esa-sceva/satcom-chunk-collection huggingface dataset:
# Revisiting Label Smoothing Regularization with Knowledge Distillation
## 3 Preliminaries  
### Label Smoothing Regularization  
We considered a standard classification problem. Given a training dataset \(\text{D}=\{(x^{i},y^{i})\}_{i=1}^{N}\), where \(x^{i}\) is the \(i_{th}\) sample from M classes and \(y^{i}\in\{1,\,2,...,\,M\}\) is the corresponding label of sample \(x^{i}\), the parameters \(\theta\) of a deep neural network (DNN) that best fit the dataset need to be determined.  
The softmax function is employed to calculate the \(m\)th class probability from a given model:  
\[p_{m}(\tau)=\frac{exp(z_{m}/\tau)}{\sum_{i=1}^{M}exp(z_{i}/\tau)}. \tag{1}\]  
Here, \(z_{m}\) is the \(m_{th}\) logit output of the model's fully connected layer. \(\tau\) indicates the temperature of the softmax distribution normally set to one in traditional cross-entropy loss, but greater than one in knowledge distillation loss [13]. A larger \(\tau\) means a softer probability distribution that reveals more detail than a hard softmax output (\(\tau=1\)).  
For M-class classification, the traditional cross-entropy loss of a sample is as follows:  
\[L_{CE}(p(1),q)=-\sum_{m=1}^{M}q_{m}log(p_{m}(1)), \tag{2}\]  
where \(q_{m}\) is the \(m_{th}\) element of one-hot label vector \(q\). Note that the temperature \(\tau\) is set to one. The pipeline is depicted in Figure 1b.  
For a training example with one-hot label vector \(q\), the label smoothing regularization method (Figure 1c) [19] replaces \(q\) as \(q^{\prime}\):  
\[q^{\prime}=(1-\alpha)\times q+\alpha\times u, \tag{3}\]  
where \(u\) is a uniform distribution. Then, the cross-entropy loss of LSR is:  
\[L_{LSR}(p(1),q^{\prime})=-\sum_{m=1}^{M}q^{\prime}_{m}log(p_{m}(1)), \tag{4}\]  
Compared to the traditional cross-entropy loss, minimizing the loss between the model output and smoothed label \(q^{\prime}\) can help the model generalize better on the validation dataset.
## 2 Principle of Vortex Bladeless Wind Turbines  
### Vortex-Induced Vibration  
The blackeless wind turbine (BWT) adopted in this study is a cylindrically structured type that works on the principle of vortex-induced vibration (VIV), as shown in Figure 1.  
When air flows past the cylindrical mast, a periodic, irregular low-pressure vortex is formed at the back of the mast, depending on the Reynolds number. The Reynolds number (\(Re\)) characterizes the state of airflow and can be estimated from  
\[Re=\frac{\rho v\Phi}{\mu}, \tag{1}\]  
where \(\rho\) is the air density, \(v\) is the air velocity, \(\Phi\) is the cylinder's diameter, and \(\mu\) is the air viscosity. It is known that a turbulent vortex street occurs around circular cylinders when \(Re>3.5\times 10^{6}\)[23].  
Figure 1: Overview of the proposed hardware design of the BWT prototype.  
The Karman vortex street, which causes fluid pressure differences in the body of the cylinder, results in periodic forces on the cylinder body due to the vortex shedding. The force perpendicular to the air velocity is the lift force and is distributed along the surface. The lift force is assumed to be periodic at the frequency of the vortex-induced vibration (\(f_{v}\)), which depends on the velocity of the air \(v\) and the diameter of the cylinder \(\phi\) as follows:  
\[F_{L}=\frac{1}{2}\rho\phi v^{2}C_{L}\sin(2\pi f_{v}t), \tag{2}\]  
where \(C_{L}\) is the lift force coefficient, and \(\rho\) is the fluid density [24; 25].  
The VIV frequency (\(f_{v}\)) can be approximated with the Strouhal number (\(St\)), wind speed (\(v\)), and cylinder diameter (\(\phi\)) according to the following.  
\[f_{v}=\frac{St\cdot v}{\phi}, \tag{3}\]  
where the Strouhal number for a cylinder body is 0.21 over a wide range of wind speeds [4].  
When the VIV frequency (\(f_{v}\)) due to vortex shedding is close to the structural frequency of the BWT (i.e., \(f_{v}\approx f_{\text{BWT}}\)), it causes the resonance of the system to have a maximum magnitude. The range of air velocity (\(v_{lock}\)) that causes the resonance of the structure is known as the lock-in range [7] and can be approximated as  
\[v_{lock}\approx\frac{\phi}{St}f_{\text{BWT}} \tag{4}\]  
Even if the vortex frequency does not precisely match the system frequency, the BWT will still resonate as long as the air velocity is within the lock-in range.  
Since the BWT harvests electrical energy from vibration energy, it is preferable to design the structure of the system to resonate under the typical wind speed in the local area. Therefore, the BWT should be designed to closely match the structural frequency to the VIV frequency of the target wind speed.  
Typically, a VIV-based BWT is a simple structure of a damped-mass system that has a resonant frequency in terms of  
\[f_{BWT}=\frac{1}{2\pi}\sqrt{\frac{k}{m}-\left(\frac{c}{2m}\right)^{2}}. \tag{5}\]  
Thus, given the design criteria for the average wind speed \(v_{avg}\) and diameter \(\phi\), the mast mass \(m\), the stiffness of the structure \(k\), and the damping \(c\) are carefully selected to meet the lock-in condition for the vortex frequency of \(f_{v}\).
# A Review of Deep Learning Applications for Railway Safety
## 2 Overview of Deep Learning Approaches
### Tasks
#### 2.2.3 Segmentation  
Segmentation is a method of extracting an object of interest from an image in units of pixels. By giving each pixel a label, it is possible to know which pixel belongs to which object. Segmentation is necessary for identifying shapes of target objects in detail, such as in traffic safety, autonomous driving services, and in reading magnetic resonance imaging (MRI). Depending on the purpose of use, segmentation can be divided into semantic segmentation and instance segmentation. Semantic segmentation assigns a class label to every pixel in an image, such as a person or car. The objects of the same class have the same label. However, instance segmentation identifies each object separately, even if they belong to the same class.  
You Only Look At CoefficientTs (YOLACT) is a real-time model that improves the processing speed of instance segmentation by omitting the localization step [28]. Instead, this model solves the problem by dividing the segmentation process into two parallel,instead of sequential, subtasks. The first task generates a dictionary of non-local prototype masks over the entire image and another predicts the linear combination coefficient for each instance. Then, YOLACT produces instance masks by linearly combining the prototypes with the mask coefficients.
# Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model
## 2 The BF-ELECTRE II Method
### Construction of a Decision Matrix  
Consider a MCDM problem involving the bipolar fuzzy information contains a set of \(r\) alternatives such as \(\mathcal{P}=\{\mathcal{P}_{1},\mathcal{P}_{2},\cdots,\mathcal{P}_{r}\}\), each of which is assessed by \(s\) conflicting criteria \(K=\{K_{1},K_{2},\cdots,K_{s}\}\). The preference values of alternatives \(\mathcal{P}_{m},\;\;m=1,2,\cdots,r\) with respect to the criteria \(K_{n},\;\;n=1,2,\cdots,s\) is given in the form of decision matrix as  
\[\mathcal{L}=\left[\begin{array}{cccc}\ell_{11}&\ell_{12}&\cdots&\ell_{1r}\\ \ell_{21}&\ell_{22}&\cdots&\ell_{2r}\\ \cdot&\cdot&\cdot&\cdot\\ \cdot&\cdot&\cdot&\cdot\\ \cdot&\cdot&\cdot&\cdot\\ \ell_{s1}&\ell_{s2}&\cdots&\ell_{sr}\end{array}\right].\]  
Each entry \(\ell_{mn}=(\mu_{mn},\
u_{mn})\) is a bipolar fuzzy set in which \(\mu_{mn}\in[0,1]\) denotes the degree of satisfaction and \(\
u_{mn}\in[-1,0]\) represents the degree of dissatisfaction. Furthermore, the weight vector for criteria is given by \(\omega=[\omega_{1}\;\omega_{2}\;\cdots\;\omega_{s}]^{T}\), such that \(\omega_{n}\in[0,1]\) and \(\sum\limits_{n=1}^{s}\omega_{n}=1\). Since each criterion has different importance and may be partially or completely unknown, the weights of criteria are determined by an optimization method, which is based on a maximizing deviation technique. Thus, the normalized weight for the criterion \(K_{n}\) is calculated by using the formula as:  
\[\omega_{n}=\frac{\sum\limits_{m=1}^{r}\sum\limits_{k=1}^{r}\left|\mu_{mn}-\mu_{ kn}\right|+\left|\
u_{mn}-\
u_{kn}\right|}{\sum\limits_{n=1}^{s}\left(\sum \limits_{m=1}^{r}\sum\limits_{k=1}^{r}\left|\mu_{mn}-\mu_{kn}\right|+\left|\
u_ {mn}-\
u_{kn}\right|\right)}. \tag{1}\]
# Error Analysis and Calibration Improvement of the Imaging Section in a Mueller Matrix Microscope
## 3 Application and Discussion  
We applied the proposed calibration method to a standard sample of air (under stable measuring conditions with a temperature of 23.1 \({}^{\circ}\)C and humidity of 51%), based on the configuration illustrated in Figure 1, to demonstrate the influence of birefringent parasitic polarization on the Mueller matrix elements of the objective lens in the imaging section and to validate the adaptability of the proposed calibration procedure when using the improved error-propagation model. To examine the general performance, we visually examined and quantified the measurement results of the elements \(M_{24}\), \(M_{34}\), \(M_{42}\), and \(M_{43}\), together with the polarization parameter \(t3\) derived from these elements, as expressed in Equation (10) [24] (which characterizes the amplitude value of birefringence), for an air sample and a quarter-wave plate sample as the standard samples and a Daphnia organism sample with complex internal structure.  
\[t3=\frac{\sqrt{\left(M_{42}\right)^{2}+\left(M_{34}\right)^{2}}}{2}=\frac{ \sqrt{\left(M_{43}\right)^{2}+\left(M_{24}\right)^{2}}}{2} \tag{10}\]
# Matching-Based Resource Allocation for Satellite-Ground Network
## 3 System Model
### The Profitability of Satellite Model  
This subsection describes and calculates the profitability of satellites. Since the primary purpose of commercial satellites is revenue generation, the profitability of the satellite constellation is also a major consideration.  
The binary variable \(\varphi_{i,j,t}\) represents whether the user \(u_{i}^{t}\) is connected to the LEO satellite \(l_{j}^{t}\) at slot \(t\)  
\[\varphi_{i,j,t}=\begin{cases}1,\,if\,user\,\,u_{i}^{t}\,\,is\,\, connected\,\,\,\text{LEO satellite}\,\,l_{j}^{t}\,\,in\,slot  
of participant has a preference list, which only includes one type of another agent, that is, GEO satellites only rank users, users only rank LEO satellites, and LEO satellites only rank GEO satellites. TMSC allows each agent to have multiple partners sorted by preference [35]. Let \(\Delta^{t}=\mathcal{U}^{t}\times\mathcal{L}^{t}\times\mathcal{G}^{t}\) represent the set of all possible triples. Therefore, \(\Sigma^{t}\in\Delta^{t}\) is a subset. Definition 1 gives the blocking triples representing TMSC stability.  
**Definition 1**.: _Blocking Triple: A triple (\(u_{i}^{t}\), \(l_{j}^{t}\), \(\delta_{k}^{t}\))\(\in\Delta^{t}\), but (\(u_{i}^{t}\), \(l_{j}^{t}\), \(\delta_{k}^{t}\))\(\
otin\Sigma^{t}\), If all three members of \(u_{i}^{t}\), \(l_{j}^{t}\), and \(\delta_{k}^{t}\) prefer triple (\(u_{i}^{t}\), \(l_{j}^{t}\), \(\delta_{k}^{t}\)) to at least one of their existing matched partners, then the triple is blocking._  
The aforementioned definition shows that each agent in the blocking triple is more eager to match each other as a triple than the current partners in matching \(\Sigma^{t}\). The matching is deemed steady if \(\Sigma^{t}\) lacks a blocking triple. The TMSC challenge in this study, which is inspired by [14], is to look for a stable matching with the highest cardinality \(|\Sigma^{t}=\{(u_{i}^{t},\ l_{j}^{t},\ g_{k}^{t})\ \}|\).  
**P1:** max \(|\Sigma^{t}|\) (14) s.t. \(\mathcal{N}\big{(}\Sigma^{t},u_{i}^{t}\big{)}\leq 1,\forall u_{i}^{t}\in \mathcal{U}^{t}\) (15) \[\mathcal{N}\big{(}\Sigma^{t},g_{k}^{t}\big{)}\leq N_{G},\ \forall\ g_{k}^{t}\in \mathcal{G}^{t}\] (16) \[\mathcal{N}\Big{(}\Sigma^{t},\ l_{j}^{t}\big{)}\leq N_{L},\forall l _{j}^{t}\in\mathcal{L}^{t}\] (17) \[\mathcal{BT}(u_{i}^{t},\ l_{j}^{t},\ g_{k}^{t})=0\] (18)  
where, \(\big{|}\Sigma^{t}\big{|}\) is the number of triples. Constraint (15) states that a user can communicate with only one LEO satellite each time. Constraint (16) indicates the capacity limit \(N_{G}\) of the GEO satellite, constraint (17) shows that a LEO satellite can only connect to, at most, \(N_{L}\) GEO satellites, and constraint (18) ensures no blocking triple in \(\Sigma^{t}\).
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 6 Analysis of Simulation Results
### Input-Joint-Trajectory Simulation Analysis  
The controllers of the three joints were successively optimized, and the trajectory planning was carried out in a Cartesian coordinate system. The end effector was moved along the gate-shaped trajectory, and the inverse kinematics were used to map the operation space to the joint space. The trajectories planned by interpolating quintic polynomials were used as the input. The simulation time was set to 3 s. The traditional PID control, fuzzy control, fuzzy PID control, and fuzzy PID control strategies optimized by GAs were used  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Controller Type** & **Setting Time/(s)** & **Rise Time/(s)** & **Steady-State Error/(\({}^{\circ}\))** \\ \hline PID & 0.752 & 0.612 & 0.1713 \\ FPID & 0.654 & 0.496 & 0.0209 \\ GAFPID & 0.508 & 0.380 & 0.0194 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results of the step response.  
Figure 11: Sinusoidal input signal at 2 Hz: (**a**) tracking response; (**b**) local enlarged view.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**The Controller Type** & **Maximum Phase Error/(\({}^{\circ}\))** & **Maximum Magnitude Error/(\({}^{\circ}\))** \\ \hline PID & 0.573 & 0.2614 \\ FUZZY & 1.146 & 0.6527 \\ FPID & 0.344 & 0.1263 \\ GAFPID & 0.229 & 0.0855 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation data of the frequency response under loading.  
to control the PM. The trajectory tracking capabilities of the four control methods were compared.  
Figures 12-14 show the trajectory tracking of the three joints, respectively. The average absolute error was about 0.0327 using the GAFPID control strategy for Q1 joint motion. The enlarged partial image presents that the joint trajectory based on the GAFPID control strategy was the closest to the ideal trajectory, with the optimal control effect.  
According to Tables 6 and 7, the minimum integral time absolute error (ITAE) value of the joint trajectory is based on the GAFPID control strategy. The dynamic response overshoot was small and the adjustment time was short. The integral absolute error (IAE) index was the smallest, which means that small deviations in the control system can be suppressed.  
Figure 12: Joint trajectory Q1: (**a**) tracking response; (**b**) local enlarged view.  
Figure 13: Joint trajectory Q2: (**a**) tracking response; (**b**) local enlarged view.
# Mining Pool Selection under Block WithHolding Attack +
## 4 Case Study with Two Mining Pools
### One-Side Attack Case  
We first consider the one-side attack case, where one pool attacks the other. Suppose that pool 1 attacks pool 2, i.e., \(a_{12}>0\) and \(a_{21}=0\). According to (2), the revenue densities of the two pools are given by  
\[r_{1}=\frac{\alpha(1-a_{12})+Na_{12}r_{2}(x_{1}\omega_{1}(1-a_{12})+x_{2} \omega_{2})}{N(x_{1}\omega_{1}(1-a_{12})+x_{2}\omega_{2})}, \tag{14}\]  
\[r_{2}=\frac{\beta\omega_{2}x_{2}}{N(x_{1}\omega_{1}a_{12}+x_{2}\omega_{2})(x_{ 1}\omega_{1}(1-a_{12})+x_{2}\omega_{2})}. \tag{15}\]  
Substituting (15) into (14), we have  
\[r_{1}=\frac{\alpha(1-a_{12})(a_{12}\omega_{1}x_{1}+\omega_{2}x_{2})+a_{12} \beta\omega_{2}x_{2}}{N((1-a_{12})\omega_{1}x_{1}+\omega_{2}x_{2})(a_{12} \omega_{1}x_{1}+\omega_{2}x_{2})}. \tag{16}\]  
Substituting (16) and (15) into (1), we can obtain the following expected payoffs of miners in both pools:  
\[y_{1}=\omega_{1}\bigg{(}\frac{\alpha(1-a_{12})(a_{12}\omega_{1}x_{1}+\omega_{ 2}x_{2})+a_{12}\beta\omega_{2}x_{2}}{N((1-a_{12})\omega_{1}x_{1}+\omega_{2}x_{ 2})(a_{12}\omega_{1}x_{1}+\omega_{2}x_{2})}-p\bigg{)}, \tag{17}\]  
\[y_{2}=\omega_{2}\bigg{(}\frac{\beta\omega_{2}x_{2}}{N((1-a_{12})\omega_{1}x_{ 1}+\omega_{2}x_{2})(a_{12}\omega_{1}x_{1}+\omega_{2}x_{2})}-p\bigg{)}. \tag{18}\]  
When pool 2 attacks pool 1, similar to (14) and (15), the revenue densities are expressed as follows:  
\[r_{1}=\frac{\alpha\omega_{1}x_{1}}{N(x_{1}\omega_{1}+x_{2}\omega_{2}a_{21})(x _{1}\omega_{1}+x_{2}\omega_{2}(1-a_{21}))}, \tag{19}\]  
\[r_{2}=\frac{\beta(1-a_{21})+Na_{21}r_{1}(x_{1}\omega_{1}+x_{2}\omega_{2}(1-a_{ 21}))}{N(x_{1}\omega_{1}+x_{2}\omega_{2}(1-a_{21}))}. \tag{20}\]The expected payoffs are given by  
\[y_{1}=\omega_{1}\bigg{(}\frac{\alpha\omega_{1}x_{1}}{N(x_{1}\omega_{1}+x_{2} \omega_{2})(x_{1}\omega_{1}+(1-a_{21})x_{2}\omega_{2})}-p\bigg{)}, \tag{21}\]  
\[y_{2}=\omega_{2}\bigg{(}\frac{\beta(1-a_{21})(x_{1}\omega_{1}+x_{2}\omega_{2}a _{21})+a_{21}\alpha\omega_{1}x_{1}}{N(\omega_{1}x_{1}+(1-a_{21})\omega_{2}x_{2 })(\omega_{1}x_{1}+a_{21}\omega_{2}x_{2})}-p\bigg{)}. \tag{22}\]  
According to (10), the equation system of the ODEs for the replicator dynamics can be expressed as follows:  
\[\dot{x_{1}}=x_{1}(y_{1}-\overline{y})=0, \tag{23}\]  
\[r_{2}=\frac{\beta\omega_{2}x_{2}}{N(x_{1}\omega_{1}a_{12}+x_{2}\omega_{2})(x_{ 1}\omega_{1}(1-a_{12})+x_{2}\omega_{2})}. \tag{15}\]0  
where \(\overline{y}\) is  
\[r_{2}=\frac{\beta\omega_{2}x_{2}}{N(x_{1}\omega_{1}a_{12}+x_{2}\omega_{2})(x_{ 1}\omega_{1}(1-a_{12})+x_{2}\omega_{2})}. \tag{15}\]1  
Since \(x_{2}=1-x_{1}\), letting \(x_{1}=x\) and \(x_{2}=1-x\), we can simplify the system of ODEs to the following equation:  
\[r_{2}=\frac{\beta\omega_{2}x_{2}}{N(x_{1}\omega_{1}a_{12}+x_{2}\omega_{2})(x_{ 1}\omega_{1}(1-a_{12})+x_{2}\omega_{2})}. \tag{15}\]2  
Thus, the population state can be expressed as \((x,1-x)\).
# Niching Global Optimisation: Systematic Literature Review
## 2 Niching Paradigms
### Parallel Niching Paradigms
#### 2.2.3 Restricted Tournament Selection (RTS)
pling strategy-based memetic differential evolution (MMDE) algorithm which combines speciation and clustering by forming niches from elite individuals using clustering and restricting crossover for individuals with poor fitness at the beginning of the search, favouring exploration, with a growing probability for fitter individuals at the end of the search favouring exploitation. The authors of [184] proposed a clustering-based genetic algorithm which uses density-based clustering to form niches and dynamic sharing to modify the raw fitness of population individuals within each niche using the niche population count. Tables 9-11 give an exhaustive overview of the surveyed methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Year** & **Algorithm** & **Theoretical Ground** & **Type** \\ \hline
[185] & 1994 & SSE & Stochastic schemata exploiter utilises schemata processing to exploit local search capabilities while maintaining global search through stochastic sampling. & Embedded \\ \hline
[186] & 2008 & IK-DCA & Immune quantum evolutionary algorithm based on chaotic searching technique integrates immune principles, quantum computing, and chaotic searching to enhance global optimisation by maintaining solution diversity and improving convergence. & Embedded \\ \hline
[187] & 2009 & MA-GAO & Memetic algorithm for optimising high-inclination multiple gravity-assisted orbits combines evolutionary strategies with local search heuristics to maintain multiple solutions. & Embedded \\ \hline
[188] & 2010 & UGOMAC & Unbiased geometry optimisation of morse atomic clusters combines geometry optimisation with unbiased search strategies to improve performance in multimodal optimisation. & Embedded \\ \hline
[22] & 2012 & N(JDE & Differential evolution local mutation using m-neighbour vectors (i.e., crowding or sharing and speciation). & Embedded \\ \hline
[189] & 2013 & HN-CA & Hybrid niche cultural algorithm integrates cultural evolutionary processes with niching strategies, enhancing global optimisation by combining cultural knowledge sharing and individual learning to explore and exploit multiple optima simultaneously. & Embedded \\ \hline
[190] & 2013 & S-DPSO & Self-adaptive differential particle swarm using a ring topology integrates self-adaptation and differential evolution within a ring topology to enhance diversity and convergence. & Embedded \\ \hline
[191] & 2013 & SELFCCDE & Adaptive parameter differential evolution with radius-based speciation and crowding. & Embedded \\ \hline
[192] & 2014 & PNPCDE & Differential evolution with crowding and parent-centric neighbourhood speciation mutation operator. & Embedded \\ \hline
[175] & 2014 & CBDE-WCA & Differential evolution with niche formation using fuzzy c-means, exploration enhancement using crowding, and redundancy reduction via clearing. & Embedded \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of niching algorithms: hybrid methods (embedded).
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 3 Microbial Safety of Meat and Meat Products
### Pathogens in Meat
#### 3.2.4 Clostridium Perfringens  
As of late, a few outbreaks of food poisoning, brought about by _Clostridium perfringens_, were segregated in meat and meat items. _Clostridium perfringens_ is considered as one of the most significant food contamination microscopic organisms, which may allude to various deadly poisons, including the _C. perfringens_ enterotoxin. This poison is liable for food contaminations linked with human gastrointestinal illnesses, causing gastrointestinal diseases of the bowels and stomach squeezing. [29]. A sum of 125 irregular raw and half-cooked chicken meat tests addressed by breast, thigh, chunks, panee and sausages were gathered from different retail locations and grocery stores in the Qualyubia governorate to examine the presence of _C. perfringens_ bacteriologically. The results showed that 6 out of 25 raw breast samples (24%), 8 out of 25 raw thigh tests (32%), 5 out of 25 pieces tests (20%), 4 out of 25 panee tests (16%), and 4 out of 25 sausage tests (16%) were discovered to be contaminated with _C. perfringens_. Twenty-seven positive segregates obtained from these examples were recognized as _C. perfringens_ dependent on the biochemical tests and microbial detection [30]
In Mexico and Costa Rica, in AFS of _Erythrina poepiggiana_ associated with coffee, a storage of C from 115 to 195 t ha\({}^{-1}\) per year was shown, while it was 93 t ha\({}^{-1}\) per year in systems with _Gliricidia sepium_ and cocoa grown in the Philippines [19,20]. In the Ecuadorian Amazon, there have been few studies on the amount of C stored in AFS. [PERSON] et al. [21] pointed out that in cocoa AFS, the amount of carbon stored was 141.4 t ha\({}^{-1}\). In addition, [PERSON] et al. [8] determined that in AFS of pitahaya with _E. poeppigiana_ and _Flemingia macrophylla_ and with _G. sepium_ and _F. macrophylla_, it was possible to obtain an acceptable fruit yield (13 and 17 t ha\({}^{-1}\), respectively) and high sequestration of C (C:N 15 and 16 ratios, respectively). In addition to C storage, AFSs also have a potential impact on belowground biodiversity and the physical, chemical, and biological properties of the soil [22]. The population and diversity of earthworms vary according to soil moisture and temperature, soil properties, abundance of litter on the surface, types of vegetation, land use management, human intervention, and microclimate variation [23, 24, 25, 26]. In an AFS located in Canada, the number of earthworms was higher near trees (182 individuals m\({}^{-2}\)) and lower when it was assessed in alleys (between 95 and 117 individuals m\({}^{-2}\)) [27]. Research on C capture in AFS associated with fruit crops is limited and zero with the naranjilla crop. Considering this situation, the aim of this study was to find out the influence on the crop yield, carbon sequestration, presence of earthworms, and the nutritional contribution of legume species associated with the naranjilla (_Solanum quitoense_) crop in an agroforestry system.