esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results of Simulations
### SHJ Cell Characterization under Illumination  
AFORS-HET simulations are carried out under AM1.5G illumination assuming that carrier transport across the a-Si:H/c-Si hetero-interface occurs by thermionic emission [47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61], which fundamentally occurs due to the inevitable presence of the valence band discontinuity \(\Delta\)E\({}_{\text{V}}\) at the a-Si:H/c-Si hetero-interface. In such a case, the hetero-interface is considered to be a boundary with the thermionic current setting the boundary condition. Hence, it is expected that the simultaneous solution of the continuity, current and Poisson's equation carried out by the simulator would depend on the thermionic emission parameters, namely, the Richardson coefficient, the temperature, and the thermionic barrier height, which has been confirmed in [35]. In some situations, hole transport across the hetero-interface is assumed to occur by drift-diffusion. In that case, the thermionic emission barrier is ignored such that holes are fully transported across the hetero-interface, and the losses attributed to the barrier are avoided. Therefore, the I-V characteristics simulated assuming drift-diffusion transport will only be used as a reference for comparison. The front contact is assumed always to be at flat band unless stated differently. Like in [35], the default value for the Richardson constant A\({}^{*}\) = 9.56 A K\({}^{-2}\) cm\({}^{-2}\) implemented in AFORS-HET is maintained. Such a value is considered to be reasonable taking into account the uncertainties in the value of the hole effective mass [62; 63; 64; 65] and in the probability of hole reflection at the hetero-interface [50; 51; 59; 60; 61].
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 1 Introduction
### Related Work in Other Countries/Latitudes
In this sense, these technological innovations not only improve precision and efficiency in resource and crop management but also promote better human-machine collaboration and mass customization. However, the mass adoption of these technologies faces significant challenges, such as those related to infrastructure, cybersecurity, and the ongoing training of farmers. These previous studies on Agriculture 5.0 at the international level have shown implementation projections on the integration of 6G for the integration of digital technologies with the objective of optimizing agricultural efficiency and productivity. However, these advances face significant barriers that limit their effective implementation, especially in rural contexts in Colombia. Insufficient adequate digital infrastructure and lack of specialized training for farmers are critical challenges preventing widespread adoption of these innovations. Despite acknowledging the transformative potential of emerging technologies, such as 6G, the existing literature has been insufficient in addressing the social, economic, and political challenges inherent in this transition. It is imperative that future analyses go beyond the simple implementation of technologies and focus their efforts on overcoming these structural barriers. The transition to the Agriculture 5.0 model requires not only the adoption of advanced technologies but also the strengthening of digital infrastructure, especially in the most vulnerable rural areas. Moreover, the incorporation of 6G could empower the Colombian agricultural sector, but it must be accompanied by strategies that promote continuous training for farmers, equitable access to digital resources, and the creation of public policies that foster an enabling environment for the inclusion and sustainability of the sector. In this sense, the future of Agriculture 5.0 in Colombia must be centered on a multidimensional approach that not only drives technological innovation but also ensures real change in the socio-economic conditions of rural producers.
# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 2 Literature Review  
### Physical Characteristics of Architectural Spaces and Their Influence on Emotions and Preferences  
Several studies have confirmed that the physical attributes of an architectural space significantly affect users' psychological responses and spatial preferences (see Table 1) [14; 15]. Regarding spatial form, curved structures can provide psychological comfort and lead to more positive evaluations, while an overabundance of curves sometimes creates visual confusion or stress [16; 17; 18; 19; 20]. As for lighting, natural daylight tends to reduce stress and foster positive emotional states, thereby enhancing preference, whereas artificial lighting can elicit a sense of emotional distance in certain cases [21]. High ceilings can increase openness and creativity, whereas lower ceilings may enhance concentration but risk inducing a sense of confinement [22]. Other design elements--such as color, materials, and acoustics--also deliver diverse psychological experiences to users and serve as key factors in shaping overall spatial preference [14]. For example, warm colors and natural materials often foster psychological comfort and increase preference, while cooler tones or synthetic materials may create a sense of detachment.  
Such emotional responses triggered by various physical elements have a direct impact on the assessment of buildings and act more broadly as a crucial indicator of how well a space might be utilized or succeed.  
These studies have relied on subjective surveys to evaluate emotions or preferences regarding space. The self-reporting approach can be limited because of biases in cognition or memory distortion, underscoring the need for a more objective, quantifiable approach to understanding how architectural environments relate to user emotions.  
To address this limitation, this study proposes leveraging EEG-based bio-signal analysis. EEG captures real-time neural responses to specific architectural stimuli--such as images of spaces or virtual reality (VR) environments--thereby quantifying the user's neurophysiological reactions. By examining ERP components, one can derive meaningful differences in emotional responses. This approach overcomes some of the shortcomings of traditional subjective evaluation methods and may help interpret systematically the causal relationship between emotional responses and the physical properties of architectural spaces.  
This study aims to contribute to user-centered architectural design by measuring emotional and cognitive responses objectively and quantitatively via EEG, employing it for preference classification in architectural contexts. An approach like this can effectively integrate emotional factors--often overlooked in traditional design processes--and ultimately enhance user satisfaction and the potential success of a space.
## 6 Performance Analysis
### Numerical Results
#### 6.4.3 Impact of Efficient Grouping on the Overall mMTC Energy Consumption  
In this subsection, it is essential to consider energy consumption because the mMTC devices are mainly powered by batteries. Hence, we analyze the impact of collisions on the energy consumed by the devices. We consider the total energy consumed for a two-step RACH mechanism as 264 \(\upmu\)[PERSON] (2016). Without loss of generality, we run the simulations for 1000 devices performing 10 tasks. As observed in Figure 7, when the collision rate is high, the energy consumption increases exponentially. The increase is the consequence of collision because the collided devices need to re-transmit with no guarantee of success in the following attempts. We can also observe that the RL-based model offers better energy efficiency than other grouping mechanisms, mainly because we took advantage of the devices' and applications' specific completion time constraints to implement an optimized scheduling mechanism. Hence, the flow ratio of attempts can be relaxed over time while guaranteeing that those with low latency requirements can be prioritized, improving the QoS.  
Figure 6: Miss rate results of the different scenarios.
## 5 Hotspot Optical Fronthaul Using OFDM Multi-IF Band Transmission
### Fiber Length Study  
To investigate the longest reach that the BBU box can be placed in the backhaul or the metro-core of the network, we performed a fiber length study with the main criteria being the EVM value of the received data signal after the fourth ROADM to be within the acceptable threshold set by 3 GPP. To this end, we considered the same topology for the hotspot fronthaul, as shown in Figure 6b, with the fiber length between the BBU and the first ROADM extended up to a few km. Figure 11 depicts the ROP versus the length of the SMF fiber between the BBU and the first ROADM, where the red line corresponds to the actual ROP in dBm reaching the PDs of the fourth RRH. The actual ROP was calculated by taking into account the insertion and propagation losses of the building blocks used in the fronthaul link as it is shown in Table 2.  
In this power budget analysis, when the SMF length was equal to 0 (back-to-back), the actual ROP was calculated equal to \(-\)11.16 dBm for an EML modulated average output power of 2.8 dBm [55], MUX and ROADM losses of 1.5 dB and 3.1 dB, respectively. As the SMF fiber length increased, the actual ROP was linearly decreasing, reaching the value of \(-\)13 dBm at a length of 7 km due to the SMF propagation losses of 0.25 dB/km [59]. The other four curves correspond to the four WDM channels and show the ROP required at the PDs of the fourth RRH in order to achieve EVM of 8% for the worst performing SC modulated with 64-QAM. As it can be observed, the EVM degraded for all channels as the fiber length increases due to the signal impairments stemming from the chromatic dispersion, resulting in slightly higher ROP for the highest wavelength channel of 1539.25 nm. By comparing the actual and the required ROP, it can be seen that these curves cross at a length of 4.8 km. After this crossing the required ROP was higher than the available power budget, implying severe performance degradation. This reveals that the longest acceptable distance between the BBU and the hotspot area can be 4.8 km without using any repeater.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Building Block** & **Value** \\ \hline Externally Modulated Laser (EML) modulated average output power & 2.8 dBm \\ Multiplexer (MUX) insertion losses & 1.5 dB \\ Single Mode Fiber (SMF) propagation losses & 0.25 dB/km \\ Reconfigurable Optical Add-Drop Multiplexer (ROADM) losses & 3.1 dB (max.) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power budget parameters.
## 4 Results
### Numerical Results
#### 4.3.2 Comparison with the Current Timetables
From these results, when compared to CT, it can be observed that the MILP approach is particularly efficient in generating schedules that excel in terms of the number of successful transfers. However, the MILP approach is also able to improve the cost of the system as well. This dual benefit underscores the versatility and effectiveness of MILP in optimizing public transportation schedules, striking a favorable balance between passenger convenience and cost effectiveness. When the instances are grouped per \(\lambda\), it can be depicted that the higher \(\lambda\) is, the smaller the improvement of the compromising and extreme solutions in terms of successful transfer  
\begin{table} \begin{tabular}{c} **\%impr.** \\ **Comp. sol.** \\ \end{tabular}  \\ \hline
37.100.1 & 263.41 & 17.04\% & 18.74\% & 8721.80 & 27.24\% & 27.24\% & 439 & 13.21\% \\
37.30.1 & 110.4 & 172.98\% & 180.29\%  
(Figure 7a). This pattern can be associated such that, when the tolerable waiting time of users is smaller, the model has a smaller gap for synchronizing buses schedules to achieve successful transfers. Regarding the number of successful transfers, the average percentage improvement via the MILP model goes from 161.06% when \(\lambda=30\) to 17.89% when \(\lambda=100\) regarding the compromising solution. Similarly, the average percentage improvement of the extreme solution goes from 169.00% when \(\lambda=30\) to 19.40% when \(\lambda=100\) regarding the compromising solution. On the other hand, the increment of \(\lambda\) affects less the capacity of the MILP model to obtain solutions with better costs, as is depicted in Figure 7b. The average percentage improvement of the cost of the system via the MILP model goes from 26.83% when \(\lambda=30\) to 27.64% when \(\lambda=100\) regarding the compromising solution. Moreover, the average percentage improvement of the extreme solution remains almost fixed around 28.33% for any value of \(\lambda\).
## 4 Numerical Modelling for Structural Analysis of TEL-Gridshells
### Shear Stiffness of the joints  
The shear stiffness of the joints was estimated according to the design recommendations of Eurocode 5 [36] for timber construction. This standard provides expressions to determine the slip modulus for each shear plane, \(K\), of different connection elements. In serviceability limit state (SLS), the slip modulus for dowel-type fasteners such as bolts or screws, referred to as \(K_{\text{ser}}\), can be obtained from Equation (1):  
\[K_{\text{ser}}=\frac{d}{23}\rho_{\text{mean}}^{1.5} \tag{1}\]  
where \(K_{\text{ser}}\) is expressed in N/mm, \(d\) is the diameter of the fastener (in mm), and \(\rho_{\text{mean}}\) is the mean density of the wood species (in kg/m\({}^{3}\)). When the shear plane is derived from two timber elements with different densities, \(\rho_{\text{mean}}\) can be estimated as \((\rho_{1}\cdot\rho_{2})^{0.5}\).  
According to the above considerations, the stiffness of the connection element depends exclusively on the diameter of the dowel and the density of the timber members it connects. Based on the connection types described above, the following springs for the gridshell model were defined: _Spring 1_ for the four shear planes of joint _Type 1; Spring 2A_ for the shear plane formed by the two eucalyptus members of joint _Type 2; Spring 2B_ for the shear plane formed by one eucalyptus member and the LVL support of joint _Type 2_; and _Spring 3_ for the two shear planes of joint _Type 3_. Table 3 shows the stiffness assigned to each of the springs determined by Equation (1). The axial stiffness of all joints was assumed to be infinite.  
Figure 8: Joint _Type 1_. Layout detail and aligned couplings for the numerical model.  
Figure 7: Connector concept by coupling used for modelling of the joints.
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
## 2 Results and Discussion  
In the in vitro studies focused on the A549 volatilome, diverse sampling techniques were used, inter alia, an exhaustive technique with a packed sorption tube monolith monotrap, but in most cases VOCs were extracted on SPME fibers coated either with carboxen-divinylbenzene [8; 10; 15], or a carboxen [16] phase. The in vitro SPME sampling was performed by placing aqueous medium in a gas tight vial followed by preincubation and exposure of a fiber assembly to the headspace [15]. In another protocol, we exposed the SPME fiber directly to the A549 cell headspace inside cultivation bottles (flasks) stored in an incubator [9; 10; 16]. In ref [9; 16] cultivation flasks were separated from the incubator atmosphere by turning caps to the gas tight position \(1\) h before sampling. Although such an approach allows for a \"cleaner\" background (lesser exogenous VOCs), lack of CO\({}_{2}\) may have an impact on cell growth. A novel approach utilizing a silica monolith monotrap attached with a syringe to the cultivation flask with a modified gas-tight cap was recently proposed by [PERSON] et al. [17]. In this protocol, sample flow through the monotrap was generated by bidirectional movement of the syringe plunger for the entire extraction time. Another sampling method applied in the study of A549-derived VOCs used TD sorption tubes, where a continuous flow of purified synthetic air enriched with 5% CO2 was used as a carrier gas to draw headspace sample through a solid adsorbent [18]. In turn, ex vivotissue sampling was performed by placing resected lung tissue in a gas-tight vial followed by exposure of the SPME fiber for static headspace sampling [8], or by purging the vial content with synthetic air with an outlet connected to a sorbent-packed tube [19]. In this work, the protocol for Thin-Film Microextraction is optimized and presented for the first time to analyze emission of volatile metabolites from A549 cancer cells.  
Altogether 68 VOCs were detected in all headspace samples from A549 cells using the GC method described here. Out of these, 40 reference substances were available for us to unambiguously confirm identification of metabolites. Hence, to ensure high reliability of the data, we focused only on those 40 VOCs in optimization of the TFME sampling procedure and further statistical evaluation of results gathered for A549 cells. Compounds for which a statistically significant difference was observed between the A549 cell culture medium and the cell-free reference medium were further calibrated and Detection Limits with Quantitation Limits were determined according to the European Medicines Agency guidelines. Metabolites which did not fulfill the EMA recommendations were excluded from further consideration.
## 4 Application Classification of Intelligent Algorithms for Hydrodynamic Noise
### Control Strategy Optimization  
Active or passive control strategies based on intelligent algorithms also play an important role in the effective suppression of hydrodynamic noise. Conventional noise control strategies are typically based on experience or static models, which are often unable to adapt to rapidly changing conditions. However, machine learning algorithms, especially reinforcement learning, enable the optimization of control parameters in real time, thereby ensuring the system can maintain optimal control performance in dynamic environments.  
For instance, the ant colony algorithm has been demonstrated to optimize a combination of multiple schemes in real-time control [101] to address noise control requirements under varying operating conditions. Conversely, genetic algorithms can attain an optimal balance between noise suppression and energy consumption through multi-objective optimization. In a related study, [PERSON] [102] employed linear regression (LR) and artificial neural network (ANN) methods to establish hydraulic loss and noise prediction models based on vortex eigenvectors, respectively. This approach enabled the minimization of the entropy yield and average total sound pressure in the pump using the NSGA-II genetic algorithm. This intelligent-algorithm-based control strategy is capable of adjusting the control parameters based on real-time feedback, thus realizing efficient noise management in complex environments. Moreover, when integrated with the Internet of Things (IoT) technology, the intelligent control system can monitor environmental noise changes in real time and automatically adjust the control strategy. This further enhances the response speed and flexibility of noise control. The continuous advancement of technology has enabled the implementation of these intelligent algorithms, leading to significant advancements in the control and optimization of hydrodynamic noise. These algorithms have the potential to reduce the cost and time investment required for experiments, thereby providing an efficient solution for practical noise control.
# Dynamic Rating of Three-Core XLPE Submarine Cables for Offshore Wind Farms
## 5 Discussion  
On the basis of this study, there are several topics to discuss and, furthermore, to highlight as potential future study areas and applications for use. In comparison with previous works with similar hypotheses, the proposed proved to be a very efficient approach to performing thermal temperature analysis. Previous works on cables or overhead lines have been conducted to improve the current ampacity of existing lines and often used a DTS system for emergency rating. The method presented in this article is a simple process to evaluate the temperature progress with acceptable deviations compared to an FEM-based simulation. The point where this method is preferable is that, instead of a simulation time measured in a couple of hours, this method can determine a dynamical rating in just a few minutes. Therefore, this method can be used together with the FEM to reduce the number of simulations for an initial cable guess, since the LNM provides a good starting point for the cable rating phase. As pointed out in the study, some of the approaches from the approved standards can be considered questionable. In particular, the calculation of armour losses has been found to be significantly higher using IEC's method. Using a two-loop reduction method of the thermal ladder network creates some deviations. The deviation is caused by the difference in the calculation method, as the transient temperature evaluation using the FEM is divided into an infinite number of small thermal capacities that are continuously charged along the thickness of the layer inside the cable. Using the LNM, the thermal capacitances are modelled as two dominant thermal capacitances, \(Q_{A}\) and \(Q_{B}\). This method is directly able to take a quick first shot at a dynamic rating of a three-core XLPE submarine cable. In addition, it will be possible to use a method like this to check the cable supplier's calculations. Looking at the proposed method in a broader context, it would be available for making continuous temperature estimations on the basis of a loading forecast and the use of a DTS system to measure the reference temperature. In this case, users should be aware of the limitation of the individual time constant of the cables found in Equation (53) [8].  
\[K_{\tau}=0.63\cdot\left[\sum T\cdot\sum Q\right] \tag{53}\]  
Figure 18: Transient temperature response to a full-load no-load current step for COMSOL Multiphysics finite element method (FEM) validation of a 220 [KV] 3\(\times\)1800 [mm\({}^{2}\)] Al XLPE SL-type submarine cable.