esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiments  
### Sample Preparation  
(1) The preparation steps of 3D-printed and 3D-printed + modified samples are as follows: firstly, according to the design criteria of the superhydrophobic cylindrical array structure, use 3D Solidworks software to design the cylindrical array model with a cylinder diameter \(d\) of 180 \(\upmu\)m, gap between adjacent cylinders \(s-d\) of 280 \(\upmu\)m, and the cylinder height H of 400\(\upmu\)m, and export the STL format. Secondly, import the STL model into the slicing software (Magics Print Metal for HanBang). Then, use an HBD-80 metal 3D printer (Hanbang Laser Technology Co., Ltd., Zhongshan, China) to print and obtain 3D-printed samples (laser power of 160 W, scanning speed of 1000 mm/s, scanning layer thickness of 30 \(\upmu\)m, the scanning strategy is strip scanning, the shielding gas is argon, and the printing material is 250 mesh 316L stainless steel powder (Hanbang Laser Technology Co., Ltd., Zhongshan, China)). The above process is shown in Figure 1. Then, soak the sample in the anhydrous alcohol solution of fluoro-silane (Aladdin Reagent Co., Ltd., Shanghai, China). Take out the sample after soaking for 12 h, and finally put it into a DHG-9075A constant temperature blast drying oven (Yiheng Scientific Instrument Co., Ltd., Shanghai, China) at 90 \({}^{\circ}\)C for 2 h to obtain 3D-printed + modified samples. Fluoro-silane easily forms hydrogen bonds with the hydroxyl groups on the surface of the sample, which greatly improves the hydrophobicity of the surface and prevents direct contact between the surface and the outside world.  
(2) The preparation steps of 3D-printed + corroded samples are as follows: Firstly, use Solidworks software to establish the cylindrical array microstructure model, and the characteristic dimensions of the cylindrical microstructure are the same as above. Slice the established 3D model and set the parameters as above. Import the sliced file into the 3D printer and start printed after finishing the pre-processing steps such as powder spreading. Then, put it into the ferric chloride dislocation etching solution(pH < 2) of 75 mL deionized water (Hugke Water Treatment Equipment Co., Ltd., Shenzhen, China), 25 mL hydrochloric acid (Spartan Chemical Co., Ltd., Dongguan, China), and 10 g of anhydrous ferric chloride (Aladdin Reagent Co., Ltd., Shanghai, China) for 5, 10, 15, and 20 h, respectively, then take it out using deionized water for ultrasonic cleaning 10 min, and finally put it in a DHG-9075A constant temperature blast drying oven (Yiheng Scientific Instrument Co., Ltd., Shanghai, China) at 90 \({}^{\circ}\)C for 2 h to obtain 3D-printed + corroded samples.  
(3) Because 316L stainless steel itself has good corrosion resistance, in order to compare with the above samples, the polished samples are 316L plates bought on the market and cut into blocks of size (length \(\times\) width \(\times\) thickness) 10 \(\times\) 10 \(\times\) 4 mm, with high density and few defects.
## 2 Results and Discussion
### Aromatic Profile Analysis
(\(\pm\))-\(\beta\)-Citronellol, linalool, limonene, isoamyl acetate and ethyl hexanoate were present at concentrations above the perception threshold (OAVs > 10). 3-Methyl-1-butanol, nonanal, geraniol, (Z)-3-hexenyl acetate, ethyl butyrate, ethyl caproate, hexenyl acetate, ethyl decanoate and phenylethyl acetate were also present at concentrations above the perception threshold (OAVs > 1). An aromatic series can be defined as a group of volatile compounds with similar odor descriptors. Based on its odor descriptors, each compound was assigned to one or more aromatic series: solvent,floral, sweet, green (vegetal or herbaceous), fatty or fruits, which were chosen because these terms are commonly used to describe young wines [28]. The value of each aromatic series was obtained as the sum of the OAVs of the compounds in the series. Therefore, the contribution of a specific compound to each series can be determined. This procedure allows the quantitative information obtained by chemical analysis to be related to sensory perception. The impact of different fermentation technologies on the OAVs of the odorant series for Muscat Hamburg wine is summarized in Figure 1. The fruity and floral series had the greatest impacts on the OAVs of the wine, followed by the sweet and green series. The solvent and fatty series were not detected in the wines. The contributions of all the odorant series were significantly different in the different wines (\(p<0.001\)). The OAVs of the Muscat Hamburg wine made by carbonic macceration were significantly higher than those of the wines made by classical white- and red-winemaking processes (\(p<0.001\)). Carbonic macceration exposes intact grape berries to an oxygen-deprived environment, allowing enzymatic fermentation to occur within the berries, typically resulting in richly aromatic wines with a fruity bouquet and palate softness [11]. These results provide clear evidence that carbonic macceration can be adopted by wineries to obtain Muscat Hamburg wines with superior quality.
## 4 Numerical Simulation and Experimental Work  
The capacitive probe is the key part of the instrumentation and requires to be designed carefully. For sensors with a constant overall electrode area, the penetration depth increases with the distance between the electrodes. A larger distance between electrodes will however decrease the signal strength (the measured output) as the coupling between the driving and sensing electrodes would be weaker and would reduce the image resolution as they are sampling a larger area of the sample and more importantly it is physically disallowed if the overall probe size is constant. To balance these trade-offs, triangular-shaped electrodes can be applied [20]. This electrode geometry is an interesting choice for different defect detection in various material as it would likely allow deep penetration into the specimen, with a symmetrical electric field distribution. As stated in the previous section, the electric field distribution is a function of the electrode geometry and so this aspect has to be designed with care [5]. The coplanar electrodes with the same overall sensing areas are used in this work, as shown in Figure 3, namely capacitive probes with back-to-back triangular electrodes. Generally, this pair of triangular electrodes could be specified by the overall size, the base (b), the height (h) of each triangle and the separation distance between the closest points of the two triangles (s). In this work, the triangular electrodes have the same overall size with b = 15.0 mm, h = 15.0 mm and s = 4.0 mm. In addition, a shielding plate and guard electrode are employed for this coplanar capacitive sensor. Note that the field distribution predicted by the finite element (FE) model, as discussed below, is determined primarily by these geometric factors in the model.
# Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
## 2 Materials and Methods
### Inference Workflow for Volumetric Data-Sets
#### 2.3.1 Adapting SAM for Volumetric Data-Sets
We either combined all slice within the current field of view regardless of their content or stopped at the first empty slice, i.e., a slice without foreground voxels. We also tested various rules based on different thresholds of overlap or Intersection over Union (IoU) between the proposed segmentation of the current slice and the preceding slice or a foreground volume obtained through global Otsu thresholding followed by a morphological closing step. As an optimization strategy, slice-wise prediction was performed in an alternating manner, starting from the centre of the current sub-volume and moving outward slice-wise in both directions. This approach was implemented to save computational time and prevent the segmentation of unconnected segments, ensuring that only cohesive regions were accurately identified. In situations where the segmentation results in an identification of unconnected segments, the algorithm may inadvertently continue segmenting entire regions composed of non-cohesive segments. This phenomenon occurs when the segmentation quality is significantly compromised. During the subsequent hyperparameter search, we also permitted segmentations without applying these rules. However, it appears that these deviations have only minimal impact on the output quality. Subsequently, a new target volume is constructed. Voxels are included in the output volume if they are segmented as the foreground in at least one and depending on the configuration, up to three slice-wise predictions. Additionally, we employed post-processing techniques such as slice-wise and volume-based median filtering and CCA prior to and after merging the slices into volumes to smooth scattered and miss segmented voxels. We also conducted experiments with different variants of SAM's outputs. Since SAM has the ability to generate multiple outputs per prompt, such as separating a backpack from a person wearing it, we investigated whether selecting any of these outputs could improve the segmentation quality. Specifically, we examined whether it is better for volumetric segmentation to use the segmentation proposal provided by SAM with the highest probable IoU or the one with the maximum IoU of the approximated foreground volume. Additionally, as SAM often tends to under-segment and include background or neighbouring segments as part of the foreground, we investigated whether selecting the output with the smallest count of voxels among the multiple outputs would improve the segmentation quality. In this context, experiments were conducted using both the binarized output of SAM and the raw probability values, which are available at a lower resolution than the binary mask. After upscaling, different threshold values can be applied to the probability outputs for further processing and experimentation.
# Efficient IoT-Based Formal Model for Vehicle-Life Interaction in VANETs Using VDM-SL
## 3 Methodology
### Architecture of the System  
Many design decisions made in nature are related to system architectures; these decisions are generally commercial and nontechnical. We are building an efficient IoT-based formal model for vehicle-life integration allowing fog-based roadside units (RSUs) with the use of several techniques such as VANETs, graph theory, UML, as well as VDM-SL formal methods in this study. Figure 2 depicts a flow of the proposed techniques: After analyzing the system's requirements, IoT will be adopted in which sensors will be used to monitor the real-time environment, and actors will be used to take action in case of violation. Then UML diagrams will be used to describe the informal behavior of the model. Because of its use as a data format and computing capability, graph theory is frequently used. VDM-SL may also be used to turn a graph-based model into formal specifications. The formal model will be converted into a verified model using the VDM-SL toolbox. Two separate modules are incorporated in our suggested model: a unicast routing module for V2V communication and an emergency service module.
# N-Trans: Parallel Detection Algorithm for DGA Domain Names
## 4 Experiment and Analysis
### Experimental Results and Analysis
#### 4.3.1 Evaluation Indicators  
Accuracy (_Acc_) was used in the experiments to evaluate the classification accuracy of the algorithm for domain names, and _recall_ was used to evaluate the model's classification of malicious domain names. The accuracy formula is shown in Equation (8).  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN}, \tag{8}\]  
where _TP_ denotes the number of malicious domain names predicted to be malicious, _FN_ denotes the number of malicious domain names predicted to be legitimate, _TN_ denotes the number of legitimate domain name predicted to be legitimate, and _FP_ denotes the number of legitimate domain names predicted to be malicious.  
The formula for calculating the _recall_ is shown in Equation (9).  
\[recall=\frac{TP}{TP+FN}. \tag{9}\]  
From Equation (9), we can see the total number of malicious domain names in the dataset, so the _recall_ can calculate the model's effectiveness in detecting malicious domain names. The detection effects of different network models will also be compared using the _precision_ and _F1-score_.
# A Strong Noise Reduction Network for Seismic Records
## 3 Results and Discussion  
### Dataset  
The data for this study were sourced from the STEAD dataset, with a sampling rate of 100 Hz and a length of 60 s. The STEAD dataset contains over two million three-channel waveform recordings covering seismic activity and environmental noise on a global scale. We randomly selected 25,000 instances of high signal-to-noise ratio seismic signals and various types of noise signals from them. These selected signals and noise were normalized by subtracting the mean and dividing by the standard deviation. Subsequently, the noise was randomly scaled within a predefined range and combined with the seismic signals to generate 25,000 low SNR seismic signals, all with SNRs below 0 dB. These noisy signals were then randomly allocated to a training set, validation set, and test set in an 8:1:1 ratio. Figure 2 illustrates a set of clean signals (Figure 2a), noise (Figure 2b), and noisy signals (Figure 2d). Additionally, it presents the denoised signal (Figure 2e) and the noise (Figure 2f) output by the model. Each subfigure's left image depicts the time domain plot of the respective signals, while the right image shows the spectrograms produced by applying a short-time Fourier transform to the signals. Figure 2c shows the masks for the clean signal and the noise.  
Figure 2: The left panels in (**a,b,d–f**) display the clean signal, noisy signal, denoised signal, and recovered noise, respectively, in the time domain. The right panels present the corresponding spectrograms. (**c**) The mask of the signals and noises depicted in (**a,b**).
# The Car-Following Model and Its Applications in the V2X Environment: A Historical Review
## 3 Research on Car-Following Behavior in the V2X Environment
### Modelling the Car-Following Behavior
#### 3.2.3 Information of Multiple Following Vehicles  
Driving experience suggests that drivers will pay attention to the motion state of vehicle(s) in the back when they are in the car-following process. Keeping focus on the vehicle(s) in the back while driving in the car-following process, it is much easier to obtain the motion state information of vehicle(s) in the back with the V2X environment. [PERSON] et al. firstly considered the following vehicle of the object vehicle, defined the behavior of the driver paying attention to the vehicle(s) in the back during car-following as the \"Back Looking Effect\", and extended the OV model to the Black Looking Optimal Velocity (BLOV) model [53]. Limited by attention and energy, it is hard for drivers to keep focusing on the motion state of vehicle(s) in the back. In the V2X environment, the driver of the object vehicle can not only obtain the motion state information of preceding vehicles in the current lane but can also obtain some kinds of information which are difficult to obtain or successively obtain without V2X technology, such as the information of backward vehicles. Thus, it is
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 4 Depth Distribution Algorithm Based on Linear Block Codes
### Depth Distribution Algorithm Identifies Bit Rate and Generation Matrix  
After the code word starting bit and code length are identified by the improved matrix analysis based on Gaussian elimination, the code rate \(t\) and matrix \(G\) are further identified by the depth distribution feature. The method of solving the code word depth value is described first. In this paper, a codeword \(e=(1001011)\) of \((7,3)\) cyclic codes on \(GF(2)\) field is taken as an example in Figure 4:  
Figure 3: Flow diagram of algorithm.  
A differential algorithm is consequent minus referred to in the preceding paragraph in the mathematical theory, and in the \(GF(2)\) for a binary linear block code is the binary addition of adjacent operations, which means every two adjacent elements add to each other in \(c\) to get a new code word, and a loop operation until the code word element is all of 0 and the emergence of a new code word number is the depth value. The depth value of code word \(c\) is calculated to be 5.  
According to the example, we conclude that the depth value of the random code word \(n\) with the generated length of random sequence is 0 1 2 \(\ldots\)\(n\), and its depth distribution is \(\left\{2^{0},\,,2^{0},\,,2^{1},\,,2^{2},\,\ldots,\,,2^{n-1}\right\}\). According to Theorem 1, the depth distribution of linear block codes can be known. In view of the noise in the actual situation, the algorithm in this paper can still achieve the bit rate and generation matrix under the condition of code error.
Modeling of Unmanned Aerial Vehicles for Smart Agriculture Systems Using Hybrid Fuzzy PID Controllers  
[PERSON]  
1 High School of Automation and Robotics, [PERSON] Saint Petersburg Polytechnic University, 195202 Saint Petersburg, Russia 1  
drones to date. Researchers have focused on achieving the best controller performance for UAV systems in order to smooth stability issues. Among the frequently used controllers for this purpose are PIDs. However, due to their limited adaptability, sensitivity to noise, lack of robustness, limited multi-objective control, and computational complexity, PID controllers are not well suited to stabilizing the desired parameters of UAVs [4; 5; 6; 7].  
Although PID controllers have stability problems, it is possible to combine them with intelligent controller algorithms (fuzzy logic controllers). This combination would be less sensitive to parameter variations and disturbances, making it more robust and reliable in various operating conditions. Furthermore, fuzzy PID controllers can be easily tuned and adapted to changing system dynamics, providing better control performance over a wider range of operating conditions. These advantages motivated the authors of this paper to conduct a study on this topic.  
The aim of this study is to model an UAV system for agricultural fields. The UAVs collect images and other information about the agricultural status first, which is processed by the controller. The process related to the UAVs is the pre-processing; the process related to the controller is the solution. Sometimes, UAVs cooperate with ground robots in order to facilitate this process quickly. In this case, different combinations of different vehicle platforms may be used to complete different tasks.  
The innovation presented by this paper is that it uses two control methods to combine different control schemes that can be applied to the UAV robot system. In order to observe the UAV state performance, namely to stabilize, regulate, and optimize the process of agricultural field activities, the mechanism of combining different control algorithms is established. This will lay the foundation for future UAV robot control applications. Future UAV robot control applications can operate according to the new combination of different control scheme concepts established in this paper.  
In order to achieve this, a fuzzy PID controller is essential. Since the controller continuously measures the drone's location, orientation, and other pertinent parameters, compares them to the desired values, and modifies the control inputs as needed to achieve the goals, a mathematical model was developed. Then, the entire model was simulated using the Simulink/MATLAB platform. The remainder of this paper is structured as follows: Section 2 describes the literature review; Section 3 carries out mathematical modeling and control design of the quadcopter for smart agriculture applications; Section 4 presents and discusses the results of the simulations; and Section 5 presents some conclusions.
# Formal Analysis of Ratchet Protocols Based on Logic of Events
## 2 Logic of Events Theory
### Fundamental Concepts
#### 2.2.2 Strong Authentication Properties of Protocols  
Principals [PERSON] and [PERSON] (hereafter referred to as [PERSON] and B) are both honest participants who abide by the agreement. Principal A initiates a sequence of conversations, and Principal  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Notation** & **Semantics** & **Example** \\ \hline ID & Identifier of protocol participant & A, B \\ Atom & Unpredictable information & nonce, key, \\  & & signature \\ Data & Message content or plaintext & “Hello”, session key \\ e & An individual event & Encrypt(e) \\ E & A set of events & E = \{e_{1}, e_{2}, e_{3}\} \\ nonce & Random number generated in protocol & r = New(e) \\ has & Predicate indicating data is contained in event & e has a \\ bs & Basic event sequence for a participant & I1, R2 \\ \textless{} & Causal ordering of events & e\_{1} \textless{} e_{2} \\ loc(e) & Principal executing event \(e\) & loc(e) = A \\ key(e) & Cryptographic key used in event \(e\) & key(e) = PrivKey(A) \\ New(e) & Random atom introduced by event \(e\) & New(e) = r \\ Send(e) & Sending message event & Send(e) = “msg” \\ Rev(e) & Receiving message event & Rev(e) = “msg” \\ Encrypt(e) & Encryption event with message, key, atom & Encrypt(e) = \(\langle m,k,a\rangle\) \\ Decrypt(e) & Decryption event corresponding to Encrypt(e) & Decrypt(e) = \(\langle m,k,a\rangle\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic notations and semantics in Logic of Events theory (LoET).  
B responds with a corresponding sequence of replies, forming a legally matching session. If Principal B completes an instance of the full reply sequence, then a matching session exists with Principal A--meaning the content of the received messages matches the corresponding sent messages. Both parties complete identity authentication, ensuring that no attacker can launch a camouflage session attack using previously intercepted messages. The matching session process satisfies the strong matching property. The basic sequence \(bs\) in protocol \(Pr\) authenticates \(n\) messages, and the authentication process satisfies the Equation (3)  
\[\forall Pr\vDash auth(bs,n)\equiv_{def}\forall A,B.\forall thr_{1}.\]  
\[\left(\begin{array}{c}Honest(A)\wedge Honest(B)\wedge\forall Pr(A)\wedge \forall Pr(B)\\ \wedge A\
eq B\wedge loc(thr_{1})=A\wedge bs(A,B,thr_{1})\end{array}\right) \tag{3}\]  
\[\Rightarrow\exists thr_{2}.loc(thr_{2})=B\wedge thr_{1}\overset{n}{\approx}thr_{2}\]
# Mapping Gaps in Sugarcane Fields in Unmanned Aerial Vehicle Imagery Using YOLOv5 and ImageJ
## 2 Literature Review
### Related Works  
In [14], the authors used the linear discriminant analysis (LDA) technique to assess the quality of a sugarcane crop on a farm in Nicaragua, achieving an overall accuracy of 92.9%.  
In [11; 12], the authors tested the performance of the Inforow software ([[https://inforow.com.br/en](https://inforow.com.br/en)]([https://inforow.com.br/en](https://inforow.com.br/en))) in detecting planting failures using pixel sizes of 3.5, 6.0, and 8.2 cm in an experimental field with plant heights of 0.5, 0.9, 1.5, 2.0, and 2.5 m. The software was unable to identify planting failures smaller than 1.0 m when using pixel sizes larger than 3.5 cm. Even with the highest spatial resolution of 3.5 cm, the software could not detect planting failures between 0.5 and 1.0 m for sugarcane plants that were 0.9 m or taller.