esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Whole Cell Protein Extraction and Profiling by SDS/PAGE  
The 2 De strain was cultivated in medium K with 0.5 mM MnO\({}_{2}\) or with the sterile soil at a concentration of 4% (_w/v_) at 28 \({}^{\circ}\)C. Untreated strain cultivated in medium K was used as a control. After 18 days of incubation, equal amounts of proteins were normalized to the optical density of cultures at 600 nm. For this purpose, 1 mL of each culture was centrifuged (15 min, 4 \({}^{\circ}\)C, 13000 rpm), and the cell pellets were washed with phosphate buffer (pH 7.4) followed by re-centrifugation (15 min, 4 \({}^{\circ}\)C, 13000 rpm). The pellets were suspended in sample buffer (0.5 M Tris-HCl 12.5 mL; 10% SDS 20 mL; glycerol 10 mL, 2-mercaptoethanol0 mL; bromophenol blue 0.01 g; final volume of 100 mL with Millipore H2O) at a ratio of 100 \(\mu\)L/OD\({}_{600}\) = 1, boiled (100 \({}^{\circ}\)C, 30 min), and centrifuged for 15 s in a microcentrifuge. The obtained samples were used for protein profiling using SDS/PAGE (Mini Protean, Bio-Rad, Hercules, CA, US). Then, 20 \(\mu\)L of the protein sample was separated using a 5% stacking gel and a 12% separating gel. After electrophoresis for 3 h at 100 V, the gel was stained with Coomassieie brilliant blue in 10% (_v/v_) acetic acid and 45% (_v/v_) methanol for one hour. Destaining was carried out using 10% (_v/v_) acetic acid and 20% (_v/v_) methanol for 10 min. In order to obtain a clear background, the gel was rinsed in distilled water.  
The relative migration distance (Rf) of the protein standard and the unknown proteins was determined. For this aim, the Rf was calculated by dividing the migration distance of the analyzed protein by the migration distance of the dye front. A calibration curve was created based on the logarithmic molecular mass of proteins relative to the Rf factor. Using the resulting equation of the calibration curve, the molecular sizes of analyzed proteins were determined.  
Moreover, the quantification of the protein content of the 2 De strain from each of the above-mentioned cultures was performed. A 5 mL portion of the culture was centrifuged (15 min, 4 \({}^{\circ}\)C, 4700 rpm). The pellet was re-suspended in 1 mL of lysis buffer (50 mM Tris-HCl; 100 mM NaCl; 1 mM Tween 20, 5% glycerol, 1 mM EDTA). After centrifugation (4 min, 4 \({}^{\circ}\)C, 4700 rpm), 1600 \(\mu\)L chilled acetone was added to 200 \(\mu\)L of supernatant and incubated for 30 min at 4 \({}^{\circ}\)C. Then, acetone was evaporated under N\({}_{2}\), and the pellet was suspended in 100 \(\mu\)L of phosphate-buffered saline (PBS). Protein estimation was carried out using the Bradford Protein Assay (Bio-Rad, Hercules, CA, US) using bovine serum albumin (BSA) as a standard, with a concentration ranging from 0 to 80 mg mL\({}^{-1}\).
## 3 Design Response Spectra for Lebanon
### Design Response Spectra According to PS92  
According to Anne A of PS92, the following formulas should be used to obtain the four branches of the normalized elastic design spectrum in terms of acceleration \(S_{e}\) as a function of the period \(T\):  
\[S_{e}(T)=A_{n}\;\tau\;[(R_{A}+(R_{M}-R_{A})]\left(\frac{T}{T_{B}}\right)\;for \;0<T\leq T_{B} \tag{1}\]  
\[S_{e}(T)=A_{n}\;\tau\;R_{M}\;for\;T_{B}<T\leq T_{C} \tag{2}\]  
\[S_{e}(T)=A_{n}\;\tau\;R_{M}\;\left(\frac{T_{C}}{T}\right)\;for\;T_{C}\leq T\leq T _{D} \tag{3}\]  
\[S_{e}(T)=A_{n}\;\tau\;R_{M}\;\left(\frac{T_{C}}{T}\right)\left(\frac{T_{D}}{T }\right)\;for\;T\geq T_{D} \tag{4}\]  
Here \(A_{n}\) is the nominal peak ground acceleration (taken as equal to 2.5 m/s\({}^{2}\) according to Assumption 2), while \(\tau\) denotes the topographic amplification factor, the values of which may range between 1 and 1.4, depending on the downstream and upstream slopes of the site, the greater the slope the higher the value of \(\tau\). Both the lowest and the highest values of \(\tau\) are used here to obtain the DRS for the two extreme topographical conditions.  
Four site types, namely S0, S1, S2 and S3, are considered by PS92, depending on the ground characteristics and on the depth of the soil layer above the bedrock. Three soil categories are classified by PS92, namely \(a\) (good or very good mechanical characteristics), \(b\) (medium mechanical characteristics) or \(c\) (low mechanical characteristics). Site S0 encompasses rock-like soils and/or layers of \(a\) soil not exceeding 15 m deep; site S1 includes more than 15 m deep \(a\) soil layers and/or less than 15 m deep \(b\) soil layers; site S2 includes soil \(b\) with depth between 15 m and 50 m and/or less than 10 m soil \(c\) layers; site S3 is characterized by more than 50 m deep layers of b soil and/or layers of \(c\) soil with depth between 10 m and 100 m. The reader may refer to PS92 for a more detailed description of the different site and soil types.  
The horizontal normalized elastic DRS relevant to the four site types and to \(\tau=1\) and \(\tau=1.4\) are provided in Figures 2a and 2b, respectively. The spectra are obtained by assuming the values given in Table 2 for the characteristic periods \(T_{B}\)\(T_{C}\) and \(T_{D}\) and for the parameters \(R_{A}\) and \(R_{M}\), all these values being in accordance with the Lebanese code indications. It can be noted that on the third branch of the spectra the accelerations (and thus the design forces) are higher for softer soils, which is due to the well-known soft soil amplification effect.
## 3 Evaluation with Expected Total Costs
### Cost Analysis
#### 3.2.1 Relationship between Building Cost and Initial SF  
The building cost is assumed to be a function of the safety factor (SF) in this paper, although some previous research on the minimum expected total cost portrayed the building cost as a function of the reliability index [50; 51].  
A low SF naturally lowers the building cost; however, the ratio depends on the piling method and has the following three types: the simplest type is that in which the building cost is proportional to the SF [52]; the second type is where a low additional cost is required to increase the SF, or where other costs constitute the majority of the costs, for example, earth-retaining walls or foundations whose superstructures are large; the last type is where a large additional cost is required to increase the SF, for example, where the weight of the structure itself harms the function, or the structure has a bad scale effect. As for open-ended steel pipe piles, the SF is unlikely to be increased by increasing the pile diameter (i.e., cost) because the unit resistance decreases as the pile diameter increases, which is known as the plugging effect [53].  
Figure 6 shows examples of cost estimations for open-ended steel pipe pile foundations [54] and steel pipe pile-retaining walls [55]. As the cost estimations conform to the function with the constant power, \(k_{b}\), of the SF (Figure 6), this paper assumes that the normalized \(C_{b}\) is modeled as follows:  
\[\frac{C_{b}}{C_{b;T}}\cong\left(\frac{\mu_{i}}{\mu_{T}}\right)^{k_{b}} \tag{13}\]  
Figure 5: Probability of failure of the proposed method (\(\rm COV_{R;c}=0.35\), \(\rm COV_{Q}=0.10\), \(\beta_{T}=3.0\)).  
As \(k_{b}\) increases, the incentive to reduce the SF functions better (i.e., relative costs of safety measures noted in [44]. In this paper, \(k_{b}\) is assumed to range between 0.5 and 1.5.
## 1 Introduction
In fact, this tool not only allows to overcome the problem of the high number of modes resulting from the identification but also to identify and differentiate local modes from global ones, especially regarding tight couplings between vertical and horizontal modes. In the case of bridges [22], the local damage can be detected often at very high modes, better identified by a surrogate model. Similar results can be obtained on specific schemes by using model reduction techniques, as far as applicable. The main purpose of the present work is to propose a methodology to approach interconnected diaphragmatic structures (interacting at their joints, at the external constraints, and the surrounding environment, e.g., embankment), and the identification of their modal parameters, aided by parametric analyses on simplified/reduced analytical models. To accomplish the scope stated above, the case study of the Pavilion V of Turin Exhibition Center is analyzed. This hypogeum pavilion, designed in 1959 by [PERSON], represents a fascinating case study of a structure composed of three macro blocks separated by two joints. The fundamental static scheme of the structure is a version of [PERSON]'s balanced beam. The diaphragmatic and massive behavior of the roofing system, with post-tensioned concrete ribs, the uncertainties related to the soil-structure interaction, and the effectiveness of the joints are just a few elements that contribute to the high complexity of the building's dynamics. The paper is organized as follows. In Section 2, the dynamic equation for rigid diaphragms interacting at linear elastic joints is developed. The methodology is then applied in Section 3 on a numerical benchmark to demonstrate the effective contribution of the joints to the dynamic behavior of the structure. As a result, the effects of the variation of the stiffness of the springs governing the interaction are investigated, and, consequently, a discrimination between the global and the local modes is provided. In Section 4, the case study of Pavilion V is first introduced and then the description of the experimental setups of a test campaign carried out in 2019 is reported. The modal identification of the structure is then finally carried out by exploiting a simplified analytical model and the modal parameters are extracted in Section 5. The outcomes of an analysis to investigate the effectiveness of the joints are reported in Section 6. Conclusions are drawn in Section 7.
# Analysis of LTE-M Adjacent Channel Interference in Rail Transit
## 4 Interference Analysis of LTE-M Ground System
### Simulation Results
From the power control model, it can be seen that the \(\text{PL}_{x-\text{ile}}\) of Set 1 is smaller than that of Set 2, so the transmitted power of Set 1 is larger than that of Set 2, and the throughput loss of the LTE-M system is also larger. When the power control parameter sets and ACIR are fixed, the larger the number of users is, the larger the throughput loss will be. Since the number of resource blocks corresponding to each bandwidth length is constant, the greater the number of LTE-FDD users, the higher the overall interference power and the greater the LTE-M system's throughput loss can be achieved. Table 11 shows the required ACIR values when using different parameter sets and the number of users to keep the average relative throughput loss below 5%. In general, the offset between two systems should be minimized during their deployment to avoid the occurrence of interfering system construction near the disturbed system's cell boundary. For the selection of power control parameters, Set 2 can be adopted for power control in places with dense users and Set 1 is suitable for places with sparse users to reduce link interference while fulfilling the full power transmission needs of most users.
# Ultrafast Optical Signal Processing with Bragg Structures
## 6 Conclusions  
We have discussed several methods to realize an ultrafast optical signal process in 1D Bragg structures, including resonantly absorbing Bragg reflectors (RABR), chirped BGs (Bragg gratings), and transparent but highly nonlinear CLCs (cholesteric liquid crystals) that exhibit properties of 1D photonic crystals. The result shows that an ultrafast pulse can be buffered and subsequently released in the RABR Additionally, based on the reduction of the light speed, a nonlinear frequency conversion can be achieved over a short propagation distance. Using a linearly chirped BG concatenated with a uniform one, we have demonstrated the possibility of achieving the efficient slow-down of light with the right mix of forward and backward propagating fields. Such chirped gratings can also function as all-optical photodiodes. Finally, we have discussed a unique class of self-assembled BGs based on CLCs. They feature extraordinarily large and ultrafast-responding optical nonlinearities, which enable direct compressions, as well as the stretching and recompressions of femtoseconds laser pulses over very short (sub-mm) propagation lengths.  
Figure 16: Results of pulse modulations by two tandem 550 \(\upmu\)m thick CLC cells. Observed pulse shape for the input (upper trace), output after traversing the first cell (middle), and output after traversing the second cell (bottom trace). Initial pulse width: 100 fs laser pulse; wavelength: 780 nm [18].  
**Acknowledgments:** This work is supported by the Chinese National Natural Science Foundation (61505265, 11374067, 11534017). [PERSON] is also supported by the Fundamental Research Funds for the Central Universities. I am [PERSON]'s travel and local support is provided by the [PERSON] Professorship of PSU.  
**Author Contributions:** [PERSON] coordinated the work on the paper. [PERSON] collected the data and wrote the draft. [PERSON], [PERSON], and [PERSON] took part in drafting the paper and contributed to the critical analysis of the data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Atrial fibrillation (AF) is associated with systemic thrombo-embolism and stroke events, which do not appear significantly reduced following successful pulmonary vein (PV) ablation. Prior studies supported that thrombus formation is associated with left atrial (LA) flow alterations, particularly flow stasis. Recently, time-resolved three-dimensional phase-contrast (4D-flow) showed the ability to quantify LA stasis. This study aims to demonstrate that LA stasis, derived from 4D-flow, is a useful biomarker of LA recovery in patients with AF. Our hypothesis is that LA recovery will be associated with a reduction in LA stasis. We recruited 148 subjects with paroxysmal AF (40 following 3-4 months PV ablation and 108 pre-PV ablation) and 24 controls (CTL). All subjects underwent a cardiac magnetic resonance imaging (MRI) exam, inclusive of 4D-flow. LA was isolated within the 4D-flow dataset to constrain stasis maps. Control mean LA stasis was lower than in the pre-ablation cohort (30 \(\pm\) 12% vs. 47 \(\pm\) 18%, \(p\) < 0.001). In addition, mean LA stasis was reduced in the post-ablation cohort compared with pre-ablation (36 \(\pm\) 15% vs. 47 \(\pm\) 18%, \(p\) = 0.002). This study demonstrated that 4D flow-derived LA stasis mapping is clinically relevant and revealed stasis changes in the LA body pre- and post-pulmonary vein ablation.  
Keywords: atrial fibrillation; 4D-flow; stasis; pulmonary vein ablation +
Footnote †: journal: Applied Sciences
###### Abstract  
Single-walled carbon nanotubes (SWCNTs) loaded with magnetic iron oxide nanoparticles were prepared by the arc discharge method and air heat treatment. The nanocomposite was characterized by X-ray diffraction, scanning electron microscopy, Raman spectroscopy, vibrating sample magnetometry, etc. The results showed that the heat-treated nanocomposites (SWCNTs/Fe\({}_{\text{x}}\)O\({}_{\text{y}}\)) had iron oxide phases and superparamagnetic properties with a saturation magnetization of 33.32 emu/g. Compared with the non-heat-treated materials, SWCNTs/Fe\({}_{\text{x}}\)O\({}_{\text{y}}\) had a larger specific surface area and pore volume. Using SWCNTs/Fe\({}_{\text{x}}\)O\({}_{\text{y}}\) to remove the organic contaminant (bisphenol A, BPA), it was found that under the conditions of pH = 3 and adsorbent dosage of 0.2 g/L, the maximum adsorption capacity of the composite was 117 mg/g, and the adsorption could reach more than 90% in only 5 min when the BPA content was below 0.05 mmol/L. The fitting results of the Langmuir and D-R models are more consistent with the experimental data, indicating a relatively uniform distribution of the adsorption sites and that the adsorption process is more consistent with physical adsorption. The kinetic calculations showed that the SWCNTs/Fe\({}_{\text{x}}\)O\({}_{\text{y}}\) exhibits chemical effects on both the surface and the gap, and the adsorption process is controlled by the \(\pi\)-\(\pi\) bonds and the hydrophobicity of the SWCNTs/Fe\({}_{\text{x}}\)O\({}_{\text{y}}\).  
SWCNT composites; Bisphenol A; magnetic iron oxide nanoparticles; adsorption; adsorption mechanism +
Footnote †: journal: Applied
## 3 Supply Chain Mathematical Model
### Supply Chain Network Configuration
#### Uncertain Supply Chain Network Optimization Problem  
Optimizing supply chain networks (SCNs) involves addressing the intricate combinatorial optimization problem of managing the movement of goods across multiple nodes under uncertainty. These nodes include suppliers, manufacturers, distribution centers, and customers. The key variables and assumptions are outlined below.  
Key Variables and Hypothesis Effective supply chain management requires a thorough understanding of the dynamic factors affecting its efficiency and robustness. To enhance supply chain performance under uncertainty, it is crucial to identify and measure the key variables that influence decision-making. The following key variables have been identified as critical to our integrated model.  
To effectively model and optimize supply chain operations under uncertainty, it is essential to identify the fundamental variables that impact performance. These key variables form the foundation of our integrated model, capturing the core elements of supply chain dynamics.  
* **Demand rates:** These determine the quantity of products required by end consumers and are assumed to be uncertain random variables.
* **Supply availability:** This indicates the availability of raw materials and finished goods, which are also subject to uncertainty.
* **Production capacity:** This defines the maximum output of manufacturing facilities and can vary based on several factors.
* **Transportation logistics:** This encompasses the movement of goods between different nodes in the supply chain and is influenced by various uncertainties.  
Hypotheses To ensure the robustness of our model, we test the following additional hypotheses related to the operational aspects of the supply chain:  
* All supply chain nodes are operational at time zero, and demand rates, supply availability, and transportation logistics are uncertain variables.
* Each manufacturing facility can process a limited number of products at any given time.
* Each product can only be processed on one manufacturing unit at any time.
* Production processes must be completed without interruptions or preemptions.
* Transportation routes are activated as soon as the first batch of goods is ready and cease once the last batch is delivered.
* Transportation routes remain idle between successive deliveries.
# High Brightness Diode Lasers Based on Beam Splitting and Polarization Combining
###### Abstract  
A new method to improve the brightness of diode lasers based on beam-waist splitting and polarization combining was proposed and demonstrated. The beam waist was split by a precisely cut prism into two parts and combined with a polarization beam combiner. The advantages of simple setup, high efficiency, brightness, and universality were presented. The slow axis \(M^{2}\) factor of a broad-area diode laser with a ridge beam width of 180 \(\upmu\)m was reduced from 18.5 to 9.8, and a brightness of 42.4 MW cm\({}^{-2}\) sr\({}^{-1}\) was realized; this brightness was a 84.21% improvement of the same emitter at 8 A. The slow axis \(M^{2}\) factor of a commercial broad-area diode laser array combined by spectral beam combining was reduced from 9.08 to 4.78, and 80.6% improvement of brightness was realized on the same commercial broad-area diode laser array. A brightness of 195.8 MW cm\({}^{-2}\) sr\({}^{-1}\) was realized at 36 A by this diode laser array. This method can be applied in a highly polarized light source to improve the beam quality and brightness.  
high brightness; diode; beam combining +
Footnote †: journal: Applied Sciences
## 5 Results and Discussion  
### Analysis of Hybrid Models  
The selection of the most suitable explicit (e.g., quiz results) and implicit (e.g., behaviour patterns) learner signals begins with a thorough analysis of machine learning models. By combining these models, the system accurately predicts detailed learner information, including preferences, progress, and learning behaviours. This enriched learner profile guides the DKPS in recommending appropriate target learning modules through the use of hybrid models.  
The final results of the hybrid model analysis, based on 10 iterations, are presented in Table 16, highlighting the performance across four metrics: accuracy, precision, recall, and F1-score. Over the iterations, there is a clear and consistent improvement in the system's performance. Accuracy increased from 70% to 93%, indicating that the model became significantly better at correctly identifying the most suitable learning modules. Precision improved from 0.68 to 0.90, meaning the system made more relevant recommendations with fewer false positives. Recall also improved, moving from 0.75 to 0.89, reflecting a growing ability to identify all relevant modules for a learner without missing important ones. The F1-score, which balances both precision and recall, rose from 0.71 to 0.89, demonstrating that the model's improvements were steady and well-rounded across all performance aspects.  
To confirm the reliability of these improvements, a statistical analysis was conducted. The probability values (\(p\)-values) for the observed increases in all four metrics across the 10 iterations were found to be less than 0.01 (\(p\) < 0.01), indicating that the improvements are statistically significant and not due to random chance. Furthermore, 95% confidence intervals for each metric were narrow, signifying low variance and strong stability in the model's predictions.  
These results confirm that the hybrid ML model used in the DKPS framework is robust and adaptive. With each iteration, the model becomes more accurate, reliable, and effective in delivering personalized learning recommendations based on well-structured learner profiles.