esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Analysis of Systematic Batched Network Codes
## 4 Triangular Embedding: A Structured Systematic Outer Code Design
### Decoder Design  
It is possible to use the decoders discussed in Section 2.3 to decode the batches generated by triangular embedding. However, due to the structure of the triangular embedding encoding, the decoder can be simplified.  
We design a decoder \(\text{DEC}_{n_{s}}\) using only the first \(M_{i}\) packets of the \(i\)th batch, \(i=1,\ldots,n_{s}\). The overall generator matrix \(\mathbf{\tilde{G}}^{*}\) of \(\text{ENC}_{n_{s}}^{*}\) is of the form in Figure 6.  
Figure 5: Illustration of encoding using triangular embedding. The gray part contains non-zero entries and the white part contains only zero.  
An inactivation decoder can be applied to decode the message packets:  
* First, inactivate all the packets used by the first \(n_{s}\) batches among the last \(A-K\) active packets;
* Second, apply belief propagation decoding to solve all the batches;
* Last, solve the inactive packets.  
Note that as, at most, \(\min\{A-K,N_{\text{inac}}\}\) packets are used among the last \(A-K\) active packets during encoding, the total number of inactive packets is no more than \(N_{\text{inac}}+K^{\prime}-A\).
## 3 Data, Modelling Methods and Analysis Tools  
### Empirical Series of Malaria Incidence  
The empirical part of our investigation comprehends eight series of malaria incidence as presented in Figure 1. These series are available from previous studies namely [PERSON] et al. (Kasena Nankana municipality in Ghana, 2017) [21], [PERSON] et al. (Ejisu-Juaben municipality in Ghana, 2017) [16], [PERSON] et al. (Ethiopia, 2014) [22], [PERSON] et al. (Kucha district in Ethiopia, 2016) [23], [PERSON] et al. ([PERSON] in Burundi, 2007) [24], [PERSON] et al. (Est Mono district in Ethiopia, 2016) [23], [PERSON] et al. ([PERSON] in Burundi, 2007) [24], [PERSON] et al. (Est Mono district in Togo, 2012) [25], [PERSON] et al. (Uganda, 2017) [26], [PERSON] et al. (Kenya, 2008) [27]. The data sets were chosen as representative of regions with malaria incidence diversity in Africa. They have a time duration ranging from 60 [16] to 132 months [22], while showing different trends and periodicity--see Figure 1 and Table 1. Malaria incidence was measured at each month as the expected number of new malaria cases per 100 inhabitants in a full year, if malaria incidence were to be kept constant. In this way, to obtain the precise incidence at each month, the yearly presented graphical value must be divided by 12 months.  
Figure 1: Eight different series of malaria incidence from different studies in the literature: [PERSON] et al. (Kasena Nankana municipality in Ghana, 2017) [21], [PERSON] et al. (Ejisu-Juaben municipality in Ghana, 2015) [16], [PERSON] et al. (Ethiopia, 2014) [22], [PERSON] et al. (Kucha district in Ethiopia, 2016) [23], [PERSON] et al. (Karuzi in Burundi, 2007) [24], [PERSON] et al. (Est Mono district in Togo, 2012) [25], [PERSON] et al. (Uganda, 2017) [26], [PERSON] et al. (Kenya, 2008) [27]. All data series are available from the respective original references from the authors.  
As can be seen from Figure 1, the eight cases differ in their level of malaria transmission and epidemic behavior. All empirical series show some form of irregular periodicity as a consequence of climate seasonality. Declining malaria incidence is clear in series from Aregawi and Okech, as well as in Alhassan and Bedane, although with a final disease outbreak in these last two cases. High levels of malaria transmission occur in Gomez-Elipe (identified solely as Elipe time series in some of the figures, for clarity) for a brief period of time. Malaria incidence remains quite stable in the Muwanika, Appiah and Landoh empirical series, with a consistent upward trend in the last case.
# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 3 Energy-Efficient Game-Theory-Based Spectrum Decision
### Data Transmission Mode (D Mode)  
#### 3.3.1 Spectrum Sensing Module of D Mode  
The spectrum sensing module of D mode exploits partial spectrum sensing (PSS), in which the sensor nodes perform spectrum sensing only on their operating channels and backup channels selected from a previous frame. In the D mode, the clustered topology is preserved and the sensor nodes are either cluster heads (CHs) or cluster members (CMs).  
Figure 4: CM coordination tasks ((\(t\)s)\({}_{t}\) means time slot \(t\)).  
Firstly, CMs check the connectivity to their CHs by sending a control packet. Meanwhile, the CHs examine whether the backup channel is empty or not. When the CHs receive the control packets from their respective CMs, CHs reply with an acknowledgement and the status of the backup channel. After control packets exchanges are finished, CHs and CMs perform PSS. If there is no PU transmission detected by sensor node \(S_{i}\) on its operating channel selected from the previous frame ((C\({}_{i}\))_{prev}\), then:  
\[Status(C_{i})_{prev}=(available) \tag{28}\]  
otherwise:  
\[Status(C_{i})_{prev}=(obsolete) \tag{29}\]  
The backup channel is sensed when it is not empty, i.e., backup channel selected from the previous channel (C\({}^{\prime}\))_{prev}\
eq\mathcal{O}\). Similarly, if there is no PU transmission detected by sensor node \(S_{i}\) on (C\({}^{\prime}\))_{prev}\), then:  
\[Status(C^{\prime}_{i})_{prev}=(available) \tag{30}\]  
otherwise:  
\[Status(C^{\prime}_{i})_{prev}=(obsolete) \tag{31}\]  
The CMs send the spectrum-sensing results to their CH. As the CRSN continues to operate, the sensor nodes eventually deplete its energy and become inactive. If a CH becomes inactive, its CMs would not receive acknowledgement packets; thus, they would go to sleep and wake up at the last time slot of the current frame to send urgent requests for C mode because an inactive CH causes the entire cluster to be inactive. If a CH does not receive any control packet (all of its CMs are inactive), it proceeds to the next activities and modules, and also sends a normal request for C mode at the last time slot. Aside from the energy depletion of the sensor nodes, the operation channel's quality degradation also causes the CHs or CMs to be unable to receive packets from each other. However, our proposed algorithm does not differentiate those causes of failed transmissions.  
The outcomes of PSS are a list of operating channels and backup channels, their status (available or obsolete), and the variables to calculate the SNR. The activities of spectrum sensing modules, both FSS and PSS, are shown in Figure 5.
# Enabling End-Users in Designing and Executing of Complex, Collaborative Robotic Processes
## 4 User Studies
### Results
#### 4.2.2 User Study RxT02  
Generally, the second user study showed positive feedback from the participants from both the healthcare and the industry domains. The participants appreciated the design of the platform prototype and the simplification of programming. Participants highlighted the benefit of initial training to get an introduction to the platform functionalities and the programming. They also showed interest in using the platform in the future. In addition, participants pointed out some negative aspects of the platform and future improvements they would like to see. Participants from both areas commented as follows on the tasks they had to complete during the assessment:  
* Interaction with user registration on the platform was considered very easy.
* The tasks that focus on an action that requires only a few blocks, such as exchanging messages, grabbing an object, or moving to a position, were considered easy to perform and required minimum assistance from the study facilitator.
* Tasks requiring the design of a complete process were considered more challenging and required more support in interpreting the task and translating the task into blocks. The feedback is presented in more detail in Table 4.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Positive Feedback** & **Negative Feedback** \\ \hline \hline  & * The registration page would need to be made more user friendly (e.g., option to see password, password guideline, link to End User License Agreement). * Dashboard screen terminology is unclear at first. * _Account_ and _Company_ tabs can be combined. * _Configure_ functionality is unclear. * Some icons could be improved (e.g., _Download_, _Search_). * For some functionalities (e.g., _Configure_, _Download_) the participants don’t understand whether users items or the online version are meant. * The meaning of the _Skills_ and _SupportedModules_ assets is unclear. * On the _TaskDesign_ screen the difference between _Blocks_, _TaskModules_ and _Processes_ is unclear. * Interaction with the _TaskDesign_ screen is a little bit confusing. * For experienced programmers the VPL prototype is too simple and does not offer enough flexibility. * Initial training and documentation is required. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Feedback from participants of the user study RxT01.  
In the second study, the implications focused on specific aspects of certain functionalities. For example, the \"Save\" button should directly save the user's program as an XML file and not open the XML code in another browser tab, the \"Load\" button should open the Windows Explorer instead of providing an interface for copying and pasting the XML file, the type of the blocks' parameters should be clearer, access to the free text field should be easier, or more information should be added when hovering.
## 2 Materials and Methods
### Gridded Precipitation Products
#### 2.3.5 Persiann  
The PERSIANN provides hourly precipitation estimates at the spatial resolution of 0.04\({}^{\circ}\) for the quasi-global coverage of 60\({}^{\circ}\)N-60\({}^{\circ}\)S from 2003 to the present [55]. PERSIANN, developed at the University of Arizona and now operated by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California Irvine (UCI), is based on an adaptive Artificial Neural Network (ANN) model that estimates fine-scale precipitation distribution using IR information (10.7 \(\upmu\)m) from geostationary satellites in analyzing local and regional cloud properties [56]. The PERSIANN-CCS algorithm converts rain rates from satellite cloud images in several steps, which consist of: (i) separating cloud images into recognizable cloud patches, (ii) extracting cloud properties based on their shape, texture, and coldness, (iii) clustering cloud patches into orderly subgroups, and (iv) calibrating cloud-top temperature and rainfall (\(T_{b}\)-\(R\)) correlations for the various cloud categories using gauge-corrected radar hourly rainfall data [55]. The product is available at: [[http://chrsdata.eng.uci.edu/](http://chrsdata.eng.uci.edu/)]([http://chrsdata.eng.uci.edu/](http://chrsdata.eng.uci.edu/)) (accessed on 3 June 2021).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Full Name** & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & \
\begin{tabular}{c} **Timescale (Highest** \\ **Resolution**) \\ \end{tabular} &
\begin{tabular}{c} **Period of** \\ **Availability** \\ \end{tabular} & **Reference** \\ \hline ARC2 & African Rainfall Climatology version 2 & 0.10\({}^{\circ}\) & Daily & 1983–Present & [45] \\ CHIRPS & Climate Hazards Group Infrared & 0.05\({}^{\circ}\) & Daily & 1981–Present & [47] \\  & Precipitation with station data & 0.10\({}^{\circ}\) & Hourly & 1979–Present & [50] \\ ERA5 & ECMWF Reanalysis version 5 & 0.10\({}^{\circ}\) & Hourly & 1979–Present & [50] \\ IMERG & Integrated Multi-satellite Retrievals & 0.10\({}^{\circ}\) & Half-hourly & 2000–Present & [53] \\  & for Global Precipitation Measurement & 0.10\({}^{\circ}\) & Half-hourly & 2000–Present & [53] \\  & Precipitation Estimation from & & & & \\ PERSIANN & Remotely Sensed Information using & 0.04\({}^{\circ}\) & Hourly & 2003–Present & [55] \\  & Artificial Neural Networks—Cloud & 0.04\({}^{\circ}\) & Hourly & 2001–Present & [57] \\  & Classification System & 0.010\({}^{\circ}\) & Daily & 2001–Present & [57] \\ RFEv2 & Climate Prediction Center (CC) & 0.010\({}^{\circ}\) & Daily & 2001–Present & [57] \\  & African Rainfall Estimates version 2 & 0.010\({}^{\circ}\) & Half-hourly & 2001–Present & [57] \\  & African Rainfall Estimates version 2 & 0.
## 2 Material and Methods
### Data Classification and Factor Analysis
#### 2.4.2 Classification of Erosion Grades  
The Soil Erosion Classification and Grading Standard (SL190-2007) [46], which assigns six categories of soil erosion intensity: slight, mild, moderate, strong, very strong, and extreme erosion, is used to classify erosion grade in the present research. Slight erosion refers to minimal soil loss, often barely noticeable. It can be caused by light rain or wind action. In this category, mild erosion means that soil loss is still relatively low, but it is more noticeable than light erosion. It can be caused by moderate rainfall or other factors. Moderate erosion means that soil loss becomes more significant. It is caused by moderate rainfall, runoff, or other erosive processes. Vegetation cover may begin to decline. Strong erosion here means that soil loss is significant. It can lead to gullies, rills, and significant damage to the landscape. Vegetation cover is severely affected. Very strong erosion means that this level is associated with severe soil loss, often resulting in deep gullies, extensive land degradation, and reduced soil fertility. Vegetation cover is severely affected. At this  
\begin{table}
\begin{tabular}{c c} \hline
**Judgment** & **Interaction** \\ \hline \(\mathrm{q}(\mathrm{XI}\cap\mathrm{X2})<\mathrm{Min}(\mathrm{q}(\mathrm{XI}), \mathrm{q}(\mathrm{X2}))\) & Non-linear weakening \\ \(\mathrm{Min}(\mathrm{q}(\mathrm{X1}),\mathrm{q}(\mathrm{X2}))<\mathrm{q}( \mathrm{XI}\cap\mathrm{X2})<\mathrm{Max}(\mathrm{q}(\mathrm{X1}),\mathrm{q}( \mathrm{X2}))\) & Single-factor nonlinear attenuation \\ \(\mathrm{q}(\mathrm{XI}\cap\mathrm{X2})>\mathrm{Max}(\mathrm{q}(\mathrm{XI}), \mathrm{q}(\mathrm{X2}))\) & Two-factor enhancement \\ \(\mathrm{q}(\mathrm{X1}\cap\mathrm{X2})=\mathrm{q}(\mathrm{X1})+\mathrm{q}( \mathrm{X2})\) & Independent \\ \(\mathrm{q}(\mathrm{X1}\cap\mathrm{X2})>\mathrm{q}(\mathrm{X1})+\mathrm{q}( \mathrm{X2})\) & Non-linear enhancement \\ \hline \end{tabular}
\end{table}
Table 1: Types of interaction between two covariates.  
highest level, extreme erosion means that soil loss is catastrophic. It leads to severe land degradation, loss of topsoil, and significant environmental impacts. Vegetation cover is almost non-existent.
## 2 Materials and Methods
### Support Vector Machine Regression
#### 2.4.2 SVR for Nonlinear Regression  
However, the linear relationship assumption is often too simple to characterize the dynamics of the time series, and thus it is necessary to consider the case when \(g\) is nonlinear. The idea of SVR for nonlinear regression is to build a mapping \(x\rightarrow\phi(x)\) from the original \(m\) dimension feature space \(X\) to a new feature space \(X^{\prime}\) whose dimension depends on the mapping scheme and is not necessarily finite. In the new space \(X^{\prime}\), the relationship between the new feature vector \(\phi(x)\) and label \(y\) is believed to be in a linear form. By building a proper mapping, the nonlinear relationship can be approximated by doing in the new feature space \(\phi(x)\) exactly the same thing as is done for the linear case, and it can be proven that the nonlinear version of (2) is  
\[y=f(x,\alpha,b)=\sum_{k}a_{k}y_{k}K(x_{k},x)+b \tag{17}\]  
where \(K(x_{k},x)=\phi(x_{k})\cdot\phi(x)\) is the kernel function and \".\" represents the inner product in the new feature space \(X^{\prime}\). The new feature \(\phi(x)\), which can be an infinite dimension vector, is usually not necessary to be computed explicitly, since we normally work with the kernel function in the training and forecasting phases. Accordingly, the kernel function is essential to the performance of SVR [25].  
The SVM regression used for the \(h_{c}\) values was made by the SMOreg sequence in the Waikato Environment for Knowledge Analysis (WEKA) 3.8.1 program. The WEKA program has been developed at Waikato University. The WEKA Program is open source software and this program supports many algorithms for classification, clustering and association rules. SMOreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorithm was selected by setting the RegSMOImproved. The most popular algorithm (RegSMOImproved) is due to [PERSON], [PERSON] et al. [26].  
Various accuracy criteria were used for SVM regression. Accuracy criteria and formulas used in SVM were shown in Table 1.  
It is essential to determine the kernel function to be used for a classification operation to be performed by SVM and optimum parameters of this function. The most commonly used radial basis function, Pearson VII (PUK) function and normalized polynomial kernels in the literature are presented together with formulas and parameters in Table 2. As shown in the table, some parameters for each kernel function must be specified by the user.
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 4 Experimental Results and Discussion
### Quantitative Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **PreProcess** & **Inference (ms)** & **PostProcess** & **Total Time** \\  & **(ms)**\(\downarrow\) & \(\downarrow\) & **(ms)**\(\downarrow\) & **(ms)**\(\downarrow\) \\ \hline YOLOv8n & 1.4 & **6.7** & 1.6 & **9.7** \\ YOLOv8s & 1.3 & 8.5 & 1.6 & 11.4 \\ YOLOv8m & 1.7 & 15.6 & 2.2 & 19.5 \\ YOLOv8l & **1.2** & 24.5 & 1.6 & 27.3 \\ \hline YOLOv9t & **1.2** & 14.2 & 1.3 & 16.7 \\ YOLOv9s & 1.3 & 16.9 & 1.3 & 19.5 \\ YOLOv9m & **1.2** & 18.6 & 1.7 & 21.5 \\ YOLOv9c & **1.2** & 20.0 & 1.4 & 22.6 \\ \hline YOLOv10n & **1.2** & 9.2 & 0.5 & 10.9 \\ YOLOv10s & 1.3 & 9.7 & 0.5 & 11.5 \\ YOLOv10m & **1.2** & 15.0 & 0.5 & 16.7 \\ YOLOv10l & **1.2** & 23.3 & **0.4** & 24.9 \\ \hline YOLOv11n & 1.3 & 8.2 & 1.3 & 10.8 \\ YOLOv11s & 1.3 & 9.1 & 1.4 & 11.8 \\ YOLOv11m & **1.2** & 14.7 & 1.3 & 17.2 \\ YOLOv111 & **1.2** & 19.6 & 1.4 & 22.2 \\ \hline \hline \end{tabular} Note: The best result is shown in **bold** and the second best in underline. \(\downarrow\) indicates that lower values are better.  
\end{table} 
Table 6: Average processing time of YOLO models for image detection. YOLOv8s achieved the best balance between detection accuracy and inference time, likely due to its optimized backbone (CSPDarknet53+) and SE attention modules that enhance focus on relevant features without significantly increasing computational load. In contrast, although YOLOv10 and YOLOv11 introduce more advanced components--such as CSPNet++ and transformer-based necks--these models require more processing time and, in some cases, do not outperform simpler versions in terms of mAP. The YOLOv11 variants, for example, use CSP-DenseNet backbones and anchorless heads, which may offer theoretical improvements but can also lead to instability or overfitting when applied to datasets with limited diversity like BRACOL. Furthermore, models with a smaller number of parameters (e.g., YOLOv8n, YOLOv10n) are faster but tend to underperform in complex images due to limited representation power.
## 1 Introduction  
Salbutamol [1-(4-hydroxy-3-hydroxymethylphenyl)-2-(terbutylamino) ethanol] (SAL) and [1-(3,5-dihydroxyphenyl)-2-(terbutylamino) ethanol] (TEB, chemical structures are shown in Figure 1) are both selective \(\beta_{2}\)-adenocopter agonists used as anti-asthmatic drugs for the treatment of asthma and chronic obstructive pulmonary disease. But in recent years, traces of \(\beta_{2}\)-agonist used as an animal growth promoter into animal feed lawlessly resulted in toxicity to animals and veterinary drug residues that could cause extensive harm to public health. SAL and TEB are at the top of the list. Analytical methods reported for the determination of SAL and its sulfate include fluorescence spectroscopy [1,2], capillary electrophoresis [3,4], HPLC [5,6], quantum dot techniques [7], and electrochemistry [8,9]. Detection for TEB include HPLC [10,11, 12], and electrochemistry [13], etc. Many of these analytical techniques are expensive or require time-consuming derivatization steps. Unlike other methods, chemiluminescence (CL), due to its more sensitive, economical, convenient and lower detection limits, has attracted considerable attention in several fields such as clinical research, biotechnology, pharmacology, and environmental chemistry [14, 15, 16, 17] in the recent years. Several chemiluminescence systems has been successfully used in determination for SAL and TEB in variety of samples [18; 19; 20; 21; 22; 23; 24]. Frequently used CL systems are based on the reactions of luminol (Lu) with different oxidants, such as K\({}_{3}\)[Fe(CN)\({}_{6}\)] [18; 19], H\({}_{2}\)O\({}_{2}\)[20], and Cu(III) complex [21], others are such as Immunoassay CL [22], Micro Flow Sensor [23] and Liquid-Core Waveguide CL detection system [24].  
Over the last few years, part of our research effort has been devoted to the studies of oxidation reactions by an unusual oxidation states of transition metal complexes (UTMC), Ag(III) ([Ag(HIO\({}_{6}\))\({}_{2}\)]\({}^{5-}\)) [25; 26]. The Ag(III) complex can react with luminol (Lu) to produce CL, which has been successfully utilized to determine several biological samples [27; 28; 29; 30] with unusually high sensitivities. In this work, we developed a CL system based on another UTMC complex (i.e., Ni(IV) complex, [Ni(OH)\({}_{2}\)(HIO\({}_{6}\))\({}_{2}\)]\({}^{6-}\)), which is fairly stable in alkaline medium, and has steady oxidation characters than Ag(III) complex. Its structure [31] is illustrated as follows (Figure 2).  
The Ni(IV)-Lu CL system incorporated with a flow injection analysis (FIA) has been established for SAL and TEB determination. The proposed method is based on the CL reaction of luminol with Ni(IV) in alkaline solution, whereas SAL and TEB can significantly enhance CL intensities. This method has suitable linearity, higher sensitivity, precision, and potential capability for the residue analysis of \(\beta_{2}\)-agonists in urine and swine feed samples. Possible reaction mechanisms for the CL system are suggested based on the UV absorption spectra, CL spectra, and results of the electron spin resonance (ESR) experiment performed in this study.