections. For quantitation, 1/x weighted, multi-point calibration curves were used. All calibration curves, developed using MultiQuant software, which were used to determine mycotoxin content, are presented in Figure S5 in the Supplementary Materials. The method was validated statistically and by recovery studies for linearity, precision, repeatability, and reproducibility. Validation experiments were performed by spiking the matrix control sample with the stock standard mixture and by using several European Reference Materials (ERM-BE375, ERM-BE376, ERM-BC717), produced and certified under responsibility of the Institute for Reference Materials and Measurements (IRMM) of the European Commission's Joint Research Centre (Sigma Aldrich, Steinheim, Germany). The results showed that recoveries were in the range of 70-120% and precision (RSD) \(\leq\) 20%. The method's suitability was confirmed in proficiency tests organized by TestQual Prodiiciency Testing Schemes (Murcia, Spain) and in the Check Sample Survey (CSS) organized by Romer Labs. The limit of quantification (LOQ) determination for mycotoxins was performed using a calibration approach recommended by \"Guidelines for the estimation of LOD and LOQ for the measurement of contaminants in feed and food\" [35]. The LOQ was estimated as equivalent to 3.3 \(\times\) LOD, based on a linear multipoint calibration curve including the lowest calibration level (LCL), characterized by R\({}^{2}\)\(\geq\) 0.998. For simplicity, however, the LOQ for all mycotoxins was assumed to be equivalent to the lowest calibration level (LCL) except for HT-2 and T-2, for which it was established as \(\upmu\)LCL. Consequently, the LOQ was set at 0.01 \(\upmu\)g kg\({}^{-1}\) for AFLB2 and AFLG2, 0.04 \(\upmu\)g kg\({}^{-1}\) for AFLB1 and AFLG1, 0.2 \(\upmu\)g kg\({}^{-1}\) for OTA, 20 \(\upmu\)g kg\({}^{-1}\) for FB1, 2 \(\upmu\)g kg\({}^{-1}\) for FB2 and ZEA, 1 \(\upmu\)g kg\({}^{-1}\) for HT-2 and T-2, 8 \(\upmu\)g kg\({}^{-1}\) for DON, 3-AcDON, 15-AcDON, and NIV.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 6 Models of Communication in the Internet of Vehicles
### Vehicle-to-Home (V2H)  
With the advent of vehicle-to-home communications, the integration and control of many smart home systems from a car is seamless and comfortable. Tasks like remotely controlling home climate control, security, lighting, and even recharging EVs can be accomplished with V2H connectivity. V2H can also function as a backup electricity source for homes by using the vehicle's energy storage system to supply power during an outage [85].  
On the way to smarter and more interconnected living environments, V2H brings greater convenience and energy efficiency to home and EV owners.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 2 Methodology
### Data Preprocessing
An ANN is effective in capturing nonlinear associations because of the establishment of layers and a flexible architecture, allowing the model to learn complicated relationships present in the data. This architecture is particularly efficient in forecasting time-series data, which is imperative in predicting energy consumption patterns over longer time intervals. Additionally, an ANN can have multiple input features and become amenable to different data preprocessing and feature engineering methods. The FUHEM dataset only includes 100 data points for regular buildings and can benefit from technical strength in the case of RF and an ability to resist overfitting in the model. Working off random samples, RF produces predictions based on a compounded decision tree, minimizing the risk of overfitting while promoting better generalization. This is especially important for our work, as the limited size of the FUHEM dataset offers a restrictive sample for modeling and we need to ensure that the prediction can be reliable while maximizing interpretability.
## 4 Project Examples
### Data Acquisition  
To effectively predict and analyze track and subgrade settlement deformation during construction, ensuring the safe and smooth implementation of the rectangular pipe-jacking operation, on-site monitoring has become an indispensable component of this project. Based on field conditions, the monitoring points are arranged as follows: a pier monitoring point is arranged at each end of the D24 type temporary beam, six monitoring points are arranged at equal intervals on the temporary beam, one group of monitoring points is arranged at an interval of 10 m for each track of the railway line, and five groups of roadbed monitoring points are arranged on the roadbed between the tracks. The schematic layout of monitoring points is shown in Figure 8.  
Field monitoring was conducted using an intelligent automatic monitoring method, with a fully automated measuring robot as the core data acquisition device. Figure 9 shows the on-site observation device diagram. The measuring robot employed a Leica TS30 Ultra-High Precision Total Station, featuring an angular measurement accuracy of 0.5 s and a distance measurement accuracy of 0.6 mm + 1 ppm. Data are automatically collected every hour. Due to the influence of weather and train obstruction during data collection, the data may be missing or abnormal. We use linear interpolation to fill these missing  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Name of Soil** & \begin{tabular}{c} **Characteristic** \\ **Value of Bear** \\ **Capacity/kpa** \\ \end{tabular} & \begin{tabular}{c} **Compression** \\ **Modulus/Mpa** \\ \end{tabular} & \begin{tabular}{c} **Cohesion /kpa** \\ \end{tabular} & \
\begin{tabular}{c} **Friction Angle/o** \\ \end{tabular} &
\begin{tabular}{c} **Void Ratio** \\ \end{tabular} \\ \hline Plain fill & / & / & / & / & / \\ Muddy and & 60 & 4.5 & 12.2 & 10.5 & 1.028 \\ Powdery clay & & & & & \\ Fine sand mixed & & & & & \\ with silt & 110 & 6.6 & 12.2 & 19.5 & 0.648 \\ Muddy clay & 60 & 2.8 & 10.9 & 8.6 & 1.217 \\ Muddy clay & 65 & 2.7 & 15.9 & 9.8 & 1.330 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil parameters table.  
Figure 7: Schematic diagram of a cross-section of a pipe jacking tunnel and an existing railway line.  
values. The data collected was then organized into a dataset for this study, resulting in a monitoring dataset consisting of 3212 samples. Each sample comprised 82 input parameters and one output parameter, both of which represented the displacement of the sample point in the direction perpendicular to the ground. The dataset follows the principle of using 70% of the data as the training set and 30% as the validation set.  
Figure 8: Schematic diagram of Monitoring Point.  
Figure 9: On-site observation device.
## 3 Results  
### Effects of Shading Treatments over Temperature and PAR Radiation  
Outside solar radiation was progressively decreasing in maximum radiation intensity and the number of solar hours, from crop transplanting (17 th September) to December. From December onward, outside radiation increased progressively until the end of the cropping season in May (Figure 7).  
The photosynthetic active radiation (PAR) inside the greenhouse was also studied. Figure 7 shows PAR radiation for the control treatment (0% shade). The variation of maximum radiation intensity and the number of exposition hours to PAR radiation showed similar behavior to the outside radiation description, with the lowest radiation in December and maximum in May.  
Figure 8 shows, for the studied period, the average daily evolution of the monthly reduction of PAR radiation produced by shading treatments (15%, 30% and 50%) in regards to the control treatment (0% shade). We could observe that generally, every month, PAR radiation reduction was proportionally higher than the shading surface in the roof-top. Specifically, the shading treatments of 15%, 30% and 50% of the shadowed surface in the roof-top resulted in a reduction in real PAR radiation for the crop of 28.8%, 46.6% and 66.3% respectively (Figure 9).  
Figure 7: Outside average monthly radiation (W m\({}^{-2}\)) evolution for the studied period (**left**). Average monthly photosynthetic active radiation (PAR) in the control treatment (0% shade) for the studied period (**right**).  
Figure 8: Average daily evolution of the monthly reduction of PAR radiation produced by shading treatments (15%, 30% and 50%) in regards to the control treatment (0%) shade for the studied period.  
The variation of the average monthly temperature between shadow treatments is shown in Figure 10. Results show that the highest average monthly temperature inside the greenhouse was registered in the control treatment (0% shade) for all the months studied, with an exception in January, where the maximum temperature was registered in the 50% shadow treatment. The highest temperature in the studied period was registered in May in the control treatment (0% shade) with 34.5 \({}^{\circ}\)C.  
The lowest inside greenhouse temperatures were registered before sunrise. In the studied treatments, the lowest temperature was registered in the 50% shade treatment for all the months of the period, with the exception of February, that were registered in the control treatment (0% shade) and in the 30% shade treatment. This February temperature (9.2 \({}^{\circ}\)C) was the lowest registered in the whole studied period.
# Multi-Objective Optimal Control Method for the 6-DOF Robotic Crusher
## 1 Introduction
Among them, the swarm intelligence optimization algorithm has attracted much attention because of its obvious advantages. The swarm intelligence optimization algorithm can take advantage of the advantages of groups, and provide a basis for finding solutions to complex distributed problems without centralized control and a global model. In recent years, the research direction has focused on multi-objective ant colony optimization [20], artificial bee colony algorithm [21], cat colony algorithm [22], and other swarm algorithms. In previous research [23; 24], the impact of specific material and operating parameters on the crushing products, crusher energy consumption, and liner wear. In order to achieve optimal crushing, the 6-DOF crusher can theoretically adjust its working parameters freely. Therefore, it is necessary to expand the research on the results of previous research [23; 24], study the performance of the crusher under different working conditions, and based on this, carry out optimization calculations to provide reference for real-time adjustment of the 6-DOF crusher. In this paper, the quality fraction of crushed products below 12 mm, total energy consumption, wear amount, energy efficiency, and output are taken as the performance indicators of the six degrees of freedom crusher. The cavity shape of the PYGB1821 cone crusher is taken as the research object, and the suspension point position, precession angle, and speed are variables under different working conditions. Combinedwith experimental data on feed particle size, the performance indicators of the 6-DOF crusher are researched under different working conditions. And the weight determination method of fuzzy multi-attribute decision-making problem is used to determine the weight of each crushing cavity depth wear. Furthermore, an optimization function was established. This function takes the above performance indicators as the optimization objectives, and the takes the above working conditions as the optimization variables. The weight determination method of fuzzy multi-attribute decision-making problem is used to determine the weights of different crushing chamber heights and optimization objective functions. Based on the experimental data of AnSteel Group, combined with the weight determination method of the fuzzy multi-attribute decision-making problem, the gray wolf optimization algorithm is used to solve the problem, and the optimization results of different crushing strategies and preferences are obtained. The model is universal, and the result can be used as an adaptive adjustment strategy for a 6-DOF robotic crusher. It can also provide a reference for the design of a cone crusher with a traditional structure.