esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of the Technological Innovation of the Invention  
In order to assess the novelty of the device, a verification was conducted to ensure that no identical or substantially similar device exists within the publicly accessible technical knowledge, thereby affirming its innovative nature. Furthermore, its distinguishing features compared to prior designs were emphasized. The primary objective of this report is to present a comprehensive analysis of prior technological developments and existing advancements in the field, providing a contextual framework for the invention within the available technical knowledge. The databases consulted include PatFT (USPTO), Espacenet (EPO), PatentScope (WIPO), Google Patents, and SIGA (IMPI), which contain relevant information on patents, utility models, and industrial designs at both the global level and in Mexico. The invention under analysis features a design and configuration adaptable to any human skull size, enabling its use across all age groups. Moreover, it integrates manually operated mechanical mechanisms that ensure the precise positioning of each electrode at designated points on the head, thereby ensuring accurate measurements. The technological background search conducted in the aforementioned patent databases identified eighteen patent documents deemed relevant to the present invention, as presented in Table 7. \begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Helmet**} & \multirow{2}{*}{**Subject**} & **Mean PSD** & **SD PSD** & **Mean PSD** & **SD PSD** \\  & & **(Active State)** & **(Active State)** & **(Relax State)** & **(Relax State)** \\  & & \(\upmu\)V\({}^{2}\)/Hz** & \(\upmu\)V\({}^{2}\)/Hz** & \(\upmu\)V\({}^{2}\)/Hz** & \(\upmu\)V\({}^{2}\)/Hz** \\ \hline
**Emotiv** & 4 & 0.08 & 0.20 & 0.21 & 0.26 \\  & 5 & 0.12 & 0.22 & 0.20 & 0.24 \\  & 6 & 0.14 & 0.16 & 0.18 & 0.17 \\
**Total Mean** & & **0.11** & **0.19** & **0.20** & **0.22** \\ \hline
**Chameleon** & 4 & 0.22 & 0.20 & 0.35 & 0.27 \\  & 5 & 0.18 & 0.22 & 0.45 & 0.31 \\  & 6 & 0.23 & 0.21 & 0.30 & 0.26 \\
**Total Mean** & & **0.21** & **0.21** & **0.37** & **0.28** \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of normalized mean and standard deviation (SD) for power spectral density (PSD) in the 8–13 Hz range between Emotiv and Chameleon helmets. Finally, to complement the technological background search, an internet search was conducted using Google, identifying four websites that market brain measurement equipment, which were deemed relevant to the invention. These are presented in Table 9.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 4 Model Development and Training
### Improved RAG Model Through Manual Construction of Hyundai Motor Sentence Similarity Learning  
To overcome the structural limitations of basic RAG models, this study developed an improved RAG model centered on retriever improvement based on domain sentence semantic similarity learning and generator design that adapts to maintenance query types. The proposed system consists of a dual pipeline structure utilizing manually constructed maintenance sentence similarity data and domain-based question-answering data, aiming to simultaneously improve search performance and response relevance.  
First, for maintenance domain retriever improvement, we manually constructed a total of 1200 pairs of sentence similarity learning data from Hyundai Staria manuals. Each sentence pair was evaluated by five automotive maintenance experts on semantic similarity using a five-point scale, with interrater agreement (ICC) measured at 0.87. Based on this data, contrastive learning was performed using the BAAI-bge-m3 embedding model with MultipleNegativesRankingLoss.  
During training, all sentence pairs except positive sentence pairs within batches were automatically considered negative samples, allowing the model to learn to minimize distances between similar sentence embeddings and maximize distances between dissimilar sentences. This method secured learning efficiency and practicality by enabling effective embedding fine-tuning through batch-based negative sampling alone without constructing separate explicit negative samples.  
For generator configuration, we used the bLLossom-8B model as a base, performed domain continual pretraining, then applied Instruction Tuning using Simple QA, RAG QA, and Multi-Turn QA datasets. The model was trained to automatically adjust response styles and structures according to query types (definition, procedural, comparative, etc.), with LoRA-based parameter-efficient tuning (rank = 64, alpha = 128, dropout = 0.1) training only about 0.1% of total parameters.
## 4 Results
### Architecture of Modified AlexNet-33 (MAlexNet-33)
#### 4.2.2 Parameter Modification  
The AlexNet contains three fully connected layers with parameter values of 9216, 4096, and 4096 for the inputs, and 4096, 4096, and 1000 for the outputs. In this experiment, we reduce the output parameters across the first two fully connected layers within the network through scaling. The results achieved from this experiment are reported in Table 7.  
In here, FC6 refers to the output of the first fully connected layer, and FC7 refers to the output of the second fully connected layer. It is important to note that the output of the last fully connected layer corresponds to the number of classes the system aims to identify and is not determined by parameter scaling.  
As observed from Table 7, a notable improvement is observed through scaling the output parameters of the fully connected layers through a division of 24 (from the input parameter and fully connected sizes of the original network), which provided slightly higher F1-score compared to the original AlexNet. Further, this results in an almost 90% reduction in size of the network compared to the original (23.82 MB as opposed to 221.4 MB). Uniform scaling also returns better performance compared to keeping an equal number of parameters across all fully connected layers. Further, it also achieved a higher weighted F1-score than the combination used by previous recent studies, for which the exact parameters used are represented by the last entry on Table 7 [66]. It is important to note that the input size for FC6 is automatically calculated for the modified networks. After the convolution stages, this is found to be 4608 parameters. Quantitatively, it is implied that the output parameters of all fully connected layers subsequent to the last fully connected layer can be scaled down extensively, depending on the number of classes that the model is designed to predict, keeping in mind that the fully connected output parameters are higher than the number of possible predictions.  
The number of epochs required is determined through the training accuracy and losses graph. Generally, a lower number of output parameters slows down the training, requiring more epochs in order to reach a well-learned network. Figure 7 displays the difference between a traditional AlexNet and a version with lower numbers of output parameters in the fully connected layers. The comparison was done for 10 epochs.
## 3 The SensoTube Architecture
### Power Management Layer (PML)  
Energy is a very critical factor for the real-world WSAN applications, especially in the agricultural environment, and can influence the lifetime and the reliability of the overall application [18]. As the WSAN technology evolves, the need for power management is increasing [23]. Unfortunately, there are several trade-offs in the commercial WSAN solutions, regarding the use of energy [11]. Traditionally, the WSAN hardware solutions being based either on the traditional architecture and COTS, or on OSH architectures, have been designed without paying particular attention to the energy implications. Furthermore, regarding the provisions for the energy sources, these systems just provide some kind of connection through the use of pins or screw-drive terminal blocks and they leave the users to take care of supplying power, under their own responsibility. In practice, this can jeopardize the overall system's performance and reliability.  
Additional pressure for energy management is coming from the need to exploit challenging energy-related technologies [34]. The WSAN nodes in agriculture, except from the use of photovoltaic panels [123], can also make use of other, more sophisticated, energy harvesting techniques [124; 125; 126]. Except for the mature battery types, the harvested energy can be also stored in relatively new media such as the Li-Ion batteries [127], supercapacitors [94; 128; 129], hybrid ultracapacitors [42], or combinations of thin-film batteries and supercapacitors [130]. The spread of such technologies in real-world WSAN applications entails sound evaluation and modeling. Otherwise they will be limited to pilots and demonstration implementations. In this context, the existence of a separate shield that accommodates all the energy requirements of the overall system is very critical.  
A SensoTube-based system could have more than one PML shield, which can be replaced on an occasional basis, in order to fulfill the scalable energy requirements of the system. With PML shields, the power electronics researchers and designers have a discrete functional shield, into which they can contribute towards the design of energy optimized WSAN systems. At the same time, the PML shields ensure a unified and well-organized energy management that can guarantee the reliability and the lifetime of the WWSAN system. Additionally, with the provision of the SensoTube inter-layer service for energy management, an MCU-based PML shield through the use of on-board electronic switches can power-ON and OFF, in real time, all of the other functional shields. The incorporation of an MCU in the PML shield that will manage the overall systems' energy could uplift the prospects for optimization.
## 2 Materials and Methods  
### Reagents  
All synthesis reagents were used without any treatment, as follows: titanium foil (99.99% purity), hydrofluoric acid (HF) solution (0.5 M), \(\alpha\)-terpinol ethyl cellulose, acetone and ethyl alcohol. The CuMnO\({}_{2}\) powder was synthesized by the ultrasound-assisted co-precipitation method, which used the nitrate precursors [Cu(NO\({}_{3}\))\({}_{2}\)\(\cdot\)3H\({}_{2}\)O and Mn(NO\({}_{3}\))\({}_{2}\)\(\cdot\)4H\({}_{2}\)O] in a NaOH solution, a synthesis time of about 40 min, and 80% ultrasound amplitude [36].  
Fabrication of the Sensors Based on the \"n-p\" Type Heterojunction Ti-TiO\({}_{2}\)-CuMnO\({}_{2}\)  
The Ti-TiO\({}_{2}\)-CuMnO\({}_{2}\) heterostructure of the sensor was achieved in two steps (Figure 1). The first step consisted of obtaining the TiO\({}_{2}\) layers that were grown on Ti foil (with an effective area of 1 cm\({}^{2}\)) by thermal oxidation (Ti-TiO\({}_{2}\)), as follows: prior to the chemical treatment, the Ti foil was degreased in an ultrasonic bath in acetone and ethyl alcohol solutions; the Ti foil was then etched in an HF solution (0.5 M) for different periods of time (1 h, 2 h, 3 h) at room temperature. After the chemical process, the titanium foils were washed with distilled water, then air-dried. For the formation of crystalline TiO\({}_{2}\), the process of thermal oxidation was carried out in a tubular furnace in a controlled atmosphere consisting of a mixed gas flow of Ar and O\({}_{2}\) at a temperature of 500 \({}^{\circ}\)C for 4 h. The second step consisted of depositing the CuMnO\({}_{2}\) film onto the surface of the Ti-TiO\({}_{2}\) layers by using the Doctor Blade method. Thus, for the deposition of the CuMnO\({}_{2}\) film, a homogeneous paste was prepared as previously described in our paper [26]. The Ti-TiO\({}_{2}\)-CuMnO\({}_{2}\) heterostructures were then air-dried for 30 min, and calcinated at 250 \({}^{\circ}\)C for 1 h. Before and after deposition, the films were UV cleaned using the UV Ozone Cleaner and then deposited in hermetically sealed dishes. In order to facilitate data collection, gold wires were affixed to the Ti and \(p\)-CuMnO\({}_{2}\) films by using Ag paste. Table 1 presents the sample codes and synthesis conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Codes**} & \multicolumn{2}{c}{**Etching (Ti Foil)**} & \multirow{2}{*}{**Oxidation**} & \multirow{2}{*}{**Sensor Type**} & \multirow{2}{*}{**Codes**} & \multicolumn{1}{c}{**Thermal**} \\  & & & & & & & **T(\({}^{\circ}\)C)/t(h)** \\ \hline \multirow{3}{*}{Ti-TiO\({}_{2}\)} & S1 & 1 & & & S1a & \\  & S2 & 0.5 & 2 & 500/4 & Ti-TiO- & S2a & 250/1 \\ \cline{1-1}  & S3 & 3 & & CuMnO\({}_{2}\) & S3a & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The sample codes according to the synthesis conditions.  
Figure 1: Fabrication process of the sensors.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 4 Scenarios Simulation Under Adjacent Building Conditions
### Effect of Changing the Load Factor of Adjacent Buildings
#### 4.2.3 HSS Model  
This section uses the HSS model to analyze the impact of varying adjacent building loads, comparing 5 F (91 kPa) and 12 F (195 kPa). The comparison was conducted with fixed adjacent building distances and basement depths. According to Figure A6 in Appendix A, the load significantly affected the wall deformation at all three distances. Compared to the MC and HS models, the HSS model highlighted the impact of the load factor more clearly. The analysis across the three models underscores the importance of considering the load of the above-ground structure.
# Improving Adversarial Robustness of CNNs via Maximum Margin
## 3 Proposed Method
### Comparisons to Other Adversarial-Based Defense Methods  
First, the main idea of many variants of adversarial training is to weaken the adversarial examples when training [21, 28] or use attacks from the weak to the strong drawing on the ideas of curriculum training [22, 26]. Unlike these methods, AT-SVM does not change the attack. Moreover, stronger adversarial examples are more likely to be misclassified by the SVM auxiliary classifier and have the opportunity to be trained by the original network rather than being discarded.  
Second, some methods treat adversarial data differently by explicitly assigning different weights to their losses, such as MART [27] and GAIRAT [23]. This kind of method is similar to ours because our method is equivalent to assigning a weight of 0 or 1 to all examples. However, our method is more concerned with finding more important examples near the decision boundary than assigning reasonable weights to all examples. We believe that as long as the training is carried out under the appropriate data, the original surrogate loss will be sufficient. Furthermore, MART regards natural examples which are misclassified as outliers and suppresses the influence of adversarial examples corresponding to these examples. On the contrary, AT-SVM will train more on these misclassified natural examples to avoid the decline in the accuracy of natural examples caused by adversarial training.  
Last, max-margin adversarial (MMA) training [41] ([PERSON] et al., 2019) is also declared to improve adversarial robustness by the maximum \"margin\" like SVM. We emphasize that our AT-SVM is different from MMA in the following aspects: (1) the \"margin\" in MMA means the \"shortest successful perturbation\" which is obtained from a PGD attack while the \"margin\" in AT-SVM is just the loss in SVM auxiliary classifier. So AT-SVM can increase the margin more simply, without using other objective function indirectly like MMA; (2) MMA will first test whether the classification of clean examples are correct or not. For correctly classified examples, MMA adopts cross-entropy loss on adversarial examples; for misclassified examples, MMA directly applies cross-entropy loss on natural examples. OurAT-SVM treats all examples the same because we believe that the importance of examples can be adequately reflected by the SVM auxiliary classifier.
## 3 Results and Discussion
### Optimal Dosage Range Determination  
Flocculant dosages from 10 to 200 mg\(\cdot\)L\({}^{-1}\), at the optimal pH previously determined (Table 3), were tested to select an optimal dosage range for flocculation. The harvesting efficiencies obtained with each flocculant are presented in Table 4, and the appearance of the culture medium after flocculation and sedimentation can be observed in Figure 1.  
Inorganic flocculants were very effective, with harvesting efficiencies varying from 89.8 to 96.4% and 96.7 to 97.7% for aluminum sulfate and ferric sulfate, respectively. Chitosan reached efficiencies from 91.2 to 99.3%, also proving to be capable of harvesting _C. vulgaris._ An interesting effect was observed with chitosan, especially at 50 mg\(\cdot\)L\({}^{-1}\). Some biomass floated instead of sediment, and gas bubbles were visible in the flocs. As the bubbles burst, the flocs quickly settled. The same phenomenon was reported by [PERSON] et al. [29] at chitosan dosages of 60-90 mg\(\cdot\)L\({}^{-1}\). According to the authors, chitosan molecules establish bonds with negatively charged cells, but others experience repulsion against themselves. As chitosan molecules have long chains, large flocs can be formed, but destabilization makes them less dense. Gas bubbles were most likely formed from the dissolved air in the medium [27]. Few flocs floated in the remaining concentrations (10, 100, 150, and 200 mg\(\cdot\)L\({}^{-1}\)). At 10 mg\(\cdot\)L\({}^{-1}\), it is possible that the available binding sites of chitosan were all occupied with microalgal cells, minimizing the repulsion. In the other concentrations, [PERSON] et al. [29] suggested that the solution concentration increased, pushing flocs downward; thus, almost no flotation was observed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Flocculant** &
\begin{tabular}{c} **Initial Biomass** \\ **Concentration (mgDW\(\cdot\)L\({}^{-1}\))** \\ \end{tabular} & **Optimal pH** \\ \hline Aluminum sulfate & \(342\pm 9\) & 9 \\ Ferric sulfate & \(342\pm 9\) & 9 \\ Chitosan & \(339\pm 5\) & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal pH for flocculation obtained for aluminum sulfate, ferric sulfate, and chitosan. Overall, high harvesting efficiencies were achieved with both inorganic and organic flocculants. However, the dosage required to achieve those efficiencies varied. The optimal dosage ranges for each flocculant are compiled in Table 5. Inorganic flocculants which
# Economic and Environmental Impact of Energy Saving in Healthcare Buildings
###### Abstract  
The purpose of this article is to estimate the economic and environmental impacts of energy consumption derived from healthcare buildings and proposes several energy-saving options in the sector. An experimental energy consumption study was development between 2005 and 2013 in 12 hospitals and 70 healthcare centres in Spain, built between 1980 and 2005 through audits carried out between 2005 and 2012, performed by the Extremadura Energy Agency. The study focused on electric energy, HVAC, DWH, lighting systems, renewable energies, maintenance strategy, thermal insulation, and optimal building size. Specifically, the following parameters were evaluated: energy savings, investment emission of CO\({}_{2}\), NO\({}_{2}\), and SO\({}_{2}\) gases, and payback. The results revealed that through an appropriate energy management of healthcare buildings it is possible to save up to 8.60 kWh/m\({}^{2}\) per year, for buildings of less than 5000 m\({}^{2}\) (with no beds), which represents an expense of 1.55 \(\upmu\)/m\({}^{2}\). In healthcare buildings larger than 5000 m\({}^{2}\) (with beds), it was possible to save up to 6.88 kWh/m\({}^{2}\) per year, which represents an expense of 1.25 \(\upmu\)/m\({}^{2}\).  
energy saving; energy efficiency; healthcare engineering; healthcare building Article
## 1 Introduction
studied the infrared radiation polarization effect mechanism and typical characteristics of solar panels through theoretical simulation modeling and outdoor experiments and analyzed the influence of view angle, refractive index, surface state, temperature and other factors on the infrared polarization effect characteristics; they found that the thermal infrared polarization characteristics of solar panels are obvious and strongly correlated with the emission angle [7]. [PERSON] et al. proposed a defect detection method for crystalline silicon photovoltaic cells based on electroluminescent polarization image fusion. It was found that polarization imaging can highlight the contour edges and texture details of photovoltaic cell defect images, and the characteristics of photovoltaic cell defect images after the fusion of polarization image and light intensity image are more prominent [8]. The above research mainly focuses on the polarization characteristics of reflection or thermal radiation on the surface of photovoltaic panels. However, the polarization characteristics and generation mechanism in the electroluminescence process of photovoltaic cells has not been studied in-depth thus far. In 2011, [PERSON] observed that some material structures of silicon solar cells produce strong polarization characteristics, and found the polarization characteristics are related to the anisotropy of the internal charge of defects in silicon solar cells during the optical recombination process, especially for high dislocation density areas, where the partial polarization characteristic of the sub-band gap electroluminescence was highlighted; additionally, the polarization direction is consistent with the dislocation direction [9; 10]. In 2013, [PERSON] found that the relationship between the electroluminescence intensity of solar cells and the rotation angle of the linear polarization analyzer is sinusoidal through experiments and 3D data analysis, indicating that the electroluminescence of solar cells has polarization characteristics which are related to the internal structural and defect of photovoltaic cells [11; 12]. The above research shows that the electroluminescence of solar cells has polarization characteristics, but it does not tell us which external objective factors are related to the polarization characteristics of electroluminescence, and it only aims at the research object of polysilicon solar cells, which have limitations. Based on this, this paper studies the polarization mechanism of electroluminescence and explores which external factors are related to its polarization characteristics. In this paper, the generation mechanism of electroluminescence polarization characteristics was investigated, taking the silicon solar panel as the object; the method for acquiring the polarized electroluminescence images was given by constructing a short wave infrared polarization imaging platform and conducting experiments. In addition, the effects of view angle and bias voltage on the polarization characteristics of silicon solar panel electroluminescence were analyzed.