esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Statement
### Minimum-Time Trajectory Planning Problem Description of Robotic Manipulators  
For the minimum-time trajectory problem of industrial robots, the performance index is expressed as below:  
\[J=t_{f}\to min. \tag{6}\]  
The RHS equations can be derived from the dynamics of the system. The general dynamics equation of manipulators is expressed on this function:  
\[\mathbf{\tau}=M(\mathbf{q})\dot{q}+\mathbf{v}(\mathbf{q},\dot{\mathbf{q}})+\mathbf{g}(\mathbf{q}), \tag{7}\]  
where \(\mathbf{q}\) indicates the states representing the relative angle between adjacent arms, \(\mathbf{\tau}\) represents the controls representing the output torque of the joint actuators, \(M(\mathbf{q})\) is the positive definite and symmetric matrix of the moment of inertia, \(\mathbf{v}(\mathbf{q},\dot{\mathbf{q}})\) is the moment caused by Coriolis and centrifugal forces, and \(\mathbf{g}(\mathbf{q})\) is the moment caused by gravitational forces.  
After several transposition operations on (7), the RHS equations become:  
\[\ddot{\mathbf{q}}=M^{-1}(\mathbf{q})[\mathbf{\tau}-\mathbf{v}(\mathbf{q},\dot{\mathbf{q}})-\mathbf{g}(\bm {q})]. \tag{8}\]  
It is quite important to derive the RHS equations, because it helps to predict the states of the system. Finding methods to instead the explicit expression of (8) is also the main difficulty in solving the OCP.  
As a result, the state and the state equations of robotic manipulators can be written as below:\[\mathbf{x}=\begin{pmatrix}\mathbf{q}\\ \dot{\mathbf{q}}\end{pmatrix},\dot{\mathbf{x}}=\begin{pmatrix}\dot{\mathbf{q}}\\ M^{-1}(\mathbf{q})[\mathbf{\tau}-\mathbf{v}(\mathbf{q},\dot{\mathbf{q}})-g(\mathbf{q})]\end{pmatrix}. \tag{9}\]  
The boundary conditions \(\mathbf{\phi}\) are taken into consideration as below:  
\[\mathbf{q}(t_{0})=\mathbf{q}_{0},\mathbf{q}(t_{f})=\mathbf{q}_{f}, \tag{10}\] \[\dot{\mathbf{q}}(t_{0})=\dot{\mathbf{q}}_{0},\mathbf{q}(t_{f})=\dot{\mathbf{q}}_{ f}. \tag{11}\]  
Generally, \(\mathbf{q}_{0}\) and \(\mathbf{q}_{f}\) refer to the start and the end, and they are both constant and can be defined by users. The stationary boundary conditions are taken into consideration, i.e., \(\dot{\mathbf{q}}_{0}=\dot{\mathbf{q}}_{f}=0\).  
The inequality constraints can be categorized into the following two kinds:  
* The control constraints on the torque voltage; \[\mid\mathbf{u}_{i}(t)\mid\leq\mathbf{u}_{i,max},i=1...n.\] (12)
* The state constraints on the angles and the angular velocities. \[\mid\mathbf{q}_{i}(t)\mid\leq\mathbf{q}_{i,max},i=1...n.\] (13)  
\[\mid\dot{\mathbf{q}}_{i}(t)\mid\leq\dot{\mathbf{q}}_{i,max},i=1...n. \tag{14}\]  
where \(i\) refers to the number of the robotic link, and \(n\) refers to the max number of the robotic link, which is equal to the degrees of freedom of robotic manipulators.
###### Abstract  
An adaptive fractional-order fuzzy control method for a three-phase active power filter (APF) using a backstepping and sliding mode controller is developed for the purpose of compensating harmonic current and stabilizing the DC voltage quickly. The dynamic model of APF is changed to an analogical cascade system for the convenience of the backstepping strategy. Then a fractional-order sliding mode surface is designed and a fuzzy controller is proposed to approximate the unknown term in the controller, where parameters can be adjusted online. The simulation experiments are conducted and investigated using MATLAB/SIMULINK software package to verify the advantage of the proposed controller. Furthermore, the comparison study between the fractional-order controller and integer-order one is also conducted in order to demonstrate the better performance of the proposed controller in total harmonic distortion (THD), a significant index to evaluate the current quality in the smart grid.  
active power filter; fractional sliding mode control; backstepping strategy
# A Strawberry Ripeness Detection Method Based on Improved YOLOv8
## 1 Introduction  
Strawberries (_Fragaria_ spp., family Rosaceae) are perennial herbaceous plants widely appreciated for their nutritional value, sweet flavor, and juiciness. Owing to their distinctive aroma, they are often referred to as the \"Queen of Fruits\" [1]. China is the world's largest producer of strawberries, with Xinjiang Province recognized for cultivating particularly high-quality varieties due to its arid and sunny climate, which contributes to elevated sugar content and unique flavor profiles. In 2022, the United Nations FAO (Food and Agriculture Organization) reported that China's strawberry cultivation area reached approximately 221.18 km\({}^{2}\), with Xinjiang's share expanding steadily as strawberry farming becomes a key component of local agriculture [2].  
Despite the widespread cultivation, strawberry harvesting and sorting in Xinjiang still rely heavily on manual labor, which is both time-consuming and costly. To improve efficiency and reduce labor intensity, the integration of robotics and intelligent technologies in strawberry production has become an important research direction. Accurate identification of strawberry ripeness is critical for enabling intelligent harvesting systems, as it directly impacts sorting precision, reduces post-harvest losses, and ensures fruit quality.  
Traditional fruit ripeness detection methods typically rely on manually extracted features such as color, shape, and texture [3, 4, 5], followed by classification based on pixel-leveldifferences between the fruit and background. These classical image processing approaches have been applied to various fruits, including grapes [6], dragon fruit [7], and bananas [8]. While hyperspectral imaging has shown impressive accuracy in controlled environments--for example, [PERSON] et al. achieved a 91.25% classification accuracy for plums using a PLS discriminant model [9]--the high cost of the equipment and its limited performance under natural occlusion pose challenges for its practical use [10, 11, 12, 13].  
The rise of deep learning has introduced new possibilities for fruit ripeness detection in complex environments. CNNs (convolutional neural networks) and target detection algorithms have been successfully applied to various fruits such as oil palms [14], blueberries [15, 16], apples [17, 18, 19], mangoes [20], and tomatoes [21]. Notably, lightweight YOLO variants have enabled real-time fruit detection on mobile platforms [22, 23], and enhanced models such as RFACConv-YOLOv8 have been used for the multi-stage classification of coffee cherries with competitive accuracy [24]. However, studies focusing specifically on automated strawberry detection and ripeness classification remain limited.  
To address this gap, the present study proposes an improved strawberry detection model based on YOLOv8, aiming to enhance detection accuracy and speed in complex field environments. This work seeks to provide a real-time, accurate solution to support automated strawberry harvesting and intelligent agricultural production.
## 2 Materials and Methods
### V. ceranae Molecular Identification  
_V. ceranae_ presence and differentiation from _V. apis_ was confirmed by duplex PCR, in which two small-subunit 168 rRNA gene regions are simultaneously amplified using two primer sets: 218 MITOC FOR/REV amplifying a 218 bp fragment for _V. ceranae_ and 321 APIS FOR/REV amplifying a 321 bp fragment for _V. apis_[24]. Briefly, DNA was amplified in a 50 \(\mu\)L reaction mix comprising 1 mM dNTPs (Invitrogen, Waltham, MA, USA), 1\(\times\) buffer A (Nippon Genetics, Tokyo, Japan), 2 mM MgCl\({}_{2}\) (Nippon Genetics), 20 pmol of each primer, 2U Fastgene TaqMan Polymerase (Nippon Genetics) and double-distilled nuclease-free water of up to 50 \(\mu\)L. The thermal cycler conditions were 3 min at 95 \({}^{\circ}\)C for initial double strand denaturation followed by 40 cycles of DNA denaturation at 95 \({}^{\circ}\)C for 30 s, primer annealing at 50 \({}^{\circ}\)C for 30 s and extension at 72 \({}^{\circ}\)C for 1 min, with a final 2 min extension at 72 \({}^{\circ}\)C. Amplicons were visualized on a 1.5% (\(w/v\)) agarose gel (Nippon Genetics) under UV light.
## 2 Theory
The third term in Equation (1) becomes \(\mathbf{m}_{p}\frac{\mathrm{d}I_{p}(t)}{\mathrm{d}t}+\mathbf{m}_{c}\frac{ \mathrm{d}I_{c}(t)}{\mathrm{d}t}\) and the total transient magnetic field \(B(t)\) becomes  
\[B(t)=-[(B_{ISR}^{(p)}*\frac{\mathrm{d}I_{p}}{\mathrm{d}t})(t)+(B_{ISR}^{(c)}* \frac{\mathrm{d}I_{c}}{\mathrm{d}t})(t). \tag{8}\]  
In this study, the \(B_{p}\) coil is connected in series with the cancellation coil and they are both completely switched off in 2 ms. We take this moment as \(t_{\mathrm{s}}=2\) ms. When \(t>t_{\mathrm{s}}\), \(\frac{\mathrm{d}I(t)}{\mathrm{d}t}=0\) in Equation (1). Equation (1) can be written as  
\[\mathbf{M}\frac{\mathrm{d}j(t)}{\mathrm{d}t}+\mathbf{R}j(t)=0. \tag{9}\]  
Then  
\[\frac{\mathrm{d}j(t)}{\mathrm{d}t}=-(\mathbf{M}^{T}\mathbf{M})^{-1}\mathbf{M} ^{T}\mathbf{R}j(t). \tag{10}\]  
Finally, we can get  
\[j(t)=e^{-(\mathbf{M}^{T}\mathbf{M})^{-1}-\mathbf{M}^{T}\mathbf{R}t}+K, \tag{11}\]where \(K\) is a constant. \(K\) equals zero because in practice the eddy current in a SR will decay to zero. Combined with Equation (2), we can get  
\[ \begin{array}{ll}B_{r}(t)&=\beta^{T}j(t)\\ &=\beta^{T}e^{-(\mathbf{M}^{T}\mathbf{M})^{-1}-\mathbf{M}^{T}\mathbf{R}t}.\end{array}  \tag{12}\]  
Once the currents through \(B_{p}\) coil and CC completely switched off, we write \(B_{r}(t)\) as \(B(t)\). Then combining Equations (8) and (12), gives  
\[-[(B_{ISR}^{(p)}\ast\frac{\mathrm{d}l_{p}}{\mathrm{d}t})(t)+(B_{ISR}^{(c)} \ast\frac{\mathrm{d}l_{c}}{\mathrm{d}t})(t)=\beta^{T}e^{-(\mathbf{M}^{T} \mathbf{M})^{-1}-\mathbf{M}^{T}\mathbf{R}t} \tag{13}\]  
Based on Equation (13), it can be shown that the residual eddy-current magnetic field can be divided into two parts. The first part is produced by \(B_{P}\) coil and the second part by CC. According to reference [20], the residual eddy-current magnetic field can be expressed by  
\[B_{r}(t)=\sum_{i=1}^{N}A_{i}e^{-t/\tau_{i}}+C. \tag{14}\]  
Combining Equations (13) and (14), when \(t>t_{s}\) = 2 ms  
\[B_{r1}(t)=-(B_{ISR}^{(p)}\ast\frac{\mathrm{d}l_{p}}{\mathrm{d}t})(t)=\sum_{i= 1}^{N_{1}}A_{i}e^{-t/\tau_{i}}+C_{1}, \tag{15}\]  
\[B_{r2}(t)=-(B_{ISR}^{(c)}\ast\frac{\mathrm{d}l_{c}}{\mathrm{d}t})(t)=\sum_{j= 1}^{N_{2}}A_{j}e^{-t/\tau_{j}}+C_{2}. \tag{16}\]  
Here, \(N_{1}\) and \(N_{2}\) are positive integers; \(A_{i}\), \(\tau_{i}\) and \(\tau_{j}\) are positive numbers; \(A_{j}\) is a negative number; \(B_{r1}(t)\) and \(B_{r2}(t)\) represent the eddy-current magnetic field generated by \(B_{P}\) coil and CC coil in the SR, respectively and they are mutually offset.
## 2 Materials and Methods
### Site Location and Sea Conditions  
As a case study, the developed MMFAR was assumed to be deployed in a Marine Protected Area (MAP), in Esposende--Portugal. This site represents a promising location for the implementation of MMFAR for ecosystems restoration and protection, and for recreational or scientific diving. Figure 5 shows the geographical location of the Littoral North Marine Park, as well as the bathymetry in the area. The ideal installation depth of the MMFAR was defined as 20 m, allowing recreational diving and avoiding the direct effect of high waves. The present study is related mainly to the structural design of an underwater structure; therefore in Table 1 the typical wave parameters in the area for different sea conditions of the study area are presented. Further details may be found in [58; 59; 60; 61].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Typical Storm** & **Extreme Storm** & **Abnormal Extreme** \\
**Event** & **Condition** & **Condition** & **Condition** \\
**E\({}_{\mathbf{w}}\)** & **E\({}_{\mathbf{w},\mathbf{storm}}\)** & **E\({}_{\mathbf{w},\mathbf{maxstorm}}\)** & **E\({}_{\mathbf{w},\mathbf{e}}\)** \\ \hline \hline H\({}_{\text{S}}\) = 2.20 m & H\({}_{\text{S},\text{storm}}\) = 4 m & H\({}_{\text{S},\text{maxstorm}}\) = 9 m & H\({}_{\text{S},\text{40}}\) = 15.61 m \\ T = 7.89 s & T = 10 s & T = 14.6 s & T\({}_{\text{p},\text{40}}\) = 20 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wave height and period for different sea conditions [58; 59; 60; 61].  
Figure 4: 3D model of MFMAR with the connectors system.
# A Secure IoT-Based Authentication System in Cloud Computing Environment
## 2 Related Works
### Review of [PERSON] et al.'s Scheme
#### 2.1.1 Registration Phase  
User Registration  
First, user \(U_{i}\) selects four values (i.e., identity \(ID_{i}\), pseudo-identity \(PID_{i}\), password \(PW_{i}\), and a random number \(b_{i}\) to calculate \(HP_{i}=h(PW_{i}\parallel\|b_{i})\). The \(U_{i}\) then sends the \(ID_{i}\) and \(PID_{i}\) to the control server \(CS\). When \(CS\) receives (\(ID_{i}\), \(PID_{i}\)), CS will check whether or not \(ID_{i}\) is in the database. If not, \(CS\) uses secret key \(\mathrm{x}\) to calculate \(C_{1}\)\({}^{*}=h(PID_{i}||DCs\|x)\) and \(C_{2}\)\({}^{*}=h(ID_{i}||x)\); otherwise, \(CS\) will stop the authentication. \(CS\) stores \(ID_{i}\) in its database and sends (\(C_{1}\), \(C_{2}\), \(ID_{cs}\)) to \(U_{i}\). When \(U_{i}\) receives (\(C_{1}\), \(C_{2}\)\({}^{*}\), \(ID_{cs}\)), \(U_{i}\) calculates three values, \(C_{1}=C_{1}\)\(\oplus HP_{i}\), \(C_{2}=C_{2}\)\(\oplus h(ID_{i}||HP_{i})\), and \(C_{3}=b_{i}\oplus h(ID_{i}||PW_{i})\), then stores (\(C_{1}\), \(C_{2}\), \(C_{3}\), \(PID_{i}\), \(ID_{cs}\)) in a smart card.  
Cloud Server Registration  
Cloud server \(S_{j}\) sends (\(SID_{j}\), \(PSID_{j}\)) to \(CS\), where \(SID_{j}\) is the identity of \(S_{j}\) and \(PSID_{j}\) is the pseudo-identity of \(S_{j}\). When \(CS\) receives (\(SID_{j}\), \(PSID_{j}\)), \(CS\) calculates \(B_{1}=h(PSID_{j}||DC_{cs}||x)\) and \(B_{2}=h(SID_{j}||x)\). Finally, \(CS\) stores \(SID_{j}\) in a database and sends (\(B_{1}\), \(B_{2}\), \(ID_{cs}\)) to \(S_{j}\), and \(S_{j}\) stores (\(B_{1}\), \(B_{2}\), \(SID_{j}\), \(PSID_{j}\)) in a memory.
## 1 Introduction
For the former option, a multi-epoch manner such as a Kalman filter-based approach can be adopted, and the constraints such as a priori antenna coordinates can also be utilized to enhance the strength of the GNSS model, thereby improving the success rate of ambiguity resolution, but the unknown large PCCs might be absorbed in the ambiguity parameters; therefore, more work is needed to detect the subsequent incorrect solutions among the accepted ambiguity solutions. In contrast, the latter approach may be more efficient, where we can directly eliminate the ambiguities by triple difference after a cycle slip detection, thus avoiding the complicated ambiguity resolution process. In this research, we used the latter method and designed an antenna calibration method specifically for the low-cost GNSS antennas. Our contributions can be summarized as follows:  
* We present an approach to construct PCC measurements with triple-differencing operations between two involved antennas, two satellites, and two epochs with a relatively long interval such as one hour. The reference satellite was selected by comparing the angular distance it passed across the sky during a time-differencing interval. The time-differencing operation bore the task of eliminating carrier-phase ambiguities, which meant that a cycle slip detection procedure was necessary. * We propose a weight setting method for the spherical harmonic fitting of phase patterns. The weights were set according to the area of a spherical quadrilateral enclosed by the terminal points of four unit vectors, the PCCs on whose directions were the components of the PCC measurements. * We discuss the appropriate durations of data collection for the Global Positioning System (GPS) and the BeiDou Navigation Satellite System (BDS). For a relative field calibration method, since the antennas involved were static, the duration of the data collection was determined by the distributions and the ground trace repeat period of satellites. * We evaluated the performance of the proposed antenna calibration method using real GPS and BDS observations and a set of simulated phase patterns. Moreover, we discuss the test results in terms of the cycle slip, weighting, and elevation mask angle. In the following, first, based on the model of antenna-induced phase bias, a new antenna calibration method is introduced. Next, test results are presented, and the performance of the proposed method is analyzed. After that, some related factors are discussed. Finally, a conclusion is given.
## 2 Results
### Reduction in Pathogen Adhesion to Caco-2 Cells by CAR1 and THY5  
To evaluate the potential reduction ability exerted by 0.02% CAR1 and THY5 against pathogen adhesion on Caco-2 cells, the indicator strains _L. monocytogenes_ OH and _S._ Typhimurium LT2 were selected. Both strains had a similar ability to adhere to Caco-2 cells, quantified in about 10% of the initial inoculum, which consisted of approximately \(1\times 10^{8}\) CFU/mL for each strain (Figure 5A,B). The presence of CAR1 significantly reduced the adhesion of _L. monocytogenes_ OH as compared to the oil-free controls, with a decrease of approximately 1 log CFU/mL, while THY5 was not effective (approximately 0.5 log CFU/mL, Figure 5A).  
Both CAR1 and THY5 were able to significantly reduce the number of adhered viable bacterial cells of _S._ Typhimurium LT2 to Caco-2 cells (Figure 5B). Thus, the two pathogens tested showed a different susceptibility to CAR1 and THY5.  
Such adhesion reduction was not ascribable to the presence of 0.4% ethanol, as preliminary experiments did not show any effect of this ethanol concentration (contained in 0.02% EOs) on the number of viable bacterial cells adhering to the intestinal cell monolayer.  
Figure 4: CAR1 and THY5 effects on Caco-2 cells monolayer integrity: Transepithelial electrical resistance (TEER) and phenol red apparent permeability (Papp). Cells were untreated (Control, C) or treated with different CAR1 or THY5 concentrations (0.01–0.1%). TEER values ((**A**) for CAR1 and (**C**) for THY5), recorded for up to 24 h, were calculated as the % of C for each timepoint. Phenol red Papp ((**B**) for CAR1 and (**D**) for THY5) was measured at 24 h and values are reported as cm s\({}^{-1}\). A black line set at \(1\times 10^{-6}\) cm s\({}^{-1}\) represents the Papp threshold, indicating destroyed cell monolayer integrity for values above. Values represent the mean \(\pm\) SD of the two independent experiments, carried out in triplicate.
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
## 4 Numerical Results and Discussions
### NOMA-Enhanced Versus OMA-Based D2D Communication  
In Figure 8, the average D2D data rate performance of three algorithms versus the number of the CUs on the same SC is shown in two accessing ways (NOMA and OMA). From Figure 8, the average data rate of the D2D devices decreases with the rising of the CU number on the same SC. Because of the increasing number of CUs, the probability of the D2D users receiving more interference from the CUs increases, which leads to the monotonous decreasing of data rates with the increasing of \(n\) according to (20). In addition, the NOMA based on D2D scheme achieves larger data rate than the conventional OMA based on D2D scheme. In NOMA, the performance of FIPA is worst, while that of FRPA is improved. Our proposed algorithm has the best data rate.
# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
## 3 System Model and System Architecture  
The data acquisition and transmission model of the Industrial Internet of Things is one of the hottest issues in the current research field. We have broken away from the traditional way of networking, the \"edge sensor-Central computer\". As shown in Figure 2, a three-level networking model of distributed sensor self-networking and networking with cloud server platforms is presented. The model is composed of three parts: a dataset collector, repeater, and server. In this model, the most important part is the data collection module, which consists of two small modules. They are a wireless communication module and a data fusion module.
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 3 Inter-SubNet with New Loss Function
### Presented Method  
The method presented here modifies Inter-SubNet by adjusting its loss function during training. Let us denote the estimated and ground-truth time signals as \(\mathbf{s}\) and \(\mathbf{\hat{s}}\). We provide three options for the loss function:  
1. The new loss is calculated using the mean Huber loss measured with the estimated and ground-truth cIRMs for \(\mathbf{s}\) and \(\mathbf{\hat{s}}\). It is expressed as follows: \[\mathcal{L}_{1}=\mathcal{L}_{cIRM,Huber}(\mathbf{s},\mathbf{\hat{s}})=\sum_{f} \sum_{t}\Big{(}\mathcal{L}_{Huber}(\mathbf{M}^{r}_{f,t},\mathbf{\hat{M}}^{r}_{ f,t})+\mathcal{L}_{Huber}(\mathbf{M}^{i}_{f,t},\mathbf{\hat{M}}^{i}_{f,t}) \Big{)},\] (13) where \(\mathcal{L}_{Huber}(.)\) is from Equation (7).
2. The new loss is a combination of TAP loss with the MSE-based cIRM loss: \[\mathcal{L}_{2}=\mathcal{L}_{cIRM,MSE}(\mathbf{s},\mathbf{\hat{s}})+\lambda_{ 1}\mathcal{L}_{TAP}(\mathbf{s},\mathbf{\hat{s}}).\] (14)
3. The new loss is a combination of TAP loss with the mean Huber-based cIRM loss: \[\mathcal{L}_{3}=\mathcal{L}_{cIRM,Huber}(\mathbf{s},\mathbf{\hat{s}})+\lambda _{2}\mathcal{L}_{TAP}(\mathbf{s},\mathbf{\hat{s}}).\] (15)  
In Equations (14) and (15), \(\mathcal{L}_{cIRM,MSE}(.)\), \(\mathcal{L}_{TAP}(.)\), and \(\mathcal{L}_{cIRM,Huber}(.)\) are from Equations (6), (11), and (13), and \(\lambda_{1}\), and \(\lambda_{2}\) are the weight hyper-parameters for TAP loss.  
The gradient of the employed loss is backpropagated to update both the complex ideal ratio mask (cIRM) and the other parameters in Inter-SubNet during training. The following is a detailed explanation of how this process works:  
1. Forward Pass: In the forward pass, the network processes the noisy speech signal to generate the complex ideal ratio mask (cIRM). This mask is then applied to the time-frequency representation of the noisy signal, resulting in enhanced speech output.
2. Loss Calculation: The estimated cIRM mask is evaluated against the ground-truth mask using MSE or Huber loss. Simultaneously, we assess the enhanced speech against the clean target speech using TAP loss. This loss function specifically measures how effectively the enhanced speech retains crucial temporal acoustic parameters.
3. Backward Pass: The total loss, which combines both MSE/Huber loss and TAP loss, is utilized to compute the gradient with respect to all network parameters by applying the chain rule of calculus through each layer. Compared to MSE loss for the cIRM, TAP loss additionally incorporates layers that perform the inverse STFT and those responsible for estimating TAPs.
4. Updating Network Parameters: The computed gradients are used to update the parameters of the Inter-SubNet framework with an optimizer.  
This process is repeated for multiple epochs over the training dataset, allowing Inter-SubNet to learn and adjust its parameters based on the feedback from the employed loss.