esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Random Forest for Image Classification  
Random forest (RF) is an ensemble machine learning method, which consists of multiple uncorrelated decision trees. It is widely used in image classification tasks due to its high accuracy, easy parameterization and robustness against overfitting [30]. Figure 3 illustrates how the random forest model works. Given the dataset with \(N\) samples \(D=\left\{(\mathbf{x}^{1},y^{1}),\ldots,(\mathbf{x}^{t},y^{t}),\ldots,(\mathbf{x}^{N},y^{N} )\right\}\), where \(\mathbf{x}^{t}=[x^{t}(1),x^{t}(2),\ldots,x^{t}(172)]\) and \(y^{t}\in\{1,2,3,4,5,6,7,8\}\) denote the input 172 features and the output label of sample \(l\), the general idea of random forest can be described as,  
1. Randomly select \(N\) samples with replacement from the original dataset, and obtain \(N\) subsamples for constructing each tree.
2. Select features for constructing decision tree nodes from a random subset of all 172 features, and construct a decision tree.
3. Repeat step (1) and (2) for \(B\) times and construct a random forest with \(B\) trees. The final prediction result is obtained by the majority vote of the trees in the forest.  
As an ensemble model, random forest model fits the input data in a shorter time as each decision tree is independent, making parallel computing and modeling possible [31; 32]. We also test the performance of the other five machine learning methods, including Fine Tree, Radial Basis Function kernel Support Vector Machine (RBF SVM), Weighted K-Nearest Neighbors (KNN), Linear Discriminant and Subspace KNN [33; 34].
# An Event Extraction Approach Based on a Multi-Round Q&A Framework
## 3 Models
### Event Extraction Process Based on Multi-Round Q&A Framework  
In this paper, we model the event detection task and the argument element extraction task as a multi-round Q&A framework via a pipeline model [17]. The complete multi-round flow of event extraction in this model is given in Figure 2. After inputting a sentence, the trigger word <Trigger> is first extracted from the sentence by the trigger word question-and-answer model. The extracted trigger words and their locations are input into the trigger word classification Q&A template, and the corresponding event type <Event Type> is extracted. Based on the obtained event type, the thesis element role <Event Type; role> is introduced into the construction of the thesis element question template, and the entity corresponding to the thesis element role is extracted. Then combine the answer entity information into the next round of question templates and further extract the other argument element roles of that event type. The above process is repeated to extract the remaining argument elements until the extraction of all event information is completed. The whole extraction process constitutes a machine reading comprehension task in a multi-round question-and-answer framework, which can flexibly model the event detection and argument element extraction tasks to achieve efficient event extraction.
## 4 Simulation Results
### Total Consumed Energy  
We observe the total energy consumption required by the WSN over various rounds. The number of sensors is 200 and the initial energy of the sensors is 0.25 J. Thus, the initial total energy is 50 J. In the process of data transmission, when the execution rounds increases, the total consumed energy of sensors also increases. Figure 6 illustrates how GB-PEDAP effectively reduces energy consumption compared to the other three schemes.
# New Protocol and Architecture for a Wastewater Treatment System Intended for Irrigation
## 2 Materials and Methods
### Protocol Description  
In this subsection, the protocol utilized to make the proposed system work is presented.  
Our proposed protocol, as can be seen in Figure 7, is divided into four fundamental phases, which are: Discovering Neighbors, Send Data, Storage and Data Processing, and Action. As its name suggests, in the Discovering Neighbors phase, the different sensor nodes send wireless signals to discover the nodes that are part of the group. Then, the sensor nodes determine the Central Node, and the Central Node identifies the gateway which is the Central Node in Urban Area 0. Therefore, a LoRa multi-hop network is established as in [15; 16].  
Once the nodes are activated and their connection is established, they go into the Send Data phase. The different sensors that are connected to each node begin to measure their respective parameters. The data collection period has been set to take place every minute. If the observed data differ by an amount greater than a previously established threshold, the measurement is sent to the Central Sensor Node of the group, and this node will store the data and compare them to check if they exceed the alarm threshold. It also checks if the alarm has been detected by more than one of the sensors of the group in order to provide fault tolerance capabilities. In case of exceeding the alarm threshold, the data are forwarded to the Central Sensor Node of Urban Area 0. Then, it is forwarded to the central storage system in the data center. In case of not exceeding the alarm threshold, the data are stored in the Central Sensor Node of the group until all the data are sent every 24 h period. Every day, the Central Sensor Node of Urban Area 0 will send all data to the Storage Server through the wired network.  
The next phase is responsible for making the decisions to correct anomalies. It is the Data Processing phase. In this phase, the data are sent from the Storage Server to the AI system. This system will be responsible for making the pertinent decisions that will be sent to the actuators.  
Finally, during the Action phase, the operation that the AI system considers adequate to solve the problem detected by the sensors will be sent to the Actuator Nodes.  
Figure 7: Proposed protocol.
## 4 Data Descriptions
### Rainfall Data and Rain Estimation  
Rainfall information was obtained from the Korea Meteorological Administration. Then, the rainfall intensity on the road was estimated based on the rainfall data obtained from six weather stations selected from the 30 weather stations in Seoul (Figure 5). The K-nearest neighbor methodology, one of the most common classification algorithms used to predict the class of a record with an unspecified class based on the classes of its neighbor records [56], was adopted to estimate the rainfall intensity. The average, maximum, and minimum distances between the six weather stations and the research site are 1.9 km (1.2 mi), 3.38 km (2.10 mi), and 0.29 km (0.18 mi), respectively. Snow data were excluded from this study because of the difficulty of collecting them.  
Figure 4: Research site map.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 4 Trust-Based Proximal Policy Optimization (TBPPO)
### Markov Process Transition Module  
After employing the trust value mechanism relying on the KL divergence for transformation, the SDN control plane becomes capable of presenting the network's real-time status, which encompasses information like the delay matrix, node categories, and node trust values. This issue is subsequently structured as a Markov Decision Process (MDP) and is characterized by a four-tuple (S, A, P, R); here, S represents the state space, A is the action space, P is the probability distribution function, and R represents the reward function, which is composed of five components: delay reward, delay variation reward, KL trust value reward, node diversity reward, and node redundancy reward. The state, denoted as S, is composed of three components at time \(\tau\): the delay values of the selected route group \(\mathbb{L}\), represented as \(D_{\mathbb{L}}(\tau)\); the trust values of each node at time \(\tau\), denoted as \(Trust(\tau)\); and the types of each node, referred to as \(\delta\). This can be formulated as:  
\[\text{S}(\tau)=[D_{\mathbb{L}}(\tau),Trust(\tau),\delta] \tag{10}\]  
where the action space A has a size of \(L_{0}\), which corresponds to the size of the preselected route group \(\mathbb{L}\). \(A(\tau)\) is a subset of \(\mathbb{L}\), and it consists of \(\mathbb{L}\) routes, where the \(l\)-th route is expressed as \(a_{l}\):  
\[A(\tau)=[a_{1},a_{2},\ldots,a_{l},\ldots,a_{L}] \tag{11}\]  
where the probability distribution P is based on the current policy \(\pi_{\theta_{\tau-1}}\), representing the probabilities of different actions for different states S(\(\tau\)). For any given time window \(\tau\), the current policy \(\pi_{\theta_{\tau-1}}\) based on parameters \(\theta\) will generate an action \(A(\tau)\) according to the probabilities in P:  
\[A(\tau)=\pi_{\theta_{\tau-1}}(S(\tau)) \tag{12}\]
## 4 Conclusions  
We have designed the compact indirect slot-fed wideband patch array using an ASIWC for a high directivity and a low mutual coupling in Ka-band. The rectangular, printed patch radiator was co-operated with the ASIWC to achieve a high-directive pattern, and the indirect feeding structure was used to obtain the wideband impedance-matching characteristics. The 4 \(\times\) 1 linear array antenna was designed and fabricated to obtain the beam-steering performances. The proposed array obtained the measured gains of 11.7 dBi (\(\theta_{0}\) = 0\({}^{\circ}\)), 10.63 dBi (\(\theta_{0}\) = 15\({}^{\circ}\)), and 9.54 dBi (\(\theta_{0}\) = 30\({}^{\circ}\)) according to the steering angles.  
Conceptualization and methodology, W.K., T.-H.L., G.B., S.A., J.-H.J. and Y.K.; validation and formal analysis, W.K., T.-H.L., G.B., S.A., J.-H.J. and Y.K.; resources and data curation, W.K., T.-H.L., S.A. and J.-H.J.; writing--original draft preparation, W.K.; writing--review and editing, T.-H.L., G.B. and Y.K.; visualization, W.K., G.B. and Y.K.; supervision and project administration, T.-H.L., G.B. and Y.K. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Measured bore-sight gains at different beam-steering angles.  
Figure 7: Simulated and measured peak gains at different beam-steering angles.  
**Funding:** This work was supported by LIG Nex1 Co., Ltd.  
**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.
## 2 System Architecture and Methods
### Implementation Details
#### 2.3.1 Voice Recognition Module  
Understanding voice instructions from children involves a three-step process. The first step is to recognize when a child is about to give an instruction, signaled by a wake-up word. This wake-up word is a specific term that the system continuously monitors for, alerting it to the start of the interaction. For this purpose, we utilize Picovoice [16], a renowned web API that has been recognized for its proficiency in multilingual voice recognition capabilities, ensuring reliable detection across various languages.  
Once the wake-up word is detected, a six-second window is provided for the child to deliver their instruction. This duration was carefully chosen based on our observations; initially, we allocated four seconds, but this proved insufficient, especially for 4-year-olds. Six seconds offers a more comfortable time frame for children to articulate their instructions.  
The spoken message is captured and forwarded to an STT system for transcription in the subsequent step. This system converts the audio input into text, which the semantic analysis module will then process to discern the child's intention. For the STT system, we tested various deep learning models and web services, including Whisper Base [6], Whisper Tiny [6], Whisper Small [6], Whisper Medium [6], Whisper Large [6], Vosk Small [17], Vosk Normal [17], and Google STT [18] with contextual information, as well as Leopard [16]. Ultimately, we opted for Google's STT service, which uses contextual information, i.e., providing hints of the expected words.  
For a detailed comparison of the various STT models and services evaluated in this work, please refer to the results section. The result section provides an in-depth analysis of the effectiveness and suitability of different models and services in converting children's voice instructions to text.
# Vectored-Bloom Filter for IP Address Lookup: Algorithm and Hardware Architectures
## 2 Related Works  
### Bloom Filter  
A Bloom filter [17] is a multi-bit probabilistic data structure used for membership querying to determine whether an input is the element included in a given set. Bloom filters have been applied to many network applications due to their space-efficient attributes [18; 19; 20; 21] and hardware-friendly features [22; 23; 24; 25; 26].  
A Bloom filter has two operations: programming and querying. In programming, the membership information of each element in a given set is stored using a number of hash indexes, which are obtained by entering each specific element as the input of a hash function. In querying, the membership of each input is checked using the same hash function as used in programming [27]. Even though a Bloom filter requires multiple hash indexes for the programming and the querying, the hash indexes can be easily obtained using a single hash function and a few simple operations [28; 29].  
In programming set \(S=\{x_{1},x_{2},\cdots,x_{n}\}\), every bit of the \(m\)-bit Bloom filter is initially 0 and \(k\) different hash indexes are used to map each element to \(k\) locations of the \(m\)-bit array. Let \(n\) be the number of elements in the programming set. The optimal number of hash indexes is defined as follows.  
\[k=\frac{m}{n}\ln 2 \tag{1}\]  
In order to program an element in the set, the \(k\) bits pointed by the \(k\) hash indexes obtained using the element are all set to 1. If a specific bit location in the \(m\)-bit array was already 1, it is not changed.  
A Bloom filter provides whether or not an input is a member of the programmed set by querying. In querying input \(y\), the \(k\) hash indexes obtained by the same procedure as in the programming are used. The querying to the Bloom filter produces two types of results: _negative_ or _positive_. If any of the \(k\)-bit locations are 0, \(y\) is definitively not a member of set \(S\) and is thus termed a _negative_. If all of the \(k\)-bit locations are \(1\), \(y\) is considered a member of set \(S\) and thus termed a _positive_. However, a Bloom filter can produce a false positive owing to hash collision even if \(y\
ot\in S\). For \(n\) elements, the false positive rate \(f\) of an \(m\)-bit Bloom filter is obtained as follows [27].  
\[f=\left(1-(1-\frac{1}{m})^{kn}\right)^{k}\approx(1-e^{-kn/m})^{k} \tag{2}\]  
As shown, the false positive rate can be reduced by increasing the size of the Bloom filter but cannot be completely eliminated. Larger Bloom filters require larger \(k\) according to Label (1), resulting in more overhead. However, since the on-chip processing overhead for the Bloom filter stored in an on-chip memory is much smaller compared with the off-chip processing overhead for accessing off-chip memories, this paper aims to reduce the number of off-chip memory accesses by utilizing an on-chip Bloom filter.
# Examination of Landslide Susceptibility Modeling Using Ensemble Learning and Factor Engineering
## 3 Methodology
### CatBoost  
CatBoost (Categorical Boosting) is a gradient boosting decision tree algorithm specifically designed to handle categorical factors effectively. It operates under three key assumptions: (1) categorical features exhibit stable target statistics, (2) ordered boosting mitigates target leakage in sequential data, and (3) symmetric trees balance computational efficiency and overfitting. These features give CatBoost a distinct advantage in tasks involving large amounts of categorical data. As an open-source machine learning library, CatBoost simplifies practical applications by directly processing datasets with categorical factors, eliminating the need for cumbersome preprocessing. To enhance performance and model accuracy, CatBoost employs several strategies. First, it uses a symmetric tree structure, where all the nodes at each level split based on the same factor. This approach accelerates training, while reducing overfitting. Second, CatBoost introduces an ordered boosting strategy, which calculates residuals for each sample using data unseen by the previous model, effectively preventing target leakage and ensuring unbiased model evaluation. Another core feature of CatBoost is its automatic handling of categorical factors. It uses a unique encoding mechanism to process categorical data, preserving the integrity of categorical information, while mitigating overfitting caused by high cardinality. CatBoost excels in regard to binary and multi-class classification tasks, demonstrating efficient and accurate performance. Notably, it can directly handle categorical data in string format without requiring one-hot encoding, offering significant convenience and efficiency in data science applications.
## 2 Vehicle Configuration
### Longitudinal Vehicle Dynamics Model  
When the vehicle travels in a straight line, the longitudinal traction forces are usually simplified as \(F_{xr}=F_{xr1}=F_{xr2}\) and \(F_{xf}=F_{xf1}=F_{xf2}\). The force with subscript 1 is the force exerted on the left tire, while 2 on the right tire. The tractive force \(F_{x}\) and the normal forces \(N_{f}\) and \(N_{r}\) of the front and rear wheels were obtained by the following equations:  
\[F_{x}=M\dot{V}_{x}+Mg\sin\theta+\frac{1}{2}\rho C_{d}A_{f}V_{x}^{2}+2C_{t} \Big{(}N_{f}+N_{r}\Big{)} \tag{1}\]  
\[N_{f}=\frac{M_{v}gL_{r}\cos\theta-\frac{1}{2}\rho C_{d}A_{f}V_{x}^{2}h_{a}-[M_{ v}b_{lm}+2\Big{(}m_{wf}+m_{wr}\Big{)}r_{t}]\dot{V}_{x}}{2\Big{(}L_{f}+L_{r} \Big{)}}+m_{wf}g\cos\theta \tag{2}\]  
\[N_{r}=\frac{M_{v}gL_{f}\cos\theta+\frac{1}{2}\rho C_{d}A_{f}V_{x}^{2}h_{a}+[M_ {v}b_{lm}+2\Big{(}m_{wf}+m_{wr}\Big{)}r_{t}]\dot{V}_{x}}{2\Big{(}L_{f}+L_{r} \Big{)}}+m_{wr}g\cos\theta \tag{3}\]  
\[F_{x}=2\Big{(}F_{xf}+F_{xr}\Big{)} \tag{4}\]  
\[M=M_{v}+2\Big{(}m_{wf}+m_{wr}\Big{)} \tag{5}\]  
where \(M\) is the total vehicle mass; \(M_{v}\) is the vehicle mass excluding tire and wheel mass; \(m_{wf}\) is the front tire and wheel mass; \(m_{wr}\) is the rear tire and wheel mass; \(g\) is the gravity acceleration; \(\theta\) is the slope angle in degrees; \(\rho\) is the air density; \(V_{x}\) is the longitudinal velocity of vehicle; \(F_{xf}\) and \(F_{xr}\) are the traction forces exerted on the front and rear tires; \(L_{f}\) is the distance from mass center to front tire; \(L_{r}\) is the distance from mass center to rear tire; and \(r_{t}\) is the tire radius. Other vehicle specifications used in this paper are described in Table 1.
## 3 Results and Discussion  
### Platsome Features of A. japonicum  
The _A. japonicum_ plastome was constructed using the 3.15 Gb trimmed sequences from 21,588,275 PE reads. The whole plastome is a circular ring structure measuring 185,875 bp in length with a guanine-cytosine (GC) content of 37.9% (Figure 1). Its structure comprises four parts; each region has a different nucleotide length: LSC (83,795 bp), SSC (35,912 bp), and IRb/1 Ra (33,084 bp).  
A total of 109 genes were obtained through the removal of 23 duplicate genes, of which the _psbB_ gene had three copies, the _trtrL-CAA_ gene had four copies, and 20 genes had two copies. From 109 unique genes, we annotated 75 protein-coding genes and 34 RNA-related genes. Twenty-three genes have introns as non-coding sequences, including the _ycf3_, _clpP_ (\(\times\)2), and _rps12_ (\(\times\)2) genes with two separate introns, and seventeen genes (_atpF_, _petD_, _petB_,  
Figure 1: The plastome multi-layer circular map of _A. japonicum_. The ring map has six circular tracks showing repeat sequences, conserved regions, GC base content, and annotated gene names. The colored boxes represent the gene names in clockwise and counter-clockwise directions.  
rpl2, rpl16, rpoC1, ndhA (\(\times\)2), ndhB, trnA-UGC (\(\times\)2), trnG-UCC, trnl-GAU (\(\times\)2), trnl-UAA, trnk-UUU, and trnV-UAC) have one intron.  
Additionally, RNA splicing events, a major cause of protein diversity [28], were observed, such as 12 cis-spliced genes (_atpF_, _clpP_ (\(\times\)2), _petD_, _petB_, _ndhA_ (\(\times\)2), _ndhB_, _rpl2_, _rpl16_, _rpoC1_, and _ycf3_) and an _rps12_ trans-spliced gene (Figure S1). In the cis-splicing structures, two _clpP_ copies and _ycf3_ have two introns. Previous studies indicate that _ClpP_ encodes a protease enzyme crucial for protein processing [29], and _ycf3_ plays a role in regulating the photosystem of the plant membrane [30].  
The functional genes were categorized into three categories: forty-six genes of photosynthesis metabolism, fifty-seven genes of self-replication, and six genes in the \"other genes\" category (Table 1). In the photosynthesis category, photosystems are crucial multi-subunit complexes for photosynthesis [31]. Photosystem I group contains five _psa_ family genes and two additional genes (_ycf3_ and _ycf4_). Although _ycf3_ and _ycf4_ genes are not essential for photosynthesis, they are often included in photosystem I [32]. Photosystem II group contains 15 _psb_ family genes that encode protein subunits in photosystem II [33]. In the other categories, ribosome groups contained twelve _rps_ and seven _rpl_ family genes. The unknown group contains two hypothetical genes (_ycf1_ and _ycf2_) which have been presumed to be non-functional pseudogenes in plants [34]. However, these pseudogenes were recently found to have potential use as molecular markers [35].
## 5 Cells and Polymer-Nanocomposite Interaction
### Cell Morphology
Furthermore, the deposition of a conductive layer on the sample reduces thermal damage, inhibits charging, and improves the secondary electron signal required for SEM analysis. As an example of the application of the above investigation tools, is the evaluation of the morphology of different types of stem cells (human adult adipose stem cells (hASCs), human bone-marrow mesenchymal stem cells (hBM-MSCs), and human umbilical matrix stem cells (hUCMSCs) seeded on the surface of PLLA polymer film [151]. This work was conducted to explore the effects of the interaction at interface of human stem cells and PLLA polymer films, and in turn to improve our previous studies that had demonstrated the molecular cross-talk between stem cells and PLLA nanocomposites on guiding the stem cells fate [10; 12]. Briefly, the above stem cell types were seeded on films of pristine PLLA polymer and on film of PLLA with their surface modified by the radiofrequency plasma method under oxygen flow (PLLA + O\({}_{2}\)). As a consequence of the treatment the surface of PLLA films became hydrophilic, whereas the bulk properties of the polymer film were not affected. The analysis of the cytoskeleton, by the specific staining of F-actin with FITCH-Phalloidin, demonstrated that while hASCs cultured on pristine PLLA polymer films acquired a spheroid conformation, the hASCs seeded on PLLA+O\({}_{2}\) film surface kept the fibroblast-like morphology classically observed on tissue culture polystyrene. These data suggested that the surface hydrophilicity is involved in the acquisition of the spheroid conformation of adipose stem cells. Moreover, the cytoskeleton staining permitted to demonstrate that the oxygen treatment had no effect on the other two type of stem cell (hBM-MSCs and hUCMSCs) cultures seems both stem cells maintained the same shape observed on PLLA films [150]. (Table 2). In another work, the same procedure was employed to study the interaction of primary cells on multifunctional nanocomposites of PBCE-based copolyesters and single walled carbon nanotubes [38].