esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Recognition and Classification of Farmland Pests Based on Improved Yolox-Tiny Algorithm
## 3 Methods
### Analysis of Detection Accuracy of the Original Yolox-Tiny Algorithm Model  
The original Yolox-tiny model demonstrates low recognition accuracy in identifying and classifying agricultural pest images. Analysis of pest images and the model structure reveals the following main reasons for insufficient accuracy:  
1. Significant size variation exists among different types of pests in agricultural images. For example, the Rice Leafroller is notably small, whereas the Turnip Moth is substantially large;
2. Some images feature overly complex backgrounds with numerous dried leaves and weeds, which often match the coloration of the target pests;
3. The Yolox-tiny model structure incorporates three downsampling operations, potentially leading to information loss about small-sized target pests and decreasing recognition accuracy;
4. The absence of an attention mechanism in the Yolox-tiny model makes it susceptible to interference from non-target objects in images, resulting in incorrect identifications.  
To address these issues, this paper proposes enhancements to the Yolox-tiny model, including optimizations in image preprocessing and algorithmic structure, enabling more accurate identification and classification of agricultural pests.
# LTDA-MAC v2.0: Topology-Aware Unsynchronized Scheduling in Linear Multi-Hop UWA Networks +
## 2 Linear TDA-MAC
### Greedy Optimization Algorithm
```
1:Create \(\mathbf{\mathcal{N}}\) via initial network discovery
2:Set the desired guard interval \(\tau_{\text{g}}\) and time step \(\tau_{\text{step}}\)
3:Initialize collision-free schedule \(T_{\text{tx}}\) using (5)
4:for\(i\in\{1..N_{\text{sn}}\}\)do
5:for\(n\in\{1..\left(N_{\text{sn}}-i+1\right)\}\)do
6: Calculate the packet index \(k=n+i-1\)
7: Calculate \(T_{\text{m}}[n,k]\) using (3) if \(n=k\), or (4) if \(n\
eq k\)
8: Initialize Tx delay: \(T_{\text{tx}}[n,k]=T_{\text{m}}[n,k]\)
9:while\(n_{\text{col}}\left(\mathbf{\mathcal{N}},T_{\text{tx}},\tau_{\text{g}}\right)>0\)do
10: Increment Tx delay: \(T_{\text{tx}}[n,k]\gets T_{\text{tx}}[n,k]+\tau_{\text{step}}\)
11:endwhile
12:endfor
13:endfor
```  
**Algorithm 1** The proposed greedy optimization algorithm for LTDA-MAC scheduling  
The Tx delay matrix is initialized as follows:  
\[\forall n,k\in\{1..N_{\text{sn}}\},\,k\geq n,\,T_{\text{tx}}[n,k]=\left(N_{ \text{sn}}n+k\right)T_{\text{large}}, \tag{5}\]  
where \(T_{\text{large}}\) is an arbitrarily long time interval, e.g., we use \(T_{\text{large}}=10^{6}\), that makes sure that the initial Tx delay matrix is collision-free. It also allows every Tx delay in \(T_{\text{tx}}\) to be optimized separately and replace its initial arbitrary value without affecting the rest of the schedule.
## 3 Results and Discussion
### Evaluation of Volatile Aroma Compounds
#### 3.1.9 Benzenoids  
No significant differences in benzaldehyde concentration were observed in young wines (Table 1). Significant increases in benzaldehyde with longer maceration durations have been observed previously [13; 61], while short macerations had no notable effect [62]. Enzymatic hydrolysis and the formation of vinylphenol via cinnamate esterase activity also contribute to increased benzenoid concentrations [63]. Benzaldehyde levels are linked to benzyl alcohol and may result from yeast oxidation of amino acids or from precursors like glycosides or phenolic compounds [64]. Consistent with this study, no significant differences in benzaldehyde concentrations with pre-fermentation heating treatments were observed previously [19]. Conversely, a decrease in benzaldehyde with pre-fermentation heating has been reported [31]. The increase in benzaldehyde after aging is likely due to oxidation processes [65]. Benzaldehyde concentrations increased after aging in TM21-A and TPHT-A treatment wines, with the highest value in the latter. Higher benzaldehyde levels in wines aged in oak barrels compared to stainless steel tanks have been observed previously [53], which was attributed to lignin decomposition [66]. However, no significant changes in benzaldehyde concentrations after 12 months of maturation in oak barrels have also been reported [37].
## 5 Performance Evaluation
### Simulation Results
#### 5.3.2 Impact of Bit Error Rate (BER)  
We investigate the impact of wireless link bit error rate on the performance of the protocols considering various metrics as shown in Figure 5. In this study, we set the data generation rate as \(1.5\) packets per second.  
Figure 5: Performance Comparison for different Bit Error Rate. (**a**) Average End-to-End delay varying Bit Error Rate; (**b**) On Time Packet Packet Delivery Ratio varying Bit Error Rate; (**c**) Reliability varying Bit Error Rate; (**d**) Average Temperature Rise varying Bit Error Rate; (**e**) Average Energy Consumption varying Bit Error Rate.  
Examining Figures 4 and 5, it is evident that bit error rate influences significantly on the performance metrics compared to traffic load. The reason is that, high bit error rate increases the number of retransmissions that in turn increases the end-to-end latency and diminishes the reliability to a great extent.  
As a non-QoS-aware protocol, with the increasing bit error rates, both TARA and ALTR show considerably poor performance in average end-to-end latency, on-time packet delivery ratio and reliability as shown in Figure 5a-c respectively. However, as depicted in Figure 5d,e, ALTR excels all the protocols in average temperature rise and average energy consumption even in high bit error rate due to its selection of least temperature node as a routing metric, and involving less number of nodes in packet transmission through exploiting the shortest path routing after a certain hop-count threshold. Although TARA exploits temperature as its routing metric, but during poor channel condition, it shows the worst performance in temperature rise and average energy consumption as it engages more nodes in packet transmission due to its withdrawal strategy and more retransmissions occurred at higher bit error rate.  
Due to the dependence on global view for route selection, TMQoS exhibits poor performance at higher bit error rates than TLQoS in all the performance metrics as shown in Figure 5a-c,e. With the increasing bit error rate, especially when it exceeds \(10^{-3}\), the convergence time increases considerably for TMQoS resulting inaccurate routing information that boosts the end-to-end latency, average temperature rise and average energy consumption, and decreases the on-time delivery ratio and reliability considerably. Because of employing localized and hybrid potential based greedy approach in route selection, TLQoS comparatively shows better performance than TMQoS in all performance metrics during poor channel condition. However, at a very high bit error rate of (>10\({}^{-3}\)), a sharp decline is also observed in case of on-time delivery ratio and reliability performance for TLQoS since, during this channel condition, the packet loss rate for all the nodes increases significantly.
## 4 Markov Detection and Prediction (MDP) Model
### MDP Model
#### 4.2.1 Defining States Using Markov Chains  
A state refers to the likelihood of a system being in a particular state, which can be estimated using data-based models, sensor technology, or machine learning techniques [38].  
The MDP involves three steps; therefore, in the first step, we use the Markov chain to define the sequence of possible events for different requests (Authentic, Suspicious, and Malicious requests) by turning any possible incident into different states and probabilities that identify this incident.  
The MDP model is designed to support M2M traffic. The MDP model is characterized by the following properties:  
* Initial phase (\(i=j=0\)).
- Authentic phase (\(0<i+j\leq C_{1}\)).
- Suspicious phase (\(C_{1}<i+j\leq C_{2}\)).
- Malicious phase (\(C_{2}<i+j\)) or (\(D_{R}\geq 1\)).
* Transition Probabilities: For each pair of states, there is a probability of transitioning from one state to another in one time step.
* Balance equations, also known as the equilibrium equations or steady-state equations. These equations are based on the principle that the inflow of probabilities into a state is equal to the outflow of probabilities from that state in the steady-state. In other words, the probabilities do not accumulate or deplete over time in equilibrium states.  
In an MDP model, any request is classified by its nature and categorized in one of the three types:  
* Read Request (\(R_{R}\)) denoted by the variable (\(i\)).
* Modify Request (\(M_{R}\)) denoted by the variable (\(j\)).
* Delete Request (\(D_{R}\)) denoted by (\(D_{R}\geq 1\)).  
The two traffic streams \(R_{R}\) and \(M_{R}\) are characterized by two average arrival rates (\(\lambda_{i}\), \(\lambda_{j}\)), respectively, which are assumed to conform to a Poisson distribution. Meanwhile, the two service rates (\(\mu_{i}\), \(\mu_{j}\)) are assumed to follow an exponential distribution.  
The transition between states in the system is possible upon the occurrence of an event (increase or decrease of \(i\) or \(j\)). The Initial phase represents the start of our system (\(i=j=0\)), while the Authentic phase represents the normal cycle of our system (\(0<i+j\leq C_{1}\)), where \(C_{1}\) is the threshold of Authentic phase. As for the Suspicious phase, it represents doubtful requests where (\(C_{1}<i+j\leq C_{2}\)), where \(C_{2}\) is the threshold of Suspicious phase. Finally, in the Malicious phase, there is a clear evidence of harmful intents or actions (e.g., deleterequests (\(D_{R}\geq 1\)) or a huge and unusual traffic that exceeds the threshold \(C_{2}\) (\(C_{2}<i+j\)), as shown in Figure 4.  
Assuming that \(C_{1}=2\) and \(C_{2}=3\), Figure 5 illustrates the MDP model with four phases: Initial, Authentic, Suspicious, and Malicious.
## 2 Methods: Simulations with GATE
### Definitions  
This subsection defines several terms that are used throughout this work. Any references to outputs of the simulation, such as \"simulated location data\" and \"simulated data\", refer to the location data that were derived from the lines of response produced by the GATE simulation of the HR++ scanner. The location data were not produced directly by the simulation; instead, PEPT-ML was used to derive the locations from the simulated lines of response. Equivalently, \"experimental location data\" or \"experimental data\" refer to the location data that were derived from the lines of response recorded by the HR++ scanner during physical experiments. The terms \"tracked location\" and its time series equivalent \"tracked path\" can refer to the location data derived from either the experimental or simulated lines of response. This is also used generally to define metrics, and if a specific application was required, this is given. The \"true\" path refers to the ground truth path, which is used as an input to the simulation. It does not relate to the use of \"true\" in \"true fraction\" or \"true coincidence\", which refer to the pairing of gamma rays to define coincidences when recording listmode data with the PET scanner. The term \"PEPT measurement\" is reserved for general application: it can be used for both experimental and simulation applications and refers to the entire process of obtaining an output location or series of locations, from the recording of the coincidence data to the derivation of a location.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Crystal Quantum Efficiency & 0.88 \\ Minimum Crystal Energy Resolution Blurring & 0.20 \\ Maximum Crystal Energy Resolution Blurring & 0.30 \\ Crystal Reference Energy & 511.0 keV \\ Paralysable Block Deadtime & 700.0 ns \\ Coincidence Window & 12.0 ns \\ Coincidence Multiples Policy & KeepIfAnyIsGood \\ Minimum Sector Difference & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Digitiser parameter values used by the HR++ GATE simulation [41; 42].
## 5 Evaluation
### Datasets
#### 5.1.2 Rule-Based Gold Standard Dataset  
The creation of the rule-based gold standard dataset has been guided by a qualitative analysis of misclassifications obtained from the skill classifier. An analysis of these classification errors revealed that, in many cases, humans could apply simple rules to assign these skills to the correct class. Therefore, we asked domain experts to identify rules that help classify these skills. These rules have been used to create a separate dataset that aims to identify mistakes that are particularly obvious to humans. Tables 10 and 11 provide examples of these expert-created classification rules.  
Based on these rules, we created a corpus that covers 13,937 skills. In contrast to the random selection gold standard, the rule-based gold standard only considers obvious classifications that are covered by these heuristics. It therefore does not contain any challenging cases in which expert assessments differed or required discussions and further clarifications to decide on the skill's correct class.  
Table 8 provides dataset statistics for both gold standard datasets. Please note that the manually annotated random selection corpus assigns offshorability and automatability classifications to each skill. The rule-based corpus, in contrast, only covers the class outlined in the rule for that particular skill (i.e., either offshorability, automatability, or both). It is also important to note that class distribution differs significantly between both datasets. Offshorable skills are even more frequent in the rule-based corpus, while the distribution for automatability is reversed between the datasets.
## 3 Results
### In-Orbit Results
#### 3.2.3 Magnetometer  
During early operations, the magnetometer was also operated in real-time mode. The obtained data shown in Figure 13 demonstrate that even though the instrument has been mounted on the body of the spacecraft, the readings can be obtained with no significant noise coming from the other spacecraft subsystems. In Figure 13, the sinusoidal oscillations are a consequence of the spinning of the satellite. For attitude determination purposes, the magnetometer information is processed together with the Sun sensor and gyroscope data. As for the GEO field readings, the local field at the spacecraft position is obtained by using the calibrated model of the instrument. This calibration process and the comparison of the results with geomagnetic field models like the World Magnetic Model [30] have to be discussed in a separate publication in order to be able to explain it in sufficient detail to be reproducible. Further readings of the magnetometer were not always possible (the instrument returned only zeros), which is discussed below.
## 2 Numerical Model and Verification
### Model Specifications: Fluid-Structure Interaction and Boundary Conditions  
We used FEM to approximate fluid-structure interactions using displacement-based fluid approximations. Accordingly, the ANSYS software was used to simulate the water tank system. An analysis of the interaction between the fluid structure and internal fluid flow in a flexible tank domain was carried out in this study. The fluid-structure interaction was investigated regarding a rectangular concrete above-ground liquid storage tank with a fixed base.  
Figure 1 shows a cube element, which is used to discretize both the fluid and tank domains. In order to incorporate the effects of wall flexibility in the tank response, the tank was meshed using three-degree-of-freedom eight-node SOLID65 elements, which have successfully been used to simulate concrete's nonlinear behavior [57; 58].  
The liquid is represented by 8-node fluid elements, known as FLUID80 in ANSYS [59]. This element type uses a displacement-based formulation, in which the fluid is characterized by its bulk modulus (K). Studies have demonstrated that this element can be used to simulate fluid-structure interactions, fluid sloshing, and hydrostatic pressure [60]. In addition, unique surface effects were incorporated into the fluid element, represented by gravity springs. To accomplish this, springs were added to each node and positioned at the top of the element with positive constants. The springs have positive stiffness at the top nodes, while the bottom nodes have negative stiffness. Thus, they balance one another's positive and negative impact on an intermediate node.  
Fluid elements were attached to the tank elements by coupling them in the normal direction, normal to the interface, which permits them to move vertically and tangentially. The dynamic behavior of the tank was evaluated using the direct integration method. In order to solve equations, the Newmark time integration technique was implemented using ANSYS software [59].  
In order to prevent settling, the spring closes off the nodes of the water and the wall of the container at the base of the water, assuming that the bottom of the container is rigid. This spring should be neutralized by setting the liquid's free surface to the coordinate z = 0 in this case, ensuring that only positive springs are used on the free surface of the water.  
Figure 1: The element used to discretize the water and the tank domains (1 to 6 represent the element’s surface numbers).  
Compared with experimental results, this modeling approach has been proven to be capable of accurately modeling the sloshing response [61]. Studies such as [62; 63] also employed this approach in order to examine the sloshing and other seismic responses of liquid storage tanks.
# A Multimode Fusion-Based Aviation Communication System
## 4 Application Testing
\\ \cline{1-1}  & ADS-B messages from aircraft are transmitted simultaneously to the ground control center through dedicated ad hoc and public network links. This allows for analysis and display of the current air situation. \\ \hline \multirow{4}{*}{High-precision Positioning Navigation} & \multirow{2}{*}{RTK Link} & Providing high-precision timing, speed measurement, direction finding, and precise positioning services for aircraft. \\ \cline{1-1}  & & The aircraft’s flight control computer receives ADS-B messages in real-time from other aircraft, which are analyzed and processed to determine the current air situation. \\ \cline{1-1}  & & The aircraft’s flight control computer receives ADS-B messages in real-time from other aircraft, which are analyzed and processed to determine the current air situation.
## 2 Simulation Method of Three-Dimensional DFN Based on Monte-Carlo Technique
### Construction of Probability Models for Geological Parameters of Rock Fractures
#### 2.1.4 Model of Center Point of Fractures  
The locations of fractures are frequently described by specifying representative points (e.g., center points) on each fracture, and the spatial distribution of these representative points can be assumed in a statistical process. Currently, the most widely used method for describing the locations of fractures is the Poisson process. It is generally accepted that any small non-overlapping block in rock masses has an equal probability of containing the center point of a fracture. In order to determine the center points of fractures, we divide the study area \(V\) into \(N\) non-overlapping sub-regions \(V_{i}\) (\(i\) = 1, 2,..., \(N\)) of equal volume. The center point of the \(i\)th fracture is assumed to be randomly and uniformly distributed within \(V_{i}\). Then, if the range of coordinates of sub-region \(V_{i}\) is \(x_{i}\leq x\leq x_{i+1}\), \(y_{i}\leq y\leq y_{i+1}\) and \(z_{i}\leq z\leq z_{i+1}\), and the coordinates of the center of the fracture are \((x_{ic},y_{ic},z_{ic})\), the following can be obtained:  
\[\left\{\begin{array}{l}x_{ic}=x_{i}+(x_{i+1}-x_{i})R_{x}\\ y_{ic}=y_{i}+(y_{i+1}-y_{i})R_{y}\\ z_{ic}=z_{i}+(z_{i+1}-z_{i})R_{z}\end{array}\right. \tag{8}\]  
where \(R_{x},R_{y},R_{z}\) are random variables that follow a uniform distribution in the interval [0, 1].
# A Middleware-Based Approach for Multi-Scale Mobility Simulation
## 5 Experiments and Results
### Setup  
We use two simulations that we have developed in the context of the MSM project. The considered territories correspond to the use case described earlier (Ile de France region and La Defense business district).  
* The first simulation is implemented in Java, and operates with traveler flows, on an aggregated network and with a time step of 10 min. It assigns travelers' flows on the shortest route at Wardrop's equilibrium using the transfer-and-equalize method [41]. The input of the simulation is: 1. The temporoized origin-destination matrix on a region scale. These data are based on a National survey [42]. 2. The aggregated network of the region (main streets only). Its output is the valuated graph every ten minutes.
* The second simulation is implemented in Java, works with agents representing individual travelers, on a very detailed network and with a time step of 20 s. It assigns travelers using a K-shortest path algorithm [43]. This simulation uses a car-following model for travelers movements (Simplified Gipp Model from [44]). The inputs of this simulation are: 1. The temporoized origin-destination matrix on the local scale. To obtain a matrix that is not too different from the regional matrix, we use the method described in [38] to infer a local matrix from a regional one. Then, we introduce some noise, by randomly multiplying the number of travelers in the matrix by a number in \([-10,\ldots,10\%]\). 2. The detailed network of the local area.  
In the following, we assume that the regional simulation corrects the Demand and Assign functions, while the local simulation corrects the Move function. Therefore, the mid-dleware applies the M-DA Algorithm (Algorithm 3).  
Since the simulations and the middleware are all implemented in the same language, there is no need for an interface between them, and all interactions are performed using Java RMI (cf. Section 6 for a discussion on this aspect).  
All the experiments have been performed using a unit under Linux Mint 17.2 Rafaela (kernel version 3.16.0-38-generic) with an Intel(r) Core processor CPU (Santa Clara, CA, USA) i5-7400 with 16 GB of memory.  
Figure 6: Middleware implementation.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\[\begin{split}\text{AWGN}&\text{ Additive white Gaussian noise}\\ \text{IoT}&\text{ Internet of Things}\\ \text{ADMM}&\text{ Alternating directional multiplier method}\\ \text{MIMO}&\text{ Multiple-input multiple-output}\\ \text{FSO}&\text{ Free-space optical}\\ \text{BER}&\text{ Bit error rate}\\ \text{CSI}&\text{ Channel state information}\\ \text{NLOS}&\text{ Non-line-of-sight}\\ \text{AI}&\text{ Artificial intelligence}\\ \text{MMSE}&\text{ Minimum mean square error}\\ \text{MADDPG}&\text{ Multi-agent deep deterministic policy gradient}\\ \text{PPM}&\text{ Pulse-position modulation}\\ \text{OOK}&\text{ On-off keying}\\ \text{SPAD}&\text{ Single-photon avalanche diode}\\ \text{NN}&\text{ Neural network}\\ \text{HCE}&\text{ Hybrid cross entropy}\\ \text{LED}&\text{ Light-emitting diodes}\\ \text{DC}&\text{ Direct current}\\ \text{PD}&\text{ Photo detector}\end{split}\]