esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Materials and Specim Fabrication  
To apply polymer composites to automotive fog lamp reflectors, PBT and PA6 were used as the polymer matrix, and alumina was selected as a filler for improving the thermal conductivity of the polymers.  
Ultraudr B 4560 from BASF SE was used as PBT. It is a material created for the injection molding of automotive lamp bezels and has a medium viscosity grade. PA6 with Hyosung 1011 br, RV 2.4 (Korea), was used as matrix resin in this work. In addition, alumina (Al\({}_{2}\)O\({}_{3}\)) was applied to improve the thermal conductivities of the polymers. The Al\({}_{2}\)O\({}_{3}\) used in this study was provided by Dongkuk R&S Co. Ltd. (Gimhae, Korea). Spherical alumina with mean particle diameters of 5, 10, and 25 um was selected. For the chemical composition, DSP-AS series with over 99.9% Al\({}_{2}\)O\({}_{3}\), 245 ppm of free Na\({}^{+}\), and 11 ppm of free Cl\({}^{-}\) were used. Spherical alumina was used to improve the flowability of the material during injection molding, and various sizes of 25, 10, and 5 um were mixed to increase the packing density [22; 23] required for thermal conductivity improvement [24].  
Pellets for injection were manufactured by a TSE 32 twin-screw extrude machine (UNEEPLUS Co. Ltd. Hwaseong, Korea) at a screw speed of 300 rpm, melt temperature at 250 \({}^{\circ}\)C. The screw diameter in the extruder machine was 32 mm, and the length-diameter ratio was 40.  
Injection molding for specimen fabrication used NE-80 (80 Ton) (Woojinplaimm Co. Ltd. Boeun, Korea). For the fabrication of tensile specimens and spirals, the nozzle temperature was set to 260 \({}^{\circ}\)C. Specimens were fabricated under the same injection conditions, including the injection rate of 50%, an injection pressure of 6.5 MPa, and the cooling time of 20 s. Specimens for the measurement of the heat transfer coefficient of each sample were fabricated using a heat press. The specimens were fabricated by setting the temperature to 270 \({}^{\circ}\)C, the pressure to 0.6 MPa, and the pressing time to 5 min. The testing samples had a diameter and a thickness of 12.7 and 2 mm, respectively [25].  
Table 1 shows the compositions of the samples used in this study. The compositions were used to analyze the characteristics of the samples based on the polymers of PBT and PA6 according to the alumina content. For PA6, three cases with alumina contents of 50%, 60%, and 70% were compared and  
analyzed. For PBT, only 60% alumina was used because it was difficult to fabricate samples through injection molding after mixing alumina due to cooling shrinkage.
## 4 Nonlinear Finite Element Analysis Considering Fire Damage  
Based on the experimental results reported in the previous section, a nonlinear FE analysis was performed using the general-purpose FE program ABAQUS/CAE [35], and the rationality of the presented modeling technique was verified by comparing the analysis results with the test results. In addition, a parametric analysis was performed using the verified FE model, in which the HCS depth, span length, hollow ratio in a section, cover thickness of concrete, and load ratio were set as variables, and the fire resistance performance of the HCS according to each key variable was investigated in detail.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 2 Language Models and Attention Mechanism  
### Language Models  
Language models primarily have two main steps: pretraining and transfer learning. In the pretraining phase, some objective function is used to learn the network's initial parameters (language representation). Pretraining is then followed by the transfer learning phase, where the initial learned parameters are adapted or finetuned on a target downstream task. The pretraining is conducted in a self-supervised manner on the unlabeled corpus. Transfer learning, on the other hand, follows a supervised approach, where each of the downstream tasks, although having separate finetuned models, are initialized with the same pretrained parameters. This approach helps in learning a universal representation of language, which requires little adaptation when transferred to downstream tasks. Therefore, the target tasks do not need to be from the same domain as the unlabeled corpus. Unlike task-specific techniques, no architectural modifications are required for PLMs when applied to downstream tasks. However, the PLMs depend on a large corpus of unlabeled data to be effective across various tasks.  
Figure 1: Large language model phases.  
As stated in [PERSON] [22], language modeling refers to the task of modeling the probability of a sequence of tokens in a text, where a token can be a unit of text, such as a word, subword, character, byte, etc. Normally, in the pretraining phase of language models, a next-word prediction objective is used, which is conditioned on the previous tokens as context. So, for a given input or source sequence \(S=(s_{1},s_{2},\dots,s_{n})\), the model predicts the joint probability of the output or target sequence \(T=(t_{1},t_{2},\dots,t_{n})\), shown in Equation (1).  
\[P(t)=\prod_{i=1}^{n}p(t_{i}|s_{1},s_{2},\dots,s_{i-1}) \tag{1}\]  
This approach, where the probability of the next token is iteratively predicted, is referred to as autoregressive language modeling and is represented using Equation (2).  
\[\begin{split} p(x)&=p(x_{1},x_{2},\dots,x_{T})\\ &=\prod_{t=1}^{T}p(x_{t}|x_{1},x_{2},\dots,x_{t-1})\end{split} \tag{2}\]  
Here, to deal with different downstream tasks (question answering, translation, summarizing, etc.), each task is casted or converted into a text-to-text framework. In this way, the language model can be applied or used to handle different downstream tasks. The pretrained model with parameters \(\theta\) is then adapted during finetuning of dataset \(D\) to minimize the loss over the target tokens conditioned on the source tokens and previously seen target tokens. Equation (3) highlights this loss function '\(L\)'.  
\[L(D;\theta)=-\sum_{i}\sum_{j}log\ p_{\theta}(t_{ij}\mid s_{i},t_{i,<j}) \tag{3}\]  
LLMs follow a similar mechanism of pretraining and finetuning to language models, except the parameter size of LLMs is in billions and/or trillions.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### Airborne Laser Scanning (ALS)  
#### 2.4.1 Data Acquisition Methods and Characteristics  
In airborne laser scanning (ALS), a LiDAR sensor is mounted on an aircraft, along with an inertial measurement unit and a global navigation satellite system (GNSS) receiver. The LiDAR sensor transmits narrow laser pulses towards the ground and generates a scanning pattern over the target area. ALS systems, typically based on an oscillating mirror and scanning patterns, receive the return signal, measure the time of signal travel and associate each return pulse with the GNSS time and scan angle at which the pulse was transmitted. The travel time can be converted to distance and then to height. The ALS technique can produce georeferenced 3D point clouds in the target area [75; 159; 160].  
The operational ALS systems are mostly based on single-wavelength single-pulse linear-mode LiDAR. The new emerging multispectral ALS systems use a combination of LiDARs at different wavelengths. These sensors provide intensity data that can be used to derive colour images, such as optical imagery. The chief advantage of this technique is that the acquired data are independent of illumination conditions and are without shadows. Therefore, multispectral ALS systems have great potential for increasing the automation level in mapping. Geiger-mode LiDAR and single-photon LiDAR (SPL) are new ALS techniques that are sensitive to a single photon and can provide dense point clouds from higher flight altitudes, owing to their higher system sensitivity.
## 2 Related Work  
Word-embeddings are an important part of NLP and have become an inescapable choice for text representation for NLP tasks. The representations based on word frequency and others usually are high dimensional, ignore the order of the text, are sparse, etc.  
To deal with the problems of word frequency representation, different approaches were tested. In a previous study, authors used a bag of words with word weighting by TF-IDF scores. They tested different classifiers including support vector machines (SVM), random forest (RF), and extreme gradient boosting (XGB). They tested on 2-, 3-, or 7-stage cancer labels with an F1-score from 0.80 to 0.99 [11]. Similarly, another group used skip-gram and paragraph vectors-distributed bag of words (PV-DBOW) with multiple discriminant analysis (MDA) to generate document embeddings. Regarding multiclass classification (five classes), they obtained an F1-score ranging from 0.68 to 0.97 using an extreme learning machine (ELM) [12].  
The use of word-embeddings for classification is common in the health sector to classify different diseases and other challenges. For instance, in the mental field [13], researchershave used weighted document vectors as a combination of TF-IDF with Word2 Vec for challenging behavior classification. They report an accuracy of 84.3-98.5% in a binary class between challenging behaviors in Autism Spectrum Disorder (ASD) using SVM. In [14], researchers focused on comparing different word representations, including TF-IDF, Word2 Vec, batch-Word2 Vec, and Doc2 Vec. The objective was to classify cardiovascular diseases according to eight ICD codes. They obtained a better result in representation based on word frequency. In [15], the authors compared three different representations of the EHR notes, with a convolutional neural network classifier to predict a visual prognosis. They obtained an F1-score on this model of 67%.  
Most of the previous works focus on EHRs in English. Nevertheless, there are some reports using NLP on EHRs in other languages. For instance, in [16], researchers developed different pipelines for the identification of adverse drug reactions (ADRs) in Dutch clinical notes. Likewise, there are a few studies using Spanish that use EHR notes to classify different health conditions. For example, in [17], they classified cancer diagnoses using different word-embedding techniques and machine learning. The results for the binary classification was an F1-score of 98%. In [18], they developed a predictive model for early and late progression to first-line treatment of HR+/HER2-negative metastatic breast cancer. According to their results, the best NLP-based model achieved an AUC of 0.752. In addition, we identified works focused on other problems such as the ICD-10 coding in Spanish, which included word-embeddings like the one presented in [19], or using a contextualized language model (BioBERT) with rule-based approaches [20].  
To our best knowledge, there are no studies to classify SMM using NLP neither in English nor Spanish.
## 3 RAR: Reliability-Aware Routing
### Network Structure and Nodes' Deployment  
The proposed network is a cube that starts from the surface of the water and ends at a depth of 500 m as shown in Figure 1. Within the network, senor nodes deployment is random, which is due to the reason that attributes that are to be sensed may occur at any part of the network and are not specific to any particular region of the network. Also, due to random deployment of nodes, the density of the nodes may vary in different parts of the network.  
On account of greater attenuation of the radio waves in water, nodes involve acoustic signals to interact. Sink is hoisted at network's uppermost central surface as a master node that establishes communications with sensor nodes using acoustic waves. It gets data from nodes and advances it to the onshore data center. The onshore center gets packets from sink node by radio waves as it is the terrestrial environment where radio waves are generally used for fast communications. Unlike the usual approach that deploys two sensor nodes at network bottom to detect an attribute of interest [17], the proposed scheme assumes that detection of an attribute can be accomplished by every node. It provides greater coverage area in terms of detecting an attribute or event of interest.  
Since every node has a limited communication range, packets routing to sink by a node is achieved using multi-hopping, in case sink is beyond the node's range of communication.
# Design of Time-Synchronized Switch Based on Zynq
## 4 Clock Synchronization Module Design
### RTU Module  
The RTU module is responsible for determining which port to forward each frame of data received from any port. The RTU consists of two parts: hardware and software. The hardware portion of the RTU is responsible for the switching process, while the software portion is responsible for the learning and aging processes, managing the filtering database and VLAN table, as shown in Figures 11 and 12.  
The main processes of the RTU are as follows:  
(1) The RTU performs initialization.  
(2) The hardware portion of the RTU receives the Protocol Data Unit (PDU) sent from the source MAC address to the destination MAC address.  
(3) If the source MAC address is not in the MAC table, the hardware portion of the RTU will place an \"Unrecognized Request\" into the learning queue (UFIFO) and trigger a remote RTU interrupt.  
(4) If the destination MAC address is not in the MAC table, the hardware portion of the RTU will broadcast the received PDU on every port except the receiving port while placing an \"Unrecognized Request\" into the user UFIFO and triggering a remote RTU interrupt.  
(5) The software portion of the RTU's learning process captures the remote RTU interrupt and reads the \"Unrecognized Request\" from the learning queue.  
Figure 11: RTU hardware block diagram.  
Figure 12: Block diagram of the first version of the RTU software.  
(6) The software portion of the RTU creates a filtering entry in the filtering database.  
(7) The aging process of the RTU software portion tracks the duration of existence for each entry.  
When an entry has existed for too long, the aging process of the RTU software portion will remove that entry from the filtering database.
# Optimal Design Parameters for Hybrid DC Circuit Breakers Using a Multi-Objective Genetic Algorithm
## 4 Simulation Results
### Results and Analysis  
#### 4.2.1 Results of Optimizing Parameters for the HCB by Using the MOGA  
The convergence process of the objective functions is shown in Figure 5, and the total computation time for the whole training process is about 2 h. The multi-objective value with the sum of three values of objective functions is shown in Figure 5a. The single-objective values are represented in Figure 5b-d. The value of objective function 1 represents the peak fault current, objective function 2 represents the interruption time, and objective function 3 represents the recovery time. It is obvious that the convergence curve of the objective functions decreases with an increasing number of generations. Initially, the multi-objective value is evaluated based on the initial population, and therefore the multi-objective value is high at 0.38, but as the number of generations increases, the multi-objective value improves due to the application of the three operations of selection, crossover, and mutation in the MOGA procedure. This continues until, at 200 generations, the optimal solution is found. With the optimal solution, the model has a multi-objective value equal to 0.016. As a result, the optimal values of the HCB parameters are [R, L, L\({}_{\text{P}}\), C\({}_{\text{P}}\)] = [54 (\(\Omega\)), 3.74 (mH), 0.32 (mH), 2.63 (uF)].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Type** & **Value** \\ \hline Population size (n\({}_{\text{p}}\)) & Randomly normalized & 30 \\ Selection probability (p\({}_{\text{s}}\)) & Roulette wheel & 2/30 \\ Crossover probability (p\({}_{\text{c}}\)) & Single point & 0.8 \\ Mutation probability (p\({}_{\text{m}}\)) & Uniform random & 0.2 \\ Stopping criteria (n\({}_{\text{g}}\)) & Number of generations & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the MOGA.  
Figure 4: Configuration of the test system for optimizing parameters in the HCB.
## 4 Numerical Modeling of a Bored Pile with a Protective Shell in the Plaxis 2D Discussion  
The modeling of a bored pile with a protective shell was carried out in an axisymmetric formulation of the two-dimensional Coulomb-Mohr model. The dimensions of the geometric model were taken on the condition that the stress distribution would be negligible within a given zone.  
Figure 13 shows a geometric model for the numerical modeling of a bored pile with a protective shell, including a geotechnical element of the site, a pile model with a shell, and a linearly distributed force for a static loading pile.  
Figure 12: Comparative diagram of the bearing capacity of ordinary piles with and without wetting.  
A simulation was carried out for static tests of a 4 m pile with a protective shell. The pile and the shell represent a single conditional foundation, so there was no need to designate an interface between the pile and the shell; in other words, the possibility of the pile slipping through the protective shell was excluded. According to laboratory and field studies of the soil massif adjacent to the pile, at this construction site, the thickness of the protective shell when the sodium silicate is fed under a pressure of 5-6 atm, on average, is 0.95-1.15 d, where d is the pile diameter. In the numerical modeling, the average value of the containment thickness was taken to be 1.05 d = 0.42 m.  
The boundary conditions of the model walls were set in the form of hinged-movable supports with free displacement along the \(y\)-axis and displacement along the \(x\)-axis = 0. The base of the model was set as a solid embedment, displacement along the \(x\)-axis = 0 and \(y\)-axis = 0. In the Plaxis software package, these types of boundary conditions were set automatically, as it is suitable for solving most geotechnical problems. The finite element mesh was generated automatically by the program and was a system of triangles. The construction method was based on the stable triangulation principle, which helped to find the optimal mesh sizes.  
Before the start of the calculation, the initial conditions were determined, which included the initial geometric structure of groundwater and the initial state of effective stresses. The first computational stage included modeling of the natural stresses caused by gravitational forces; in the second stage--a uniformly distributed load, emitting a static load test loading, was applied to the pile model (Figure 13).  
The main design parameters of geotechnical elements for the Coulomb-Mohr model were taken based on the geotechnical surveys described in Table 1. The parameters of the containment shell, presented in the form of soil hardened with sodium silicate, were taken  
Figure 13: Geometric model of numerical simulation of a bored pile with a protective shell.  
based on numerous laboratory tests of hardened soil samples. The physical and mechanical values of the soil, shell, and pile accepted for modeling are presented in Table 5.
## 7 Mapping and the Morphological Interpretation of Deposits
### Stratiform Deposits  
These are all located to the west of the Anti-Atlas and are spatially distributed along the NE-SW trend from the southern IIni inlier to the Siroua massif (Figure 1). They are either in the Adoudou Formation, at the bottom of the \"basal series\" in contact with the Precambrian bedrock, or higher up in the Tamjout carbonate Formation, and rarely in the \"Lower Limestones\" Formation, but always in terrigenous facies with carbonate cement and/or in pure carbonate rocks. The main mineralized horizon, known as the Talat n'Ouamane horizon, is in the \"Serie de base\" Formation between the top of its basal conglomerate and the Tamjout dolomite [7]. From bottom to top, it consists of coarse detrital levels with dolomitic cement, which grade to sandstone siltstone levels, followed by a series of dolomite formations topped with siltstone [7]. Within this horizon, mineralized zones form stratiform bodies that fill sedimentary joints and interlayered sliding surfaces. These bodies are thicker (tens of meters), closer together, have higher copper content, and exhibit significant lateral extension (over a kilometer) in those deposits where the hosted Adoudou Formation tends to lose strength such as in the Agjgal and Tazalaght deposits. Conversely, as the Adoudou Formation strengthens, the ore bodies depleted copper and thinned to disappear completely [7].  
In stratiform bodies, mineralization occurred in different textural aspects (Figure 9): (i) filling voids of various sizes ranging from intragranular pores in carbonate-cemented terrigenous facies (Figure 9a) to those associated with open cavities in carbonate facies due to karstification and/or tectonic processes (Figure 9b), in the latter, undissolved carbonate residues are cemented by ore; (ii) in thin ribbons or veneers and coatings that develop along sedimentary fabrics and interlayered slipping planes parallel to the stratification planes (Figure 9c,d); (iii) in venules, sometimes branching into fine networks with no preferential orientation; (iv) in the dissemination of speckles of sulfides at first glance seemingly isolated, but interconnected by microcracks filled with calcite; (v) in granules lining internal geodesics parallel to the bedding or directly connected to the veinlet network.