esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Qualitative Interpretation
#### 4.1.1 Complete Bouguer Gravity Anomaly Map  
Based on the map shown in Figure 6a, the pattern of the Bouguer gravity anomaly can be classified into three distinctive zones of low (LG), medium (MG), and high (HG) gravity anomaly values ranging from, \(-30\) to \(0\) mGal, \(1\) to \(10\) mGal, and \(11\) to \(30\) mGal, respectively. The LG regions can be observed and situated mostly in the southeastern to eastern part that consists of the Mahang Formation and granitic relief; mark the area of high topographic land as shown in Figure 4. The MG region is comprised of Mesozoic sedimentary formations, which are Sungai Petani, Kubang Pasu and Semanggol Formations, while the (HG) zone has been identified in Quaternary sediments at the western side, near to the coastal area of the study area.  
Generally, the gravity anomaly values decrease laterally from southwest to northeast indicating the decrease in density due to the thinning of Mesozoic sediments towards the eastern and southeastern parts. On the other hand, the shallowing of the granitic basement as the presence of two major uplifted granite ranges, namely Main Range and Bintang Range at the eastern and southeastern parts, plays a major role in the sediment thinning. The negative gravity anomaly region, (LG), indicates the presence of Main Range and Bintang Range above the surface that have an effect of the low-density root of mountains and performed isostatic equilibrium phenomenon, causing a further gravity deficit. Consequently, the high-density earth crust compensates for these granite ranges. The cause of the low Bouguer anomaly value of the granite might be due to its less mean density compared to the mean density of the surrounding rock [22]. [PERSON] et al. [42] reported that the gravity anomaly shows a prominent gravity minimum centered over the Main Range which is due to a thickening of granite batholith in the area. This idea is strongly supported by and reported in [PERSON] et al. [43].  
Variations in gravity anomalies were normally caused by variation in the density of the subsurface rocks [44] and they usually indicate faults or lithological contacts [45]. As aforementioned, the negative gravity anomaly covered in the granite and Mahang formation has continued southward, indicating the presence of extended granite basement overlain by younger formations and the continuity of the Main-Bintang granite ranges. Sungai Petani, Kubang Pasu and Semanggol formations were classified by irregular medium (MG)-high (HG) gravity anomaly zones, suggesting the deposition of relatively dense sedimentary formations. The highest gravity (HG) anomaly zone was detected in the Quaternary alluvium overlying the Pleistocene Simpang formation and indicating deep sedimentary basin thickness [46].
## 2 Methods
### Model for Eliminating the Mixed Noise Based on Linear Signal Filtration
Subsequently, the variance of a random process with a uniform function of spectral power density \(S_{\eta}(\omega)\) is determined by the formula:  
\[\sigma^{2}=\frac{1}{2\pi}\int\limits_{-0.5/\Delta t}^{0.5/\Delta t}\frac{N_{0}}{ 2}d\omega=\frac{1}{2\pi\Delta t}\frac{N_{0}}{2} \tag{11}\]  
For a single spectral density \(S_{\eta}(\omega)=N_{0}/2=1\),, we obtain that the discrete implementation of white noise \(\xi(n)\) for the \(N\) samples should be performed in accordance with the expression:  
\[S_{\xi}(\omega)=\frac{b_{M}\omega^{2M}+b_{M-1}\omega^{2(M-1)}+\cdots+b_{0}}{a _{N}\omega^{2N}+a_{N-1}\omega^{2(N-1)}+\cdots+a_{0}} \tag{4}\]0  
where \(x(n)\) are random independent numbers with a normal standard distribution (a Gaussian distribution with zero mathematical expectation and unit variance). In modeling a linear filter in order to eliminate mixed types of noise based on a linear filtering method, its input \(x(t)\) and output \(y(t)\) signals are presented in the form of lattice functions \(x[n]\) and \(y[n]\), and are nonzero for time instants:  
\[S_{\xi}(\omega)=\frac{b_{M}\omega^{2M}+b_{M-1}\omega^{2(M-1)}+\cdots+b_{0}}{a _{N}\omega^{2N}+a_{N-1}\omega^{2(N-1)}+\cdots+a_{0}} \tag{4}\]1  
where \(\Delta t\) is the sampling period of the signals. Perfect equality of the input \(x[n]=x(n\Delta t)\) and output signals \(y[n]=y(n\Delta t)\) for the digital model of a linear filter is not achievable. In practice, approximate equality \(y[n]\approx y(n\Delta t)\) is achieved with an error that has a normal distribution \(S_{out}(\omega)\) with a dispersion equal to:  
\[S_{\xi}(\omega)=\frac{b_{M}\omega^{2M}+b_{M-1}\omega^{2(M-1)}+\cdots+b_{0}}{a _{N}\omega^{2N}+a_{N-1}\omega^{2(N-1)}+\cdots+a_{0}} \tag{4}\]2  
The application of this filtration model to an MRI image of the heart eliminates impulse noise as well as the geometric and fluctuation noise of the system (Figure 2). Gaussian noise was added to the original MRI image in order to study the effectiveness of applying the model to eliminate mixed noise (Figure 2). The Gaussian noise was a simulation of the random noise in an MRI image in the DICOM format after a real biological object was scanned. It is assumed that the random noise in an MRI image of the heart is due to impulse noise and the geometric and fluctuation interference of the scanning system. Thus, a digital model of a linear filter to eliminate mixed types of noise that represent a random process can be implemented while using a discrete Fourier transform by choosing the number of harmonics \(M\approx 2\pi/\Delta\omega\) and sampling frequency \(\Delta t\geq M\) for the random process \(\xi(n),n=\overline{1,N}\). \[S_{\xi}(\omega)=\frac{b_{M}\omega^{2M}+b_{M-1}\omega^{2(M-1)}+\cdots+b_{0}}{a _{N}\omega^{2N}+a_{N-1}\omega^{2(N-1)}+\cdots+a_{0}} \tag{4}\]3 Heart MRI: (**a**) image with white noise and (**b**) denoised image.
## 2 Literature Review
In [31], a greedy model small world (GMSW) is proposed for applications based on IoT to improve the robustness of the topological structure. To our knowledge, no one has combined QoS and energy consumption challenges in a clustering-based routing model for 5G and IoT-based smart healthcare. Because of the variety of the network and the limited energy available to nodes, balancing QoS and energy consumption is a difficult challenge. Table 1 shows the summary of different clustering-based routing schemes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Latency** & **Energy** & **Reliability** & **Simulators** & **Benchmarks** \\ \hline LEACH & NO & YES & NO & Matlab & Direct, MTE, Static \\ LEACH-C & YES & YES & NO & NS-2 & LEACH, Static \\ HEED & NO & YES & NO & Matlab & LEACH \\ BEEM & YES & YES & NO & Matlab & LEACH, HEED \\ TEEN & NO & YES & NO & NS-2 & LEACH, LEACH-C \\ RINtraR & NO & YES & YES & Matlab & LEACH \\ LEACH-TL & NO & YES & NO & NS-2 & LEACH \\ DWEHC & NO & YES & NO & NS-2 & HEED \\ AWARE & NO & YES & YES & TOSSIM & Unaware LEACH \\ EECS & NO & YES & NO & Matlab & LEACH \\ ADAPTIVE & NO & YES & YES & Matlab & PARNET, Clustering TDMA \\ EEHC & NO & YES & NO & Matlab & MAX-MIN-D \\ HGMR & YES & YES & YES & NS-2 & HRPM, GMR \\ MOCA & NO & YES & NO & Matlab & No Comparison \\ UCS & NO & YES & NO & Matlab & Equal Cluster Size \\ CCS & NO & YES & NO & Matlab & PEGASIS \\ BCDCP & NO & YES & NO & Matlab & LEACH, LEACH/LC, PEGASIS \\ FLOC & NO & NO & NO & Matlab & No Comparison \\ APTEEN & NO & YES & NO & NS-2 & LEACH, LEACH-C, TEEN \\ EEUC & NO & YES & NO & Matlab & LEACH, HEED \\ PEACH & NO & YES & NO & Matlab & LEACH, HEED, EEUC, PEGASIS \\ ACE & NO & YES & NO & Matlab & HCP \\ S-WEB & NO & YES & NO & Matlab & Short, Direct \\ PANEL & NO & YES & NO & TOSSIM & HEED \\ TTDD & YES & YES & YES & NS-2 & No Comparison \\ PEGASIS & NO & YES & NO & Matlab & LEACH, Direct \\ MPRUC & NO & YES & NO & Matlab & HEED \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of existing clustering-based routing algorithms.
## 2 Materials and Methods
### Proposed Improve Centernet Model  
CenterNet network adopts the \"encoder-decoder\" structure. After 32 times downsampling of the model, the feature layer of high-level semantic information is obtained via deconvolution for object detection. However, this model does not consider the detection ability of multi-scale objects, and the detection ability of small objects is weak. In the process of feature extraction, multiple downsampling will cause the feature aggregation of small objects, leading to the problem of missing detection and wrong detection. Additionally, the different object types of maize leaf pests and diseases, as well as the variation in object size, create a significant increase in detection difficulty, given the small and dense characteristics of these objects.  
To address these issues, we propose a maize leaf pest and disease detection model based on the CenterNet algorithm. As shown in Figure 2, the model consists of three main parts: backbone network, neck network and detection network. The backbone network uses ResNet50 to extract features and embeds the convolutional attention module (CBAM) in the second to fourth layers, which helps to learn the distribution law of features and improve the weights of semantic and location features for pests and diseases. The neck network uses multi-scale feature fusion module (MFF) to replace the deconvolutional layer of the original model. The module fuses the input feature maps using a weighted bidirectional feature fusion network and balances the contributions of feature maps with different resolutions in the output by adding additional weights. Finally, the feature maps at different scales are concatenated and fused with each other and fed into the detection network.  
By improving the CenterNet model, the maize leaf pest detection model is able to better detect small objects, as well as improve detection efficiency and accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pest and Disease Name** & **Sample Size** \\ \hline Northern leaf blight & 249 \\ Northern leaf spot & 213 \\ Gray leaf spot & 262 \\ Maize borer & 424 \\ Aphids & 879 \\ Red spider & 160 \\ Armyworm & 206 \\ Peach borer & 382 \\ Total sample size & 2775 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data samples with the same statistics.
# Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training
## 3 Local Back-Propagation Algorithm
### Model Training  
In models constructed using the AE series (Figure 3a-c), the encoder transforms the input data into a latent vector, and the decoder is then trained to reconstruct the input from this latent representation. During training, the encoder's output latent vector from one cell is provided as input to the next cell.  
In models that incorporate GAN (Figure 3d), the generator takes random noise as input. Within the generator, the encoder produces a latent vector, and the decoder subsequently generates fake data from this latent vector. The discriminator receives both real and generated data, learning to distinguish between the two. Through this process, the generator refines its ability to produce data that the discriminator deems authentic. After training, the encoder's latent vector from the generator, using input from the previous cell, serves as the input to the subsequent cell.  
In Figure 1c, the final layer in each model is trained to produce the desired outputs. Depending on the task, this may involve classification, regression, or generation. As with the other cells, the final layer is trained locally.  
For input reconstruction tasks, we employ mean squared error as the loss function. When training the discriminator in GAN, binary cross-entropy is used. The final classification layer utilizes cross-entropy as its loss function. In all models, the AdamW [28] optimizer is applied, and ReLU [29] is used as the activation function.  
To further stabilize training and enhance the generalization, layer normalization [30] is applied to every input of each cell. This step helps mitigate issues such as vanishing or exploding gradients, leading to more stable and efficient learning.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **BP** & **FF** & **LBP (Ours)** \\ \hline Training method & Forward–Backward & Forward-only & Forward–LocalBackward \\ Input format & Raw data & Positive/Negative & Raw data \\ Layer isolation & No & Yes & Yes \\ Memory efficiency & Low & High & High \\ Training stability & High & Low & Moderate \\ Loss function & General-purpose & Specialized & General-purpose \\ Compatibility & Yes & Limited & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of back-propagation (BP), Forward-Forward (FF), and the proposed Local back-propagation (LBP) across core aspects such as training flow, input requirements, loss function types, memory efficiency, and compatibility with standard deep learning pipelines.
## 3 Proposed Method
### Cross-Layer Routing Using ECTSA
#### 3.4.1 Iterative Process of ECTSA  
An optimal path over the network is identified using the ECTSA based on the residual energy, communication cost, data success rate, and mobility. Generally, the jet propulsion is established in three conditions: avoiding conflicts among the search agents, following the location of the optimal solution, and keeping close to the optimal solution.  
The new search agent location \(\overset{\rightarrow}{A}\) is calculated for avoiding the conflicts among the search agents. This search agent location identification is calculated based on Equations (3)-(6).  
\[\overset{\rightarrow}{A}=\frac{\overset{\rightarrow}{G}}{\overset{ \rightarrow}{M}} \tag{3}\]  
\[\overset{\rightarrow}{G}=c_{2}+c_{3}-\overset{\rightarrow}{F} \tag{4}\]  
\[\overset{\rightarrow}{F}=2.c_{1} \tag{5}\]  
\[\overset{\rightarrow}{M}=P_{min}+c_{1}.P_{max}-P_{min} \tag{6}\]  
where the gravity force is denoted as \(\overset{\rightarrow}{G}\); water flow advection in the deep ocean is denoted as \(\overset{\rightarrow}{F}\); the random numbers generated in the range of \(\left[0,\ 1\right]\) are represented as \(c_{1},\ c_{2}\) and \(c_{3}\); and \(\overset{\rightarrow}{M}\) denotes the social forces among the search agents. Moreover, the initial and subordinate speeds among search agents are denoted as \(P_{min}\) and \(P_{max}\). Here, \(P_{min}\) and \(P_{max}\) are considered 1 and 4, respectively.  
The search agent is moved in the direction of the current best solution, once conflict is avoided among neighbors. This helps to reach the optimal solution. The optimal location of the best agent is identified using Equation (7).  
\[\overset{\rightarrow}{PD}=\left|X_{best}-r_{rand}.\overset{\rightarrow}{P }\left(x\right)\right| \tag{7}\]  
where \(\overset{\rightarrow}{PD}\) indicates the distance among the optimal solution \(\left(X_{best}\right)\) and location of tunicate \(\overset{\rightarrow}{P}_{P}\left(x\right)\); the current iteration is represented as \(x\); and the random number generated in the range of \(\left[0,1\right]\) is \(r_{rand}\).  
The location of the tunicate is calculated as shown in Equation (8) to maintain the tunicate location near to the optimal solutions.  
\[\overset{\rightarrow}{P}_{P}\left(x\right)=\begin{cases}X_{best}+\overset{ \rightarrow}{A}.\overset{\rightarrow}{PD},\ if\ r_{rand}\geq 0.5\\ X_{best}-\overset{\rightarrow}{A}.\overset{\rightarrow}{PD},\ if\ r_{rand}<0.5 \end{cases} \tag{8}\]Equation (6) returns the updated locations of the best transmission path according to the optimal solution \(X_{best}\).  
The location of the current tunicate is updated according to the locations of two tunicates for modelling the swarming action, which is expressed in Equation (9).  
\[\overrightarrow{P_{p}}\left(x+1\right)=\frac{\overrightarrow{P_{p}}\left(x \right)+\overrightarrow{P_{p}}\left(x+1\right)}{2+c_{1}} \tag{9}\]  
In this ECTSA, the optimal solution \(X_{best}\) is identified based on the fitness function estimation, which is clearly described in the following section.
## 3 Results and Discussion
### Pesticide Residues in Apples and Citrus Fruits--Comparison
#### 3.1.1 Apples
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Pesticide**} & \multirow{2}{*}{**Group**} & \multicolumn{4}{c}{**Range of Pesticides Concentration**} & \multirow{2}{*}{**MRL**} & \multirow{2}{*}{**LOQ**} \\ \cline{3-3} \cline{5-8}  & & \multicolumn{2}{c}{**Whole Applies**} & & \multicolumn{2}{c}{**Apple Peets**} & & \\ \cline{3-8} \cline{5-8}  & & **2012** & **2020** & **2012** & **2020** & **2012** & **2020** & \\ \hline boscalid & F & <LOQ-0.0613 d & <LOQ-0.1300 c & <LOQ-0.3245 b & <LOQ-0.7820 a & 2 mg/kg [22] & 2 mg/kg [23] & 0.0005 \\ \hline carbendazin & F & <LOQ-0.0298 b & <LOQ c & <LOQ-0.1691 a & <LOQ c & 0.2 mg/kg [24] & not approved & 0.0001 \\ \hline chlorpyrifos & A,1 & <LOQ-0.0490 b & <LOQ c & <LOQ-0.2685 a & <LOQ c & 0.05 mg/kg [25] & not approved & 0.0001 \\ \hline buprimate & F & <LOQ-0.0098 b & <LOQ c & <LOQ-0.5000 a & <LOQ c & 0.3 mg/kg & 0.3 mg/kg [27] & 0.0001 \\ \hline difenconazole & F & <LOQ-0.0096 b & <LOQ c & <LOQ-0.0432 a & <LOQ c & 0.5 mg/kg [28] & 0.8 mg/kg [29] & 0.0002 \\ \hline diphenylamine & PCR & 0.02820-0.130 b & <LOQ c & 0.1536-0.6773 a & <LOQ c & 5 mg/kg [30] & not approved & 0.0025 \\ \hline disuidron & I & <LOQ-0.0321 b & <LOQ c & <LOQ-0.1888 a & <LOQ c & 0.01 mg/kg [20] & not approved & 0.0001 \\ \hline hychirazo & A,1 & <LOQ-0.0119 b & <LOQ c & <LOQ-0.0088 a & <LOQ c & 1 mg/kg [33] & 1 mg/kg [33] & 0.0001 \\ \hline franzaquin & A & <LOQ-0.0117 b & <LOQ c & <LOQ-0.0444 a & <LO Q c & 0.1 mg/kg [34] & 0.1 mg/kg [35] & 0.0005 \\ \hline malnutrition & A,1 & <LOQ-0.0291 b & <LOQ c & <LOQ-1.0534 a & <LOQ c & 0.02 mg/kg [36] & 0.02 mg/kg [37] & 0.0001 \\ \hline propagrigte & A & <LOQ-0.3540 b & <LOQ c & <LOQ-1.4943 a & <LOQ c & 3 mg/kg [30] & not approved & 0.001 \\ \hline pyraclockrobin & E, PCR & <LOQ-0.0370 d & <LOQ-0.087 c & <LOQ-0.1928 b & <LOQ-0.4470 a & 0.3 mg/kg [39] & 0.5 mg/kg [40] & 0.0002 \\ \hline pyrimethanil & F & <LOQ-0.1950 b & <LOQ c & <LOQ-0.6658 a & <LOQ c & 5 mg/kg [39] & 15 mg/kg [35] & 0.0005 \\ \hline thiophanate & F & <LOQ-0.2380 b & <LOQ c & <LOQ-1.3310 a & <LOQ c & 0.5 mg/kg [24] & not approved & 0.0005 \\ \hline thiacloprid & I & <LOQ-0.0161 b & <LOQ c & <LOQ-0.0867 a & <LOQ c & 0.3 mg/kg [41] & not approved & 0.0001 \\ \hline triflumuron & I & <LOQ-0.0082 b & <LOQ c & <LOQ-0.0508 a & <LOQ c & 0.5 mg/kg [25] & not approved & 0.0001 \\ \hline fuslazole & F & <LOQ-0.0145 a & <LOQ b & <LOQ-0.0132 a & <LOQ b & 0.02 mg/kg [22] & not approved & 0.0002 \\ \hline pirrimcarb & I & <LOQ-0.092 b & <LOQ c & <LOQ-0.5001 a & <LOQ c & 2 mg/kg [44] & 0.5 mg/kg [45] & 0.0001 \\ \hline triflosytrobin & F & <LOQ-0.0116 b & <LOQ c & <LOQ-0.0793 a & <LOQ c & 0.5 mg/kg [41] & 0.7 mg/kg [46] & 0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Levels of individual pesticides in apples and the values of MRL.
# Valorization of Cassava By-Products: Cyanide Content and Quality Characteristics of Leaves and Peel
Some researchers have already used fresh leaves to explore the properties of cassava leaves and their products after screw pressing [21; 27]. However, in our experiments we used frozen cassava leaves because freezing is a fast, convenient, and popular method for food preservation with minimal nutrient loss. In addition, freezing leads to various structural changes in the fresh leaves, such as cell lysis, cracking, and dislocation [28], which could increase the availability of proteins for extraction and may also have a positive effect on the leaching of cyanide in the juice fraction during pressing. Moreover, frozen leaves could be readily available throughout the year. In the case of cassava peel, in addition to cyanide, the presence of insoluble fiber fractions, such as neutral detergent fiber (NDF) which includes cellulose, hemicellulose, and lignin; acid detergent fiber (ADF) comprising lignin and cellulose; and acid detergent lignin (ADL) consisting of lignin, poses a great challenge in processing. Under natural conditions, the hemicellulose and lignin form a protective matrix around the cellulose, which interferes with the digestion of the peel [29]. Therefore, it is necessary to hydrolyze the insoluble components of the peel so it can subsequently be used as feed or converted into other usable products. The peeling methods employed can also affect the insoluble fiber fractions by modifying the peel's composition and structural properties. Various methods, such as manual, mechanical, chemical, and thermal methods, are currently used to remove the peel from the tubers. However, despite the fact that mechanical peeling is widely used in both small- and large-scale industries because of its simplicity and speed, it results in a significant loss of flesh of about 25-44% [30; 31]. To address this problem, [PERSON] et al. [7] discovered that enzyme treatment prior to mechanical peeling can increase the efficiency of the peeling process by softening the cell wall components of the peel. This study is an extension of previous research and aims to determine if enzyme-assisted mechanical peeling can enhance the hydrolysis of insoluble fibers in cassava peel. Additionally, this study examines whether enzyme treatment prior to mechanical peeling could also contribute to the detoxification of cassava peel by promoting contact between linamarin and endogenous linamarase [32]. Thus, overall, this study had the following objectives: (1) To explore the influence of mechanical pressing on the composition, protein, and cyanide levels of frozen cassava leaf fractions: juice, and press cake. (2) To examine the effects of enzymatic pretreatment on the composition, insoluble fiber, and cyanide content of cassava tuber peel.
# Optical Camera Communication as an Enabling Technology for Microalgae Cultivation +
## 3 Communications Modelling  
The proposed architecture presents some specific characteristics from the communication system point of view. First, the light propagates through different media from the emitter to the camera, which modifies the received optical signal. On the other hand, the camera position affects the communications' performance since it determines the received power and the emitter image size. Furthermore, the distribution and size of the photobioreactors also impact the complete OCC system performance. In this section, all these aspects are addressed.