esa-sceva/satcom-chunk-collection huggingface dataset:
# The Predictive Grey Forecasting Approach for Measuring Tax Collection
## 2 Literature Review and Conceptual Framework
### DirectTax-to-GDP Ratio  
The tax-to-GDP ratio in India is the lowest among major nations ([PERSON], 2019). When compared globally, the cost of tax collection is quite low ([PERSON], 2019). Because some compliance procedures are complex, India requires next-generation tax administration improvements ([PERSON], 2019).  
The direct tax-to-GDP ratio has fluctuated throughout time, reflecting changes in tax collection efficiency and the growth of the formal economy. The percentage fluctuated between 3.03% in 2001-02 and 6.3% in 2007-08, reflecting changes in tax policies, economic conditions, and tax administration. The direct tax-to-GDP ratio rose steadily until 2007-08, hitting a peak of 6.3% during that fiscal year (Figure 3).This growing trend shows that the tax base is expanding, compliance is increasing, and tax collection techniques are improving. The ratio fluctuated in subsequent years, driven by economic development, tax rate adjustments, and policy revisions.  
In recent years, the ratio has decreased, falling to 4.78% in 2020-21 and marginally recovering to 5.97% in 2021-22. The drop in the ratio can be linked to causes such as the economic impact of the COVID-19 epidemic, reduced economic activity ([PERSON] et al., 2023), and the government's response to the crisis ([PERSON] et al., 2022).  
The direct tax-to-GDP ratio measures the tax system's progressivity and the distribution of tax burden. A higher ratio shows that a greater proportion of the country's economic production is paid in direct taxes, implying a more equitable allocation of tax burdens. It also suggests that the government can raise tax money from personal and corporate income. The direct tax-to-GDP ratio can affect revenue generation and economic growth. A greater ratio indicates that tax revenues are growing faster than GDP, showing positive buoyancy and the ability to support public expenditures. A lower ratio, on the other hand, may indicate income generation issues, probable budget deficits, and financial constraints for public services and investments.  
The buoyancy factor assesses how responsive direct tax income is to fluctuations in GDP. A factor greater than one suggests that tax revenue is growing faster than GDP, indicating a growing tax base and increased tax compliance. A factor smaller than one suggests that tax revenue growth lags GDP growth, indicating the need for tax reforms or  
Figure 3: Comparison of the tax growth rate, GDP growth rate, and Direct tax-to-GDP ratio (Source: MOSPI).  
improved tax administration. The direct tax-to-GDP ratio can help policymakers evaluate the efficiency of tax programs, identify areas for reform, and ensure a stable revenue base. A balanced approach to tax reform that focuses on increasing the tax base, improving compliance, and encouraging economic growth might help boost the ratio over time.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 1 Introduction
### Literature Base  
In this document, the term 'orchard' is used to refer to a management block of fruit trees, i.e., of a common genotype, planting date, irrigation scheduling, management history, harvest unit, etc. A 'typical' orchard size is around 1000 plants in 2 ha, but this is dependent on commodity and management practice.  
A recent review of the use of machine learning for crop yield prediction reported 567 relevant studies; however, this work primarily refers to annual crops [1]. For the narrower topic of tree fruit yield forecast, the scientific publication rate has risen steeply from a low base in the last decade [2] (Figure 1).  
Publications on tree fruit yield estimation have focused on the major crops of apple and citrus, and the tropical crop, mango (68% of records), with researchers primarily located in China, the USA and Australia (Table 1). The majority of work has addressed the use of machine vision using RGB cameras (65% of records), but attention has also been given to the correlation of fruit load to tree structural parameters, as measured proximally (in field) or remotely (by an Unmanned Aerial Vehicle, UAV, or satellite), and to the use of time series and climate data. Work on the use of stereology to streamline manual sampling methods was dominated by the work of [PERSON] and co-workers. However, to our knowledge, the only reviews on tree fruit crop load estimation published in the last decade have been on the use of deep learning for tree fruit detection using machine vision [3] rather than fruit load estimation per se.  
Figure 1: Annual scientific paper publication rate as indexed by Scopus records (doa 18/4/2021) for the keywords ‘tree AND fruit AND yield AND estimation’.  
With respect to the topic of the prediction of the time of harvest maturity, a Scopus keyword search (doa 9/4/2021) using the keywords of 'prediction AND fruit AND harvest AND maturity AND timing' yields 196 results over the ten-year period (2011-2020). Of these reports, the majority refer to studies using a heat unit methodology, with a few papers referring to the development of heat unit models, or to the use of other indices for the estimation of harvest maturation.
## 5 Experiments and Evaluation
### Accumulated Number of Generated Packets  
The purpose of the second experiment was to examine the number of generated packets on WSN when different data update cycles are given. Since the data update cycle means periodic time for fetching latest data from WSNs, a request interval of conventional WSN and a reporting cycle of  
Figure 11: Response time as increasing the number of applications.  
TinyONet can be considered as the data update cycle. In the case of the conventional WSN, a single sensor application is used to issue a request to each sensor by changing the request interval of 60, 120, or 180 seconds respectively. If the sensor application selects the request interval of 120 seconds, each sensor will receive a request at every 120 seconds. For TinyONet, a single sensor application was connected to TinyONet, which changed the sensor's reporting cycle of 60 or 120 seconds. We also added a random reporting cycle to the case of TinyONet. The random reporting cycle emulates a realistic sensing environment where sensor nodes report only if they have the sensing data to report. The range of random reporting cycle spanned from 30 seconds to 600 seconds.  
We made the _TxRx_ module of TinyONet record all incoming and outgoing packets regardless of its transmission success. The packet on the _TxRx_ module is stored in the TOS_Msg format [23]. The message format consists of seven bytes header, payload and two bytes CRC (Cyclic Redundancy Check). The TinyONet packet is stored in the payload of the TOS_Msg. In the case of the conventional WSN, a BYPASS packet (17 bytes) was used to broadcast data gathering message and to transmit a request packet to a specific sensor node. Each sensor node also used the same BYPASS packet to report its sensing data. In the case of TinyONet, each sensor node used a REGISTER packet (23 bytes) to register itself and a GET packet (17 bytes) to report its sensing data.  
Undoubtedly, the shorter data update cycle will generate more data packets on WSN. In Figure 12, we can see the fact that the packet number of 'Conventional-60 sec' exceeds that of 'Conventional-120 sec' and 'Conventional-180 sec'. Although the shorter data update cycle generates more packets, it lets external sensor applications and virtual sensors have more recent data. For the same data update cycle, the generated packets of TinyONet are much less than those of conventional WSN. While a single session of conventional WSN is composed of a request and a response phases, a reporting phase solely consists of a single session in TinyONet. In view of the data update, fewer packets of TinyONet can make the same effect of data update in the conventional WSN. In addition, Figure 12 shows that TinyONet having the random reporting cycle generates much fewer packets on WSN. It means TinyONet's performance can be much better under the realistic sensing environment.
## Appendix 1 Deduction of the Mean Footprint Reflectance (HCRF)
### Footprint Reflectance  
Let \(f(\varPhi_{r})\) [sr\({}^{-1}\)] be the hemispherical-directional reflectance distribution function (HDRDF) of the small area \(dA\) to the direction \(\varPhi_{r}\). Note: for clarity purpose, we do not express azimuth angle or wavelength explicitly in the denotation. The reflected radiance of a hemispherical incoming radiance \(E\) onto \(dA\) is:  
\[L_{\theta}=E\cdot f(\varPhi_{r}). \tag{14}\]  
Rewrite Equation (12) by using Equation (14):  
\[\bar{L}_{r}=\frac{\int_{\Omega_{A}}E\cdot f(\varPhi_{r})\cdot d\omega^{\prime }}{\Omega}. \tag{15}\]  
The footprint hemispherical-conical reflectance factor (HCRF) is computed by:  
\[\bar{\rho}=\frac{\pi\bar{L}_{r}}{E}=\frac{\pi\int_{\Omega_{A}}E\cdot f( \varPhi_{r})\cdot d\omega^{\prime}}{E\cdot\Omega}=\frac{\int_{\Omega_{A}}\pi f (\varPhi_{r})\cdot d\omega^{\prime}}{\int_{\Omega_{A}}d\omega^{\prime}}.\]  
Writing this in the Cartesian coordinate system yields:Sensors_**2011**, _11_  
\[\bar{\rho}=\frac{\int_{\mathbf{A}}\ \pi f(\theta_{r})\cdot\frac{\cos\left(\theta_{r} \right)}{r^{2}}dxdy}{\int_{\mathbf{A}}\ \frac{\cos\left(\theta_{r}\right)}{r^{2}}dxdy}. \tag{10}\]
## 1 Introduction
In the multi-classification part, we use K-Nearest Neighbor (KNN) to determine the type of packets and an innovative combinatorial architecture based on a neural network to determine the particular type of data packet. The main contributions of this work are as follows:  
1. Introduction of CM-NIDS: This work presents a new network intrusion detection framework called Combined Multi-Classification-NIDS (CM-NIDS). This framework is based on an improved neural network and supports other security systems by accurately matching the attack type of the packets. The introduction of CM-NIDS addresses the need for effective network intrusion detection and enhances overall system security and parallelism. 2. KNN-GA-Based Feature Selection: This work proposes a feature selection approach using a combination of the K-Nearest Neighbors (KNN) algorithm and Genetic Algorithm (GA). This approach significantly reduces the number of parameters the neural network uses, leading to reduced communication and memory overhead. Additionally, it helps mitigate the impact of feature incompleteness, thereby improving the model's robustness and performance. Introducing this feature selection technique contributes to more efficient and effective network intrusion detection. 3. Data Pre-Processing and Feature Selection: This work emphasizes the importance of data pre-processing methods and specifically highlights the significance of feature selection. The authors provide detailed insights into the pre-processing techniques employed, which contribute to improving the quality and relevance of the input data. * Comparative Evaluation: This work evaluates the proposed CM-NIDS framework alongside existing frameworks. The evaluation demonstrates that CM-NIDS achieves higher attack-type matching rates than other methods. The remainder of this paper is as follows. Section 2 describes the methodology of our proposed CM-NIDS approach. Section 3 illustrates the results and discussion. Section 4 concludes this research work.
## 3 The KLSVM Model
### Linearly Non-Separable Data
#### 3.6.1 Slack Variable  
The error that results from our prediction is caused by some of our solutions allowing some of our data points to violate the margin constraint. Thus, a Soft Margin is assigned a cost, for example, the distance by which they violated the constraints scale by a factor \(R\). If the factor \(R\) is chosen to be very large, it pays a lot of attention to make sure no data violate the constraints margin if possible.  
On the other hand, if \(R\) is very small, maximize the margin for most data to allow some of them to violate it. Figure 4 shows we do so by adding the so called Slack Variables \(\epsilon^{(i)}\) one for each data points, defined as  
\[w^{*}=arg\min_{w,\epsilon}\sum_{j}w_{j}^{2}+R\sum_{i}\epsilon^{(i)} \tag{16}\]  
subject to \(y^{(i)}(w^{T}x^{(i)}+b)\geq+1-\epsilon^{(i)}\) violating margin by \(\epsilon^{(i)}\geq 0\).  
\(\epsilon^{(i)}\) measures the amount of data point \(i\) violating the margin constraint. \(\epsilon^{(i)}\) is always non-negative or zero if the constraints are satisfied and adds a penalty to epsilon to our objective function balancing the margin in terms of margin squared \(w_{j}^{2}\) with the amount of slack \(R\sum_{i}\epsilon^{(i)}\). Notice the formulation of \(w^{*}\) in Equation (16) remains a quadratic program; its quadratic objective in \(w\) and \(\epsilon\) subject to linear constraint \(y^{(i)}(w^{T}x^{(i)}+b)\geq+1-\epsilon^{(i)}\). For any weight \(w\), we can choose \(\epsilon\) to satisfy constraints by writing \(\epsilon^{*}\) as a function \(J\) and optimizing directly. For any weight vector \(w\), we can always choose a value \(\epsilon\) to minimize its term and satisfy constraints. It means, first, we can always initiate a solution \(w\), \(\epsilon\) and \(b\) to something that satisfies the constraints even if it does not minimize the objective. Secondly, the optimal value \(\epsilon\) given \(w\) is easy to select if data point \(i\) is not satisfied, the margin with \(\epsilon^{(i)}=0\), then the smallest value \(\epsilon\), enforces these inequalities to be true and will accept two sides to be equal.  
By choosing an optimal value for a given \(w\), we can then optimize the resulting cost as function of \(J\) directly as  
\[J_{i}=\max[0,1-y^{(i)}(w\cdot x^{(i)}+b)] \tag{17}\]  
where \(\epsilon^{*}\) written as a function of \(J\) can be optimized directly to  
\[w^{*}=arg\min_{w}\sum_{j}w_{j}^{2}+R\sum_{i}J_{i}(y^{(i)},w\cdot x^{(i)}+b) \tag{18}\]  
We find that the cost of \(J\) is only non-zero for data points that do not satisfy the margin constraint and for those points equal linearly with their distance to the margin.
## 5 Conclusions  
The multi-beam satellite often faces the mismatch problem of spot allocation and data requirements, which can cause an overload traffic jam or a waste of resources. In this work, we introduced a modified K-means method to automatically place the spot centers with appropriate beam widths in line with the density of the traffic demands and to realize the uniformity of the beam occupation. Compared with the conventional K-means algorithm, two adjustable parameters \(\alpha\) and \(\beta\) were introduced: one for tuning the ratio of determination matrix and the other for setting the obligatory minimum number of objects per beam. The impact of the parameter range on the results was also analyzed. A larger value of \(\beta\) with a relative smaller value of \(\alpha\) tends to obtain the uniformity of the beam occupation; the minimum standard deviation of objects per beam is achieved when \(\alpha\) is 0.2 and \(\beta\) is 0.8. When \(\alpha\) is larger than 0.6 and \(\beta\) is smaller than 0.4, the results tend to the uniformity of beam width, similar to conventional K-means, with a relatively larger but stable standard deviation of objects per beam. The algorithm would be theoretically degenerated to the conventional K-means when \(\alpha\) equals 1. The results have proven the validity of the proposed algorithm, which helps to introduce a trade-off strategy between the uniformity of the beam occupation and the uniformity of the beam width and would be implemented in the engineering practices in the field of multi-beam satellite design. In the future, the proposed strategy to balance the uniformity of cluster occupation will be considered to transfer to other unsupervised learning algorithms, such as Fuzzy C-means, and be implemented in more comprehensive applications.  
Conceptualization, H.L. and Y.C. ([PERSON]); methodology, H.L. and Y.C. ([PERSON]); software and experiments, H.L. and Y.C. ([PERSON]); writing--original draft preparation, H.L.; writing--review and editing, Y.C. ([PERSON]) and Y.Z.; supervision, W.H., Y.L. and Y.Y.; funding acquisition, Y.C. ([PERSON]) and Y.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
[PERSON] gratefully acknowledges the financial support from the China Postdoctoral Science Foundation (2020M670361, 2021T140392) and the Shuimu Tsinghua Scholar Program.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: The computational time of the proposed algorithm in terms of the number of objects.
## 3 Methods
### Optimization Algorithm  
#### 3.3.1 PSO  
The PSO algorithm is a new swarm intelligence algorithm proposed by [PERSON] and [PERSON] in 1995 to simulate the foraging behavior of birds in nature [30]. In PSO, particles in a swarm are produced in a \(d\)-dimensional space. The position of each particle in the population represents a candidate solution of the corresponding optimization problem. Different particles cooperate and compete to realize the search process in the optimization space and find out the optimal solution to the problem. The position of the optimal particle is finally obtained through a continuous iterative update of velocity. The velocity and position of the \(i\)th particle are represented by \(\mathbf{v}_{i}\left(t\right)=(v_{\text{i1}}(t),v_{\text{i2}}(t),\dots,v_{\text{id }}(t))\) and \(\mathbf{x}_{\text{i}}(t)=(x_{\text{i1}}(t),x_{\text{i2}}(t),\dots,x_{\text{id}}(t))\), where \(t\) and \(d\) denote the number of iterations and the dimension of the optimization problem, respectively. The velocity of the \(i\)th particle in the \(t\)+1 th iteration, \(\mathbf{v}_{\rm i}(t+1)\), is updated by using three vectors: (1) \(\mathbf{v}_{\rm i}\left(t\right)\), the velocity of the \(i\)th particle in the \(t\)th iteration; (2) \(\mathbf{p}_{\rm i}=(p_{\rm i1},p_{\rm i2},\ldots,p_{\rm iid})\), the best position _pbest_ in the history of the \(i\)th particle; and (3) \(\mathbf{p}_{\rm g}=(p_{\rm g1},p_{\rm g2},\ldots,p_{\rm gd})\), the best position _gbest_ of the whole swarm in history.  
The renewal equation of \(\mathbf{v}_{\rm i}(t+1)\) is as follows [27]:  
\[\mathbf{v}_{\rm i}(t+1)=w\mathbf{v}_{\rm i}(t)+c_{1}r_{1}(\mathbf{p}_{\rm i}(t)-\mathbf{x}_{\rm i }(t))+c_{2}r_{2}(\mathbf{p}_{\rm g}(t)-\mathbf{x}_{\rm i}(t)) \tag{16}\]  
where \(w\) is the inertia weight; \(c_{1}\) and \(c_{2}\) are the acceleration coefficients that are called the cognitive and social parameters, respectively; and \(r_{1}\) and \(r_{2}\) are two random numbers that are between intervals [0, 1].  
Accordingly, the position of the \(i\)th particle is updated as follows:  
\[\mathbf{x}_{\rm i}(t+1)=\mathbf{x}_{\rm i}(t)+\mathbf{v}_{\rm i}(t+1) \tag{17}\]
# Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment
## 2 Materials and Methods
### Data Collection  
Three datasets were received from an actual WWTP process, including online measurements from the automation system. Data from the related paper machine were also received. Online data were stored at a one-minute frequency. Table 1 shows the relevant information about the datasets used.  
Dataset 1 was the largest of the received datasets, containing one year's worth of data. The initial dataset from the plant included 44 online measurements. From these measurements, 27 were chosen for the next step after the data pre-processing phase. Some variables were neglected because they contained no useful information. The measurements in Dataset 1 included data on temperature, pressure, flow rate, liquid level and various quality measurements from the wastewater process. Dataset 2 included similar data to Dataset 1, i.e., measurements from the wastewater treatment plant, but it covered only the summer period. Dataset 3 included measurements about WWTP influent obtained from the paper machine automation system and covered about the same time period as Dataset 2. These data were crucial for the development of the influent soft sensor. Dataset 2 spanned approximately 4.5 months, while Dataset 3 covered a period of six months. Datasets 2 and 3 were aligned and merged, and thus, about 1.5 month period from Dataset 3 was removed.  
Dataset 1 was utilised as training data in developing the predictive effluent model. Dataset 2 served two purposes. Firstly, it was used as validation data for the predictive effluent model. Secondly, it was utilised in conjunction with Dataset 3 for developing an influent soft sensor model.  
The device responsible for online COD measurement extracts periodic wastewater samples at a four-hour frequency. These samples undergo thorough analysis giving the online data that are promptly recorded within the automation system. This means that  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data** & **Length** & **Variables** & **Variable Info** & **Dataset Usage** \\ \hline Dataset 1 & 1 year & 44 & Wastewater treatment process data & Effluent model development \\ Dataset 2 & 4.5 months & 31 & Wastewater treatment process data & Influent and effluent model development \\ Dataset 3 & 6 months & 17 & Wastewater data from paper machines & Influent and effluent model development \\ \hline \end{tabular}
\end{table}
Table 1: Dataset content information and its usage for modelling.  
online data for the targeted variables are updated roughly every four hours. This applies to both the influent and effluent COD measurements.
## 1 Introduction
An alternative, less researched strategy for determining pharmaceuticals in environmental samples is using QuEChERS-based extraction methods (fast, easy, cheap, effective, robust, and safe) [41]. The QuEChERS method was initially designed to extract pesticide residues from fruits and vegetables [42]. The method was subsequently applied to the analysis of other food matrices, such as cereals [43, 44]; legumes [44]; oils [45]; and environmental samples, e.g., soil [1, 46, 47, 48]. Due to its simplicity and \"low bias\" toward specific groups of compounds, the technique was also developed to study residues of alkylphenols, bisphenol A [49], mycotoxins [50, 51], pyrrolizidine alkaloids [52], polycyclic aromatic hydrocarbon (PAHs) [53], and surfactants [54], including per- and polyfluoroalkyl substances (PFASs) [55] in a variety of matrices. Due to the possibility of overcoming the complexity of the wastewater matrix and the great diversity of pharmaceuticals in physicochemical properties, modifications of the QuECHERS method appear to be a promising technique for extracting these compounds from wastewater. The technique avoids several drawbacks of traditional SPE, such as high solvent consumption, lengthy procedures, and poor selectivity in complex wastewater matrices. When combined with graphene oxide (GO) as a clean-up sorbent, the method enhances overall extraction efficiency and limits matrix effects, making it a more effective tool for detecting pharmaceutical residues in wastewater. This work aims to improve the QuECHERS technique for simultaneously determining a range of pharmaceuticals in wastewater by improving sample extraction and clean-up procedures. Improving the accuracy and efficiency of the analysis for the detection of drugs in wastewater will support the monitoring of their levels in wastewater treatment plants and, therefore, contribute to the development of effective methods to control the efficiency of these facilities. Effective surveillance is crucial for adequate water quality management, helping to improve wastewater treatment methods and minimizing pollutants' impact on aquatic ecosystems. In addition, it will provide a low-cost and straightforward tool to track the type and quantity of substances collectively consumed by the populations served by the treatment plants.