esa-sceva/satcom-chunk-collection huggingface dataset:
# Growing Kratky Basil in Trombe Wall Cavity: Energy Performance
###### Abstract  
This year-long experimental study, conducted in Kitakyushu, Japan, evaluates the performance of a retrofitted Trombe wall designed to cultivate hydroponically grown basil plants, aiming to enhance its year-round usability. The results show that the addition of plants reduced overheating and moderated temperature fluctuations, but also led to a 30.2% decrease in absorption and a 49.4% decrease in dissipation efficiency compared to a traditional Trombe wall. Seasonal variations influenced the suitability of the space for cultivation, with optimal conditions occurring in spring and summer, while autumn and winter posed challenges due to extreme temperature fluctuations. The daily energy balance was largely unaffected by factors such as leaf transpiration, spontaneous evaporation, additional ventilation, or increased appliance use, as these were overshadowed by the primary thermal processes: solar gains and conductive losses. Although the modified TW still provided passive heating, its energy output was reduced to approximately 10,000 MJ annually, compared to the baseline 14,000 MJ. The study suggests that alternative designs, including increased thermal mass, improved ventilation and better plant selection and could improve both cultivation and energy efficiency. Ultimately, while the green TW is best suited for seasonal use, it offers ecological and social benefits, such as local food production and CO\({}_{2}\) fixation, highlighting its potential for integration into sustainable architectural practices.  
combined-use Trombe wall; Kratky basil; energy efficiency; energy gains and losses; embedded greenery; passive heating; solar energy +
Footnote †: journal: applied sciences  
the building by combining it with living plants, enabling its year-round functionality and adding value through the production of edible greens, as well as increased visual and social engagement [7; 8; 9; 10].  
The primary motivation for this experiment was to investigate whether plants could be grown within a retrofitted existing Trombe wall cavity (Figure 1) during warmer months, thereby making the space productive year-round. The study was extended beyond this initial period to a whole year, offering a more comprehensive assessment of the potential for plant growth and providing a detailed baseline for its performance under weather conditions at our location (Kitakyushu, Japan).  
Although the Trombe wall in this study underwent significant alterations to accommodate plant growth, making direct comparisons to its pre-experimental performance challenging, the thermal observational data collected before and during the experiment offer valuable insights into the system's thermal dynamics. The focus of this paper is to assess how plant integration influences the overall energy performance of the Trombe wall.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 6 The Soft-Decision Helper Data Scheme
### Code Construction and Simulations  
As with the MO scheme we can now define a code construction, with a uniformly generated secret \(s\), an encoder based on an off-the-shelf LDPC code, and where the helper data \(w^{n}\) and reliability information \(\hat{\psi}_{t}^{n}\) are constructed as explained in Section 6.3.  
The decoder observes \(r^{n}\) and the error probabilities \(\hat{\psi}_{t}\) which can be used to calculate the log-likelihood ratios needed for reconstruction of the secret. Since the SRAM cells are independently distributed, we can calculate the LLR of each bit separately. The LLR for a codebit \(c\), after observing the noisy codebit \(r\) and the error probability \(\hat{\psi}_{t}\) for \(t\) enrollment observations, is  
\[\text{LLR}^{\text{\tiny SD}}\big{(}r,\hat{\psi}_{t}\big{)}=\begin{cases}\log \frac{\hat{\psi}_{t}}{1-\hat{\psi}_{t}}&\text{if }r=1,\\ \log\frac{1-\hat{\psi}_{t}}{\hat{\psi}_{t}}&\text{otherwise}.\end{cases} \tag{39}\]  
See Appendix A.5 for a derivation.  
It is shown in Appendix A.5 that the LLRs for the SD scheme are equal to the LLRs for the MO scheme when the same enrollment observations \((x_{1},\ldots,x_{t})\) and reconstruction observation \(y\) are generated by the SRAM-PUF. Therefore, simulations for the SD scheme would give the same FER results as for the MO scheme, and we can use the plots in Figure 6 to predict the performance of the SD scheme.
# Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages
## 4 Results
### Perceived Practicality of VR Across Project Stages
The quadratic contrast (F = 8.21, \(p\) < 0.01) is also significant, supporting the idea that **VR's practicality follows a curved relationship, with its usefulness fluctuating at various stages**. However, the linear effect (F = 2.65, \(p\) = 0.109) had higher-order effects (Order 4 and 6), indicating that some transitions between stages do not consistently impact the perceived practicality of VR. These results suggest that while VR is perceived as highly practical during certain stages, such as Training and Proposal and Investor Recruitment, its perceived usefulness varies more in stages like Specification and Requirement Definition. This analysis emphasizes the need for further research to understand the factors influencing these shifts and offers valuable guidance for project managers to strategically deploy VR at the most effective stages of the project lifecycle. Significant pairwise differences were observed between stages with high and low perceived practicality of VR. Specifically, **VR was viewed as significantly more useful during the Training and Proposal and Investor Recruitment stages compared to the Specification and Requirement Definition and Development phases (\(p\) < 0.01 and \(p\) < 0.001, respectively). These results suggest that VR is perceived as significantly more useful during later and collaborative stages, whereas its utility is perceived as lower during early planning phases. **  
In contrast, no significant differences were found between several other pairs of stages, such as between Concept Formation and Design and Surveys or between Maintenance and Testing. These non-significant results suggest that the perceived usefulness of VR remains relatively consistent during these stages. The profile analysis reveals that the perceived utility of VR varies significantly across different stages of the project lifecycle. Training and Proposal and Investor Recruitment are identified as stages where VR is perceived as most practical, whereas Specification and Requirement Definition is viewed as the least favorable stage for VR application. This suggests that **VR is most valuable in stages involving decision-making, collaboration, or interactive processes, while its utility may be less evident in earlier, more conceptual or planning phases**.
# Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
## 1 Introduction
RL emerged as a practical method for robot control [14] and was successfully applied to solve complex robot manipulation problems [15] and learn complex skills like playing Go [16] and Atari games [17]. There are two categories of RL (i.e., model-based RL and model-free RL). Since we cannot provide complete models of uncertain environments for RL, model-free RL is more suitable for our scenarios. However, RL cannot be used directly to plan paths in complex environments due to two limitations: (1) sparse reward, i.e., reward is sparsely distributed in large state spaces, causing difficulties for learning feasible policies; (2) local minima trap, i.e., RL agent may be trapped in local minima, like goals on the other side of box anyons. In this paper, to overcome the abovementioned limitations of SG and RL methods, we present a hierarchical path planning framework, called SG-RL, to integrate the geometric path-planning method (i.e., SG) and the ground-level motion planning method (i.e., RL), which can plan rational paths in uncertain and continuous environments. SG-RL solves this path-planning problem in a two-phase manner. The first phase is a global abstract path-planning phase that focuses on the first requirement for rational paths. The second phase is a feasible trajectory-planning phase that focuses on the second requirement. In the first phase, SG-RL uses SSG to find global abstract paths, also called subgoal sequences, from start points to goal points with high computational efficiency. In the second phase, considering kinematic constraints, SG-RL uses an RL method, i.e., Least-Squares Policy Iteration (LSPI) [18], to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. Compared with RL methods, SG-RL can use SSG to plan paths more efficiently so as to eliminate first-move lags, and owing to the direct-h-reachability of adjacent subgoals, SG-RL can support RL agents to realize long-range navigation on complex maps by overcoming sparse reward and local minima trap. Then, compared with SG methods, SG-RL can use LSPI to generate smooth paths that follow kinematic equations and preserve G1 continuity [9], and due to the generalizability of LSPI, SG-RL can deal with small changes (i.e., unexpected obstacles) over maps. The rest of this paper is organized as follows: Section 2 discusses some related work on path-planning methods based on A*, RL, and hierarchical path planning. Section 3 presents the hierarchical path-planning approach based on SG and RL. In Section 4, the performance of the proposed approach is evaluated by simulation experiments. Finally, this paper is concluded in Section 5.
## 3 Results
### Biocompatibility In Vitro and In Vivo
#### 3.6.1 Biocompatibility In Vitro  
To assess the biocompatibility in vitro, studies of the hemolytic activity and cytotoxicity of the Fe-Mn-5 Si alloy were carried out. The conducted studies showed that incubation for 2 and 4 h with samples of the Fe-29 Mn-5 Si alloy in the state after annealing and ECAP did not lead to a significant increase in the level of hemolysis compared with the control of spontaneous activity (\(p>0.05\)) (Figure 11a). This indicates their biocompatibility in accordance with the recommendations of [45] and [46]. Even if the exposure time was extended to 24 h, the hemolysis observed in the presence of the alloy after ECAP was below 5%, which allows the use of this alloy in medical purposes.  
The cytotoxic activity of the studied alloy was assessed by measuring the activity of extracellular LDH (LDH Release), indicating cell destruction during incubation. The comparative analysis of the obtained results did not reveal signs of cytotoxicity of both types of treatments. They not only did not contribute to a significant increase in the level of extracellular LDH in comparison with the control (\(p>0.05\)), but also contributed to the stabilization of the state of cell membranes. These studies of biological properties have shown that contact with alloys after annealing and ECAP does not lead to cell destruction under the described conditions. This allows us to confirm their biocompatibility in vitro.
## 2 System Model and Problem Statement
### Secure Transmission Based on Adaptive Coding  
When using the coding method with a low code rate to encode transmitted data bits at high SNR, more redundant code bits must be added to correct errors, resulting in lower coding efficiency. However, at low SNR, using a high code rate cannot fully correct inconsistent  
Figure 4: Signal frame design of downlink commmunication.  
keys and transmitted error bits, making it difficult to ensure reliable communication. Thus, to maximize the secure transmission rate at different SNRs, an adaptive coding algorithm using BCH code \((n,k,t)\) based on the equivalent channel is presented. We use BCH code in this paper since it is widely used due to its lightweight and low-complexity advantages. An equivalent channel that contains an encryption channel and a transmission channel is constructed to compute the BER. Next, the calculation results are used to further design the optimal parameters of \(n\) and \(k\). Finally, according to \(n\) and \(k\), we can encode data bits to guarantee reliable transmission at the maximum secure transmission rate. The adaptive coding algorithm based on the equivalent channel is summarized in Algorithm 1.  
```
0: Source bits \(M_{A}\); code length \(n\), and SNR \(\gamma_{p}\), \(\gamma_{d}\)
0: Encoded bits \(X_{A}\)
1: Calculate the upper bound of the BER based on the equivalent channel \(P_{et}^{UB}=P_{e}+p_{AB}^{\text{key}}-2P_{e}P_{AB}^{\text{key}}\).
2: Maximize secure transmission rate \(R^{UB}=\frac{n-2 nP_{et}^{UB}-1}{n}\).
3: Calculate information bits \(k^{UB}=nR^{UB}=n-2 nP_{et}^{UB}-1\).
4: Fix the code length \(n\) and adjust \(k^{UB}\) according to the BER and encode source bits \(M_{A}\) to obtain encoded bits \(X_{A}\) by using \(n\) and \(k^{UB}\).
5: Return \(X_{A}\).
```  
**Algorithm 1** Adaptive coding algorithm based on the equivalent channel
## 5 Model Order Reduction Method  
The 9 th order model that results from combining (13), (14), and (16) to a common denominator can be reduced to a lower order model by utilizing an approximation of the original transfer function. This method is not a mathematically rigorous method, but rather is meant to be a practical method that is based on engineering judgement and simulation. This method may require some trails and error iterations to obtain the desired closed loop response. In this example, each 3 rd order SISO model will be approximated independently by a 2 nd order SISO model. The approximation is made based on an engineering judgment driven approximation method consisting of removing similar time constants from the nominator and denominator of transfer function or replacing these three-time constants by two slightly bigger ones. The reduced transfer functions are defined by Equations (18), (19), and (20):  
\[T_{1}(s)=\frac{Y(s)}{U_{1}(s)}=-1\frac{(120s-1)}{(90s+1)(110s+1)} \tag{18}\]  
\[T_{2}(s)=\frac{Y(s)}{U_{2}(s)}=-1\frac{(20s-1)}{(80s+1)(180s+1)} \tag{19}\]  
\[T_{3}(s)=\frac{Y(s)}{U_{3}(s)}=\frac{1}{(40s+1)(60s+1)} \tag{20}\]  
The quality of the model after the order reduction step was verified by simulation. The results are shown in Figures 10-12.  
Figure 9: GPC with the 9 th order internal model—closed loop simulation of 300 time-steps, trend for MVs signals. Controller parameters: _control horizon_\(N_{u}=20\), _prediction horizon_\(N=400\), and the \(R\) matrix = _identity matrix_.  
were reduced to a common denominator and added to meet the requirements for the GPC model form (3). This resulted in a 6 th order reduced model and this reduced model is compared with the original 9 th order model that was obtained by adding the three original SISO models. The simulations results are shown in Figure 13.  
The static gain of the 6 th order reduced model is equal to 3 and thus the reduced model compares closely to the expected model. A closed loop simulation of the resulting GPC controller utilizing the reduced order internal model (6 th order) was performed and the original 3 rd orders models, before reduction to common order given by (13), (14), and (16), were used. The resulting GPC controller was found to be stable through simulation and the results are shown in Figures 14 and 15.  
Figure 14: GPC with reduced internal model—closed loop simulation of 300 time-steps where the dashed line is the set point and the solid line is the CV. Controller parameters: _control horizon_\(N_{u}=20\), _prediction horizon_\(N=400\), and the \(R\) matrix = _identity matrix_.  
Figure 13: Comparison of the 6 th and 9 th order models—simulation of 900 time-steps (solid line—9 th order model, dashed line—6 th order model—model after order reduction).
## 3 Proposed Method
### Bag Aggregation  
After we use the noise-detection strategy to eliminate the noise, we get a new bag-level label \(\{r^{\prime}_{1},r^{\prime}_{2},\ldots\}\) instead of just one label \(r\) for \(\mathbb{S}_{\text{h},\text{t}}\). It is remarkable that maybe not all sentences in \(\mathbb{S}_{\text{h},\text{t}}\) have the positive label \(r\), and different positive sentences express \(r\) to different degrees.  
Based on the above-mentioned reasons, we adopt frequently used selective attention over \(\text{S }=\{s_{1},s_{2},\ldots\}\) to get the bag-level representation \(\mathbf{B}\). Selective attention with a query vector of relation \(r^{\prime}_{i}\) can make the noisy sentences invalid and no longer contribute to the representation of the bag. In addition, via selective attention, higher weights are assigned for the sentences expressing \(r\) more clearly. The representation of the bag is computed as follows:  
\[\left\{\begin{array}{c}\alpha_{i}=softmax(\mathbf{q}^{T}_{r^{\prime}_{i}} \mathbf{s}_{i})\\ \mathbf{B}=\sum\limits_{i=1}^{n}\alpha_{i}\mathbf{s}_{i}\end{array}\right. \tag{8}\]  
where \(\mathbf{q}_{r^{\prime}_{i}}\in\mathbb{R}^{(3\times d^{\prime})}\) is a learnable query vector of relation \(r^{\prime}_{i}\) and n is the number of sentences in the current bag.  
Due to the fact that the sentences contained by \(\mathbb{S}^{1}_{h,t}\) and \(\mathbb{S}^{2}_{h,t}\) are not an order of magnitude, \(\mathbb{S}^{1}_{h,t}\) and \(\mathbb{S}^{2}_{h,t}\) will get their own representation through Formula (8), which are denoted as B1 and B2. To balance their impact on relation prediction, they are assigned different proportions in the final sentence-bag representation y, which is formulated as  
\[y=\lambda\textbf{B}^{1}+(1+\lambda)\textbf{B}^{2} \tag{9}\]  
where \(\lambda\) is a variable from 0 to 1 and is calculated by  
\[\lambda=\sigma(\textbf{W}^{\lambda}[\textbf{B}^{1};\textbf{B}^{2};\textbf{q}_{ r}]+\textbf{b}^{\lambda}) \tag{10}\]  
where \(\textbf{W}^{\lambda}\)\(\in\)\(\mathbb{R}^{(1\times(9\times d^{n})}\) is the transformation matrix, \(\textbf{b}^{\lambda}\)\(\in\)\(\mathbb{R}^{1}\) is the bias, and \(\textbf{q}_{r}\)\(\in\)\(\mathbb{R}^{3\times d^{r}}\) is a learnable query vector of bag label \(r\).
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 3 Results
### Ablation Study  
For the following comparisons, unless otherwise noted, all training parameters are kept at the same values as stated in Section 2.2. Here, we focus on the parts of our method that diverge from the traditional methods that inspired it, as well as the changes that differentiate our U-Net architecture from the most commonly used U-Net architectures, and show that each change resulted in improved training results. A quantitative performance evaluation is performed using the synthetic data previously described. Figure 6 shows a bar graph of the mean Root-Mean-Square-Error (RMSE) [63] for each variant of the method tested with error bars showing the standard deviation of 40 runs (ten runs for each of the four synthetic images). For RMSE, lower values are better.
## 1 Introduction
There are no computer modeling studies in the literature considering the influence of burnishing process parameters and tool geometry on the conditions of nanostructured state formation in the processed steel surface layers. The modern studies of the burnishing process with a cylindrical indenter are mainly devoted to the finishing treatment of the surfaces of workpieces, which are bodies of revolution. The development of the method of nanostructuring burnishing of flat parts with a cylindrical indenter located at an angle to the treated surface, proposed by the authors of this study in [7], is of current interest. The inclination of the cylindrical tool creates a wedge of the deformable material shear favorable for nanostructuring between the processing plane and the line connecting the indenter depth point and the point corresponding to the maximum height of the pile-up. It can be noted that the inclination angles of the cylindrical indenter and the wedge of the deformable material practically coincide. According to the research results [3], the wedge angle of the quenched steel being sheared varies from 1 to 3 degrees. Of considerable interest are the studies on the application of an indenter made of CBN500 ceramic material for nanostructuring burnishing of AISI 52100 quenched roller-bearing steel. Based on the literature review and the purpose of the work, the following research objectives and the sequence of their solution are formulated:  
* Development of numerical and finite element simulations of the burnishing process with the application of a cylindrical indenter with an adjustable inclination angle to the treated surface;
* Dynamometric studies of the process and establishment of the relationship of contact forces and the friction coefficient with the inclination angle of the indenter;
* Simulation and experimental study of the influence of the cylindrical indenter inclination angle and of burnishing force on the contact pressure and shear deformation;* Establishment of microstructure change regularities, microhardness, and roughness depending on the inclination angle of the cylindrical indenter and its burnishing force. Since the value of the coefficient of friction of the CBN ceramic indenter in dry contact with AISI 52100 steel has not been studied before, for numerical simulation, it is necessary to initially conduct dynamometric studies of the process and establish the real values of the coefficient of friction at different tilt angles of the indenter.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 3 Implementation Results and Analysis
### Read Static Noise Margin  
The RSNM is defined as the maximum tolerance of an SRAM to external noise or bit line disturbances during a read operation, despite the presence of external noise or bit line disturbances. It is measured as the area of the largest square that can fit within the butterfly curve obtained by sweeping a DC voltage at the storage node. A higher RSNM value affect stronger immunity to soft errors and greater stability under noise-sensitive or radiation-exposed environments [10]. The RSNM is influenced by the implementation of read isolation structures, which enhances read stability by decoupling the storage node from direct interaction with the bit line during read operations. This prevents the read current from disturbing the storage node voltage. Other influencing factors include the number of access transistors, parasitic capacitances, transistor dimensions (W/L ratios), and the CR. As shown in Figure 7, the WRTU-16T shows a RSNM of 119 mV.  
Figure 5: RAT measurement method.  
Figure 6: Comparison of RAT with other SRAMs: (**a**) at VDD = 1 V, (**b**) at various VDD levels from 0.9 V to 1.1 V.