esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Design  
The inclusion criteria were people living independently; aged \(\geq\)50 years; being either at risk for cognitive impairment or worsening cognitive impairment or with MCI, which was measured with the Mini Mental State Examination (MMSE); and having both digital literacy and internet access. The exclusion criteria included taking drugs that could affect cognition within the previous three months; alcohol or substance abuse in the previous two years; and having any other condition that, in the opinion of the investigator, might have affected the study. All participants provided informed consent, the study was approved by the Ethics Committee of Fundacio Sant Joan de Deu, and all procedures were performed in accordance with the Declaration of Helsinki and its later amendments. The intervention plan took place through the deployment of a multicenter study with a total duration of 12 weeks (6 mandatory + 6 voluntary), with an expected total sample size of 20 professionals, 90 patients and 20 caregivers. The study consisted of the following two settings: a clinical setting and a home setting. While the clinical setting sessions included the activities conducted at the clinical institutions that were supervised by a therapist, the home sessions were individual sessions in which COGNIVITRA was used at home without supervision while doing the session (Figure 1). Within these studies, the usability and user experience (UX), workflow and impact, and overall effectiveness were evaluated. With respect to the usability and UX, the interaction between the users and the COGNIVITRA interface was analyzed, considering the parameters of the user interface, UX and overall usability of the system. In the case of the workflow and impact, tests of the utility and impact of the system in users' lives were carried out, focusing on the workflow of the professional when handling and supervising activities, as well as its impact on both patients and caregivers. Finally, overall effectiveness was used to analyze the global effectiveness of the COGNIVITRA program, which was centered on the impact of the use of the system at both the institutional and public health levels. The schedule is summarized in Table 1.  
Figure 1: Study flowchart.
## 2 Related Work
### Localization-Aware Routing Protocols
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Protocol** & **Features** & **Flaws/Deficiency** & **Advantages Achieved** \\ \hline \multirow{5}{*}{CARP [19]} & Distributed cross-layered routing protocol for multi-hop data delivery, Relay is selected on the basis of having a history of successful packet delivery to sink. & Use PING-PONG control packets for appropriate relay selection which is not efficient in relatively steady network. High mobility of sensor nodes will lead to accelerated hop-count growth. & High throughput, less energy consumption and reduced end-to-end delay. \\ \hline \multirow{5}{*}{E-CARP [20]} & Distributed cross-layered reactive routing protocol for relatively steady network topology. & Reduced throughput and show high path loss due to mobility of sensor nodes & Prolonged network lifetime and reduce energy consumption when sensory data size is very large compared to control packet. \\ \hline \multirow{5}{*}{HydroCast [15]} & Pressure based routing protocol for enhancing reliability and resolving void hole problem. & In HydroCast, the detour path may be invalid because of water current which increased its communication overhead as well as energy consumption. & High packet delivery ratio with limited co-channel interference. \\ \hline \multirow{2}{*}{FRR [23]} & Location-aware routing protocol for networks containing both static and mobile nodes. & High energy dissipation along with the delay because of RTS/CTS. & Reduced unnecessary flooding. \\ \hline \multirow{2}{*}{DR [25]} & Location-aware directional flooding based routing protocol with controlled flooding technique to increase reliability. & Due to constant transmission power, more energy is utilized because more energy is used from source to destination. In sparse networks, eligible forward cannot be found when void hole occur. & High reliability with less communication overhead. \\ \hline \multirow{2}{*}{VBF [27]} & A geographic VBF routing protocol with a position based routing approach. & The static communication range leads to higher packet drop and low performance of the network. Thus, VBF is effective in dense deployment. & Achieved robustness, energy efficiency, and high data delivery ratio. \\ \hline \multirow{2}{*}{HHVBF [28]} & A geographic VBF routing protocol with adaptive hop-by-hop location-based approach. & HH-VBF illustrate good behavior in even distribution network, however, when nodes deployment is uneven, the performance is greatly effected. & Improved the robustness of packet delivery in sparse networks with less energy consumption. \\ \hline \multirow{5}{*}{AHH-VBF [29]} & A geographic VBF scheme that changes the pipeline radius dynamically for adjusting the forwarding region. & In AHHVBF, increasing the radius of the pipeline does not resolve the void hole problem. & Reduced end-to-end delay, energy consumption and improved data delivery ratio. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Effects of the Weight and Balance of Head-Mounted Displays on Physical Load
## 5 Discussion
### Questionnaire Results  
#### 5.3.1 Physical Load  
The HMD fixation to the head and the sharpness of the graphics both received average scores of approximately 4 (slightly agree). Even though some significant differences  
Figure 12: Variation in fatigue due to weight and balance.  
were observed between certain conditions, most of the participants did not report feeling discomfort. However, in the \"front\" group, the HMD fixation and graphics sharpness scores decreased as the weight was increased. It is possible that reports of imbalance are the result of ill-fitting HMDs. Importantly, the HMD fixation and graphics sharpness of HMD did not negatively impact the following results during the short task performed in this study.  
However, some participants reported visual fatigue, and these reports were reasonably consistent across the various weight conditions and locations investigated in the experiment. The results show that, at least for short-term use, weight and balance do not affect visual fatigue. Reports of fatigue were more common among the elderly participants, which likely reflects their lack of familiarity with playing VR games. Similarly, elderly participants took longer to complete the game.  
Questionnaires Q4 and Q5 highlighted the tendency for fatigue to increase as weight was added. This observation is supported by many previous studies on head- and helmet-mounted displays [3; 4; 5; 6; 7; 8; 10; 13; 14; 15]. Evidently, reducing the weight of HMDs is beneficial for all situations.  
Nevertheless, our results indicate that fatigue can be reduced by including additional weight at the rear of the HMD where possible. Because the original COM coincides with the front of the HMD, adding weight to the rear of the HMD causes the COM to shift to the center of the head, thereby reducing the physical load. Interestingly, this reduced fatigue due to rear weighting was more common among female participants. One possible explanation is that female participants had less neck muscle mass on average than their male counterparts. This is supported by the results of the grip strength test, which we used as an indicator of neck muscle mass.
###### Abstract  
Due to the complexity of underground environmental conditions and operational incidents, advanced and accurate monitoring of the underground metro shield tunnel structures is crucial for maintenance and the prevention of mishaps. In the past few decades, numerous deep learning-based damage identification studies have been conducted on aboveground civil infrastructure. However, a few studies have been conducted for underground metro shield tunnels. This paper presents a deep learning-based damage identification study for underground metro shield tunnels. Based on previous experimental studies, a numerical model of a metro tunnel was utilized, and the vibration data obtained from the model under a moving load analysis was used for the evaluation. An existing deep auto-encoder (DAE) that can support deep neural networks was utilized to detect structural damage accurately by incorporating raw vibration signals. The dynamic analysis of a metro tunnel FEM model was conducted with different severity levels of the damage at different locations and elements on the structure. In addition, root mean square (RMS) was used to locate the damage at the different locations in the model. The results were compared under different schemes of white noise, varying levels of damage, and an intact state. To test the applicability of the proposed framework on a small dataset, the approach was also utilized to investigate the damage in a simply supported beam and compared with two deep learning-based methods (SVM and LSTM). The results show that the proposed DAE-based framework is feasible and efficient for the damage identification, damage size evaluation, and damage localization of the underground metro shield tunnel and a simply supported beam with comparison of two deep models.  
deep autoencoder (DAE); feature extraction; damage identification; moving load; structural health monitoring
# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
## 3 Model Design and Optimization
### Edge Attention Extraction Module
Consequently, it enhances the module's performance and practicality while maintaining the stability and reliability of its core functions. The engineering design of this structure provides crucial technical support for real-time applications and lightweight models. In the WETS-Net model, the first two layers typically output shallow features, primarily encompassing edge and texture features. These features aid in locating object boundaries and contours, providing fundamental cues to  
Figure 4: EAEM structure diagram. Figure 3: Edge-enhanced convolution. the image structure. Therefore, in this model's design process, EAEM is positioned at the output of the encoder's first two shallow feature layers. This placement allows for comprehensive extraction and enhancement of edge and texture features of weak targets, facilitating refinement of segmentation boundaries and details. Furthermore, it provides richer and more fine-grained low-scale information for subsequent feature fusion and segmentation tasks. Regarding the computational overhead of the EAEM module, its carefully designed architecture achieves lightweight computation. The module's efficiency can be analyzed as follows:  
1. Main Path: The 1 \(\times\) 1 convolutions applied in the first and third layers primarily serve for linear combination across channels. For RGB input images, the computational complexity is 3 \(\times\) 3 \(\times\) H \(\times\) W, which is relatively low. 2. EE-Conv Layer: As the core of the module, it employs a 3 \(\times\) 3 convolution kernel with a theoretical complexity of 3 \(\times\) 3 \(\times\) 3 \(\times\) H \(\times\) W. However, due to its optimized design, the actual computational cost is lower than traditional 3 \(\times\) 3 convolutions. This layer precisely extracts edge features while reducing computational complexity through sparse convolution or other efficient strategies. 3. Edge Attention Weight Matrix: The generation of the 1 \(\times\) H \(\times\) W edge attention weight matrix, used to enhance pixel importance in target edge regions, primarily involves convolution operations during its creation process. 4. Branch Path: The convolution operation between the weight matrix and the input image produces the edge attention feature map. Overall, the EAEM module optimizes computational costs in the 1 \(\times\) 1 convolutions and the EE-Conv layer, ensuring efficient edge feature extraction while maintaining a lightweight and real-time capable structure. This design makes it suitable for real-time applications and lightweight model requirements. The module's architecture balances the trade-off between computational efficiency and feature extraction effectiveness. By leveraging optimized convolution operations and a well-designed attention mechanism, EAEM achieves enhanced edge detection capabilities without significantly increasing computational overhead. This approach aligns with the growing demand for efficient, real-time capable models in various computer vision applications.
## 2 Materials and Methods: Ten-Koh Mission Description
### Challenges Faced during the Project
#### 2.7.3 Amount of Data Generated by the Mission Instruments  
Ten-Koh has been equipped with four SD cards of 2 GB each in order to provide on-board storage for the mission, ADS, and housekeeping data. The DLP instrument generates the largest amount of data per single measurement with 3000 bytes in the high-resolution mode, followed by the CPD with 2528 bytes (2000 from CMOS detectors and 528 from the Liulin detector). Table 4 shows the main mission instruments listed in descending order for the amount of data generated considering the different mission scenarios. Storing data on-board presents no difficulties due to the use of SD cards. However, the downlink of relatively large amounts of data can become problematic when using only the amateur UHF frequencies at the rate of 9.6 kbps supported by the used COTS transmitter. Thus, the experiment duration has to be tailored depending on team priorities and operational conditions. Increasing the number of downlink locations would improve the system data throughput at the expense of more energy needed per-orbit to operate the transmitter for longer. Ultimately, this approach was not adopted due to insufficient time to prepare additional ground stations before the launch. During the first three months of operations, maximum duration of 10 min was planned for each of the main missions. Depending on the real downlink capability, extending the duration for the main missions would be considered.
## 4 Discussion
### Opportunities and Challenges Related to the Use of Generative AI in Personalized Patient Care
#### 4.1.1 Opportunities  
1. Medical imaging: Gen AI models can improve the quality of medical imaging by generating high-resolution images, aiding in more accurate diagnoses and treatment planning;
2. Natural language processing: Gen AI models excel in natural language processing tasks, enabling the analysis of unstructured data such as clinical notes, research papers, and patient narratives for better understanding of patient conditions, reducing medical errors, and enhancing treatment outcomes;
3. Patient engagement and education: Gen AI models can generate personalized educational content for patients, providing them with relevant information about their conditions, treatment options, and preventive measures;
4. Remote monitoring and telemedicine: Gen AI can enhance remote monitoring solutions, enabling healthcare providers to remotely track and analyze patient data, leading to more proactive and personalized care;
5. Predictive analytics: Gen AI models can predict patient outcomes, helping healthcare providers identify high-risk patients and intervene early to prevent complications;
6. Reducing healthcare costs: By improving the efficiency and accuracy of diagnoses and treatment plans, Gen AI has the potential to reduce healthcare costs associated with unnecessary tests, treatments, and hospitalizations.
## 1 Introduction and Objectives
Laboratory experimentation coupled with geochemical modelling of rock-brine-CO\({}_{2}\) interactions is a suitable method to explore and understand the mechanisms and processes of CO\({}_{2}\) injection and storage [25]. In the present research, laboratory-scale experimental SC CO\({}_{2}\) injection is studied in carbonate rocks under conditions also applied by [26; 27; 28]. The rock type was selected due to its importance as potential CO\({}_{2}\) reservoir in Spain. The lithological characteristics and the structural position of the studied \"Marine Jurassic\" sequence provide favourable conditions for CO\({}_{2}\) storage [29]. The pressure (\(P\)) and temperature (\(T\)) conditions and the exposure time (\(t\)) were selected to model possible conditions in the near-well zone of a theoretical CO\({}_{2}\) injection well [7], more precisely, (i) at the CO\({}_{2}\)--brine interphase and (ii) in the dry zone where the CO\({}_{2}\) displaced the brine. The aim of the research was (i) to study the possible mineralogical-textural changes of the rock after the rock-brine-CO\({}_{2}\) and the rock-CO\({}_{2}\) interactions, respectively, by means of optical microscopy (OpM), scanning electron microscopy (SEM), and optical image analysis (OIA); and (ii) to study the chemical--compositional evolution of the brine and whole rock by comparative analyses before and after the experiment. Finally, mass balance calculation was compared with numerical geochemical computation in order to validate and interpret the laboratory results. This approach has been successfully applied in several studies (e.g., [11]). This study focuses exclusively on the textural-mineralogical changes produced by chemical interactions by X-ray fluorescence (XRF), disregarding physical alterations that may appear under dynamic conditions due to the flow of CO\({}_{2}\) and brine through the rock (e.g., particle entrainment, induced fracturing, etc. ).
# Extracting the Secrets of OpenSSL with RAMBleed
## 3 RAMBleed
### Dram  
DRAM holds one bit of information in a cell, composed of a capacitor and a transistor, as shown in Figure 0(a). The value of the bit is determined by whether the capacitor is charged, and access to the cell is controlled by the transistor. A cell that represents one and zero in the charged and uncharged states, respectively, is called a true cell. Conversely, a cell that represents zero and one when charged and uncharged is called an anti-cell. For simplicity, this study only considers true cells. Cells are arranged in the form of a matrix, with each row connected by word lines and each column by bit lines as shown in Figure 0(b). A fixed number of rows are collectively called a bank, and a DRAM chip is composed of multiple banks. Multiple DRAM chips are collectively referred to as a rank and are mounted on one side of a dual inline memory module (DIMM).  
The data in DRAM are handled in rows of 8 KiB. These data are accessed by increasing the voltage of the word line of the row in which they are located; a capacitor is connected to the bit line. This process, known as activating a row, transfers one line of data to the row buffer, as shown in Figure 0(b). Subsequently, the CPU accesses the target column from the data in the row buffer and reads/writes data. The charge stored in the DRAM cell capacitor is gradually lost over time. Thus, it is crucial to periodically recharge the capacitor to prevent data loss: this operation is termed a refresh. A refresh is performed by periodically activating each row.
## 3 Results
### Fatty Acid Composition  
The fatty acids profile varied significantly depending on the sample analysed: whole fish finger, meat, or breading. The level of saturated, monounsaturated, and polyunsaturated fatty acids in the fat fraction of both premium and budget breaded fish fingers was similar. The predominant fatty acid in the whole fish fingers samples was oleic acid, then linoleic acid, and linolenic acid (Table 2). Fat located in breading was frying fat. The predominant fatty acids in breading fat sample were oleic (62.04-63.14%), then linoleic (22.44-23.36%), and some linolenic and palmitic acids. Saturated fatty acids were less than 7%. The total trans fatty acids were at least below 1%. This reflected the composition of rapeseed oil as frying fat. However, omega-3 VLC PUFA levels were different. It was 3.56% vs. 1.26% (\(p\) = 0.0120) of total fatty acids in premium and budget fish fingers with breading, respectively. In fish finger meat (samples with breading removed) the predominant fatty acids in fat samples of premium fish fingers were EPA and DHA omega-3 VLC PUFA, whereas in budget samples it was oleic acid (C18:1) and linoleic acid (C18:2n-6). The omega-3 VLC PUFA content in the fat extracted from meat of premium and budget fish fingers was 43.08% vs. 9.47% (\(p\) = 0.0010), respectively. Figures 1 and 2 show examples of chromatograms of premium and budget fish finger meat samples. In the entire product, 100 g of premium and budget fish fingers with breading contained approximately 283.01 mg vs. 123.44 mg (\(p\) = 0.0011) of omega-3 VLC PUFA (Figure 3). In premium fish fingers, EPA, DPA, and DHA were 93.01 mg, 32.59 mg, and 157.41 mg per 100 g of product, respectively. In budget ones, it was 41.36 mg, 5.00 mg, and 77.08 mg, respectively, and these values were significantly different in comparison to premium fish fingers (\(p\) = 0.0021, \(p\) = 0.0018, \(p\) = 0.0012, respectively). Total omega-3 PUFAs, including ALA, in premium and budget fish fingers were 570.01 mg vs. 523.58 mg (\(p\) = 0.2992) per 100 mg of product, respectively, whereas the total omega-6 PUFAs were 1859.50 mg vs. 2019.12 mg (\(p\) = 0.2644), respectively. The ratio of polyunsaturated to saturated fatty acids PUFA/SFA (P/S) in premium and budget fish fingers was approximately 4.3 and 4.2 (\(p\) = 0.2111), respectively.