esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Model
### Geotextiles  
A geotextile specimen was tested by wide-width tensile tests (ISO 10319 [25]) to determine long-term tensile strength. The test data are presented in load per unit width versus strain curve, from which the modulus values could be calculated.  
\[J=\ \frac{T\times 100}{\varepsilon} \tag{1}\]  
where \(J\) is the secant tensile stiffness in kN/m, \(T\) is the tensile force per unit width at a certain strain in kN/m, and \(\varepsilon\) is the strain in \(\%\).  
A strain of \(5\%\) was used because it was close to soil failure strain. The secant tensile stiffness and tensile force per unit width at \(5\%\) strain, \(T_{5\%}\) and \(J_{5\%}\), were \(120\) kN/m and \(2400\) kN/m, respectively, for the wide-width tensile tests (see Figure 4). The long-term tensile strength of geotextile at \(5\%\) strain was:  
\[T_{5\%,\ l}=\!120/6=20\ \text{kN/m} \tag{2}\]  
The long-term secant stiffness at \(5\%\) strain was:  
\[J_{5\%,\ l}=2400/6=400\ \text{kN/m} \tag{3}\]  
where the number of \(6\) is the reduction factor value that is typically in the range from \(5\) to \(7\), as suggested in the design guidelines (GB/T 50290-2014 [26]) accounting for the negative impact from creep, durability,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil** & \(\gamma\) **(kN/m3)** & \(w\) **(\%)** & \(\varphi\) (\({}^{\circ}\)) & \(\psi\) (\({}^{\circ}\)) & \(c\) **(kPa)** & \(v\) **(\({}^{\circ}\))** & \(E\) **(MPa)** \\ \hline New perm & 19.7 & 11.2 & 32 & 0 & 10 & 0.3 & 50 \\ MSW & 10.8 & 35 & 15 & 0 & 12 & 0.35 & 10 \\ Old perm & 19 & 18 & 25 & 0 & 20 & 0.3 & 50 \\ Foundation & 20 & 24.8 & 23 & 0 & 33 & 0.25 & 63 \\ Bedrock & 25 & - & 42 & 0 & 300 & 0.25 & 5000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil propriety of the Xingfeng municipal solid waste (MSW) landfill site. \(\gamma\): unit weight; \(w\): moisture content; \(c\): cohesion; \(\varphi\): friction angle; \(\psi\): dilatancy angle; \(E\): Young’s modulus; and \(\upsilon\): Poisson’s ratio.  
Figure 3: Finite element mesh.  
and installation damage. The reinforcement was modelled using a geogrid element built in Plaxis 2D with an axial stiffness and tensile strength on the basis of the tensile test results. The soil-reinforcement interface was assumed to be fully bounded in the numerical model because the primary failure mode of the GRS berm was not governed by the geotextile pullout failure ([PERSON] and [PERSON] [27] and [PERSON] et al. [23]).
## 3 Results
### Biologicals in Asthma and Allergic Rhinitis
#### 3.3.4 Dupilumab (Anti-IL-4/13 Receptor)  
Dupilumab was approved by the FDA in 2017 [221]. It is recommended for patients aged 6 years and older who present severe eosinophil asthma, but also for the treatment of moderate to severe atopic dermatitis, moderate to severe asthma, and chronic rhinosinitis with nasal polyps (CRSwNP) [222]. It is administered by subcutaneous injection every 2 weeks in patients older than 12 years. For children between 6-11 years of age, the dose depends on weight [15].  
Several studies have demonstrated the efficacy of duplumab in the treatment of asthma and chronic rhinosinitis [223]. In 2018, [PERSON] et al. carried out a randomized, double-blind trial (NCT02414854) involving 1902 patients aged 12 years and older with uncontrolled asthma [224]. Participants received duplimumab 200 or 300 mg or placebo every 2 weeks for 52 weeks. Results showed that severe asthma exacerbations were reduced by 47.7% among patients receiving dupilumab as compared with individuals that received a placebo. Moreover, dupilumab has been shown to be more effective than OCs in the treatment of severe asthma [225]. [PERSON] and collaborators carried out a randomized phase III clinical trial (NCT02528214) in which 210 patients with OC-treated asthma were recruited [225]. They received dupilumab (300 mg) or a placebo every 2 weeks for 24 weeks. During the study, the glucocorticoid doses were reduced each week, and at the end of the study, the decrease in the dose was 70.1% for the dupilumab group and 41.9% for the placebo group. Despite dose reduction, dupilumab treatment showed a 59% lower exacerbation rate as compared with the placebo group. Nevertheless, this biological presents important adverse effects like blood eosinophil, dry eyes, keratitis, conjunctivitis, head and neck dermatitis, and arthritis [226].  
Regarding the use of dupilumab in AR, [PERSON] et al. recently reviewed a randomized, double-blind Phase IIb clinical trial (NCT01854047) in patients with asthma and perennial AR (PAR) [227]. This trial evaluates the efficacy of dupilumab at different doses: 200 mg or 300 mg subcutaneously every 2 or 4 weeks; versus placebo. There was evidence that 300 mg of dupilumab every 2 weeks in combination with intranasal corticosteroids and \(\beta\)2 agonists, significantly reduced nasal symptoms in patients with asthma and comorbidity with PAR [178]. Although dupilumab has demonstrated several benefits in the treatment of AR, as well as omalizumab, further studies are needed to corroborate their cost-effectiveness against current therapies.
# A Feasibility Study for a Nonlinear Guided Wave Mixing Technique
## 2 Theory
### Guided Wave Mixing Theory
The frequency ratio that results in the maximum mixing wave amplitude is \(\omega_{s}/\omega_{l}\)= 0.263, which is simplified as: \(u(\omega_{1})+v(0.263\omega_{1})\to v(0.737\omega_{1})\).
# Effects of Heat Treatment Processes: Health Benefits and Risks to the Consumer
## 3 Phenolic Content  
The phenolic compounds present in seaweed mainly include chlorogenic acid, phloroglucinol and phlorotannins, caffe acid, kaempferol, benzoic acids, coumaric acids, cismaritin, ferulic acid, gallic acid and syringic acid [11; 13; 17; 20]. The extractability of bioactive components from oven-dried _Gracillaria_ sp. (Phaeophycae), _Ulva rigida_ (Chlorophyta) and _Fucus vesicolus_ (Phaeophycae) was studied at different temperatures (25 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C) [18]. However, there was a demonstrated reduction in total phenolic and flavonoid content after drying. In _Hromosira banksia_ (Phaeophycae), total phenolic content (TPC) and total flavonoid content (TFC) were highly affected by the drying technique, with greater preservation of the phytochemical content through freeze-drying, de-humidification and vacuum drying, compared with oven- or sun drying. For _Saccharina latissima_ (Phaeophyceae), the drying temperature and humidity had a significant effect on TPC, with a reported drop by 10-fold compared with the fresh algae [13], with the highest amount of phenolic activity in freeze-dried samples and the lowest in samples dried at 70 \({}^{\circ}\)C. This variation is possibly due to less oxidation at lower temperatures and the absence of oxygen while vacuum-drying [20]. In accordance with these results, the drying kinetics of _Himanthalia elongata_ (Phaeophyceae) when exposed to a range of temperatures was described [16]. After a 24-h drying period, a reduction of 29-51% in total phenolic compounds was observed, with the maximum reduction of 51% observed for the 25 \({}^{\circ}\)C drying, while a reduction of 29% was observed for drying at 40 \({}^{\circ}\)C. Total flavonoid content followed a similar tendency to the phenolic content in these algae [10; 16].  
In [PERSON] et al., the effects of different drying processes (oven-drying at 40-60 \({}^{\circ}\)C, freeze-drying and microwave drying at 385, 540 and 700 W) on antioxidant potential, protein, lipid, amino acid and fatty acid content were tested in five brown seaweeds (_Fucus spiralis_, _Laminaria digitata_, _Fucus serratus_, _Halidrys siliquosa_ and _Pelvetia canaliculata_) [11]. The results demonstrated an overall loss of compounds, with high values of certain compounds linked to specific drying methods.  
Overall, heat causes cellular damage, inducing enzymatic activity and oxidative stress, leading to thermal degradation of the phenolic compounds [20]. These heat-labile compounds are usually bound within carbohydrates, protein and the fatty acid matrix in the food structure [20]. A substantial number of phenolic compounds can be degraded due to light or oxygen exposure, which are more likely to occur in an oven-dryer system in comparison with freeze-drying. This phenolic content is associated with several of the beneficial properties present in seaweeds. Assessing the best methods is crucial for optimization, in order to minimize phenolic content loss [17].
## 4 Experimental Setup and Result Analysis  
### Experimental Configuration  
The proposed model was trained using the publicly available Places2 dataset [44], which contains over 10 million images spanning more than 500 scene categories. Each category includes approximately 6000 to 40,000 images with a resolution of 256 \(\times\) 256 pixels, providing high representativeness for real-world scenarios. Based on the distortion simulation method described in Section 4.2, the original images were processed to generate synthetic wide-angle images for training purposes.  
During training, 6000 images were randomly selected for training, with 600 used for validation and 250 for testing. To enhance sample diversity, various deformation parameters and randomly selected distortion centers were applied to create a wide range of distortion patterns. For generalization evaluation, the MS-COCO dataset and real-world wide-angle surveillance images were further employed during the testing phase to assess model performance under complex scene conditions.  
All experiments were conducted on a server running the Ubuntu 20.04 operating system. The hardware configuration consisted of an Intel Core i9-10900K CPU (Intel, Santa Clara, CA, USA) and an NVIDIA RTX 4090 GPU (NVIDIA, Santa Clara, CA, USA), providing substantial parallel computing power and memory resources suitable for both training and inference tasks.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 2 System Model  
In this section, we introduce a simple signal model of an uplink MU-MIMO system, the joint transceiver design and the channel models.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 3 Results
### Proximal Soil Sensing and Profile Description
The soil profiles also differed in redoximorphic features, which indicated the depth of temporary waterlogging due to the slow deep drainage (stagnic properties). In particular, the soils of the northern part of the field, characterized by slightly lower ECa and TC_gamma (P1 and P2) showed stagnic properties starting at 1.15 or 1.30 m deep. In contrast, the soils characterized by higher ECa and lower TC, in particular in the southern side of the field, showed stagnic properties starting around 0.90-1.00 m deep. Although the difference of 0.15-0.40 m regarding stagnic properties depth is apparently similar, it could influence the water availability for plants. Water saturation at a depth of 0.9-1.0 m for a certain period of the year does not negatively affect root oxygenation but can provide water for plants for a slightly longer time than soil with deeper stagnic properties. Figure 3: The maps obtained by the soil proximal sensing, and the location of the soil profiles (P1–P6, black dots). The left and central maps showed the apparent electrical conductivity (ECa) at two reference depths, about 0–0.75 (ECa\({}_{1}\)) and 0–1.50 m (ECa\({}_{2}\)). The map on the right showed the gamma-ray total counts (TC_gamma) of the topsoil, about 0–0.30 m. The lower values of TC_gamma in the northern part of the field correspond to the area with higher surficial stoniness.
# Things2 People Interaction toward Energy Savings in Shared Spaces Using BIM
## 2 State of the Art
### Energy Consumption Prediction
Another technique that can be used for time-series predictions is the Holt-Winters exponential smoothing method which deals with seasonality. This applicability of this method was observed for forecasting short-term electrical load showing MAPE results of 3.94% with one step ahead and 4.49% for seven steps ahead [13]. Another study applied this method for energy consumption forecasting in the Cajamarca region, Peru, in the short and medium term [14]. Similar to the previous studies, the Holt-Winters method was also applied to forecast data centres power consumption to increase energy efficiency [15]. Neural networks can also be utilized for time series forecasting, with long short-term memory (LSTM) being one of the used techniques. This model was utilized to forecast energy consumption in buildings within the campuses of the City University of New York [16]. Another study showed the applicability of the LSTM method to forecast housing energy consumption to create a stable power supply since electricity is simultaneously consumed as it is generated by power plants. This method was tested with data provided by the University of California, Irvine (UCI) machine learning repository that contained an individual household's electrical power consumption and showed results of MSE of 0.37 [17]. [PERSON] et al. in the work [18] covered a combination of methods to measure and collect data on occupant behaviour, new occupant behaviour models, and the integration of these models with building simulation programs, and it highlights case studies demonstrating the use of these tools to solve real-world problems to improve building design, operation, and retrofit. The 'drivers-needs-actions-systems' (DNAs) framework, described in [19] presents an ontology providing a common technical language for the building simulation community to observe, model, and simulate energy-related occupant behaviour in buildings. The proposed framework captures the vast majority of occupant behaviour, which directly or indirectly impacts building energy use. The DNAs framework presented in the paper facilitated the quantification of the impact of occupant behaviour on building energy efficiency.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 3 Challenging Cross-Border Use Cases
### Identification of Use Cases
Vehicle QoS support could be considered a horizontal use case, whereby the vehicle is able to anticipate network QoS fluctuations and adjusts its service requirements accordingly. Moreover, the network is able to allocate connectivity resources to preserve a QoS to satisfy application needs and the application can adapt its traffic demands to meet network performance. The overall goal is to offer a smooth user experience, meaning the coordination of different network infrastructures of both sides in a cross-border context to perform seamless transitions.
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 4 Results and Discussion in Weighing on Modular WIM System
### Test Site Modular System Comprehensive Test
#### 4.1.3 Performance Comparison of Modular System Robustness  
In the actual weighing process, drivers may attempt to reduce weight by weighing at variable speeds. To accurately reflect the actual conditions and verify the robustness of the modular system proposed in this paper for vehicles traveling at variable speeds, the Shaanxi Provincial Institute of Metrology conducted a field test. The test report number was CZ30230264C, and the test results are listed in Table 8.  
It can be seen from Table 8 that when weighing a vehicle traveling at variable speeds, the five-platform modular system maintained high accuracy, with a maximum relative error of less than 0.5%. In contrast, when the one-platform system used in the experiment was faced with a vehicle traveling at variable speeds, the weighing accuracy decreased greatly, and the maximum relative error approached 15%. This was because the five-platform modular system employed whole-vehicle-load weighing, while the acceleration and deceleration of the vehicle can cause the axle loads to change, but it did not affect the calculation of the gross vehicle weight. The test results demonstrated that the modular system exhibited excellent robustness.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Vehicle Type** & **5-Platform (kg)** & **3-Platform (kg)** & **1-Platform (kg)** \\ \hline
2-axle & 8.010 & 10.00 & 41.09 \\
4-axle & 10.47 & 122.6 & 162.6 \\
6-axle & 10.55 & 106.3 & 158.6 \\ \hline \end{tabular}
\end{table}
Table 6: Standard deviation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Vehicle Type** & **5-Platform (kg)** & **3-Platform (kg)** & **1-Platform (kg)** \\ \hline
2-axle & 24.40 & 28.03 & 115.0 \\
4-axle & 30.30 & 319.1 & 409.0 \\
6-axle & 27.30 & 299.0 & 475.0 \\ \hline \end{tabular}
\end{table}
Table 7: Magnitude of error fluctuations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Vehicle Type** & **2-Axle** & **3-Axle** & **6-Axle** \\ \hline Modular System & \(-\)0.38\% & \(-\)0.10\% & \(-\)0.20\% \\ Independent System & \(-\)5.04\% & \(-\)8.63\% & 14.66\% \\ \hline \end{tabular}
\end{table}
Table 8: Maximum relative error of different systems.
# Accelerating High-Resolution Seismic Imaging by Using Deep Learning
## 2 Methods
### Network Architecture
The arrow line between the encoder and decoder layers is the skip connection, which maintains the spatial resolution and positional information and ensures that the feature maps from the up-sampling are more elaborate. Moreover, the introduction of the skip connection makes the network less sensitive to a new data set and ensures that the network has a stronger generalization ability.
## 4 Wind Loading and Vibration Mitigation
### Structural Model  
The benchmark building has the aspect ratio of its dimensions height \(H\): depth \(D\): width \(W\) of 6:1:5:1, with \(H=180\,\mathrm{m}\). With a prescribed density of \(160\,\mathrm{kg/m^{3}}\), the total mass results in 38.880 tonnes. It is a generic structure, with the mass, stiffness and damping characteristics distributed homogeneously. Further provisions give the target modes of vibrations and respective frequencies. The first three for such a cantilever structure are as follows: weak bending at \(f_{bending,weak}=0.20\,\mathrm{Hz}\), strong bending at \(f_{bending,strong}=0.23\,\mathrm{Hz}\) and torsion at \(f_{torsion}=0.40\,\mathrm{Hz}\). These eigenmodes are uncoupled.  
The initial study aimed to use this model for base-force measurements according to the High-Frequency Force Balance (HFFB) method. The peculiarity for that method is that the fixity is flexible, but the building is rigid along its height. As a result, the mode shapes are linear. We created a model setup for Finite Element Analysis (FEA). This numerical replica was built up with elements using a formulation corresponding to the FEM. It led to a structure detailed with multiple types of elements, such as shells, trusses and beams, tuned to match the prescribed geometric and mechanical properties. This directly led to more realistic shapes of vibration, similar to those known for cantilever-type structures.
## 7 Risk Analysis
### Tcu
#### 7.2.4 Information Disclosure  
1. **Ports and Communications** _Security risk_: Threats to the telematics control unit (TCU) component's information leakage may result in privacy concerns over the collection of sensitive information such as photographs shot with a camera. The most well-known attack in this area is the so-called man in the middle (MITM), which is carried out by listening to wireless transmission while utilizing a promiscuous antenna. One of the main hazards of information leakage is the packets collected in combination with bad data encryption. _Security measure_: MITM attacks cannot be avoided directly since a wireless channel is always available and can be listened to at any time, but some techniques can be used for obfuscating the content that flows on channels. Data encryption can be applied using a symmetric-key approach like Advanced Encryption Standard (AES) or session-key-based communication can be established between nodes in order to securely exchange data over the channel.
# Interference-Aware Radio Resource Management for Cognitive High-Throughput Satellite Systems
## 7 Conclusions  
In this paper, we have proposed a cognitive HTS system architecture to achieve spectrally efficient and high-throughput broadband satellite communications. The practical challenges of designing resource management in cognitive HTS systems have also been analyzed. By exploiting the spatial correlation of satellite channels, we have provided an interference-aware RRM scheme based on JSDM, which can effectively manage the hybrid interference with a reduced training and feedback overhead. Simulation results have shown the effectiveness of the proposed resource management framework and demonstrated the viability of the cognitive HTS network architecture. Finally, future research challenges have also been discussed.  
Conceptualization, Y.L.; Formal analysis, Y.R.; Investigation, R.Z.; Writing-review & editing, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China 2016 YF-B1200202, in part by the National Natural Science Foundation of China under Grant 61771365, Grant 61901333 and Grant 61901345, in part by the Postdoctoral Science Foundation of China under Grant 2018M640959 and Grant 2019M653560, in part by the 111 Project under Grant B08038, in part by the Fundamental Research Funds for the Central Universities under Grant JBF180101.
# Ambiguity-Resolved Model Tests for Carrier-Phase GNSS
## 4 The Ambiguity-Resolved Tests
### AR Detection
As shown in [18], p. 1124, the HD-formulation does justice to the multimodality of the PDF, while providing an acceptance region with the most concentrated probability. This HD test will, however, due to its infinite sum, be more difficult to execute than the one based on the quadratic form (13); at the same time, due to the commonly high ambiguity success rate carrier-GNSS usages, its performance will be similar to that of (13). To illustrate the difference in power between the AF and AK detectors, on the one hand, and the operational AR detector (13), on the other hand, we consider as an example application their detection power in the presence of a zenith tropospheric delay bias. As with the other AR test statistics, the power of the AR detector is computed using the Monte Carlo simulation approach of [19]. Figure 3 shows the detection power of the three detectors for a single-frequency, single-epoch, dual constellation (GPS and Galileo), using a mass-market receiver with relatively poor undifferenced pseudorange standard deviation \(\sigma_{p}=50\) cm. The results show that the AR detector is not as good as the AK detector would be (as the \(\mathcal{H}_{0}\) success rate is only 92%), but that it is significantly better than the AF detector. Figure 3: Tropospheric zenith delay detection power of AR detector (13) compared with that of the AF and AK detectors (7) and (10), for a single-frequency, single-epoch, dual constellation (GPS and Galileo), with undifferenced pseudorange standard deviation \(\sigma_{p}=50\) cm. Figure 2: PDF ambiguity residual: (**Left**) Stepwise construction (in blue) of \(f_{\hat{e}_{0}}(x)\) from \(f_{\hat{a}_{0}}(x)\) (cf. (15)); (**Right**) PDF comparison between \(f_{\hat{e}_{0}}(x)\) (**left**) and \(f_{\hat{a}_{0}}(x)\) (**right**) for four different values of the standard deviation \(\sigma\).