esa-sceva/satcom-chunk-collection huggingface dataset:
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 4 Analysis and Discussion  
### Buoy Performance  
On recovery, there were a number of twists in the rope connection between the GNSS buoy and the trawl float. An additional swivel on the end of the GNSS buoy connection would probably have reduced this and may lead to a more stable and free-floating platform. The use of a rubber bungee directly below the wave buoy morning [18] may be worth incorporating in this mooring in future. Both these suggestions may allow more free movement in the strong alongshore currents during the flood tide and thus improve data quality. In addition, the anchor point was completely covered with about 0.25 m of sand over just a couple of tidal cycles, so the bottom swivel was probably not working as intended by the end of this period. Overall, the mooring arrangement appears to have worked well and coped without being damaged in a very dynamic part of the intertidal beach. If the deployment was further away from the sea defences and nearer the low water mark, then spilling waves may have less impact on the results due to spending less time in the surf zone. Other authors observed the reverse [18] since they had to site their mooring closer to shore so as to pick up the breaking/spilling waves.
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 2 Materials and Methods
### Method
#### 2.2.2 Calcium Carbonate Content Test and Calculation of the Calcium Carbonate Precipitation Rate  
The calcium carbonate content of aeolian sand column samples was tested using the acid-washing method [30]. The steps were as follows: The aeolian sand column samples were removed from the temperature maintenance chamber every 12 h, and the aeolian sand in the mold was poured out and mixed well. Approximately 30 g of aeolian sand was weighed and placed in a Petri dish, and the remaining aeolian sand continued to complete the preparation and maintenance according to the method described in Section 2.2.1. The mass of the Petri dish was weighed and recorded as \(m_{0}\). The Petri dish was filled with distilled water, mixed thoroughly, and allowed to stand for 30 min, and then the upper clear layer was removed using a pipette. This process was repeated three times to wash away the dissolvable salts from the aeolian sand. The aeolian sand that was washed away from the dissolvable salt was dried in a baking oven at 100 \({}^{\circ}\)C for 12 h, and the mass of the Petri dish with dry aeolian sand was weighed at this time and recorded as \(m_{1}\). An excess of 2 mol/L hydrochloric acid solution was added dropwise to the dry aeolian sand sample and mixed until there were no more bubbles, and the process of washing away the dissolvable salt was repeated three times. The aeolian sand washed away from calcium carbonate and dissolvable salts was dried in an oven at 100 \({}^{\circ}\)C for 12 h, and the mass of the Petri dish with dry aeolian sand was weighed at this time and recorded as \(m_{2}\).  
Each experimental group was tested five times in parallel, and the calcium carbonate content per unit quantity of dry matter was calculated according to Equation (1).  
\[C=\frac{m_{2}-m_{1}}{m_{2}-m_{0}}\times 100\% \tag{1}\]  
The rate of calcium carbonate precipitation is the proportion of the actual calcium carbonate production to the theoretical calcium carbonate production. According to the comparison of the calcium carbonate precipitation rate at different maintenance periods (12 h, 24 h, 36 h, 48 h, and 60 h), the consolidation effectiveness of EICP by the mixing method was evaluated, and the optimal reaction duration was predicted.
## 7 Conclusions  
In some high-performance applications, an LC filter must be added to the auxiliary resonant snubber inverter (ARSI) to reduce the output current ripple. This makes the system quite different to the conventional ARSI without LC filter. This paper presents the modeling and the double-loop digital control of the ARSI with LC filter. The following points can be summarized to cover the paper:  
1. An improved loading scheme that the data is updating at the upper limit and lower limit of the carrier is proposed to improve the maximum duty ratio.
2. The filter capacitor current feedback is introduced to damp the resonance caused by the LC filter.
3. A step-by-step digital controller design method, including the auxiliary current controller and the output PI controller, is proposed, in which the computation and transport delay is considered.  
The control method has been verified by simulations and experiments. The maximum duty ratio is improved by 0.01 with the proposed loading method. In addition, soft-switching can be realized for the entire load range. Also, the resonance can be eliminated with the capacitor current feedback.  
Figure 21: Simulation results of the load dynamic response: (**a**) a step change from 12.5% to 75% rated output power; (**b**) a step change from 75% to 12.5% rated output power.  
Figure 22: Experimental results of the load dynamic response: (**a**) a step change from 12.5% to 62.5% rated output power; (**b**) a step change from 62.5% to 12.5% rated output power.  
**Acknowledgments:** This work was supported by the State Key Program of National Natural Science of China under Grant 51537002 and the National Natural Science Foundation of China under Grant 51607044.  
**Author Contributions:** [PERSON] conceived the proposed method and wrote the paper; [PERSON] provided technical guidance and advice for the manuscript; [PERSON] and [PERSON] performed the experiments and reviewed the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Infrastructure BIM Platform for Lifecycle Management
## 4 Infrastructure BIM Platform
### Management of Infrastructure Using State-of-the-Art Techniques  
After infrastructure construction is completed, damage is already present or begins to occur immediately. Therefore, an inspection of the infrastructure is performed, referring to the as-built BIM model shortly thereafter.  
For the management of very large infrastructure, digital-camera-embedded robots are widely adopted, as shown in Figure 4. The BIM model is updated by data gathered by multi-digital cameras embedded in climbing robots, which scan the surface of bridge piers in the vertical direction, as shown in Figure 3(a). Likewise, UAVs effectively scan the surface of bridges using only one digital camera to gather data. The gathered data are then used to build a 3D model using the photogrammetry algorithm.  
Simultaneously, as shown in Figure 4(a), damage is detected in the gathered data through a deep learning network, which is trained to detect multiple types of damage, such as cracking, spalling, rust, rebar exposure, etc. Figure 4(b) shows representative inference results of the deep learning network. The length and width of the damage is then calculated using image processing algorithms, using the segmented area (red zone) in the output image of Figure 4(b) [9]. Quantitative analysis results are saved for mapping onto the BIM model.  
Figure 5(a) shows a representative point cloud model of a bridge pier generated by the photogrammetry algorithm. Figure 5(b) shows a texture- and damage-mapped BIM model. The damage information in Figure 5(b) is saved with the BIM model as a reference model for regular updates. In the future, the grade of the infrastructure is lowered due to the damage experiencing further growth, which will be used in the decision to repair or for demolition.  
Figure 3: Representative page of web-based infrastructure BIM platform: (**a**) dashboard and (**b**) quantity takeoff page.  
Figure 4: Unmanned robot system for data acquisition: (**a**) climbing robot with multiple digital cameras; (**b**) unmanned aerial vehicles with digital camera.  
Figure 5: Deep learning-based automated damage detection: (**a**) network architecture; (**b**) automated damage detection result.  
Figure 6: Scan-to-BIM model: (**a**) point cloud model generated using the photogrammetry algorithm using digital images acquired by a digital camera embedded on a UAV; (**b**) texture- and damage-mapped BIM model.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 2 Related Works
### Set Cover  
A greedy set cover algorithm was proposed by the authors in [28]. It selects the minimum number of sensors that are disseminated into disjoint and non-disjoint sets, with the necessity that every set cover completely fulfills the scope of all its targets. They stated that the proposed algorithm is an improvement over the classical greedy set cover algorithm, and its approximation ratio is verified to be not worse than \(\log(m)\). Through simulations, they demonstrated that their algorithm performed better than others from previous research efforts.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 2 Methodology
### Classification Model for Exercise Intensity Assessment
#### 2.3.4 Neural Network Framework Employed  
In this study, the eight ECG signal features mentioned above are used as inputs to build a classification model based on the fully connected neural network (FCNN) algorithm, as shown in Figure 8. The FCNN is a classical deep learning model, where all neurons in adjacent layers are globally connected through a weight matrix, and the network parameters are optimized using the back-propagation (BP) algorithm. Unlike a CNN, which focuses on spatially localized feature extraction [46], FCNN achieves nonlinear combinatorial mapping of features through a densely connected layer structure (comprising the input, hidden, and output layers). This structure allows FCNN to offer advantages such as fast fitting speeds and high accuracy, particularly in solving low-dimensional nonlinear classification problems [47].  
Figure 8: The structure of the exercise intensity classification model.  
Figure 7: CEMS testing on the subject was conducted at different exercise durations. (**a**) t = 0 min. (**b**) t = 10 min. (**c**) t = 20 min. (**d**) t = 30 min.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 3 Results and Discussion
### Thermodynamic Study  
The adsorption experiments of Cr(VI) were carried out from 288 to 318 K for the thermodynamic investigation. Thermodynamic parameters such as Gibb's free energy change (\(\Delta G\)), enthalpy change (\(\Delta H\)), and entropy change (\(\Delta S\)) were calculated from the adsorption isotherms at different temperatures. All of the thermodynamic parameters for the adsorption of Cr(VI) on SDS-chitosan beads are tabulated in Table 6, and the adsorption capacity of Cr(VI) depending on the temperature is shown in Figure 18. The negative values of \(\Delta G\) suggest the preferred occurrence of spontaneous adsorption over the tested temperatures (288 to 318 K). As shown in Figure 18, the adsorption capability of Cr(VI) on SDS-chitosan beads increased with an increase in temperature. In addition, the minus value of \(\Delta G\) increased with the increase in temperature. This indicates that the adsorption of SDS-chitosan beads on Cr(VI) is more favorable at a higher temperature. Besides, the enthalpy \(\Delta H\) of SDS-chitosan beads (\(\Delta H>0\)) denoted the endothermic adsorption reaction, with the probable better adsorption results at a high temperature. It is known that the value of \(\Delta G\) is between 0 and \(-\)20 kJ/mol for physisorption, and that it is between \(-\)80 and \(-\)400 kJ/mol for chemisorption [72]. In certain conditions, the physisorption and chemisorption can be classified by the magnitude of \(\Delta H\) and \(\Delta G\). Bonding strengths of \(<84\) kJ/mol are typically considered as those of physisorption interaction [73]. Then, it is suggested that the adsorption of Cr(VI) on SDS-chitosan beads can be mainly dominated by physisorption judging from the values of \(\Delta H\) and \(\Delta G\).  
\begin{table}
\begin{tabular}{c c c c} \hline
**T(K)** & \(\Delta\)**H(kJ/mol)** & \(\Delta\)**S(J/mol)** & \(\Delta\)**G(kJ/mol)** \\ \hline
288 & 80.70 & 288.18 & \(-\)2.34 \\
298 & - & - & \(-\)5.22 \\
308 & - & - & \(-\)8.10 \\
318 & - & - & \(-\)10.98 \\ \hline \end{tabular}
\end{table}
Table 6: Thermodynamic parameters for the adsorption of Cr(VI) on SDS-modified chitosan beads.  
Figure 17: Intraparticle plot for the adsorption of Cr(VI) by the SDS-chitosan beads.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 7 Experimental Results
### TPC-C Benchmark Evaluation  
Dbx1000 features a pluggable lock manager that supports multiple CC schemes, enabling a direct comparison of DCoS against various baseline approaches within the same system. Specifically, we implement and evaluate the following three methods:  
* NO_WAIT is a variant of 2 PL where any conflict leads to the immediate abortion of the requesting transaction.
* Silo [27] is a representative OCC protocol.
* IC3 [8] combines the static analysis of the transaction workload with runtime techniques that track and enforce dependencies among concurrent transactions.  
To ensure an industry-standard evaluation, we conduct comprehensive experiments using the TPC-C benchmark [35] with enhanced contention control analysis. We maintain a balanced transaction mix of 50% New-Order and 50% Payment transactions to model real-world order-processing workloads closely. These two transactions comprise the vast majority of the benchmark and have been used previously to evaluate high-contention workloads to stress concurrency control. Our evaluation framework extends the classical TPC-C benchmark by varying two key factors: (i) the number of warehouses while maintaining a constant thread count, and (ii) the number of worker threads while keeping the warehouse count.  
We fix the number of worker threads at 32 and vary the number of warehouses from 1 (high contention) to 32 (low contention). As shown in Figure 9, DCoS achieves the highest throughput across all warehouse configurations, significantly outperforming others, especially under high contention. At one warehouse, DCoS is three times higher than Silo and 1.5 times higher than IC3. These results highlight the effectiveness of DCoS in mitigating contention bottlenecks and maintaining robust performance under varying workloads, since it can avoid unnecessary waiting through intelligent scheduling based on runtime information. At the same time, IC3 only leverages the static information.  
Figure 8: (**a**) Scalability comparison under different workloads. (**b**) Generalization performance with increasing concurrent transactions and thread workers.  
To assess system scalability, we vary the number of worker threads from 1 to 32 while keeping the warehouse count fixed at 1, as shown in Figure 10. The performance advantage of DCoS becomes more pronounced as the number of threads increases. At 16 threads, DCoS reaches 3.2 M tons/s, surpassing IC3 by 25% and significantly outperforming NoWait and Silo.
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 3 Implementing Physics-Informed Surrogate Model
### Injecting Physics into Models
#### 3.4.1 Database Development (Feature Engineering Technique--FET)  
The first step in developing a physics-informed surrogate model (PISM) is the construction of a database and the identification of relevant input features. This process begins by identifying a broad set of potential influencing factors, which are then refined to retain only the most significant ones. A thorough understanding of the governing physics, as established in previous sections, is essential in selecting these parameters. In cases where multiple interdependent parameters influence the response, dimensionality reduction techniques, such as principal component analysis (PCA), can help isolate dominant parameter combinations governing the output, as adopted in [130]. Where combinations of parameters are known to act together, they can be implemented as a single feature for model training, as in [132].  
To identify meaningful features from high-fidelity numerical models, parameter studies can be conducted. This involves individually varying selected features and assessing their correlation with the predicted outcomes. The strength and nature of these relationships--whether positive, negative, or nonlinear--can be evaluated using a combination of visual, statistical, and expert-driven approaches.  
* **Visual Analysis:** Plotting output values as a function of each input parameter allows for an intuitive assessment of trends (for example, in [96]).
* **Statistical Analysis:** Quantitative measures, such as Pearson correlation coefficients (PCCs) [141] for linear dependencies and maximal information coefficient (MIC) [142] for nonlinear correlations, can help determine parameter significance. Advanced techniques, such as partial dependence plots or feature importance rankings from tree-based models, provide additional insight.
* **Expert Judgment:** Domain expertise is incorporated to assess the practical significance of observed sensitivities.  
For example, [PERSON] et al. [130] applied a combination of PCC and MIC to rank input features for predicting the tensile strength of alloys. Similarly, [PERSON] et al. [132] employed [PERSON]'s sensitivity index to evaluate key features for developing their PIML model for the tensile strength of recycled concrete. [PERSON] et al. used simplified structural mechanics models for their sensitivity analysis to reveal that section dimensions and fire exposure time are among the most critical parameters, guiding their inclusion in the final surrogate model [95]. [PERSON] et al. [96] followed a similar methodology, selecting features based on governing physics and empirical equations before performing frequency-based and single-parameter analysis to detect both linear and higher-order correlations.  
Figure 9: Different stages for injecting physics in the model, from data collection to model tuning section.
## 2 Background
### Named Data Networking
#### 2.1.1 Named Data Networking Architecture  
NDN is based on the receiver-driven communications [21]. Two types of packets are used in NDN communication: Interest and Data packets [4]. The NDN router has three main architectures: forwarding information base (FIB), pending interest table (PIT), and content store (CS). An FIB table is used to forward the interest packet to the producer node by routers. The PIT keeps track of the interest packet's path. Aggregation and congestion control mechanisms can also be supported by the PIT. The CS is a temporary cache for the data objects. In the CS, all the satisfied interests and content entries are present.
# A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model
## 3 Results
### Experimental Settings and Training Procedures  
Three distinct models, named RNN, DRRN, and DBRRN, were utilized in our experiments. The explicit parameters for each model are detailed in Table 1.  
In order to facilitate an unbiased and uniform comparison across models, we harmonized several settings in all conducted experiments. Notably, the channel size for every model was fixed at 128, and we deployed five blocks as latent states in the RNN-based models.  
These blocks each follow the same architecture: an initial convolution layer, a subsequent ReLU activation layer, and a concluding convolution layer. The channel size of the convolution layer was designated as 128.  
We set the prior estimate to zero during the onset time step (t\({}_{0}\)). The learning rate for the RNN models' training was primarily fixed at 1 \(\times\) 10\({}^{-4}\), which was then attenuated by 0.1 for every 60 epochs until the 70 th epoch.  
Model optimization was accomplished by applying the Adam optimizer [17], characterized by 81 equating to 0.9, 82 being 0.999, and a weight decay of 5 \(\times\) 10\({}^{-4}\). The model's training was supervised using the pixel-wise L1 loss function. This function is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **RNN** & **DRRN** & **DBRRN** \\ \hline Input Frames & recurrent & recurrent & recurrent \\ Param. [M] & 7.204 & 2.93 & 2.94 \\ FLOPs [GMAC] & 193 & 108 & 120 \\ Runtime [ms] & 45 & 30 & 32 \\ Vid4 (\(\Upsilon\)) & 27.69 & 26.78 & 27.01 \\ SPMCS (\(\Upsilon\)) & 29.89 & 28.89 & 29.10 \\ UDM10 (\(\Upsilon\)) & 30.33 & 29.55 & 30.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: It presents a comparative analysis of PSNR values, obtained from the Vid4 dataset. The Y symbol represents evaluations conducted on the luminance channel.  
an efficient tool for diminishing the disparities between the anticipated and actual pixel values, boosting the model's capacity to generate high-resolution images closely reflecting the original ones.  
These configurations and training methods, which have proven to be efficacious in prior research, lay a robust groundwork for comparing the proposed models' performance. A detailed examination of the experimental outcomes is covered in the upcoming sections.
# The Impact of Blenderized Tube Feeding on Gastrointestinal Symptoms, a Scoping Review
## 3 Results
### Findings in the Pediatric Population
The majority of the included population (\(n=21/23\)) while on standard enteral formulas complained about upper GI symptoms, namely gagging (39%), emesis (48%), and chronic cough with concern for aspiration (4%). Ninety-five percent of them experienced an improvement in the reported symptoms over a three-month period. BTFs did not decrease the constipation rate in the patient previously suffering from it, and new onset mild constipation was reported in 21% (\(n=5\)) of patients [18]. Similar data were reported by [PERSON] et al. in 35 highly complex patients, almost all (\(n=33\)) suffering from GI symptoms before switching to partial or full BTF. Sixty percent of them (\(n=21/35\)) experienced a gradual improvement of symptoms during the follow-up visits. The most commonly improved symptoms were those related to gastroesophageal reflux disease (GERD). Two patients had worsening of constipation and GERD, respectively [19]. Furthermore, [PERSON] et al. reported a reduction in lower GI symptoms in 10 patients suffering from intestinal failure to absorb macro- and micronutrients, transitioning from SCF to tube feeding formula with real food ingredients (TFRF). Specifically, stooling patterns (consistency or volume and number in 24 h) improved in the majority of cases (90%). TFRF resulted in being well tolerated in children with 30-40 cm of small bowel, an intact ileoccal valve, and at least two-thirds of their colors in continuity [20]. [PERSON] et al. reported that fifty-two percent (\(n=17/33\)) of children with fundoplicatio experienced an extensive decrease (76% to 100%) in gagging and reching after two months on BTF. No parents reported that their child's GI symptoms worsened after starting BTF [22]. In a more extended monitoring period of six months for 33 patients, [PERSON] et al. recorded a decrease in gagging and reching from 82% pre-BTF to 47% post-BTF. Between enrolment and study exit, stool frequency of more than one/day slightly decreased (from 100% to 94%), while stool consistency did not significantly change [23]. In a larger cohort study of [PERSON] et al. on 70 children, participants receiving blenderized diets compared with those receiving SCF showed fewer GERD symptoms assessed by the Pediatric Gastroesophageal Symptom and the Quality-of-Life Questionnaire (PGSQ). Furthermore, participants on blenderized diets indicated an overall improved gastrointestinal function through Pediatric Quality-of-Life Inventory Gastrointestinal Symptoms (PedsQL). Specifically, less nausea and vomiting (64.0 \(\pm\) 22.6 vs. 49.0 \(\pm\) 37.9, \(p\) = 0.02), less abdominal pain (65.0 \(\pm\) 26.8 vs. 56.4 \(\pm\) 33.9, \(p\) = 0.04), abdominal upset (71.1 \(\pm\) 26.0 vs. 58.9 \(\pm\) 32.7, \(p\) = 0.02), less diarrhea (87.9 \(\pm\) 15.5 vs. 73.6 \(\pm\) 26.3, \(p\) = 0.004), and less worry about stool (91.5 \(\pm\) 12.8 vs. 81.4 \(\pm\) 30.0, \(p\) = 0.05) were reported [24].