esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Method for Packet Loss Types in IoT: An Naive Bayes Distinguisher
## 3 System Model and Feature Extraction
### Priority Feature  
Packets of the same flow are marked as \(p^{high}\)(packets with high priority) and \(p^{low}\)(packets with low priority) at sender. As shown in Figure 1(a), the sender sends 9 packets,\(p_{1}\),\(p_{2}\),...,\(p_{9}\), \(p_{4}^{low}\) and \(p_{8}^{low}\) are marked as low priority. When the load of the network is too heavy or the router cache overflows, lower priority packets are dropped first. As shown in Figure 1(b), \(p_{4}^{low}\) and \(p_{8}^{low}\) are dropped in router because of congestion. The \(p^{high}\) will be dropped while there are no \(p^{low}\) in queue. When packet loss occurs in the wireless link, the loss probability of packets with different priorities is the same. As shown in Figure 1(c), \(p_{3}^{high}\), \(p_{6}^{high}\) and \(p_{7}^{high}\) are randomly dropped. The receiver counts the packet loss pattern to matches the packet loss category. The statistical index can be given by  
\[F_{pri}=\lfloor 1-\frac{kl_{p^{low}}}{l_{total}}\rfloor, \tag{1}\]  
where \(k\) is the marking interval, it means that one out of every \(k\) packets is marked as \(p^{low}\). \(l_{total}\) is the total number of lost packets at receiver, and \(l_{p^{low}}\) is the number of lost packets which marked as \(p^{low}\) in \(l_{total}\). If \(\text{F}_{pri}\leq 0\), it means that, the proportion of \(p^{low}\) in the lost packets is greater than \(1/k\), thereby judging that the packet loss is caused by congestion.
## 2 Related Work
K-Medoids algorithm was used to extract the white blood cells from the image. The RBCs and WBCs images were collected from the Kasturba Medical College, Karnataka [48], Hemato-pathology laboratory [49], and Internet resources. They obtained an accuracy rate of 98% for the correct identification of anemia. In [50], the authors proposed a deep CNN to classify RBCs in sickle cell anemia. They developed a high-throughput framework that consists of three stages. A total of eight sickle cell disease patients have been selected to collect 7000 single RBCs images. Four various artificial learning methods were used by [51] to classify the general type of anemia. They used Support Vector Machines, Naive Bayes, and Ensemble Decision Tree for classification purposes on 1663 samples. They achieved the highest accuracy of 85.6% with the Bagged Decision Tree algorithm. The summary of the literature is given in Table 1.  
The literature reveals that most of the current research on blood image processing pays more attention to simple segmentation and classification, especially for WBCs. The present research work generally focused on identifying RBC and WBC elements within an image. They are not targeting the identification of any specific disease. We have targeted  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{S \#} & \multicolumn{1}{c}{**Paper**} & \multicolumn{1}{c}{**Project Aim**} & \multicolumn{1}{c}{**Database/Dataset**} & \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Targeted Features**} & \multicolumn{1}{c}{**Performance Evaluation**} \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{[41]} & Diagnosis of (1) iron & \multirow{4}{*}{793 individuals} & \multirow{4}{*}{Weka Software} & \multirow{4}{*}{CBC} & \multirow{4}{*}{Accuracy = 96.343\%} \\  & & deficiency anemia, & 184 IDA & & & & \\  & (2) a-thalassemia & 200 healthy & Hybrid vote algorithm & & & \\  & & trait and & 203 \(\beta\)-thalassemia & J48, IBK and Naïve & & & \\  & & (3) \(\beta\)-thalassemia trait & 206 \(\alpha\)-thalassemia & Bayes algorithms & & & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{[42]} & Statistical analysis & \multirow{2}{*}{NFHS-4} & Decision tree & \multirow{2}{*}{Hemoglobin level} & Accuracy with only hemoglobin = 97.35\% \\  & & & of anemia & & & & \\  & & &the identifying normal and abnormal morphological features of RBC elements considering specific diseases, i.e., anemic RBC. The proposed work helps to count the number of infected RBC elements within an image along with the severity level of anemic images as mild or chronic. The proposed work also paid attention to the separation of the overlapped structure of RBC. In the case of the overlapped structure, pathologists cannot differentiate accurately between normal and abnormal shape and size. The overlapped structure of abnormal RBC elements hides the blood element density and morphological features (shape and size) in a unit volume.
# The Feature Selection Effect on Missing Value Imputation of Medical Datasets
## 2 Literature Review
### Missing Value Imputation  
According to [24; 25; 26], the methods to deal with incomplete data containing missing values can be classified into three categories, which are case deletion, learning without handling of missing values, and missing value imputation. In case deletion, which is the simplest method, the data with missing values are removed from the original incomplete dataset to make it become a complete dataset. For the learning methods that do not involve handling of missing values, some learning techniques can be employed, such as Bayesian networks [27] and cost-sensitive decision trees [28].  
On the other hand, missing value imputation can be broadly classified into single imputation and multiple imputation methods. In the single imputation methods, the focus is on substituting each missing value, which is done using a statistical method, such as the mean and mode technique. In addition, there are several machine-learning-based techniques, such as the k-nearest neighbor [29; 30], multilayer perceptron [14], and support vector machines [31] techniques, which can be used to estimate the missing values. However, these can lead to biased estimates of variances and covariances (i.e., underestimation of standard error) [32].  
Multiple imputation methods are aimed at solving the limitations of single imputation methods so that each missing value is replaced by two or more acceptable values, which represent a distribution of possibilities. One representative method is the least absolute shrinkage and selection operator (LASSO), which is a regression analysis method that performs variable selection and prediction [33]. It has been modified for specific domain problems, such as medical data [34] and high-dimensional data [35]. However, there are limitations in that the computational complexity is larger than for single imputation methods, and different estimations produced to replace a specific missing value may be very different, which can lead to the situation where different values are obtained from the same data using the same method at different times [32]. Therefore, the research objective of this paper is to examine the feature selection effect on single imputation methods, especially by three widely used machine learning methods --MLP, KNN, and SVM.
## 2 Materials and Methods
### Geobia  
#### 2.6.1 Ms Bands Layout  
The selection of the MS bands layout depends on the type and characteristics of the vegetation that is selected [13]. Coastal blue and blue bands are significant for coastal applications, such as deep-water masks differentiation or water body penetration. The green band is applicable for distinguishing crop types and bathymetry, while the yellow band is intended for leaf coloration, plant stress, or separability of iron formations. The red band is used for the detection of chlorophyll absorption or plant species and stress. Various red edge bands are commonly used to detect vegetation health and stress, while the near-infrared band is preferred for biomass surveys, type and age discrimination, and plant stress detection. Since the objects of this research are olive tree canopies, the selection of band arrangement is conditioned by the spectral reflectance of olive trees which is the best in all bands except blue ones. Therefore, when choosing the band layout, various combinations were visually compared.
## 2 Research Methodology  
This study adopts a systematic review methodology to explore how MC has evolved after the advent of I4.0. The review is conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses 2020 (PRISMA 2020) guidelines [28]. The protocol was defined in advance, including objectives, eligibility criteria, search strategy, data extraction, and analysis procedures.  
The aim of the review is to systematically identify, analyze, and synthesize the most relevant contributions on MC within the framework of I4.0 over the period 2011-2024. The selection of documents was based on predefined inclusion and exclusion criteria to ensure the relevance and quality of the literature reviewed.  
Inclusion criteria:  
* Peer-reviewed journal articles, conference papers, reviews, and book chapters;
* Documents published between 2011 and 2024;
* Documents written in English language, only;
* Documents focused on MC and related enabling technologies;
* Documents indexed in the Scopus database;* Documents belonging to the following subject areas: Engineering, computer science, business management and accounting, decision sciences, mathematics, materials science, social sciences, and economics econometrics and finance. Exclusion criteria:
* Editorials, commentaries, letters, and non-peer-reviewed material;
* Articles unrelated to MC or Industry 4.0;
* Duplicates or retracted papers;
* Papers without accessible full text.  
The literature search was conducted using the Scopus database due to its extensive coverage of scientific publications in engineering, management, and information systems in September 2024. The article selection process followed the main, if applicable, steps of the PRISMA 2020 methodology guidelines. The term \"mass customization\" was used as the primary keyword, searched in the title, abstract, and keywords fields. The search initially yielded 4013 records. The Scopus tools were used, applying the subject area filters and listing the publications according to the number of citations and relevance criteria, leading to a pool of 349 documents with no duplicates. These publications were checked for their availability as full text, removing 119 papers. A final selection of 229 highly relevant publications was fully read and reviewed, focusing on the technological, methodological, and managerial dimensions of MC.  
The screening process was conducted independently by two authors. The initial abstract screening was performed separately by both reviewers. In case of disagreement, a discussion was held to reach consensus. Full-text screening was also performed in duplicate, and any unresolved conflicts were resolved by a third author. The inclusion/exclusion decisions were recorded in a shared spreadsheet, and screening consistency was periodically validated. The PRISMA flow diagram in Figure 1 summarizes the identification, screening, eligibility, and inclusion phases of the review process.
### Kenya RAPID Program  
The Kenya Resilient Arid Lands Partnership for Integrated Development (Kenya RAPID) program is aimed at increasing \"access to water and sanitation for people and water for livestock\" while rebuilding \"a healthy rangeland-management ecosystem\" [9]. Kenya RAPID is funded by the United States Agency for International Development (USAID) and the Swiss Development Corporation (SDC). Launched in September of 2015, Kenya RAPID is working to increase access to improved water services from 37% to more than 50% within five years through three strategic objectives: (1) responsive and accountable governance frameworks for providing water and pasture; (2) replicable and scalable business models for water, sanitation, hygiene, and livestock service provision; and (3) increased access to water, sanitation, and hygiene services and improved rangeland management [9]. With over a dozen public and private partners, including the national and county ministries of water, four non-governmental organizations (NGOs), and six private companies, the Kenya RAPID program aims to strengthen institutions and facilitate coordination across sectors to improve service delivery and empower communities.  
Kenya RAPID seeks to promote the consistent use of improved water and sanitation services through three activities. First, the program is supporting local financing and cost recovery through the creation of private and public partnership business models and improved asset management practices. Second, the program seeks to aid in the development of functioning management and maintenance systems through capacity-building of community-level structures and institutions. Finally, the program is promoting the protection and management of natural resources through integrated water resource management (IWRM); recharge, retention, and reuse (3R) initiatives; and the implementation of rangeland management plans [10]. Together, these activities are aimed at building the resilience of communities located in the arid and semi-arid regions of Kenya, so that they will be able to \"mitigate, recover, and adapt to major crises, such as droughts, and minor crises like rainy seasons that cut off transportation to markets\" [10] (p. 26).
# Analysis of Total-Forms of Cyanotoxins Microcystins in Biological Matrices: A Methodological Review
## 4 Quantification of Total MC after Base-Catalyzed Deconjugation
### Miles' Approach of Based-Catalyzed Deconjugation  
#### 4.2.1 Based-Catalyzed Deconjugation of Thiol-Conjugated MCs  
[PERSON] et al. [114] studied the deconjugation kinetics of some thiol-conjugated MCs (Figure 4). Different variants of MCs (MC-LR, -YR, -RR, -LA, -LF, -LV, -LW, -RY, [Asp\({}^{3}\)]MC-RY, and [Dha\({}^{7}\)]MC-LR) were conjugated to different nucleophilic compounds, such as mercaptoethanol, methanethiol, MeOH, and cysteine or glutathione. A base-catalyzed conjugation was performed to obtain the different thiol-conjugated MCs. Once synthesized, the stability of these MC-thiol conjugates was studied under basic conditions, e.g., the MC-RY conjugate with mercaptoethanol was deconjugated under weakly basic conditions, and the half-life of the conjugate decreased when the pH increased or when DMSO or iodoacetamide was added. Subsequently, further kinetic studies on eight MC conjugated with mercaptoethanol were performed using smooth deconjugation using a MeOH-carbonate buffer. At pH 10.7, most of the thiol derivatives had half-lives ranging from 30 to 50 h. This shows the possibility of deconjugating conjugated MCs, and further experiments were subsequently performed to accelerate the process. Three possibilities have been put forward to try to speed up MC deconjugation, firstly by increasing the pH in order to increase the rate of deconjugation while minimizing the occurrence of side reactions. The second possibility put forward by [PERSON] et al. [114] is to remove the free thiol formed during the reaction in or der to drive the reaction equilibrium towards deconjugation. Finally, the last possibility is in accordance with the authors who converted the free MCs into non-thiol-reactive derivatives in order to avoid the reconjugation phenomena. To do this, one solution is to carry out the reaction in the presence of an excess of non-natural thiols, such as mercaptoethanol. These hypotheses have been considered and were successfully demonstrated on thiol-conjugated MCs [114; 115]. However, it has been observed that the use of high pH can lead to artifact formation [114] and that the use of derivatization reagents can lead to the formation of isomers, complicating the subsequent chemical analysis [114; 115].
## 3 Aerial Survey by UAS
### UAV Regulation  
Increasingly high-performance and increasingly easy to steer, drones are widely used in photogrammetric surveying for the representation of cultural heritage. Despite such simplifications, there are countless safety issues; in fact, regulations and laws have been drawn up and passed worldwide that restrict the areas of use. Even in some countries, such as Syria, Morocco, and Iraq, the use of UASs is banned. This issue raises important questions for the protection and preservation of the cultural heritage contained in these countries.  
Regulations and laws in different states and continents are constantly changing. A globe-wide overview is offered to us by the map created by SurfShark, a virtual private network service provider, which has collected and catalogued the worldwide scenario of the different drone regulations in force [65]. Nations are coloured according to the approach of the regulations in force in that country, divided into seven different categories: (i) outright ban, (ii) effective ban, (iii) restrictions apply, (iv) visual line of sight required, (v) experimental visual line of sight, (vi) unrestricted (when flying away from private property and airports, under 500 ft/150 m height and with drones weighing less than 250 g), and (vii) no drone-related legislation.  
A WebGis listing the laws, regulations, and restrictions of each country can be found on the droneregulations.info website [66]. Indeed, the user can search by country or directly on the map (Figure 5a) and consult the rules in force in that country (Figure 5b).
## 6 Mathematical Formulation Using Linear Programming
### PDR Maximization  
In order to enhance network throughput by consuming minimum energy, packets are transmitted through multiple hops. Throughput is number of packets successfully reached the sink. Link quality is taken into consideration by defining threshold value \(\delta\) for selecting optimal neighbor nodes at each hop. Additionally, it ensures successful packet delivery. Moreover, energy needed to transmit the packet must be fulfilled during forwarding according to C1. All aforesaid constraints are considered during the formulation of the objective function given in Equation (26).  
\[Max\sum_{i=1}^{N}T_{p}(i);\quad\forall\,i\,\epsilon\,N, \tag{26}\]  
where \(T_{p}(i)\) is network throughput, \(T_{p}(r)\) represents the amount of packets received during \(r\) rounds which is expressed mathematically in Equation (27).  
\[Max\sum_{r=1}^{r_{max}}T_{p}\times P;\quad\forall\,i\,\epsilon\,N, \tag{27}\]  
such that:  
C1: \(E_{TX,RX}\leq E_{r}\)  
C2: \(P_{link}\geq\delta\)  
C3: \(E_{TX,RX}\geq E_{th}\)  
where \(E_{th}\) is the threshold for transmission and reception energies.  
C4: \(0<D_{ij}\leq D_{ij}^{max}\)  
C1, C2, C3 and C4. C1 and C3 restrictions on \(E_{TX}\) and \(E_{RX}\) are set to avoid unnecessary energy consumption. In GRMC-SM, all nodes report their sensed data to the nearest sink. PDR of the network is accumulated packets successfully received at all the sinks. Equation (27) shows the summation of all the data packets in \(r\) rounds. Feasible region for GDGOR-IA lies within these following vertex points as shown in Figure 6a.  
at \(P_{1}(0.34,150)\)  
at \(P_{2}(0.55,200)\)  
at \(P_{3}(0.60,250)\)  
at \(P_{4}(0.83,550)\)Similarly, for GRMC-SM, feasible region lies within following vertex points illustrated in Figure 6b:
## 3 Three-Tier Architecture of LS-WSNs
### Architecture Description
#### 3.1.2 The Level 2 Network  
Built on a set of sensors of a different type than those of level 1, the level 2 network is in charge of collecting big data of environmental type. However, the number of sensors deployed is relatively lower than that of the level 1 network. The sensors of this network are also organized in clusters. In addition to collecting environmental data, these sensors also serve as relays for the transfer of data collected by the level 1 network to the BS. The layer 2 network is stationary and is rated N2.  
Compared to level 1 sensors, sensors at this level perform increasingly complex tasks. They collect environmental data (presence, CO\({}_{2}\) detector, smoke detector, humidity, etc.) and relay this data including medical data to the BS. Environmental sensors have processing and aggregation capability. At the scale of a city, the deployment of environmental sensors must be optimized to cover the entire area of interest (camera field of view) and to provide better connectivity with the sink (radio range).
# Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review
## 4 Smart Grid and 5G
### Challenges of 5G Based Smart Grid Networks
To achieve this, a great deal of research has been done, some of which addressed an integrated photovoltaic system [108]. The indispensable component of a smart grid using 5G is the information exchange among the electrical devices that are located in a distributive manner in the system. The communication delay performance analysis is required in order to design a reliable network [109]. Recently, a paradigm named Mobile IoT Federation as a Service [110] has been proposed in order to enhance the performance of smart grids based on 5G. It involves a pool of devices managed by an IoT cloud provider. The requirement of an energy efficient communication model has gained significant attention from researchers in recent years. An important attribute of 5G in solving this issue is green communication [111; 112; 113; 114; 115; 116; 117; 118]. This is achieved by finding the minimum number of small cells that are required to be active at various times in order to meet user demand as well as minimize the power consumption.
# Automatic Age and Gender Recognition Using Ensemble Learning
## 3 Dataset and Methods  
### Dataset  
The Mozilla Common Voice dataset [23] was used to evaluate the models proposed in this research. It is a multilingual speech corpus consisting of audio recordings of texts taken from public domain sources and voiced by volunteers. An ordinary Internet user can contribute to this corpus as a speaker or a listener after the registration stage. This dataset is collected for speech recognition purposes, but since the age, gender, language, and accent information of the speakers are available in the dataset, it is also used for different purposes. The dataset consists of three subsections. Audio clips that are listened to by at least two listeners and whose audio-text match is approved by the majority vote of the listeners are in the \"valid\" subsection, while those that are rejected are in the \"invalid\" subsection.  
Audio clips with fewer than two votes or equal valid and invalid votes are in the \"other\" subsection. All audio clips are stored as mp3 files, and the information about each recording is available in a csv file. In the dataset, speakers are divided into nine groups according to their age. These age groups and age ranges are as follows: teens: \"\(<\)19\", twenties: \"19-29\", thirties: \"30-39\", forties: \"40-49\", fifties: \"50-59\", sixties: \"60-69\", seventies: \"70-79\", eighties: \"80-89\", and nineties: \"\(>\)89\".  
This study utilized the Turkish subset of the Common Voice dataset, version 18.0, which consists of 121 h of verified speech data. However, some recordings in the dataset do not include age or gender information and there are instances where the audio-text match received down-votes. Additionally, certain classes within the dataset have very few or no records, such as \"teens\", \"seventies male\", and \"eighties male\". To address these issues, recordings with missing age or gender information, those down-voted, and classes with insufficient records were removed from the dataset. As a result, 12 classes remained in the dataset, comprising two gender classes and seven age classes. However, the records in the dataset are clustered into certain classes. For example, while there are 670 records in the twenties female class, there are 10.555 records in the twenties male class. To eliminate this imbalance, a balanced dataset containing 8040 conversations was created by randomly selecting an equal number of records from each age and gender classes, and this dataset was used in experimental studies. The class definitions in this dataset are given in Table 1.