esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 3 Methodology
### Aspect Term Extraction Structure  
Aspect term extraction can be viewed as a sequence labeling problem. The study uses BIO labels, namely Begin, Inside, and Outside, which are the beginning of the aspect term, the inside of the aspect term, and not the aspect term, respectively. For example, this text \"Good spreads, great beverage selections, and bagels really tasty.\", is marked as {O, B-asp, O, O, B-asp, I-asp, O, B-asp, O, O, O, O}.  
The right structure of Figure 2 shows the structure of the aspect term extraction model, including the BERT pre-training model, convolutional layer, gating unit, Bi-LSTM and CRF, which further enriches the word vector information by combining global and local information, and adds constraints for determining the aspect term boundaries.
## 3 Materials and Methods
### Implementation of Dynamic Multi-Objective Optimization  
#### 3.3.1 Reinforcement Learning Training Protocol  
Algorithm Choice  
We adopted _Double Deep Q-Learning (DDQN)_ with a target network to alleviate Q-value overestimation. Preliminary experiments showed that policy gradient variants (e.g., PPO) produced comparable results but at a higher computational cost; hence, DDQN offered the best accuracy-efficiency trade-off.  
Markov Decision Process (MDP)  
At each decision step \(t\), the agent observes a system state \(s_{t}=\left[E(t)\), \(V(t)\), \(S(t)\right]\) and outputs an action \(a_{t}=(w_{1},w_{2},w_{3})\) representing the weights for the environmental, economic, and social objectives.  
Reward Design  
The scalar reward  
\[r_{t}\ =\ \lambda_{1}\,\Delta ESI_{t}+\lambda_{2}\,\Delta EG_{t}+\lambda_{3} \,\Delta SWI_{t}\ -\ \lambda_{4}\,\mathcal{P}_{t}, \tag{12}\]  
reflects one-step improvements (\(\Delta\)) of the three subsystem indices and a penalty \(\mathcal{P}_{t}\) for any constraint violation, where \(\lambda_{1}\)-\(\lambda_{4}\) are tunable scaling factors.  
Network Architecture  
The Q-network contains two hidden layers with 256 and 128 ReLU units, followed by a linear head outputting \(Q(s_{t},a_{t};\theta)\).  
Training Procedure  
Each episode spans \(T=48\) decision steps (one day in half-hour slots). The system samples mini-batches uniformly from the replay buffer and optimizes the Huber loss between predicted Q-values and DDQN targets. Training stops when the moving average episode reward has not improved by more than 0.1 for 20 consecutive epochs _or_ after \(5\times 10^{5}\) environment steps, whichever comes first. Three random seeds are run and the best checkpoint on the validation set is used for testing.
# Performance Evaluation of VANET Routing Protocols in Madinah City
## 4 Simulations
### Test Scenarios
#### 4.3.2 Communication Latency  
This test scenario aims at collecting information on the packet transmission delays when a VANET node sends a packet to all neighboring nodes within a specified communication range, determined by the communication power of the radio antenna. In this case, an accident is simulated on a vehicle in the network, blocking the road. Then, a packet alert is broadcasted to the nearest VANET vehicles, warning them of an accident. These vehicles relay the packet to their neighbors until all the vehicles in the environment have received the accident packet. Once the alert is received, the alternative route to the destination is taken by vehicles to avoid the congestion caused by the accident.
# IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies
## 3 Review of IoT-Enabled Smart City Components and Solutions
### Smart Governance
A report of the European JRC also observed that governments may benefit from the combination of various data sources (e.g., from IoT and the web) with suitable analytical techniques (including AI-based techniques) to better identify and design specific administrative policies [45]. In Barcelona, ICT has represented a key driver for the evolution of the city model, based on flexible and efficient e-government initiatives and services that aim to make the city more innovative, inclusive and self-sufficient [46]. In the early 2000s, the city of Barcelona started implementing the _22@Barcelona_ Project to enhance the technological, socio-economical and sustainable impact of the region in order to improve citizens' quality of life (QoL) [47]. In Amsterdam, a consortium consisting of the municipalities, research centers and private companies of the metropolitan area developed and launched the Amsterdam Smart City Platform (ASCP) in 2014: an online board where the aforementioned stakeholders can discuss urban issues, propose solutions and foster city innovation [48]. The local government in Rio de Janeiro has promoted city data sharing initiatives, hosting them in the so-called Intelligent Operations Centre (IOC) premises with the aim of increasing the efficiency of city services [49]. Moreover, the Rio Agora social platform has been implemented to allow citizens to propose and discuss public policies with municipal authorities [3]. Singapore has adopted several smart governance projects through the Smart Nation and Digital Governance Group (SNDGG) [50], including a digital identity system for Singapore residents that allows them to perform easier transactions with public administration and the Core Operations Development and eXchange (CODEX), which is a digital platform delivering smart services to citizens. In Toronto, the Sidewalk Labs smart city project was proposed. The project promotes data sharing standards and data governance models to profitably exploit the data gathered from citizens [51] through the urban data trust model. This allows for the collection, management and aggregation of urban non-personal data, de-identified data and personal information (according to data privacy regulations and laws) and then extracts value from them [52]. Songdo in South Korea is considered one of most advanced large-scale greenfield-based smart city projects in the world [53]. In fact, Songdo is defined as a ubiquitous U-city, in which data are continuously collected through a capillary network of sensors and equipment. Data are shared and interconnected with information from public institutions, such as the Incheon City Traffic Information Center, the Korean Meteorological Administration, the Institute of Health and Environment, Police Agency, etc. Data are analyzed by the U-Integrated Operation Center and provided to citizens through media broadcasts and control servers in order to help them to find the necessary information [54].
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 3 PIDL for Deterministic Tse
### ML Surrogate  
When physics and PUNN are jointly trained, Figure 5 further demonstrates the flow of simultaneously tuning parameters in the physics model and the hyperparameters in the PUNN. The top blue box encloses the data-driven component that contributes to the loss function, and the bottom red box encloses the physics-based component.  
Note that in Figure 5, we omit details about how \(\rho\) and \(u\) interact within a PUNN. These two quantities can be generated in sequence or in parallel. When \(\rho\) is generated first, \(u\) can be derived from an FD that stipulates the relation between \(\rho\) and \(u\). Otherwise, one PUNN can be trained to generate both \(\rho\) and \(u\) together or two PUNNs are trained to generate \(\rho\) and \(u\) separately. \(\rho\) and \(u\) need to satisfy the CL, but when they are generated out of PUNNs this cannot be guaranteed. Thus, \(\rho\) and \(u\) are then fed into the physics component to impose this constraint on these quantities.  
To inject the minimum amount of knowledge such as the universal CL defined in Equation (6) to PIDL, and leave out that based on assumptions such as FD, defined in Equation (7), we propose an ML surrogate model that replaces the mapping from traffic density to velocity as a DNN and learns the relation between these two quantities purely from data.  
A fundamental diagram that establishes a relation between \(\rho\) and \(u\), can be treated as an embedded physics component within traffic flow models. According to empirical studies, the relation between \(\rho\) and \(u\) is generally not a one-to-one mapping, especially in a congested regime. Thus, it is natural to employ an ML surrogate component to characterize the interaction between these two traffic quantities. We can further explore to what extent the addition of surrogates affects the performance and where it is appropriate to use ML surrogates.  
Table 2 summarizes the existing studies that employ PIDL for TSE. Leveraging PIDL for the TSE problem was first proposed by [25; 27; 28; 31], concurrently and independently.  
Figure 5: Flowchart of joint training of PIDL.  
Next, we will demonstrate how to design the architecture of PIDL and the corresponding PICG. We will first present a numerical example to demonstrate how the physics law of three-parameter-based LWR is injected into the PICG to inform the training of the PUNNs, and then compare all the existing architectures on a real-world dataset.
###### Abstract  
Upper molar distalization using clear aligners requires optimal force direction and control to ensure effective and predictable tooth movement. This study aimed to evaluate the biomechanical effects of different aligner trimming lengths (trimline ending at the gingival margin vs. 2 mm extended) and attachment designs (no attachment, vertical rectangular, 25-degree beveled vertical, and double horizontal) on maxillary first molar distalization using finite element analysis. A three-dimensional maxillary model was constructed from CBCT data, and eight different aligner configurations were simulated under identical distalizing force. The stress distribution within the periodontal ligament of the maxillary first molar and displacement of crown and root landmarks in three axes were analyzed. The models with no attachments and trimlines ending at the gingival margin exhibited the highest degree of uncontrolled tipping and uneven stress distribution. In contrast, models combining extended trimlines with either beveled vertical or double horizontal attachments demonstrated more controlled bodily movement, reduced palatal root mesial displacement, and more uniform vertical movement. Overall, extended trimlines were associated with increased total distalization and improved force transmission. These findings provide biomechanical insight into optimizing aligner configuration for upper molar distalization and may guide clinicians and manufacturers in improving treatment precision and predictability.  
attachment design; clear aligners; finite element analysis; molar distalization; orthodontic biomechanics; root movement; tipping control; trimline +
Footnote †: journal: Applied sciences
# Diagnosing Phosphorus Limitation in Subtropical Forests in China under Climate Warming
## 3 Results
### Changes of Additional Phosphorus Demand for Different Forest Types  
The additional P demand averaged over forest types increased greatly during 1951-2010 (Figure 6). Linear regression analyses show that the slopes of additional P demand for EBF, ENF and DBF are 3.52, 1.85 and 0.81 mg\(\cdot\)m\({}^{-2}\)-a\({}^{-1}\) for S1, and are 0.35, 0.18 and 0.08 mg\(\cdot\)m\({}^{-2}\)-a\({}^{-1}\) for S2, respectively. The positive trends in the additional P demand for EBF, ENF and DBF are all significant, at 99% confidence level.  
The AAPD values in the period of 1981-2010 are much greater than those in 1951-1980 for both EBF and ENF. Take S1 for EBF as an example; the AAPD values are 85.7 mg\(\cdot\)m\({}^{-2}\) and 142.77 mg\(\cdot\)m\({}^{-2}\) for 1951-1980 and 1981-2010, respectively, and the latter is 1.67 times more than the former. For ENF, the AAPD for 1981-2010 is 1.8 times more than that for 1951-1980. The exceptional case occurred in DBF. The AAPD for DBF is 64 mg\(\cdot\)m\({}^{-2}\) during 1951-1980 and is 26 mg\(\cdot\)m\({}^{-2}\) during 1981-2010, which is only 0.41 times the former, implying a decreased growth rate in the latter 30 years. However, DBF occupies very small areas (1.38%) of subtropical China, and the decreased growth rate in AAPD is not common in this region.
# Analysis and Reduction of Nonlinear Effects in Optical Fiber Frequency Transfer
###### Abstract  
Nonlinear effects in optical fiber frequency transfer have a significant impact on the precision of frequency transfer. We investigate the main nonlinear effects, including the Brillouin scattering and the Raman scattering, in optical fiber frequency transfer through theoretical and simulation calculations in detail. The calculation results show that the threshold powers of the Brillouin scattering and the Raman scattering decrease with the increase in the fiber length; however, the fiber length has little to no impact on the threshold powers when the fiber length is greater than 10 km. The threshold powers, including the Brillouin scattering and the Raman scattering, increase as the attenuation coefficient increases. Conversely, when it comes to the gain coefficients, the outcomes exhibit a reverse trend. When the linewidth \(\Delta\upsilon_{laser}\) of the laser source is from 1 Hz to 1 MHz, the linewidth \(\Delta\upsilon_{laser}\) does not affect the threshold powers of the Brillouin scattering. This study seeks to offer design guidance aimed at mitigating nonlinear effects in optical fiber frequency transfer. The calculated results hold considerable potential in guiding various applications reliant on Brillouin and Raman scattering properties, such as laser technology and optical fiber sensing.  
optical fiber frequency transfer; nonlinear effects; Brillouin scattering; Raman scattering; optical clock +
Footnote †: journal:
# Towards Machine Learning for Error Compensation in Additive Manufacturing
## 2 Advancements in Additive Manufacturing
### Additive Manufacturing in Industry 4.0  
The fourth industrial revolution or Industry 4.0 is the current trend in the manufacturing sector connoting the gradual fusion of traditional manufacturing and practices with automation and data exchange. Additive manufacturing is considered as an essential and prospective element in the recent movement of Industry 4.0 where cyber-physical systems monitor the physical processes of the factory and decentralized decisions are bolstered. As a whole, Industry 4.0 focuses on manufacturing technology's intelligent automation and data exchange merger aiming to redefine humans' role in the latest generation of smart factories thus offering profitable cooperation of the cyber and physical systems [64]. Figure 2 shows the flow of how smart factories flow under the Industry 4.0 concept.  
Its essential concepts are associated with virtual environment that comprises Big Data, Internet of Things (IoT), Cloud Computing, wireless networks, cloud infrastructure along with artificial intelligence to name a few, whereas its physical side includes additive manufacturing, autonomous vehicles, smart materials, nanotechnology, and intelligent robots. In contrast, the physical part is bound by the capability of the current mainstream systems which makes a fresh manufacturing technology similar to AM a very enticing route in the Industry 4.0 concept. Researchers in academia and in the industry have recognized that the Fourth Industrial Revolution initiation will change our practices completely in products manufacturing, business transaction, administration of healthcare and even how we will live our daily lives [65].  
In the cyber-physical system's perspective, IoT can be described as the concept of gathering information from physical objects using computers and accelerated wireless connections. The extracted information from the 3D printers, prototype CAD models and even the end products themselves constitutes to a substantial amount of statistical data to be exchanged and analysed.  
Together, this large quantity of data is defined as Big Data, which is another fundamental element in Industry 4.0. Furthermore, cloud computing, which is related to the gathered information processing, can be in the virtual industrial world as one of the most significant paradigms. All of these current technologies help to ensure the effective utilization of existing information for a sustainable future in smart AM [66; 67].
## 1 Introduction
### State of the Art
* trajectory analysis: the use of saliency maps [41] to analyze which elements of the environment are vital to an agent's decision-making or LIME metrics [42] to show in specific cases what actions have the highest probability in a given state. * visualization techniques: the use of GradCAM (in vision neural models) [43; 44] to identify which parts of the image have the most influence on the agent's decisions, or Heatmaps of state-action pairs [45] to observe which state-action pairs are most frequently selected. * model distillation (indirect inference): training of a simpler model (e.g., a decision tree) [46] whose task is to mimic the performance of a more complex RL agent on the assumption that a simpler model is more straightforward to interpret and can provide more comprehensible information about the agent's decisions. Detailed surveys on RL method explanation can be found in papers [47; 48; 49; 50].
## 4 Discussion
### Limitations and Recommendations  
This study's outcomes shed light on the durability and stability of thermoplastic retainer materials. The differences between clinical situations and the in vitro conditions described in this paper should be considered when analyzing the current study findings. The current investigation illustrated how thermoplastic retainer materials behave in a moist environment and how they may degrade over time. Nevertheless, some limitations must be noted with this study. In this investigation, only 1 mm thickness was analyzed. Future research should compare samples of various thicknesses. This work was able to provide insight into the time-dependent alterations of water sorption and solubility and surface molecular structure of thermoplastic retainer materials. Future research should explore a longer duration of water immersion. Moreover, the effect of saliva immersion on water absorption and surface molecular structure should be evaluated in future studies. In addition, clinical investigations should be performed to assess the impact of in-vivo use on the physical and structural properties of the various types of thermoplastic retainer materials.
# Use of Total Least Squares Adjustment in Geodetic Applications
## 2 Solution of the Predetermined Model
### Motivation
This solution is called the LS solution and can be obtained in different ways:  
* We search for the most probable LS solution for the vector of unknowns \(\mathbf{x}\) through the system of normal equations [1,2]: \[\mathbf{A}\mathbf{x}=\mathbf{b}+\mathbf{v},\;\mathbf{A}^{\text{T}}\mathbf{P} \mathbf{A}\mathbf{x}=\mathbf{A}^{\text{T}}\mathbf{P}\mathbf{b},\;\hat{\mathbf{ x}}=(\mathbf{A}^{\text{T}}\mathbf{P}\mathbf{A})^{-1}\mathbf{A}^{\text{T}} \mathbf{P}\mathbf{b}.\] (3) In the case \(\mathbf{P}=\mathbf{I}\), the solution is \(\hat{\mathbf{x}}=(\mathbf{A}^{\text{T}}\mathbf{A})^{-1}\mathbf{A}^{\text{T}} \mathbf{b}\). * We can also use a more general approach--general least squares (GLS). In this case, the solution to the unknowns is not sought directly from the measurements, but via a vector of equivalent observations, each of which is a linear combination of the original observations [2]. Model (1) would be written in the form \(\mathbf{B}\mathbf{b}=\mathbf{A}\mathbf{x}\), and the solution would be computed using the expression: \[\hat{\mathbf{x}}=(\mathbf{A}^{\text{T}}(\mathbf{B}\mathbf{Q}\mathbf{B}^{ \text{T}})^{-1}\mathbf{A})^{-1}\mathbf{A}^{\text{T}}(\mathbf{B}\mathbf{Q} \mathbf{B}^{\text{T}})^{-1}\mathbf{b},\] (4) where \(\mathbf{B}\) represents the matrix of observations' coefficients, \(\mathbf{A}\) is the design matrix or the matrix of coefficients of unknowns and \(\mathbf{Q}=\mathbf{P}^{-1}\) is the cofactor matrix of observations. This approach is useful, especially in procedures for approximating curves and surfaces to a certain set of points, where we treat as \"one observation\" the actual coordinate  
Figure 1: **Left—**system of consistent equations, **right—**system of non-consistent equations (blue lines) and the adjusted solution (red lines). pairs \((x,y)\) or triplets \((x,y,z)\), which, on the surface, are connected in a mathematical equation with unknowns in non-linear form. In this case, it would be very difficult to isolate each coordinate and express it in a linear form. The same situation applies to coordinate transformation, where each coordinate pair or triplet of all points are treated as observations. The GLS solution is much faster and simpler in this case. * We look for a solution with the help of SVD [10], which is a version of the computation according to the classical least squares method under a.). We can also take into account the weights of the observations (in matrix \(\mathbf{P}\)) when using SVD. If we consider the fact that the matrix \(\mathbf{P}\) for observations is a positive definite, then it holds that \(\mathbf{P}=\mathbf{P}_{c}\mathbf{P}_{c}^{*}\) (Cholesky decomposition) and system (1) can be transformed to the form: \[(\mathbf{P}_{c}\mathbf{b})=(\mathbf{P}_{c}\mathbf{A})\mathbf{x}.\] (5)
* We use the adjustment method, which also takes into account the errors in unknowns and is described in detail in the following section and later practically tested on a few numerical examples.
# A Polymer Inclusion Membrane for Sensing Metal Complexation in Natural Waters
## 3 Results
### Competitive Studies  
The ability of D2 EHPA to accumulate metal cations in a similar manner (non-selective carrier) was further investigated in (i) SNW solution, where Ca\({}^{2+}\) and other ions are present; and (ii) mixtures of Cu and Zn.  
In the first case, experiments were conducted to determine whether or not the presence of calcium could affect the accumulation of either Zn or Cu. Calcium is a divalent cation that is widely present in natural waters and that can also be extracted by D2 EHPA [26]. These experiments were performed using as a feed solution a SNW containing 1 mM Ca\({}^{2+}\), among other ions (see Section 2.1), and only 0.03 mM of Zn or Cu. The results are presented in Figure 6 for each metal, where the accumulation obtained in controlled medium (nitrate at pH = 6) is compared to that in SNW. In the case of Zn, the accumulated metal was not statistically different at the 95% confidence (_t_-test), while in the case of Cu, its accumulation in SNW increased slightly. Therefore, even though the high amount of Ca present in water, besides other ions, the effectivity of the PIM system to accumulate both Zn and Cu was not affected as has already been discussed in [PERSON] et al. [27].  
Moreover, competitive experiments were conducted with Zn/Cu mixtures in 0.01 M KNO\({}_{3}\) at pH 6. In these experiments, the Zn concentration was set at 0.03 mM and the Cu concentration was varied from 0.008 to 0.03 mM. In this case, the results of which are presented in Figure 7, the accumulation efficiency was calculated for each metal found in the receiving solution. As can be seen, the accumulation of Zn was not affected even though Cu was also effectively transported through the PIM in the evaluated concentration range.  
Figure 6: Effect of the matrix on metal accumulation. Feed phase: 100 mL of a 0.03 mM of Zn or Cu in 0.01 M KNO\({}_{3}\), pH 6.0 or in SNW, pH 6.0; PIM: M2; receiving phase: 5 mL 0.01 M HNO\({}_{3}\) (_n_ = 3).  
These results show the PIM system to be a suitable tool for environmental studies to evaluate the free/complexed fraction of metal in natural waters.