esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Solution-Machine Learning Modeling Coupled with Model Predictive Control
However, despite its advantages, CCC has notable limitations. It struggles to effectively mitigate torque ripple, exhibits relatively low efficiency, and lacks the capability to handle constraints such as voltage, torque, and speed. These drawbacks make it less suitable for applications requiring precise control and optimization in drive system design [76]. The authors of [77] have conducted a comprehensive analysis of MPC for application in SRM drives, covering all its subcategories. They highlight that MPC offers high accuracy, can account for non-linearities, can easily handle constraints, and has the potential to replace conventional PID controllers. Based on the above points, the integration of ML algorithms with MPC can have a positive impact on the design and optimization of SRM drives. Figure 11 illustrates that the proposed scheme, on one hand, enables efficient machine modeling through ML, with training, testing, and evaluation performed using data from the multiphysics analysis, while on the other, MPC provides advanced machine control, utilizing the predictive model, with inputs like rotor position \(\theta_{r}(k)\), and current \(i(k)\), and minimizing the error between the reference current \(i^{*}\), or torque \(T^{*}\), and the actual output value \(\hat{T}\) or \(\hat{i}\). Although multiphysics analysis is a simulation-based approach, it demonstrates a high level of accuracy and can effectively be used to train neural networks. When combined with real-world data, it enables a robust validation process, enhancing the overall reliability and applicability of the developed model [78; 79]. Figures 12 and 13 show the application of the ML-MPC for the single-level and multi-level SLDOM, respectively. This representation highlights how ML models assist the MPC  
Figure 11: MPC control strategy integration with machine learning modeling for SRM. and contribute in optimizing both the machine and controller parameters. Specifically, ML algorithms supply MPC with the necessary data for accurate predictions, such as relationships between torque, current, flux, and rotor position. Switching states can be optimized to mitigate torque ripple, enhance dynamic response time, and reduce computational burden. Additionally, MPC proves highly valuable in system-level design, as it performs online optimization during the control optimization phase [78; 79]. This is expected to reduce the number of required steps and dynamic performance simulations needed to determine the near-optimal operating conditions. Figure 12: Implementation of ML-MPC on Single-Level System-Level Design Optimization. Figure 13: Implementation of ML-MPC in multilevel system-level design optimization.
## 3 Materials and Methods
### Anti-Biofilm Assay  
The anti-biofilm activity of P13#1 and LL-37 was tested on the _P. aeruginosa_ strains PAO1 and RP73. The bacteria were grown overnight in Luria-Bertani (LB) medium at 37 \({}^{\circ}\)C. The overnight cultures were diluted to 1 \(\times\) 10\({}^{8}\) CFU/mL and incubated in BM2 biofilm-adjusted medium [81] (62 mM potassium phosphate buffer pH 7.0, 7 mM (NH4)2 SO4, 2 mM MgSO4, 10 uM FeSO4, 0.4% (\(w/v\)) glucose) in polystyrene 96-well microtiter plates (Corning\({}^{\circled{B}}\), New York, NY, USA) for 24 h at 37 \({}^{\circ}\)C. The one-day-old biofilms were than treated with increasing concentrations of P13#1 and LL-37 (0.39-30 uM) to evaluate their ability to eradicate a pre-formed biofilm after 4 h of incubation time at 37 \({}^{\circ}\)C. At the end of the incubation, planktonic cultures were removed from the wells. After washing the cultures three times with sterile PBS, each biofilm mass was stained using 0.1% crystal violet for 20 min. The excess crystal violet was eliminated via three successive washes with sterile water. Finally, the crystal violet was solubilized using 30% acetic acid, and the optical absorbance values were determined at 595 nm using a microtiter plate reader (Synergy\({}^{\text{TM}}\) H4, BioTek, Winooski, VT, USA). The metabolic activity of the cells after treatment was evaluated via a Cell Proliferation Kit II assay (XTT) (Merck KGaA, Darmstadt, Germany). The planktonic cells were removed, and the plates were thoroughly washed three times with PBS. The assay was carried out in 100 uL of PBS supplemented with 50 uL of XTT solution. The plates were incubated in the dark for 3-6 h at 37 \({}^{\circ}\)C. The reduction of the tetrazolium salt into the orange formazan dye by the metabolically active cells was measured at 490 nm using a microtiter plate reader (SynergyTM H4, BioTek, USA). The viability was compared to controls carried out in the absence of an antimicrobial agent. MBEC values were determined by counting the viable bacterial cells inside the biofilm's structure upon disrupting the biofilm with 1% Triton X-100 for 10 min. The cells were serially diluted in the LB medium and plated on LB/agar plates. After incubating for 16 h at 37 \({}^{\circ}\)C, the CFUs were counted. The percentages of surviving cells were calculated as follow: (CFU treated sample/CFU untreated sample) \(\times\) 100.
## 4 Results
DeepSeek V3 scored moderately in Reading Comprehension (M = 3.58, SD = 0.49) and Summarization (M = 3.50, SD = 0.54), but its lower Grammar & Syntax (M = 2.82, SD = 0.40) and Sentiment Analysis (M = 3.00, SD = 0.63) scores indicate limitations. Llama 3.3 70B ranked lowest, with poor performance in Reading Comprehension \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **RC** & **TS** & **SG** & **SA** \\ \hline
**GPT-4o** & 4.05 (0.68) & 3.64 (0.93) & 4.21 (0.60) & 2.94 (0.55) \\ \hline
**Claude 3.7 Sonnet** & **4.32 (0.46)** & **4.11 (0.78)** & **4.78 (0.28)** & **4.41 (0.61)** \\ \hline
**Gemini 2.0 Flash** & 3.79 (0.81) & 3.58 (0.87) & 3.64 (0.68) & 4.23 (0.90) \\ \hline
**DeepSeek V3** & 3.02 (0.90) & 3.7 (0.68) & 2.9 (0.83) & 2.76 (0.97) \\ \hline
**Llama 3.3 70B** & 2.23 (1.04) & 3 (1.06) & 1.47 (0.50) & 3.11 (0.85) \\ \hline
**Grok 2** & 3.64 (0.86) & 3.64 (1.05) & 3 (0.77) & 3.88 (0.78) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Human Evaluation Results (RC: Reading Comprehension, TS: Test Summarisation, SG: Syntax and Grammar, SA: Sentiment Analysis). \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **RC** & **TS** & **SG** & **SA** \\ \hline
**GPT-4o** & 4.41 (0.49) & 4 (0) & 3.83 (0.35) & 3.33 (0.81) \\ \hline
**Claude 3.7 Sonnet** & **4.5 (0.31)** & **4.83 (0.40)** & **4.60 (0.38)** & **4.66 (0.51)** \\ \hline
**Gemini 2.0 Flash** & 4.16 (0.40) & 4.33 (0.51) & 3.60 (0.25) & 4.16 (0.40) \\ \hline
**DeepSeek V3** & 3.58 (0.49) & 3.5 (0.54) & 2.82 (0.40) & 3 (0.63) \\ \hline
**Llama 3.3 70B** & 1.91 (0.80) & 2.5 (0.83) & 1.72 (0.39) & 3.83 (0.75) \\ \hline
**Grok 2** & 3.75 (0.41) & 4.16 (0.75) & 3.49 (0.34) & 3.83 (0.40) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Machine Evaluation Results (RC: Reading Comprehension, TS: Test Summarisation, SG: Syntax and Grammar, SA: Sentiment Analysis). (M = 1.91, SD = 0.80) and Grammar & Syntax (M = 1.72, SD = 0.39), despite a decent Sentiment Analysis score (M = 3.83, SD = 0.75), underscoring its challenges in producing reliable outputs. A comparison of average performance across the four criteria, illustrated in Figure 2, reveals identical rankings between human and machine evaluations, suggesting that LLM self-evaluation could serve as an effective preliminary assessment method. This alignment is supported by statistical analyses: [PERSON]'s W (0.561, \(p<0.001\)) confirmed significant agreement among the 17 human evaluators, while a Mantel-Haenszel test \(\chi^{2}(1)=4.98\), \(p<0.026\) indicated a linear association between human and machine scores, with higher LLM ratings corresponding to higher human ratings. Both human and machine evaluations rated Claude 3.7 Sonnet response highest for its precise correction of verb tense, prepositions, and sentence structure, while Llama 3.3 70B response retained errors (e.g., \"goes\" and \"don't finding\"), reflecting its lower performance. These results collectively position Claude 3.7 Sonnet as the standout model, with GPT-4o and Gemini 2.0 Flash as viable alternatives, while highlighting the limitations of Llama 3.3 70B, DeepSeek V3, and Grok 2 for SALLY requirements.
## 1 Introduction
In particular, ultrabroadband MIR pulse generation by using FWM of the fundamental and second harmonic output of a Ti:sapphire amplifier is one of the most attractive applications of the filamentation effect. Such MIR pulses with more than one octave at full width at half maximum are very attractive to be applied for molecular spectroscopy, e.g., pump-probe spectroscopy, two-dimensional infrared spectroscopy, and chirped pulse up-conversion. It is also possible to generate MIR coherent light with self-phase modulation of one-color ultrashort pulse through filamentation in gases [37]. However, the bandwidth is much narrower and the efficiency is much less than those achieved with the two-color input pulses. The MIR pulse generation by using FWM of two-color pulses through filamentation was firstly demonstrated in 2007 [18], and the technique was followed and slightly modified by several groups [19; 20; 21; 34]. Generation of sub-single-cycle pulses in MIR region was demonstrated with this scheme [22]. Recently, the light source has started to be used for spectroscopy [38; 39]. In this paper, we report the detail of the performance of the MIR pulses generated by using FWM through filamentation. The fundamental (\(\omega_{1}\)) and second harmonic (SH, \(\omega_{2}\)) output of a 30-fs Ti:sapphire amplifier were focused into a nitrogen gas and produced phase-stable broadband MIR (\(\omega_{0}\)) pulses through FWM processes (\(\omega_{1}+\omega_{1}-\omega_{2}\rightarrow\omega_{0}\) and \(\omega_{2}-\omega_{1}-\omega_{1}\rightarrow\omega_{0}\)). An important difference from the previous experiment [22] is the system for generating synchronized fundamental and SH pulses. In this experiment, so-called inline configuration was used to have the polarization and delay of the fundamental and SH pulses adjusted [20; 30; 38; 39]. The benefit of the inline scheme is the stability of relative phase between the fundamental and SH pulses, which enables us to clarify the relative phase dependence of the generated MIR spectrum. By adjusting the phase between the two FWM processes, the bandwidth of the MIR spectrum was optimized and the pulse duration was estimated as 6.9 fs with the center wavelength of 3.3 \(\mu\)m. The stability of the carrier-envelope phase was significantly improved with the inline scheme.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 3 Impact of Network Slicing
### CN Slicing
#### 3.1.3 5 GC Network Functions  
As commented in Section 2.2, the allocation of dedicated network functions on a network slice allows it to be tailored to the specific needs of hosted service(s). Where there is more potential to making this customization is on the 5 GC side.  
It is not the goal of this subsection to discuss which 5 GC functions are to be dedicated to a slice; indeed, as we will see in Section 5.1, this entirely depends on the business requirements of individual customers. The purpose of this section is instead to outline the importance of some 5 GC functions when building up CN slice subnets. In this regard, the UPF is the most valuable network function to be dedicated, followed by control plane network functions (SBA).  
The importance of having a dedicated UPF comes from two important reasons: (i) a tailored user plane QoS, and (ii) an improved availability and reliability. The first point refers to the ability of allocating an UPF with required resource capacity where needed, e.g., close to customer premises to ensure low latency. The second point means that having a dedicated UPF instance allows optimal redundancy level to be achieved, and the risk of service interruption for the slice to be reduced, ensuring that established sessions can survive for a period of time, even when the connection to the control plane functions is lost.  
The control plane functions are the second-most valuable assets to dedicate. For example, with a dedicated Session Management Function (SMF) [10], it is possible to make changes to established sessions and establish new sessions for a period of time, even if the connection to UPFs is lost.
###### Abstract
Compared to multiple or logistic regression analysis, which are the most common tools used to model natural ventilation with manual window opening/closing [37; 43], SEM and path modeling provide graphical representations of the causal relationships (or paths) relating input and output variables, and allow for the simultaneous use of observable variables and unobservable hypothetical constructs (factors). These latter constructs are often used in behavioral and social sciences to represent attributes of a person, group of persons, or geographical region [40; 42]. SEM helps in the identification of false models (those with a poor fit to the sample data), and as such is useful in model generation and confirmatory model analysis. Compared to machine learning methods, which aim for an improved fit to sample data [35; 36; 37; 38], the goal of SEM is to test the validity of a model's internal structure and the understanding of causal effects and factors driving the researched phenomenon. SEM has been previously used in studies related to built environment [39; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56]; however, to the best of our knowledge, SEM and path modeling have never been used to confirm a model relating natural ventilation with continuous occupancy and environmental physical variables. Moreover, most experimental studies on natural ventilation with window airing have dealt with offices and residences [26; 27; 31; 33; 34; 35; 36; 37; 38; 39; 43; 57; 58; 59; 60; 61; 62], where the group dynamics and window opening/closing dynamics are quite different from those found in classrooms [28; 63]. Using a two-year-long experimental dataset, this paper provides a path modeling approach to the study of classroom ventilation, relating occupancy and environmental physical variables with air change rate. A major objective of this paper is to use continuous occupancy and environmental data to assess the use of path modeling, and structural equation modeling more generally, for natural ventilation research. Another objective is to identify the relative importance of occupancy and the most relevant environmental (physical) determinants of natural ventilation for school buildings in Southern Europe. The rest of this paper is structured as follows: the Materials section presents the details of the classrooms and monitoring approach, followed by a description of the data screening process; the Methods section introduces the path model and the implied causal relations between the variables; the Results and Discussion section discusses the the path modeling results and the model's fit to the monitoring data; finally, the paper ends with our conclusions.
## 2 Design and Construction of RSR Abutment and Transition Zone
### Construction of RSR Abutment Transition Zone  
The construction details of the RSR are as follows: (1) first, the cast-in-place concrete pile was installed. (2) Subsequently, the bench cut was applied, and the 3% cement-treated gravel was spread out and compacted. (3) Thereafter, a 1 m-high small-strip foundation was installed. (4) Georgids and nets of welded rebars were installed, (5) the cement-treated gravel was laid, (6) followed by compaction, (7) and then the geogrid was rolled up. (8) Steps (4) to (7) were repeated to obtain the 17 th layer. (9) Subsequently, the backfill was stabilized, (10) the rebars and connectors were installed, (11) and the forms and placement of the concrete for the rigid wall were carried out. Finally, (12) the RSR was completed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Factors of Safety**} & \multicolumn{3}{c}{**Loading Conditions**} \\ \cline{2-4}  & **Dry** & **Wet** & **Earthquake** \\ \hline Circular Failure & 2.011 \(>\) 1.500 & 1.802 \(>\) 1.300 & 1.681 \(>\) 1.100 \\ Sliding & 3.546 \(>\) 2.000 & - & 3.793 \(>\) 1.500 \\ Overturning & 3.712 \(>\) 1.500 & - & 2.715 \(>\) 1.100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of stability analysis [24].  
Figure 2: Design of RC retaining wall and RSR: (**a**) originally designed RC retaining wall; (**b**) RSR design.  
The construction stages are illustrated in Figure 3, and the time schedules and major milestones in the RSRs construction are listed in Table 3. The backfill materials for the RSR that were applied to the sections in accordance with the Korean Railway Design Standard for Roadbed are depicted in Figure 4 [25]. The properties and compaction conditions for backfill materials are summarized in Table 4. The 3% cement-treated gravel was applied for the 1 section, and gravel with a maximum diameter of 63 mm was applied for the 5 section.  
Figure 3: RSR construction process. (**1**) Installation of cast-in-place concrete pile; (**2**) bench cut, spreading, and compaction processes of 3% cement-treated gravel; (**3**) installation of small strip foundation; (**4**) installation of geogrids and nets of welded rebars; (**5**) laying of 3% cement-treated gravel; (**6**) compaction; (**7**) rolling up the geogrid; (**8**) repetition of steps (**4**) to (**7**) to obtain the 17 th layer; (**9**) stabilization of the embankment; (**10**) installation of rebars and connectors; (**11**) installation of forms and placing of concrete for rigid wall; (**12**) curing and completion of RSR.
## 4 Results  
### ISR Estimation  
As one of the main goals of this contribution is to individually assess the feasibility of GNSS-based ISR estimation in a lunar scenario despite the large-scale geometry involved, presented in Figure 4, the first set of graphs in Figure 10 represents a case where both users have perfectly synchronized measurements and are not impacted by significant systematic errors. In particular, Figure 10a shows the bias over Monte Carlo simulations performed at each epoch. As anticipated in Section 2.2.2, it shows a clear correlation between the ISR values estimated following the terrestrial parallelism assumption, that is SD H\({}_{\text{aiding}}\), DD H\({}_{\text{aiding}}\), SD H\({}_{\text{aided}}\), DD H\({}_{\text{aided}}\), and the actual angles between steering vectors. Indeed, as anticipated by analyzing Figure 8, the global minimum happens between 01:00 am and 01:30 am and the global maximum between 02:30 am and 03:00 am, reaching values of 1000 km. These results also indicate that in a lunar DGNSS ranging scenario based on the terrestrial DGNSS ranging algorithms, the minimum achievable error is around km-level. This confirms that the usual DGNSS SD and DD algorithms are not applicable as such in a space scenario. Moreover, the graph shows that no matter the linearization point chosen for creating the design matrix of the terrestrial SD algorithm, the bias magnitude remains constant, as proven by the overlapping of the curves SD H\({}_{\text{aided}}\) and SD H\({}_{\text{aiding}}\) as well as the overlap of the curves DD H\({}_{\text{aided}}\) and DD H\({}_{\text{aiding}}\), respectively, based on Equations (10) and (12) but where one is using the SPP estimate of the _aided user_ as linearization point and  
results around 00:20. As also shown by this figure, the estimators SD Hideal, DD Hideal, APD and SD Haided, corrected are actually unbiased, as expected, according to Section 2.  
Figure 10: ISR estimation over time.  
The second subfigure of Figure 10 presents the standard deviation of the ISR estimators, as defined by the KPI presented in Equation (25). It can be observed that the presented techniques are split into two groups based on their estimates' variance. Roughly speaking, the DD techniques present a higher variance when compared to the SD techniques, with the exception of DD \(\texttt{H}_{\texttt{ideal}}\) which has a variance similar to the SD ranging techniques. The fact that DD techniques overall present a higher variance is expected as applying the SD twice is supposed to decrease the correlated errors but increase the uncorrelated ones, represented by the term \(\Sigma_{ij}\) in Equation (11).  
Finally, Figure 10c, which combines both mean and standard deviation in one metric, shows that the estimators SD \(\texttt{H}_{\texttt{ideal}}\), DD \(\texttt{H}_{\texttt{ideal}}\), APD and SD \(\texttt{H}_{\texttt{aided}}\),corrected are the best estimators in absolute in this configuration.
# Numerical and Experimental Characterization of a Coanda-Type Industrial Air Amplifier
## 4 Numerical Methodology
### Axisymmetric Setup  
The use of an axisymmetric model is based on the assumption that, above an inlet total pressure of \(p_{c}=0.15\) MPa, the air amplifier works under sonic conditions in the injection gap, with the nozzle flow choked. Assuming operational configurations above this \(p_{c}\) value, it can be assumed that the influence of the upstream flow information will not affect the flow downstream from the gap. Thus, eliminating the pneumatic line of primary flow and the inner stagnation chamber of the air amplifier from the numerical simulations allows for a significant simplification of the simulation and hence a considerable reduction in the associated computational cost. Instead of using the three-dimensional Navier-Stokes equations (see Equations (8)-(10)), we use the cylindrical representation of the compressible Navier-Stokes equations, assuming no azimuthal velocity for the axisymmetric flow. The geometry and boundary conditions are similar to those considered in the three-dimensional setup, except that the compressed pressure inlet is located at the injection gap and an axisymmetry boundary condition is applied on the axis of radial symmetry (see Figure 15). This setup allows for faster numerical convergence of the flow solutions and reduced computational time: 48 h (3D model) vs. 0.5 h (axisymmetric model).  
This configuration is used in Section 5.3.3 in the study of the sensitivity of the air amplifier performance to the ratio defined by the width of the injection gap, \(\delta\), and the diameter of the minimum section in the secondary flow area, \(D_{m}\).
## 4 Evaluation
### Throughput
With 4 MB, AES (Original) requires 180.71 s, while our scheme achieves 174.90, 146.11, and 120.58 s on 4, 6, and 8 nodes, respectively, yielding up to a 1.50\(\times\) speedup. For 8 MB, the runtime is reduced from 189.93 AES (Original) to 174.50, 164.58, and 134.17 s, achieving up to a 1.41\(\times\) improvement. With 16 MB, decryption time decreases from 179.52 AES (Original) to 169.65, 165.59, and 139.92 s, resulting in a maximum speedup of 1.28\(\times\). Although both the AES (Original) and our proposed scheme perform centralized, thread-based decryption, our scheme shows improved or comparable performance for large files. This is primarily due to structural overlap between communication, memory preparation, and computation. While the master node receives encrypted subsets over the network, it concurrently prepares in-memory buffers and sorts incoming data, allowing decryption to start immediately after reordering. This overlap reduces idle time and enables more efficient use of computational resources. In contrast, AES (Original) performs decryption directly on data loaded from disk, which introduces I/O latency that becomes more pronounced with larger file sizes, such as 70 and 100 GB. In addition, our proposed scheme adopts a streaming-based and offset-aware design to prevent memory exhaustion during decryption. Each decrypted subset is immediately written to the recovered file at the corresponding offset, without aggregating all data in memory. our design maintains stable and efficient memory usage regardless of dataset size and supports large-scale data processing.