esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.1 GPP Estimation  
In this study, RF was selected for GPP simulation for two primary reasons. First, RF is a powerful predictive tool that can effectively estimate and handle missing values, ensuring high model accuracy [60]. It can integrate diverse remote sensing products and meteorological data while utilizing ground-based observations to constrain model outputs. This method has been widely applied in simulating ecosystem fluxes such as GPP and NPP [61; 62; 63; 64; 65]. The RF model in this study was implemented using R version 4.3.3(R Core Team, 2024), with the training set proportion set to 0.75, with a total of 1500 decision trees and five terminal nodes per tree. Ground-based GPP observations served as the dependent variable, while environmental and climatic factors were used as the independent variables. Key variables in the model included monthly average temperature (MAT), monthly accumulated temperature (AT), monthly average precipitation (MAP), monthly accumulated precipitation (AP), vapor pressure difference (VPD), normalized difference vegetation index (NDVI), land use and land cover (LULC), digital elevation model (DEM), and the standardized precipitation evapotranspiration index (SPEI). These variables were used to simulate the growing season GPP for the QTP from 1982 to 2018. Detailed information on the modeling process is provided in the Supplementary Information. Second, the result obtained by using the RF model is more accurate than those obtained by using the multiple regression model and GLASS products.
# FL-MD3 QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites
## 3 FL-MD3 QN-Based Intelligent Access
### Data Preprocessing  
In smart construction sites, data preprocessing is performed at the BS. Input data include multi-modal and massive local data from users across sites, such as text, audio, images, and video. The goal is to convert raw traffic data into formats suitable for neural network inputs. The process involves three steps: traffic segmentation, feature extraction, and CSV format conversion. The SplitCap tool splits raw traffic by five-tuple information and filters nontarget device traffic via MAC addresses. Feature extraction considers transmission characteristics and bandwidth requirements of different data types. For example, video data require higher bandwidth than text. Features are extracted based on the method in [26], ensuring accurate reflection of transmission characteristics and device states. Extracted features (Table 1) serve as inputs to the MD3 QN model at edge servers.  
The raw traffic files are in hexadecimal format and cannot be directly used as neural network inputs. Data conversion is required. The process involves writing feature-extracted traffic data into CSV files using sliding windows, generating a CSV-formatted traffic sample set. Integrating these steps, this paper proposes a Sliding Window-Based Data Preprocessing (SW-Based DPP) algorithm. The pseudocode of SW-Based DPP is described in Algorithm 1. Let \(L\) denote the raw IoT device traffic (pcap format). After preprocessing, the unified initial sample set is represented as csvs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Features** \\ \hline Link-layer protocol & ARP/LLC \\ Network-layer protocol & IP/ICMP/ICMP6/EAPOL/payload\_1 \\ Transport-layer protocol & TCP/UDP/TCP\_w\_size \\ Application-layer protocol & HTTP/HTTPS/DHCP/BOOTP/SSDP/DNS/MDPS/NTP \\ IP selection & Padding/RouterAlert/count \\ Packet content & Size/Raw data \\ IP address & Destination IP counter \\ Port type & Source/Destination \\ \hline \hline \end{tabular}
\end{table}
Table 1: 26-Dimensional IoT traffic features.
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 8 Conclusions  
In this work we investigated the potential of the recently specified C-V2X technology to match the latency and reliability demands of platooning application. In particular, we consider two ways of implementing dynamic scheduling for CAM transmissions within platoons, currently unspecified by 3 GPP: the sequential mode, that is somehow reminiscent of TDMA solutions based on IEEE 802.11p, and the simultaneous mode with spatial frequency reuse enabled by the tight control of the eNodeB. The latter reduces the latency in the update cycle in each platoon vehicles controller fed by the exchanged CAMs. After a preliminary evaluation through theoretical bounds, a wide set of results achieved through a system-level simulator are reported under different configurations and load settings, with the ultimate goal to showcase the benefits of the C-V2X network-orchestrated radio resource allocation mode and our proposed enhancements specifically targeting platooning applications. Interesting findings are provided and hints about future works shortly discussed.  
In this paper, all the authors conceived the idea, organized the work and the proposed solutions. They conceived and designed the experiments; [PERSON] and A.V. performed the experiments. All the authors analyzed the experiments, reviewed the writing of the paper, its structure and its intellectual content.  
The authors declare no conflict of interest.
### Problem Formulation  
Researchers have worked on different methods and algorithms to protect data from replay attacks. Some researchers have developed a hybrid deep-learning-based model to protect data from replay attacks. Some researchers have designed an architecture consisting of 3-nodes and identified faulty nodes on this network. Some researchers have worked on different techniques and have implemented these techniques on different datasets, while other researchers have developed protocol-based security to prevent replay attacks. With the help of all these algorithms and mechanisms, replay attacks can be detected, and faulty nodes can be identified, but with the help of such a technique, data transmission cannot be secured, nor can malicious data be differentiated from the original data.  
The way this paper works is different from all other papers. This paper will develop a secure architecture to protect the data from replay attacks, which will be protected using security mechanisms in each phase. Whenever data is transmitted to a cloud server, the data is broadcast over the entire network, and all valid and invalid users can access the data. Cryptosystem will be used to solve this problem in which cipher values will be obtained by implementing Cryptosystem on the text, sender name, and Key received from the receiver, and a cipher catenation will be implemented on these cipher values. The reason for using cipher citation is that if an attacker tries to change any cipher value, he will not be able to understand the original text in that cipher value due to the implementation of cipher citation. Changing the cipher value and re-transferring a new value, it will affect the entire cipher text. The most significant advantage of using a username is that when the receiver decrypts the data, it will match the Key and the username. If both match after decryption, then the original text data will be shown, and if there is any change in the cipher value, then it will affect the whole data, and malicious data will not be shown, which is the novelty of this paper.  
The rest of the paper will be managed in this way. Section 2 will deal with various papers related to the existing technique. Section 3 will discuss the latest work. Testing will be undertaken in Section 4. The latest technique in Section 5 will be compared with the previous technique, and the latest work's novelty will be discussed. Section 6 will consist of conclusions.
# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain
## 3 Quality Control Using In-Situ Methods
### In-Situ Methods for R&D Purposes
#### 3.2.5 Bainite Sensor  
Another field of application for eddy current based testing is the so-called bainite sensor. This consists of an excitation coil, which generates an electric field in the component to be tested. In addition to the eddy current, this field also generates a magnetic field, which in turn generates a field in the reverse order to the first field. Thus, magnetic as well as electrical signals can be obtained by a coil system in the sensor. The signals obtained are analyzed according to their harmonic spectrum. In-situ testing of material properties is carried out by implementation in a TMTS, like the Gleeble. This allows phase fractions as well as transformation kinetics to be recorded during heat treatment or deformation. This is based on the ferromagnetic to paramagnetic change of steel during the transformation from low-temperature phases to austenite in the steel. So far, this method has been used primarily to study the bainite transformation in steel, which gives rise to the designation. This represents a simple technique to obtain in-situ information on transformation behavior in materials with allotropic phase transformation. However, this method is limited to this kind of investigation [90; 91].
## 4 Numerical Results  
This section presents the results of numerical validation of nested kriging modelling with variable-thickness domain. The method is compared to conventional surrogates (kriging and RBF) as well as the original nested kriging employing the fixed thickness parameter. The benchmark set includes two antenna structures, a dual-band dipole, and a broadband patch antenna with narrow ground plane.
# Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing
## 3 Receiver Algorithms
### Channel Estimation  
The \(m\)-th channel received signal \(\mathbf{y}_{m}(n)\), which is Doppler-corrected, can be expressed in matrix form as follows:  
\[\mathbf{y}_{m}(n)=\mathbf{S}(n)\mathbf{h}_{m}(n)+w(n), \tag{12}\]  
where  
\[\mathbf{y}_{m}(n)=[y_{m}(n)\ \cdots y_{m}(n-1)\ \cdots y_{m}(n-L+1)]^{T}, \tag{13}\]  
\[\mathbf{h}_{m}(n)=[h_{m}(n,0)\ \cdots h_{m}(n,1)\ \cdots h_{m}(n,L-1)]^{T}, \tag{14}\]  
Figure 3: Proposed TR-DFE block diagram.  
Figure 2: Conventional time reversal DFE (TR-DFE) block diagram.  
\[\mathbf{S}(n)=\begin{bmatrix}s(n)&s(n-1)&\cdots&s(n-L+1)\\ s(n-1)&s(n-2)&\cdots&s(n-L+2)\\ \vdots&\vdots&\vdots&\vdots\\ s(n-L+1)&s(n-L+2)&\cdots&s(n-2L+2)\end{bmatrix}. \tag{15}\]  
Channel estimation plays a critical role in TR underwater acoustic communications. The least square (LS), orthogonal matching pursuit (OMP), and improved proportionate normalized least mean squares (IPNLMS) are implemented to solve the above equations, and their performances are compared in this paper. UWA channels are sometimes sparse. The LS algorithm cannot take advantage of the sparsity inherent in UWA channels, which leads to a high mean squared error. The OMP algorithm is a sparse channel estimation; however, the sparsity of the channel is a necessary factor in this algorithm. The LS and OMP algorithms can be found in [16; 17]. Sparseness was taken into account to apply the IPNLMS algorithm to UWA channel estimation [21; 23]. The IPNLMS algorithm shows not only robust performance in non-sparse channels but also better performance than LMS in sparse channels. To utilize the sparse nature of the UWA channel, the IPNLMS adapts the coefficients \(\mathbf{h}_{m}\) as follows:  
\[\hat{\mathbf{h}}_{m}(n+1)=\hat{\mathbf{h}}_{m}(n)+\frac{\mu e_{m}(n)^{*} \mathbf{\Phi}_{m}(n)\mathbf{y}_{m}(n)}{\mathbf{y}_{m}^{H}(n)\mathbf{\Phi}_{m} (n)\mathbf{y}_{m}(n)+\delta}, \tag{16}\]  
where \((\cdot)\) represents the conjugate, \(\mu\) is the step size, and \(\delta\) and \(\varepsilon\) are regulation parameters. \(\mathbf{\Phi}_{m}(n)\) is a diagonal proportionate matrix:  
\[\mathbf{\Phi}_{m}(n)=\mathrm{diag}\{\phi_{m,0}(n),\cdots,\phi_{m,L-1}(n)\}. \tag{17}\]  
To guarantee its robustness in various UWA channels, the diagonal elements of \(\mathbf{\Phi}_{m}(n)\) are delineated as follows:  
\[\phi_{m,k}=\frac{1-\alpha}{2L}+(1+\alpha)\frac{|\hat{h}_{m}(n,k)|}{2\|\hat{ \mathbf{h}}_{m}(n)\|_{1}+\varepsilon},\quad k=0,\cdots,L-1. \tag{18}\]
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 6 Results and Discussion
### Consumption of Energy (mWh) in the Receive Mode  
Figure 16 depicts the amount of energy consumed by transmit mode routing protocols, such as AODV, LAR1, OLSR, Fisheye, and STAR-LORA, when combined with the applications of Telnet and Superframe for 250 nodes. As can be seen in Table 7, the minimum amount of transmitting energy that is necessary for the AODV, LAR1, OLSR, Fisheye, and STAR-LORA routing protocols to send data at their maximum size in the proposed CB UWSN is 0.021 mWh for LAR1 in the Superframe deployment application and 0.1 mWh for STAR-LORA in the Telnet deployment application. For STAR-LORA, the value for this minimum amount of received energy is the goal of the CB-UWSN, which is to reduce the amount of transmit power used as much as feasible. Regarding speed and dependability, no other routing protocol can compete with STAR-LORA in the Telnet deployment application and LAR1 in the Superframe deployment application.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{2}{c}{**Energy Consumed (mWh) in**} & \multicolumn{2}{c}{**Energy Consumed (mWh) in Idle**} & \multicolumn{2}{c}{**Energy Consumed (mWh) in**} \\  & \multicolumn{2}{c}{**Receive Mode**} & \multicolumn{2}{c}{**Mode**} & \multicolumn{2}{c}{**Transmit Mode**} \\ \cline{2-7}  & **Telnet** & **Superframe** & **Telnet** & **Super Frame** & **Telnet** & **Superframe** \\ \hline
**AODV** & 0.18 & 0.025 & 0.13 & 0.9 & 0.2 & 0.01 \\ \hline
**Fisheye** & 1.2 & 0.9 & 0.16 & **0.45** & 0.8 & 0.25 \\ \hline
**STAR-LORA** & **0.1** & 0.3 & 0.15 & 0.85 & **0.05** & 0.08 \\ \hline
**LAR1** & 0.12 & **0.021** & 0.13 & 0.95 & 0.12 & **0.009** \\ \hline
**OLSR** & 0.23 & 0.4 & **0.11** & 0.8 & 0.1 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy comparison of all routing protocols.  
Figure 15: Consumption of energy when operating in the Transmit mode using Telnet and Superframe deployment.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
###### Abstract  
In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has a significant impact on a broad range of applications, such as question-answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable of identifying the semantics of short text. Traditional methods are based on ontologies, knowledge graphs, and corpus-based methods. The performance of these methods is influenced by the manually defined rules. Applying such measures is still difficult, since it poses various semantic challenges. In the existing literature, the most recent advances in short-text semantic similarity (STSS) research are not included. This study presents the systematic literature review (SLR) with the aim to (i) explain short sentence barriers in semantic similarity, (ii) identify the most appropriate standard deep learning techniques for the semantics of a short text, (iii) classify the language models that produce high-level contextual semantic information, (iv) determine appropriate datasets that are only intended for short text, and (v) highlight research challenges and proposed future improvements. To the best of our knowledge, we have provided an in-depth, comprehensive, and systematic review of short text semantic similarity trends, which will assist the researchers to reuse and enhance the semantic information.  
short text; semantic similarity; natural language processing; deep learning; STSS
## 2 MultiNET Flexible Confident Learning
### Flexible Confidence Threshold  
The original CL method performs \(N\)-fold validation. That is, the DNN is trained on a \(N-1/N\) fraction of the database, while the remainder of the database is searched for mislabeling. The DNN classifications are designated as either confident or uncertain. The threshold for a confident detection is determined as follows:  
\[t_{j}=\frac{1}{|X_{g=j}|}\sum_{x\in X_{g=j}}p(y=j;x), \tag{1}\]  
where \(t_{j}\) is the probability threshold for the confident detection of class \(j\), \(X_{g=j}\) is the collection of images for which the DNN's estimated class is \(j\), and \(p(y=j;x)\) is the probability that the image \(x\) belongs to class \(j\) as indicated by the DNN (i.e., the \(j\)-th node of the softmax layer at the output of the DNN). The proposed threshold is an average of out-of-sample classification probabilities within a set of images that are classified as a given class. An image label is assumed to be noisy if the detection is confident and the DNN classification does not match the label.  
The CL algorithm does not allow the sensitivity threshold to be adjusted and is therefore highly dependent on the training set used. For images within the validation set that are similar to those within the training set, the probability of accurate classification is high. However, images which differ will have a lower chance of being classified accurately. In such cases, potentially mislabeled images may not be designated as such.  
Our solution allows the user to control the level of certainty so that an appropriate balance can be achieved between the detection of obvious errors and a minimal number of false positives. To achieve this, we replace the average confidence level of the original CL-method by a \(q\)-quantile. The threshold definition then takes the form  
\[t_{j}=Q\{p(y=j;x),x\in X_{g=j};q\}, \tag{2}\]  
where \(Q\{A;q\}\) returns the value of the \(q\)-quantile calculated within a set of probabilities \(A\). The value of \(q\) can be adjusted to modify the label noise detection threshold. For example, \(q=0.5\) indicates median probabilities, while \(q<0.5\) and \(q>0.5\) decrease and increase the threshold, respectively.  
In addition to increased flexibility, our proposed solution improves robustness to the presence of noisy labels within the training set. This is due to the replacement of the average value with a robust quantile-based measure.
## 4 Experimental Verification
### Test Results  
Figures 7 and 8 show a comparison between the experiment results and the simulation results under the condition shown in Section 4.1.  
Figure 5: Test device and test scene: (**a**) the source; (**b**) the receiver; (**c**) the target sphere; (**d**) the test scene.  
Figure 6: Schematic diagram of the experiment.  
Figure 7: Comparison between the experiment result and the simulation result at 20\(-\)40 kHz: (**a**) simulation result; (**b**) experiment result; (**c**) the motion trajectory.  
Figures 7 and 8 show that the striations shape of the simulation results was consistent with that of the experimental results. Figures 7b and 8b show the intensity of the experimental striations varied with frequency. The target moved from near to far, and due to the attenuation characteristics of the signal in water, the intensity of the signal trace was weak at a longer range. The shape of the striations followed the shape of the target's trajectory. The interference striations at the high frequency were denser than those at the low frequency.
## 4 Proposed Methodology
### Solution Based on the Recommendation System
#### 4.2.1 Data Collection  
The data collection phase is the first stage of our proposed system. Each of the three RS components gathers specific data as described below:  
The collaborative RS: This component collects data related to the user who requested the placement, including service preferences such as latency, deadline, CPU, memory, and storage requirements. It also retrieves the service history and preferences of neighboring users located in the same geographical area. The history reflects the Fog node(s) where similar services were previously placed, while availability refers to the current capacity of each node to accommodate new services, based on metrics such as CPU, memory, and storage.  
The content-based RS: This module gathers data on the specific requirements of the target user's requested service (e.g., CPU, memory, and storage), his history, and the characteristics of candidate Fog nodes. These characteristics indicate the resources that each node can provide, enabling a match between service needs and node capabilities.  
The hybrid RS: will retrieve the results of these two recommendation systems, as well as the location of users and Fog nodes.
## 2 Characteristics of Plant-Based Food Matrices as Carriers of Probiotics
### Main Prebiotics of Plant Food  
Dietary fibers (pectin, cellulose, hemicellulose) are widely recognized as health-beneficial components of fruits and vegetables. They are divided into two groups: soluble and insoluble fibers. In general, dietary fibers are non-digestible by humans, but in the large intestine, they can be fermented and metabolized by probiotictics carried in the food matrix [42]. Throughout the fiber metabolism, probiotics produce bioactive compounds, including short-chain fatty acids (SCFAs). Major SCFAs are acetate, propionate, and butyrate. They consist of a source of energy for the colon cells. Prebiotics could be fermented by _Lactobacillus_ and _Bifidobacterium_, promoting their growth and leading to the production of lactate and acetate as fermentation end-products, consequently impacting the consumers' health. Moreover, prebiotics, by stimulating the probiotic production of antimicrobial peptides, inhibit the growth of pathogenic bacteria and decrease pathogen adherence [43]. Thus, prebiotics' bioactivity strengthens the intestinal mucosa, regulates absorption, and mitigates inflammatory processes, contributing not only to gastrointestinal health but also to the overall host health profits [44]. Prebiotics can indirectly confer distant health effects on osteoporosis, neural and cognitive function, immune function, allergic and skin disorders, and cardiovascular disease risk factors [45].  
The main prebiotic carbohydrates present in plant foodstuffs include fructo-oligosaccharides (FOSs), galacto-oligosaccharides (GOSs), xylo-oligosaccharides (XOSs), beta-glucan, polydex-trose, and inulin. Despite direct health effects, they enhance the absorption of minerals (zinc, magnesium, iron, calcium) in the gut [46]. Gut microbiota degrade non-digestible carbohydrates into monosaccharides or into oligosaccharides, obtaining energy and promoting the growth of lactic acid bacteria (LAB) and other probiotics [43].  
Polyols are prebiotics naturally occurring in plant food (plum, strawberry, spinach, olives). Due to low calories, they can be utilized in food manufacturing as natural sweeteners (erythntrol, xylitol, sorbitol) [28].  
Phenolic and carotenoid compounds are another group of valuable substances of plant food that are classified as candidates for prebiotics. They are metabolized into phenolic acids by _Lactobacillus_ species of the intestinal microbiota. In the study of [PERSON] et al., the active phenol metabolites of fermented blueberries were shown to have antioxidant and antiproliferative activities [12; 47]. In the study of [PERSON] et al., vegetable-fruit beverages containing apples, pears, and carrots, fermented with _Lactobacillus plantarum_, had increased levels of vitamin C and significantly increased antioxidant activity [48].