esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 2 Evacuation Wayfinding Systems
### Static Wireless Sensor Network-Based Systems  
With the fast development of information and communication technology (ICT), as well as the advent of low-cost microelectronic devices, in the middle of the 1990s, research moved to the development of complex emergency cyber-physical-human systems to direct evacuees to exits with the aid of an on-site wireless sensor network (WSN), which itself may encounter communication uncertainties [15]. Until today, most of the state-of-the-art evacuation wayfinding systems and algorithms are still based on static WSNs. For instance, the research in [16] presented a static WSN-based distributed system to compute shortest safe paths for evacuees; this system employed a two-tiered architecture that contained a sensing sub-system and a decision sub-system. Since the full control of distributed information required maintaining consistency between the views related to different parts of the system [17], most proposals in the area of distributed wayfinding schemes assumed that decisions were made locally with the risk of inconsistent decisions. The study presented in [18] proposed a static WSN-based navigation system to direct evacuees with the variation of the temporally-ordered routing algorithm [19]; initially, exit sensors broadcast \"initial packets\" to assign each sensor with an \"altitude\" that was positively correlated with its distance to the nearest exit (sensors that were nearer to the exits possessed smaller altitudes than farther sensors); when a disaster occurs, the altitude of sensors inside a hazardous region would be increased and escape paths would be generated along sensors with higher altitudes to those with lower altitudes. The work in [20] utilised a self-organising WSN to guide a robot across a hazardous area; by using an \"artificial potential field\" [21], sensors could cooperatively generate a safe path without knowing the network topology.
# Wake-Up Receiver with Equal-Gain Antenna Diversity +
## 2 Background
### Equal-Gain Diversity  
In an equal-gain diversity system, the signals from the diversity branches are first co-phased and then summed at equal gain. An equal-gain diversity system provides a performance that is only marginally inferior to maximal-ratio combining systems and superior to selection diversity [14].
## 4 Discussion
### Adaptability Assessment Frameworks--Scope and Objectives
Multiple empirical studies addressed this aspect (e.g., [28]). However, only a few of those were quantitative [17]. The tools reviewed in Section 3.1 are developed to measure adaptability as a stand-alone objective. However, other building operational and design aspects also influence the decision-making processes and the overall adaptability performance. The findings and their implications should therefore be discussed in the broadest context possible. This calls for an integrated approach within wider spectrum frameworks, i.e., building circularity, where results can be interpreted from the perspective of the working hypotheses and the experiments of case-sensitive trade-offs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Tool** \\ **Designation** \\ \end{tabular} } & \multicolumn{3}{c}{**Domain**} & \multicolumn{3}{c}{**Indicators/Criteria**} & \multicolumn{3}{c}{**Methodology**} \\ \cline{2-9}  & **New Buildings** & **Existing Buildings** & **Typology** & **Qualitative** & **Quantitative** & **Mixed** & **Weighting System** & **Validation** \\ \hline ARP & & x & General & & x & Mathematical algorithm & Case studies \\ \hline ABD & x & & Office & & x & ROA and Monte & One case study \\  & & & commercial & & & carlo simulations & One case study \\ \hline IconCUR & & x & General & & x & Multi-Criteria & Case study \\  & & & & & x & Decision Analysis & Inter-model comparison \\ \hline AdappSTAR & x & & General & x & & Weighted checklist of & Case study \\  & & & & & & 26 design criteria & Inter-model comparison \\ \hline PAAM & & x & Office & & & Weighted checklist of & Illustrative \\  & & & commercial & x & & 12 indicators & case study \\ \hline FLEX 4.0 & x & & General & & & The sum of indicators & \\  & & & Office & x & & values after & \\  & & & School & & & & muliplying by & \\  & & & & & & assigned weights & \\ \hline SAGA & x & x & Residential & & x & & No final score & \\  & & & & & & Indicators are & Case study of \\  & & & & & & individually & 6 representative layouts \\  & & & & & & calculated using & \\  & & & & & & BIM software & \\ \hline ARAM & & x & Heritage & x & & & A sequence of & \\  & & & & & conditions needs to & \\  & & & & & & be met for a building & Case study \\  & & & & & & to be considered & \\  & & & & & suitable & model comparison \\  & & & & & for adaptation & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of existing adaptability models and tools (domain, indicator type and methodology). An emerging yet important issue is the visual simulation of building adaptation and the use of Building Information Modelling (BIM) technologies. Although it is still in an early stage (only the SAGA model 2019 established a BIM linkage), the development of data-driven quantitative modeling forms a potential future research direction for more efficient and multi-perspective assessments.
# Effects of Physical Exercise on Substance Use Disorder: A Comprehensive Review
###### Abstract  
Physical exercise has emerged as a promising complementary intervention for individuals with substance use disorders (SUD). This comprehensive review examines the neurobiological, psychological, and social benefits of exercise in improving quality of life (QOL), mental health, sleep quality, craving, physical fitness, and cognitive function among individuals with SUD. Aerobic exercises, particularly those of moderate intensity, demonstrate a consistent efficacy in reducing anxiety, depression, and cravings, while also enhancing cardiovascular health and psychosocial well-being. Strength training and concurrent programs provide additional benefits for muscular and cognitive function, although their effects on mental health are less consistent. Mind-body disciplines like yoga and Tai Chi offer accessible entry points for individuals with low baseline fitness but exhibit variable outcomes, especially in sleep and craving management. High-intensity interval training (HIIT) shows potential for craving reduction and cardiovascular improvements but may pose challenges for individuals with low initial fitness. This review underscores the importance of tailored, well-structured programs that align with participants' needs and capabilities. Future research should prioritize standardizing protocols, incorporating technological tools, and exploring hybrid intervention models to maximize adherence and therapeutic impact. Physical exercise remains a vital, multifaceted tool in comprehensive SUD rehabilitation strategies.  
substance addiction; physical exercise; FITT variables; quality of life; mental health; craving +
Footnote †: journal: Applied sciences
## 4 Life Cycle Analysis (LCA) Comparison between Batteries and PHES  
Life Cycle Analysis (LCA) stands as a comprehensive and efficient method utilized to assess the environmental repercussions of a product or process across its entire lifespan. This encompasses all stages from cradle to grave, encompassing the acquisition of raw materials, manufacturing, transportation, utilization, and eventual disposal. LCA's primary aim is to quantify and scrutinize the environmental impacts and benefits at each juncture, furnishing valuable insights for potential enhancements and directing decision making towards more sustainable practices. When employing LCA to juxtapose batteries and pumped hydroelectric storage (PHES), it entails a thorough examination of the environmental implications and sustainability considerations associated with each energy storage system. This entails evaluating the overall ecological footprint of each technology throughout their respective life cycles, encompassing processes from raw material extraction to manufacturing, energy consumption, operational efficiency, and end-of-life disposal. Such analysis offers invaluable guidance for optimizing environmental performance and fostering the adoption of more sustainable energy storage solutions.
# Future of IoT Networks: A Survey
## 4 Research Challenges of the IoT Network
### Discussions  
The current research on the IoT network can be summarized as follows: Service-oriented network based on CDN and P2P, Platform-centric heterogeneity support, SDN, Object-to-object security, Location- and proximity-based grouping, IoT gateway. The similarity among current IoT network research is that they assume the IP-based client-server model for communication. These studies are essential components to realize the IoT on the current network. However, the existing research direction has some limitations. Most devices are still based on IPv4, which has the issue of a limited number of available addresses. As a result, IPv6 takes the spotlight for next-generation network addressing, and many research and development effort are underway based on IPv6. However, IP-based networking is based on one-to-one communication, in which a device attempts to connect with nearby devices one by one when it wants to receive content. This retrieval procedure may require additional resources and time. Therefore, a service provider with plenty of content is required to reduce this effort. In this case, the users must send the queries to access a content-providing server, which may lead to an overhead problem for the content server. Moreover, the availability of the content is related on the status of a server that has the content, despite the fact that the user has legitimate authority. An ID-based networking concept such as NDN is necessary to solve these limitations and realize a veritable service-oriented network. It is a great challenge to institute this concept into the real network infrastructure. Furthermore, novel technologies such as SDN, content-based routing, context-based multi-connection, naming management, and gateway-based communication that are suitable for the IoT environment are required because satisfaction of ID-based networking does not imply that it can satisfy the IoT environment.
## 4 Simulation Verification  
In order to verify the effectiveness of the control strategy proposed in this paper, a hybrid AC-DC microgrid simulation model, as shown in Figure 1, is established in the Matlab/Simulink simulation platform. The simulation model contains DG1, DG2, and DG3 in the AC sub-microgrid and DG4, DG5, and DG6 in the DC sub-microgrid. The system simulation parameters are given in Table 2.
## 1 Introduction
The FE head model used in this study was previously developed at the Royal Institute of Technology (KTH) by [PERSON] [24] using LS-DYNA. It includes the scalp, skull, brain, meninges (i.e., dura mater and pia mater), intracranial membranes, cerebrospinal fluid (CSF), eleven pairs of the largest parasagittal bridging veins, and a simplified neck with the extension of the spinal cord. The brain elements were further grouped to represent the primary brain components, including the cerebral gray matter (GM), cerebral WM, corpus callosum, thalamus, brainstem, midbrain, cerebellar GM, cerebellar WM, ventricles. Responses of this model have shown a good correlation with experiments on brain-skull relative motion [28], intracranial pressure [29], skull fracture [30], and brain strain [31]. Conventionally, head impact kinematics are usually investigated with an isolated head in simulations to save computational time. This approach is also common in physical testing to reduce complexity. Although employing a full-body model can represent more realistic boundary conditions during impacts, leading to more reliable predictions compared to an isolated head model, its applicability is challenged due to the high computational costs involved [25]. In this study, a Hybrid III (HIII) 50 th percentile headform was also used in FE simulations and drop tests for validation. The effect of the protective properties, such as strain attenuation in brain tissue, kinematics alteration of the head gravity center, and energy absorption during impact, has not yet been evaluated for various EAFs. Therefore, the aims of the current study are (1) to establish FE models to describe the mechanical properties of four EAFs, (2) to develop and validate the FE models against drop tests, and (3) to compare the protective capability of different EAFs using kinematic-based criteria and tissue-based criteria in a pedestrian fall accident.
## 2 Approach of GPS/GALILEO RT PPP with Ionospheric Constraint
### Weight Factors Searching Algorithm with Moving-Window Average Filter
As the weight factor increases, the ionospheric constraints are weakened, and the post-fit weighted sum residuals of squares decreases gradually and then tends to be convergent. It was found that different positioning modes have the same characteristics, the post-fit weighted sum residuals of squares was close to stable when the distance between the point and the origin of the coordinate is the shortest. Therefore, to improve the efficiency of the searching algorithm, the Equation (12) can be replaced:  
\[D_{i}=\sqrt{i^{2}+\left(V_{i}^{T}R_{i}^{-1}V_{i}\right)^{2}}=\text{min} \tag{13}\]  
where subscript \"\(i\)\" denotes \(i-th\) search of searching algorithm, \(D_{i}\) is the distance from the \(i-th\) point to origin of the coordinate. If the distance \(D_{i+1}\) is greater than distance \(D_{i}\), the search will be stopping and the \(\lambda_{i+1}\) of \((i+1)-th\) searching will be taken as the optimal weight factor. Since the epoch-by-epoch determines the optimal weight factor, \(\lambda_{optimal}\) is not always available due to the limited number of the visible satellites and low redundancy of single-epoch data. To further enhance the reliability of the solutions, a moving-window average filter is applied to determine the smoothed optimal weight factor as follows:  
\[\lambda_{MW}(k)=\frac{1}{n}\sum_{i=k-n+1}^{k}\lambda(i) \tag{14}\]  
Figure 2: Relationships between the weight factors and the post-fit weighted sum-squared residuals for kinematic (**a**) and static (**b**). where \(n\) is the size of the smoothing window in average filter, \(\lambda_{MW}(k)\) is a smoothed optimal weight factor over \(n\) multiple epochs within a time window from epoch \((k-n+1)\) to epoch \(k\). A long window size would tend to blend these changes into the previous open sky conditions, while a short one would change quickly to the conditions but would be prone to larger errors [20]. Since the observation data of MGEX station is obtained under good operating conditions, a suitable window size is set as 10. The effectiveness of this window size will be verified through experiments later in the paper.
## 7 Defenses against Privacy Attacks
### Perturbing Gradients  
Perturbing gradients involves adding random noise to gradients during model training to protect sensitive data from unauthorized access [168; 169]. This technique disrupts gradient-based attacks used by adversaries to reverse-engineer models or extract confidential information [170; 171]. By obscuring the original gradient structure and details, perturbing gradients makes it challenging for attackers to infer sensitive data or manipulate model parameters, thereby enhancing security in scenarios where data privacy is paramount [172]. Figure 19 outlines the process block diagram of perturbing gradients.  
For example, [PERSON] et al. [173] introduced a privacy framework for over-the-air Federated Learning (OtA FL) that utilizes spatially correlated additive perturbations to enhance privacy during wireless transmission of model updates. These perturbations cancel out at the central server, ensuring strong \((\epsilon,\delta)\)-differential privacy against eavesdroppers without compromising the learning accuracy. The framework optimizes the perturbation  
Figure 19: Visual representation of perturbing gradients.  
Figure 18: Visual representation of split learning.  
parameters to balance privacy and model convergence, offering robust privacy guarantees supported by the theoretical analysis and simulations.  
Similarly, [PERSON] et al. [174] proposed a privacy framework to combat gradient leakage attacks in FL. Their approach assesses the information leakage risk across the model layers and applies local random perturbations to gradients before transmission to the server. This strategy complicates attackers' efforts to identify and exploit noisy gradients, bolstering privacy protection. Global gradient compensation techniques ensure model convergence, maintaining robust privacy while preserving the learning performance.  
In another approach, [PERSON] et al. [175] developed the Soteria framework to address the privacy risks posed by data representations learned during FL. By perturbing these representations before transmission, Soteria minimizes privacy leakage while preserving model utility. It strategically adds noise to degrade the quality of reconstructed data, ensuring analytical guarantees on privacy defense and algorithm convergence. The experimental results demonstrate Soteria's effectiveness in significantly improving privacy protection without sacrificing model accuracy.  
Additionally, [PERSON] et al. [176] proposed a privacy-preserving framework for FL that integrates a digestive neural network (DgstNN) to transform original data representations. This transformation maximizes the disparity between the gradients of original and transformed data, rendering gradients indecipherable to attackers while minimizing classification error. The framework optimizes privacy-utility trade-offs using predefined parameters, enhancing the model privacy against inference attacks compared to the traditional differentially private methods.
## 3 Results
### Cytotoxicity Study  
The effect of EFA and GSH on the viability of THP-1 cells as assessed by the MTS method is illustrated in Figure 3. An aqueous dispersion of pure EFA was prepared using 1% sodium CMC as a suspending agent. Using the data obtained from cell uptake studies, we tested the viability at a selected concentration of EFA (400 uM) to assess the cytotoxicity potential of the liposome formulation in comparison to the pure form of EFA.  
MTS results indicated that EFA formulated in liposomes significantly increased the cell viability up to 78% of untreated cells in comparison to only 5 % with pure EFA (Figure 3A,B).  
The potentiality of pure GSH, when incubated alone or co-administered with pure EFA in THP-1 cells, is depicted in Figure 3A,B. In the present work, it was observed that co-administration of GSH enhanced the viability of EFA by 4-fold despite that the viability with GSH alone was >70%. As portrayed in Figure 3C,D, the presence of GSH improved the viability of THP-1 cells in the presence of EFA to 21.9% from5.5% (about 4-fold). Further, delivery of liposome-encapsulated EFA + GSH signifies the influence of glutathione, as  
Figure 3: **(A)** Influence of EFA on viability of THP-1 monocyte/macrophage cells treated with pure EFA with and without GSH after 24 h incubation; **(B)** Influence of liposomal loaded EFA with and without GSH on viability of THP-1 monocyte/macrophage cells; **(C–F)** represent merged fluorescent images acquired at \(\times\) 40 original magnification. Control indicates untreated cells, whereas cells were treated with pure EFA without and with GSH **(C,D)**; liposomal EFA without and with GSH **(E,F)**. Nucleus is stained with DAPI (blue); R123-loaded EFA liposomes shown as green fluorescence (**** indicate \(p\) < 0.0001; **** indicate \(p\) < 0.001 and **** indicate \(p\) < 0.01).Values are means \(\pm\) S.D. of six independent experiments.  
the viability was 100% relative to control cells. MTS results clearly demonstrated that GSH improved the viability of THP-1 non-infected cells. Overall, MTS assay provides strong evidence that EFA is encapsulated into liposomes and, when co-delivered with GSH, significantly increases the cell viability by 15.6-fold and 20-fold, respectively, in comparison to pure EFA. Supportive to the colorimetric estimation by MTS assay, confocal images displayed an increase in the number of cells and increased uptake (overlay) of R123 labelled liposomes (Figure 3E,F), demonstrating the biocompatibility and non-toxicity of liposomes. This was further confirmed by the elimination of cytotoxicity of EFA at a concentration of 400 uM when encapsulated into liposomes.
# Double Unloading Gas Control Technology for Fracturing Soft Coal Seams in Overlying Key Strata
## 3 Analysis of Simulation Results
### Coal seam Permeability Change Rule  
#### 3.2.1 Change Rule of Key Strata Unloading Permeability  
After hydraulic fracturing of the key layer, the permeability of the coal seam undergoes significant changes, as depicted in Figure 7a,b. Analyzing the changes in coal seam permeability during the stress unloading process in the key layer reveals that hydraulic fracturing disrupts the stress transfer path of the overlying rock layer, thereby enhancing the permeability of the coal seam. The extent of stress unloading directly impacts the degree of permeability enhancement. For overlying rock stresses of 13 MPa, 10 MPa, 8 MPa, and 5 MPa, the corresponding permeabilities were 3.95 \(\times\) 10\({}^{-18}\) m\({}^{2}\), 1.56 \(\times\) 10\({}^{-17}\) m\({}^{2}\), 3.14 \(\times\) 10\({}^{-17}\) m\({}^{2}\), and 4.32 \(\times\) 10\({}^{-17}\) m\({}^{2}\), respectively. Notably, permeability exhibits a linear relationship with stress, represented by the equation y = \(-\)0.5087x + 6.9376. This implies that higher overlying rock stresses result in lower permeability and, consequently, reduced  
Figure 6: Energy change rule of coal seam before and after unloading of key strata. (**a**) Advanced energy nephogram of coal seam. (**b**) Energy nephogram after coal seam excavation. (**c**) Energy curve after coal seam excavation.  
gas extraction efficiency. Therefore, fracturing the key layer to relieve stress in the coal seam significantly enhances gas extraction effectiveness.