esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Wave-Induced Uplift Force onto Pipelines Buried in Sloping Seabeds
## 2 Numerical Model
### Boundary Conditions  
Under defined boundary conditions, the seabed governing equation is able to be solved to obtain the wave-caused seabed response. Figure 2 illustrates the boundaries of seabed sub-model. As for the lateral boundaries of seabed sub-model, the normal displacement is fixed, at the same time it does not allow the pore water to flow through. Additionally, as for the seabed bottom boundary, two translational degrees are fixed, and it also does not allow the pore water to flow through. Relatively complex boundary conditions occur on the seabed surface, in which all the components of displacement are free, and the wave pressure and pore pressure are assumed to be equal. The pore pressure gradient next to the interface of pipeline and soil is zero.  
Figure 2: Sketch of the boundary conditions of the seabed sub-model.  
In the present model, two user subroutines are able to be adopted to apply the wave-caused response to the mudline. The pressure response applied by the wave to the mudline is numerically simulated first using the wave sub-model. Then, the pore pressure and wave pressure applied to the seabed surface are able to be loaded using the subroutines DISP and DLOAD, respectively.  
In this study, the wave sub-model was built using the finite difference method and the seabed sub-model was established using the finite element method. In the wave simulation, the pressure solver is implicit while the viscous stress, the free liquid surface pressure, and the advection solver are explicit. Moreover, a first-order upwind differencing method is used to solve the momentum advection algorithm. In the seabed sub-model, the direct method and an unsymmetric matrix storage are used. In addition, the solution technique is Full Newton.
## 3 Methodology
### Heterogeneous Stance Network
#### 3.2.2 Network Construction  
The construction of the HSN involves extracting entities and determining relationships among nodes. We first extract entities from posts and targets using a combination of rule-based methods and LLMs. In the posts, certain entities, such as hashtags or users, can be extracted easily using regular expressions. As shown in Figure 3, for named entities, we utilize an LLM to extract them from the text. The entity extraction process is formalized as  
\[\mathcal{E}=\text{RegEx}(\mathcal{P})\cup\text{LLM}_{\text{NER}}(\mathcal{P}), \tag{1}\]  
where RegEx is a regular expression-based entity extractor, and LLM\({}_{\text{NER}}\) is an LLM with a named entity recognition (NER) prompt. Relationships between nodes are determined by analyzing the stance expressed between posts/targets and entities. An LLM is used for stance detection between pairs of nodes, represented as  
\[s=\text{LLM}_{\text{SD}}(u,v), \tag{2}\]  
where \(u\) and \(v\) represent nodes in the set of posts, targets, or entities. Using the identified relationships, we construct edges between nodes, where each edge captures the relationship type (favor, against, or neutral). To reduce query costs, stance detection is performed only for the given post-target pair and its directly extracted entities. Specifically, as demonstrated in Figure 3, for a given post-target pair, we determine stance relationships only between (1) the post and its extracted entities, and (2) the target and the extracted entities. This ensures that stance detection does not extend to entities extracted from other post-target pairs, effectively limiting unnecessary queries while maintaining a localized and computationally efficient stance representation. For subsequent training, we represent edges as undirected:  
\[\mathcal{R}=\{(u,s,v),(v,s,u)|u\in\mathcal{P}\cup\mathcal{T},v\in\mathcal{E}\} \tag{3}\]  
Finally, the heterogeneous stance network is constructed as  
\[\mathcal{G}=(\mathcal{V},\mathcal{R}), \tag{4}\]  
where \(\mathcal{V}=\mathcal{P}\cup\mathcal{T}\cup\mathcal{E}\) represents the set of nodes.  
To improve the representation of nodes that lack textual descriptions, we enhance entity nodes by generating descriptive text. This process involves using a large language model to generate a description for each entity node \(e\):  
\[\text{desc}(e)=\text{LLM}_{\text{DSC}}(e) \tag{5}\]  
The generated descriptions are used as initial features for the corresponding nodes, which aids in initializing the node embeddings for downstream tasks. This enhancement ensures that even those nodes without an original textual description have rich representations that can be utilized during feature learning and graph-based stance detection. Notably, LLMs cannot generate descriptions for all nodes as some nodes (e.g., less-known or anonymous users) may lack sufficient contextual information or publicly available data for the model to infer meaningful descriptions.
## 3 Proposed Method
### Description of the Individual Steps of the Compatibility Improvement Approach  
#### 3.2.1 First Step--Definition of Universal Item Structures  
In the course of defining universal item structures, we recommend analyzing a transfer file from the source and target systems separately. This procedure helps identify the entities and instances for the respective unit. In both programs, the item to be identified has to be created and exported analogously. It is essential that only the required information to represent an item is determined. Various methods are suitable for the recognition process, as has been elaborated on by previous researchers [15; 54].  
However, a challenge arises with the extracted units. The determined structures only refer to the considered file and cannot be used independently of the use case. This occurs due to internal factors, such as deviating keys, references, and attributes. For example, the name, position, and orientation of a coordinate system all depend on the user's preferences. We propose using operations representing parts of the item to describe these discrepancies.  
Moreover, we distinguish between special and standard operations. The former are parts that can differ across items, such as the orientation and the specific name of a coordinate system. Reference and differentiation operations are sub-specifications of special operations. With standard operations, however, we do not subdivide further. We assume that this categorization is sufficient for fully describing diverse items, as we have tested it on various examples. Our work resulted in the following three operation types:  
* Reference operation--This is a part of an instance representing a key or reference within an item.
* Differentiation operation--This is a segment of an instance that describes attributes that vary across items.
* Standard operations--This is a part of an instance without any special meaning. It is a static string that does not differ across items, and standard operations serve as the basic framework.  
For example, Figure 5 shows the operations for the item coordinate system from Figure 2. The keys and references, such as #47, are declared as reference operations. The name, the values of the coordinates of the origin, and orientation represent differentiation operations since they may differ across items. As all other data are standard operations that are identical for each item, we transfer them directly to Figure 5.  
Figure 4: Schematic illustration of the compatibility improvement approach.  
In summary, the result of the first step is a universal, file-independent item structure for both the source and the target system.
## 1 Introduction
This paper introduces a novel multi-user FTN-based DCSK system where a combination of FTN filtering and non-coherent detection allows better usage of the available spectrum for the DCSK systems. Moreover, the new system enables multi-user access using the same mechanism that enables spectral efficiency gain, i.e., the FTN sampling. The MU-FTN-DCSK enables multi-user access via different sampling rates with a gain in spectral efficiency for all the users simultaneously transmitting within the FTN regime. The BER results prove the system's capabilities for various sampling rates and spreading factors. The contributions of this paper are listed as follows:  
1. Designing the Multi-user Faster-Than-Nyquist system that enables access for users with different sampling rates, all transmitting in an FTN regime. 2. Derivation of the theoretical BER expression for the proposed system over Multi-path Rayleigh Fading channels. 3. Performance analysis of the designed system, via monte-Carlo simulation, over the AWGN and Rayleigh fading channels for different system parameters and configurations. The rest paper is structured as follows. In Section 2, the MU-FTN-DCSK system is presented. Section 3 derives the BER expressions of the system. In Section 4, the simulation results of the MU-FTN-DCSK system are presented along with their analysis. The paper is concluded in Section 5, where we highlight the most important findings related to the newly introduced system.
# Improved Simplified Engineering Fault Displacement Hazard Evaluation Method for On-Fault Sites
## 4 Discussion of the Results
### Consideration for Validation of the Method
#### 4.2.2 Checking the Self-Consistency  
The fundamental question is what percentage of the random displacement values are likely to fall into the interval between the minimum and maximum values (min-max interval) and whether the value that can be taken at a given level from the \"expected\" AER curves is an unbiased estimation of the mean displacement. In the studied case, the probability density function is not defined a priori.  
First, investigate the percentage of displacements within the calculated minimum and maximum values interval. Since the negative displacements do not make sense, a truncated normal distribution or a lognormal distribution is a reasonable assumption. However, assuming normal distribution \(X\sim\mathcal{N}\big{(}\mu,\sigma^{2}\big{)}\) with \(\sigma\ll\mu\), \(X\) is rarely negative. Therefore, as a first assumption, the normal distribution is used with the expected value for the mean \(\mu\) and half of the difference between the expected and maximum values for \(\sigma\). Figure 9 shows the density functions calculated for site position cases at \(x_{s}/FL=0.1\) and \(x_{s}/FL=0.5\) and for the \(\sim\)\(10^{-6}/\)a and \(\sim\)\(10^{-7}/\)a levels. The parameters of the density functions are shown in Table 4.  
Setting \(z=(D-D_{expected})/\sigma=1.96\) for the standard normal distribution, it can be concluded that more than the 95% of possible values fall within the calculated minimum and maximum interval except the case at \(x_{s}/FL=0.5\) and a level of \(\sim\)\(10^{-6}/\)a, where more than 90% but slightly less than 95% of values fall in the min\(-\)max interval (\(z=-1.69\) for \(D_{min}=0.26\) cm, but \(z=2\) for \(D_{max}=7\) cm). Consequently, if the decision on the safety  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{\(x_{s}/FL=0.1\)} & \multicolumn{2}{c}{\(x_{s}/FL=0.5\)} \\ \cline{2-4}
**Hazard Level** & \(\mathbf{1.0\times 10^{-6}/a}\) & \(\mathbf{2.19\times 10^{-7}/a}\) & \(\mathbf{1.1\times 10^{-6}/a}\) & \(\mathbf{2.25\times 10^{-7}/a}\) \\ \hline \(D_{min}\) & 0.10 cm & 0.26 cm & 0.26 cm & 0.63 cm \\ \(D_{max}\) & 2.85 cm & 7.0 cm & 6.99 cm & 17.13 cm \\ \(\mu\) & 1.58 cm & 4.14 cm & 3.35 cm & 9.23 cm \\ \(\sigma\) & 0.625 cm & 1.43 cm & 1.825 cm & 4.0 cm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the normal distribution density for selected site positions and hazard levels.  
Figure 9: The displacement density functions calculated for site position cases at \(x_{s}/FL=0.1\) and \(x_{s}/FL=0.5\) and for the \(\sim\)\(10^{-6}/\)a, and \(\sim\)\(10^{-7}/\)a levels within the min\(-\)max interval.  
significance should be made using the mean displacement and the min\(-\)max interval at a \(10^{-7}\)/a hazard level, the expected value and the maximum and minimum values calculated by the proposed improved engineering hazard evaluation method are of high confidence.
## 3 Results
### Scenario 1 versus Scenario 2  
Differences in NO\({}_{2}\) concentrations between SC1 and SC2 reached a maximum reduction of 4.6 \(\upmu\)g/m\({}^{3}\) (up to \(-\)41.2%. Figure S7 ES). This indicates that SC2 is more effective than SC1 in reducing NO\({}_{\rm x}\) emissions, mainly over urban areas where traffic is most intense. Diesel cars have the largest contribution to the total road transport NO\({}_{\rm x}\) emissions. The reason why SC2 shows the largest reduction over the northern part of Switzerland (not shown) is that road transport has a larger contribution of emissions to the total than other countries, and hence the change in emission factors is more evident. Additionally, most of the fleet is already Euro 6 and therefore the comparison of the Reference Case versus SC1 did not show major differences. During the summer period, the absolute differences are somewhat smaller than during the winter period. Applying Euro 6b (SC1) or Euro 6d-TEMP (SC2), shows that yearly concentrations are lower in the cities, with Euro 6d-TEMP showing the largest reduction in NO\({}_{2}\).  
Figure 2: Calculated yearly mean differences in NO\({}_{2}\) concentrations (\(\upmu\)g/m\({}^{3}\)) between Reference Case and Scenario 1 (**a**), together with the differences for three main periods, i.e., (**b**) cold/winter period (November to February), (**c**) warm/summer period (May to September), and (**d**) mild/transition period (March, April, and October).  
In Figure 3, the yearly NO\({}_{2}\) concentrations by Reference Case, SC1, and SC2 for 50 cities (the most polluted among the 150 cities considered in the PM\({}_{2.5}\) Atlas [35] are presented. For Bologna (~14 \(\mu\)g/m\({}^{3}\)), the lowest ground level NO\({}_{2}\) concentration was found, while for Paris (~33 \(\mu\)g/m\({}^{3}\)), the highest yearly average concentration was found for the Reference Case (these concentrations are the largest 7 km averaged value within the city functional urban areas [36] further denoted as \"FUA\"). Clearly, from this Figure we can see that implementing Euro 6b and Euro 6d-TEMP has a positive impact on the reduction of NO\({}_{2}\) concentrations at the city level.  
Figure 3: _Cont._  
Milan and Paris are among the problematic cities in Europe where yearly average NO\({}_{2}\) concentrations are high. In order to understand how often people are exposed to these high NO\({}_{2}\) levels, we took a look at the number of days for which the WHO NO\({}_{2}\) daily recommended concentration value of 25 \(\upmu\)g/m\({}^{3}\) (WHO new guidelines recommended high-ambition level by 2030) are exceeded in Figure 3b,c. We saw that for Milan, the NO\({}_{2}\) concentrations by the Reference Case exceeded 228 times per year and for Paris by 364 days per year. SC1 and SC2 show a decrease in the number of exceedance days, i.e., for Milan 179 and 161 times, respectively, and a reduction for Paris to 351 (SC1) and 344 (SC2) days per year. Euro 6d and Euro 6d-TEMP vehicles have therefore a clear impact on reducing NO\({}_{2}\) exposure levels in cities, although it is insufficient to reach the WHO guidelines.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 4 The LoRaWAN Protocol
### LoRaWAN MAC Commands  
LoRaWAN defines many MAC commands that allow customizing end-device parameters [23]. One of them, _LinkCheckReq_, can be sent by an end-device to test its connectivity. All of the others are sent by the network server. These commands can control the data rate and output power used by the device, as well as the number of times each unconfirmed packet should be sent (_LinkADRReq_), the global duty cycle of the device (_DutyCycleReq_), changing parameters of the receive windows (_RXTimingSetupReq_, _RXParamSetupReq_) and changing the channels used by the device (_NewChannelReq_). One command is used to query the battery level and reception quality of a device (_DevStatusReq_).  
Figure 8: LoRaWAN frame format. The sizes of the fields are in bits.
# Effect Mechanism of Material Ratio on Ultrasonic P-wave Velocity in Coal Based Paste Fill Materials
## 6 Parametric Analysis Model of Ultrasonic P-wave Velocity
### Establishment of the Predictive Model  
Utilizing the identified relationships between ultrasonic p-wave velocity and various influencing factors, and in adherence to the principles for devising a multi-parameter linear model, a predictive model encapsulating the characteristic parameters of the fill materials was formulated and is denoted as Formula (1).  
\[\{v=a_{1}v_{1}+a_{2}v_{2}+a_{3}v_{3}+a_{4}v_{4}+a_{5} \tag{1}\]  
The myriad of test findings related to the characteristic parameters dictated that Equation (1) undergo a multiple linear regression analysis, from which the association of ultrasonic p-wave velocity with diverse determinants was derived and is articulated in Formula (2).  
\[\{v=0.99997v_{1}+v_{2}+0.99982v_{3}+1.00006v_{4}-5.77648 \tag{2}\]  
The independent variable interval of the fitted wave velocity prediction equation is \(0<v_{1}<10\); \(3<v_{2}<6\); \(15<v_{3}<25\); \(27<v_{4}<35\). In this interval, the predicted equation fits the actual measured wave velocity to a high degree.
## 4 Simulation and Test Verification
### Simulated Test
#### 4.1.2 Anti-Jamming Performance Verification
Specifically, when using MSTFT and WPCT for interference suppression, the limited time-frequency resolution of these methods causes a greater energy dispersion of interference in the time-frequency domain as the interference energy increases, affecting desired signals more significantly. Although the FrFT algorithm improves the energy aggregation of PFM interference to some extent, digital FrFT suffers from spectral leakage; so, its interference suppression performance also degrades with the increase in the interference energy.
# Full-Scale Experimental Investigation of the Interaction between Trains and Tunnels
## 2 Trackside Tests  
In order to characterize the pressure signatures of different trains and to better define practical guidelines for carrying out experimental tests, the data collected in different tunnels of the Italian HS line will be shown. The experimental data were collected using absolute pressure transducers placed at different positions and acquired continuously for approximately one month. The speed of the trains was measured by two couples of photocells on both sides. Additionally, the atmospheric conditions (pressure, temperature, and wind) were measured during the experimental campaigns using a weather station. By comparing all the collected data, it is possible to highlight the influence of three main parameters: the initial conditions within the tunnel, the side of the track where to place the sensors, and the speed of the train.
# Wastewater Treatment Using Alkali-Activated-Based Sorbents Produced from Blast Furnace Slag
## 2 Materials and Methods
### Alkali-Activated Materials and Monoliths Preparation
The X-ray diffraction (XRD) measurements were performed by Bruker D8 Advance diffractometer (Bruker Italy, Milan, Italy), using a graphite-monochromated Cu K\(\alpha\) radiation (\(\lambda\) = 0.154 nm), step scan 0.02\({}^{\circ}\) 2\(\theta\) and 1 s per step, in the 5-30\({}^{\circ}\) 2\(\theta\) range. The scanning electron microscope-energy dispersive X-ray analysis (SEM-EDX) was performed with a Zeiss EVO 50 EP (Zeiss, Jena, Germany) with a spectrometer OXFORD INCA energy 2000 (Oxford Instruments, Abingdon, UK). Samples were coated with a thin layer of gold (by an EMS K500X sputter coater, Labtech-EM, Heathfield, UK) before the analysis, in order to make surfaces conductive. The SEM was operated at EHT voltage of about 20 kV, high vacuum (2.5 mPa), probe current of 100 pA, and working distance of 7.5 mm. Porosity of the material was determined by Hg intrusion by means of Autopore V9600 (Micromerities Instrument Corporation, Norcross, GA, USA). Cu(II) adsorption on porous alkali-activated monolith from monoionic copper solutions was investigated by electron paramagnetic resonance (EPR) spectroscopy, powdering the samples for the analysis. The EPR spectra were acquired by a Bruker EMX spectrometer (Bruker Italy, Milan, Italy) operating at the X-band frequency and equipped with an Oxford cryostat (Oxford Cryosystems, Long Hanborough, UK) with the following conditions: 130 K, modulation frequency 100 kHz, modulation amplitudes 5 G, and microwave power 5 mW. A 2,2-diphenyl-1-picrylhydrazyl (DPPPH) sample was used as reference and the intensity of the spectra was normalized by sample weight, receiver gain, and filling factor of the tube.
## 4 Discussion
### Experimental Insights into Color Element Differences  
Through regression analysis of \(\Delta\)E, \(\Delta\)L, \(\Delta\)C, and \(\Delta\)h using eye-tracking data and subjective questionnaires, we found the following:  
(1) Residential colors with medium to high brightness and medium to low saturation often receive higher color evaluations [4; 50; 60]. Further studies have explored architectural color combinations, finding that higher brightness contrast increases visual fatigue [58]. When accent and auxiliary colors fall within the dark range (low \(\Delta\)L), the public may develop negative emotions toward these combinations [61], which can affect the vitality of streets in the area. This experiment indicated that \(\Delta\)L had a more significant impact compared to other color difference factors (\(\Delta\)E, \(\Delta\)C, and \(\Delta\)h) on both user visual stimulation and subjective visual evaluations. Regression models further revealed that, in various models related to visual and subjective preferences, the regression coefficient of \(\Delta\)L was higher than that of other color difference factors, indicating a stronger and more precise connection between \(\Delta\)L and subjective visual quality.  
(2) In fields such as art appreciation, consumer decision-making, and targeted information retrieval, numerous studies have shown that a larger \(\Delta\)h tends to increase user visual attention, enhance visual perception, and boost consumer engagement [38; 62; 63]. However, this study found that \(\Delta\)h had a relatively smaller impact on visual perception compared to other color difference elements, based on eye-tracking data and visual perception evaluations. Additionally, its influence on subjective ratings was not particularly significant. Thus, it can be observed that in the field of architectural colors, \(\Delta\)h does not play as significant a role in visual perception ratings as it does in other domains. Conversely, when the \(\Delta\)L and \(\Delta\)C values of architectural color combinations are kept close, combinations with a larger \(\Delta\)h can still improve the visual quality of architectural color compositions, thereby enhancing the vitality of the streetscape.  
(3) In experiments exploring harmonious complementary landscapes, vibrant plant colors, such as oranges and yellows, can play a more significant role, even when combined with cooler tones [64]. This study's regression results similarly indicated that in architectural color research, a certain degree of \(\Delta\)h can enhance subjective evaluations of buildings. This may be because moderate color contrast can elevate users' EEG activity levels [65]. Previous studies have demonstrated a strong correlation between EEG activity and visual comfort [66]. Therefore, it can be inferred that controlling a certain degree of \(\Delta\)h in architectural color combinations can effectively enhance the visual quality of buildings and improve the residential experience, creating better spatial dynamics.