esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Coordinated Dispatching Model of IESs Considering Differences of Multiple Functional Areas
Thus, the objective function of coordinated dispatching of IESs is to minimize the total operating cost of the systems, and can be represented as  
\[\min\sum\limits_{s=1}^{S}p_{s}\sum\limits_{t=1}^{T}\sum\limits_{r=1}^{R}\sum \limits_{r=1}^{R}\left\{\sum\limits_{i=1}^{N_{\text{sub}}}[c_{i}^{\text{chp}}(P ^{\text{s}}_{\text{chp},r,i},H^{\text{s}}_{\text{chp},r,i})+\rho_{1}]\Big{[}P ^{\text{s}}_{\text{chp},r,i,t}-P^{\text{s}}_{\text{chp},r,i}]\Big{|}+\rho_{1} ^{\text{p}}P^{\text{s}}_{\text{buy},t}+\rho_{2}]P^{\text{s}}_{\text{buy},r,t}-P ^{\text{s}}_{\text{buy},r,t}]+\rho_{3}P^{\text{s}}_{\text{cut},r,t}] \tag{11}\]  
\[c_{i}^{\text{chp}}(P^{\text{s}}_{\text{chp},r,i},H^{\text{s}}_{\text{chp},r,i, t})= a_{0,r,i}+a_{1,r,i}P^{\text{s}}_{\text{chp},r,i,t}+a_{2,r,i}P^{\text{s}}_{ \text{chp},r,i,t}+a_{3,r,i}(P^{\text{s}}_{\text{chp},r,i,t})^{2} \tag{12}\] \[+a_{4,r,i}(P^{\text{s}}_{\text{chp},r,i,t})^{2}+a_{5,r,i}P^{\text{s}}_{ \text{chp},r,i,t}H^{\text{s}}_{\text{chp},r,i,t}\]  
\[P^{\text{s}}_{\text{cut},r,t}=P^{\text{s}}_{\text{PW},r,t,\max}+P^{\text{s}}_{ \text{PV},r,t,\max}-P^{\text{s}}_{\text{PW},r,t}-P^{\text{s}}_{\text{PV},r,t}- P^{\text{s}}_{\text{PV},r,t}-P^{\text{s}}_{\text{PV},t,t}-P^{\text{s}}_{\text{ PV}\text{H},r,t} \tag{13}\]  
The cost function of CHP units and the expression of abandoned WT and PV power could be described as (12) and (13), respectively. The constraints of the subsystems in the IESs are as follows:  
1. Constraints of electric systems Constraint of the electric power balance [32] can be represented as \[\sum\limits_{t=1}^{N_{\text{chp}}}P^{\text{s}}_{\text{chp},r,i,t}+P^{\text{s}} _{\text{buy},r,t}+P^{\text{s}}_{\text{PV},r,t}+P^{\text{s}}_{\text{PW},r,t}+ \sum\limits_{l=1}^{L_{r}}P^{\text{s}}_{\text{tie},l,t}=P^{\text{s}}_{\text{L}, r,t}+P^{\text{s}}_{\text{ch},r,t}+P^{\text{s}}_{\text{EC},r,t}\] (14) It shows that electric loads can be balanced between functional areas through tie lines and EVs. Constraints of the CHP unit can be represented as \[P^{\text{s}}_{\text{chp},r,i,t}=H^{\text{s}}_{\text{chp},r,i,t}/k_{\text{hp},r,i}\] (15) \[P^{\text{min}}_{\text{chp},r,i}\leq P^{\text{s}}_{\text{chp},r,i,t}\leq P^{ \max}_{\text{chp},r,i}\] (16) \[-U^{\max}_{r,i}\cdot\Delta t\leq P^{\text{s}}_{\text{chp},r,i,t+1}-P^{\text{ s}}_{\text{chp},r,i,t}\leq U^{\max}_{r,i}\cdot\Delta t\] (17)  
The thermoelectric constraint is described as (15), the electric output constraint is described as (16), and the climbing constraint is described as (17). Constraints of the purchasing power can be represented as  
\[P^{\text{s}}_{r,t}\geq 0 \tag{18}\]It means that electric power can be purchased but not sold.
## 2 Materials and Methods
### Study Selection Process  
The systematic literature review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure a transparent and methodical selection process. The study selection steps are summarized as follows:  
Initial Identification (N = 187):  
The initial search across databases (IEEE Xplore, ScienceDirect, Google Scholar) identified a total of 187 studies. For ScienceDirect, the search was conducted in three separate queries--(safety management) AND (Smart), (safety management) AND (AI), and (safety management) AND (Artificial Intelligence)--due to search limitations, and the results were integrated for analysis. The search was restricted to titles only, as keyword-based searches yielded an unmanageable number of results, making the process impractical within the study's timeframe. However, this approach excluded searches in the Web of Science database, as it did not return results under the title-only search constraint. These studies were retrieved based on their relevance to the research keywords ((safety management) AND ((Smart) OR (AI) OR (Artificial Intelligence))).  
Step 1: Duplicate Removal (Excluded: N = 37):  
Studies with identical or overlapping content were excluded. After this step, 146 unique studies remained for further screening.  
Step 2: Title and Abstract Screening (Excluded: N = 59):  
A comprehensive screening of titles and abstracts was conducted, excluding the following:  
* Review articles.
* Conference proceedings.  
This step ensured that only original peer-reviewed journal articles focusing on the application of AI and smart technologies in safety management were retained. A total of 87 studies passed this phase.  
Step 3: Full-Text Screening (Excluded: N = 21):  
The full texts of the 87 selected studies were reviewed. Studies were excluded if they exhibited the following:  
Full papers were inaccessible or unavailable.  
After this stage, 66 studies were finalized for inclusion in the systematic review.  
Final Inclusion (N = 66):  
The 65 studies selected through this rigorous process were included in the final analysis and categorization. These studies form the basis of the insights and findings presented in this review.  
This systematic review adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PRISMA flow diagram (Figure 1) summarizes the study identification, screening, eligibility, and inclusion process. This rigorous approach identified recurring research themes and methodologies, focusing on the integration of AI and smart technologies in safety management across diverse sectors. The findings are synthesized into detailed tables, providing a clear overview of how advanced technologies are transforming safety management practices. The final dataset includes 65 studies, categorized into eight industry sectors: construction, industrial safety, disaster and public safety, transport and logistics, energy and power, health, smart home and living, and other sectors.
# Dynamic Network Formation for FSO Satellite Communication
## 3 Minimum Spanning Tree on Dynamic Graphs (DMST)  
As explained above, an \(MST\) on a dynamic graph cannot be solved as different \(MST\)s on consecutive static graphs. Let us first define what a dynamic graph \(MST\) means and how to define it.  
We start with a discrete scenario, and then we generalize to a continuous scenario.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 3 Queueing Models
### ON/OFF Loss Model  
The ON/OFF models presented in the previous subsections do not consider the case of message losses (or message drops). All messages are either buffered before service or immediately served if the server is empty.  
In order to model message losses, we consider the case of the ON/OFF loss queue where messages still arrive in the system according to a Poisson process (Figure 2b). Regarding the server function, we consider the following: while the server is ON, messages enter the queue and wait to be served with rate \(\mu\). When the server \(\mu\) OFF, messages enter the queue, are served with rate \(\mu_{\text{loss}}>\mu\), and exit the system (i.e., messages are lost).  
The assumption that messages are \"served\" with rate \(\mu_{\text{loss}}>\mu\) was adopted because the ON/OFF loss queue is used for the performance analysis of an IoT mobile device that employs a middleware protocol. The latter introduces (message) losses since it builds atop UDP and does not install a logical sender/receiver session (i.e., there is QoS guarantee for messages). Regarding the value of \(\mu\), it can be based on the network transmission delay of the end-to-end interaction. Regarding \(\mu_{\text{loss}}\), we identify the network's point where message losses occur. As an example, if the ON/OFF loss queue is used to model the intermittent connectivity of a wireless receiver, then we parameterize \(\mu_{\text{loss}}\) via the corresponding transmission delay of the network. The definition of such a delay can  
Figure 2: Probabilistic and loss ON/OFF queues.  
be based on how the sender interacts with the access point that the receiver connects or disconnects.  
An ON/OFF loss queue \(q^{loss}_{\text{on/off}}\) can be defined according to the tuple:  
\[q^{loss}_{\text{on/off}}=(\lambda,\lambda_{\text{out}},\lambda_{\text{loss}}, \mu,\mu_{\text{loss}},T_{\text{ON}},T_{\text{OFF}}). \tag{7}\]  
where \(\lambda\) is the messages' arrival rate; \(\lambda_{\text{out}}\) and \(\mu\) are the messages' output and processing rates during ON periods, respectively; \(\lambda_{\text{loss}}\) and \(\mu_{\text{loss}}\) are the lost and processing rates during OFF periods. Both \(\lambda_{\text{out}}/\lambda_{\text{loss}}\) rates were exponential since new messages split according to the \(T_{\text{ON}}/T_{\text{OFF}}\) intervals.
# Research on the Reciprocating Friction and Installation Force of Seals in Deep-Sea Samplers
## 2 Methods
### Numerical Simulation
#### 2.1.2 Parameter Setting  
Considering the symmetry of the geometry, boundary conditions, and loads, a two-dimensional axisymmetric model is established in ABAQUS to improve computational efficiency. The stiffness of the piston and sleeve is approximately 10,000 times greater than that of the rubber; therefore, they were modeled as rigid bodies [25]. In order to enhance both the accuracy and efficiency of our calculations, we conducted a grid independence analysis. The mesh size was set to 0.025, resulting in a total of 16,732 mesh elements. Due to the incompressible nature of rubber, hybrid axisymmetric elements (CAX4 RH) were utilized in the model. Two contact pairs were established: one between the O-ring and its housing and the other between the O-ring and the sleeve. The normal contact behavior for these pairs was defined as hard contact, while a penalty friction formulation was used to describe the tangential behavior. Additionally, the piston was fully fixed during the calculations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material Type** & \(\mathbf{C_{10}}\) & \(\mathbf{C_{01}}\) & \(\mathbf{C_{20}}\) & \(\mathbf{C_{11}}\) & \(\mathbf{C_{02}}\) \\ \hline NBR (25 \({}^{\circ}\)C) & 1.5099 & \(-0.8510\) & 0.6193 & \(-1.0989\) & \(-0.2663\) \\ NBR (2 \({}^{\circ}\)C) & 5.8453 & \(-5.2729\) & \(-2.2978\) & 9.3654 & \(-10.8957\) \\ FKM (25 \({}^{\circ}\)C) & 4.9274 & \(-4.4537\) & \(-1.0643\) & 4.5278 & \(-6.3517\) \\ FKM (25 \({}^{\circ}\)C) & 5.4255 & \(-4.8963\) & \(-1.2351\) & 4.9735 & \(-6.4289\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Five-parameter Mooney–Rivlin constitutive model.  
Figure 3: Test of mechanical properties. (**a**) Test specimen. (**b**) Test instruments.  
The following three steps were set up to simulate the installation and reciprocating motion of the O-ring: (1) The sleeve moves in the negative direction along the \(y\)-axis at a velocity (\(v\)) until the O-ring is fully compressed (step time: 1 s). (2) Pressure penetration is applied to the two contact pairs (step time: 1 s) to create a pressure difference between both sides of the O-ring. (3) The sleeve continues to move for a specified duration before reversing its direction for an equivalent period, while the O-ring remains fully compressed throughout this process (step time: 2 s). The first step simulates the installation process of the O-ring, while the subsequent two steps simulate the reciprocating motion of the O-ring under a pressure of 30 MPa.
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 4 Experiment  
To evaluate the quantization method proposed in this paper, four popular CNN models for ImageNet classification are selected as benchmarks: ResNet-18 [33], ResNet-50 [33], DenseNet-121 [34], and Inception-v3 [35]. This paper tests on ImageNet ILSVRC-2012 [36] datasets. Their parameters are pre-trained on PyTorch [37]. The AGP in the Intel open-source Distiller [17] is used to prune the models. All the model pruning and quantization are implemented with CUDA 10.2 and PyTorch 1.4 frameworks for the Windows system on NVIDIA GeForce RTX 2080 Ti GPUs. The experiments in this paper will not quantize the first and last layer of the network because the first and last layer often requires higher accuracy [23].  
The models in the following experiments are all pruned models based on the AGP algorithm. Figure 8 is the pruning rate and sparsity curves of DenseNet-121 using the AGP algorithm. It is a pruner that controls the sparsity level growth by a mathematical formula and prunes the smallest magnitude weights to achieve a preset level of network sparsity [8]. Table 1 is the parameters used by the AGP algorithm, and Table 2 shows the top-1 and top-5 accuracies and sparsities of the models after pruning.  
* Top-1 accuracy denotes the accuracy at which the top-ranked category matches the actual results.
* Top-5 accuracy denotes the accuracy at which the top-5 categories match the actual results.
* Total sparsity denotes the proportion of non-zero elements.
# Role of Piezoelectric Elements in Finding the Mechanical Properties of Solid Industrial Materials
## Using Piezoelectricity to Calculate Solid-Material Properties
### Poisson's Ratio  
In most studies, \(\
u\) is associated to \(E\). Poisson's ratio is the ratio between a strain perpendicular to the load axis and a strain that occurs with the load axis. Authors in [95] utilized a PE with 0.5 MHz, and [96] also utilized it as an emitter and a sensor in the TOF method by measuring the longitudinal and transverse velocities of the wave to calculate \(\
u\) and several properties of materials for cement paste, mortar, and rock.  
Authors in [97; 98] succeeded in measuring \(\
u\), \(E\), and tensile strength for polysilicon by using PE as a motion generator to the load cell (4.5 N) and the linear air bearing. The advantage of using the air bearing is the absence of friction, which is shown in Figure 11. The motion of PE was controlled by a laboratory computer. This motion system was used to generate strain in a polysilicon specimen. The specimen strain was measured using an interferometric strain/displacement gage.  
To determine \(\
u\) and \(E\) for a thin film of fused silica and TiN/NbN, the authors in [99] developed the contact area technique using an acoustic microscope to measure surface elastic properties quantitatively. They needed a piezoelectric thin-film transducer in a pulse-echo acoustic microscope, which was used in this work, to generate an ultrasonic wave that is focused by a hemispherical sapphire lens. The focused wave passes through a coupling fluid, usually water, and interacts with the sample surface, producing bulk shear and longitudinal waves as well as a surface wave. These waves were used in the contact area technique to calculate \(\
u\) and \(E\).  
The flexibility to use a thin film of PE associated with the ability of generating deference types of waves, cannot be found in other smart materials except PE. The authors in [100] preferred to utilize piezo-spectroscopy to measure the stress and strain to calculate \(\
u\) in aluminum-alumina.  
The authors in [101] measured the \(\
u\) and \(E\) for a thin golden film using a micro tensile testing unit. This unit consisted of a piezoelectric actuator, as a load cell, a microscope, and two CCD cameras. After the results were obtained from the tensile test unit, the results were compared with those from the visual image tracing (VIT). The results were in good agreement.  
To evaluate the \(\
u\) and shear \(E\) for a concrete specimen, the authors in [102] used three pieces of PE (piezo-blender element), one as sender (500 kHz) and two pieces as receivers. The first receiver was in front of the actuator to detect the \(C_{L}\) and the other was beside it to detect the \(C_{S}\) as shown in Figure 12a,b. Then, the velocities of these two types of waves were calculated using B-scan images. [PERSON]'s ratio was calculated from the equation below:  
\[v\ =\ \frac{C_{L}^{2}\ -\ 2C_{s}^{2}}{2\big{(}C_{L}^{2}\ -\ C_{s}^{2}\big{)}}.\]  
Figure 11: The measurement system for Poisson’s ratio, modulus of elasticity, and tensile strength for polysilicon.
# ASO (Asthma-COPD Overlap) Is Independent from COPD, a Case in Favor: A Systematic Review
## 1 Introduction
### Safety Issues of ICS for Airway Infection of Patients with COPD  
Several lines of evidence have indicated a higher risk of pulmonary infection in COPD patients. A population-based, case-control study conducted in Spain including 859,033 inhabitants showed a strong relationship between COPD and community-acquired pneumonia, which was independent of other clinical factors (odds ratio (OR) 1.84 (95% confidence interval (CI), 1.32-2.59)) [32]. A prospective case-control study with 175,906 COPD subjects in Canada also demonstrated that current use of ICS further increased the risk of hospitalization for pneumonia (rate ratio (RR) 1.70 (95% CI, 1.63-1.77)) and pneumonia followed by death within 30 days (RR 1.53 (95% CI, 1.30-1.80)) [33]. Particularly, a subset of COPD treated with ICS who had both less than 100 cells/\(\mu\)L of blood eosinophils and chronic bronchial infection by potentially pathogenic microorganisms was at higher risk of pneumonia (OR 3.238 (95% CI, 1.426-7.231)) [34]. Moreover, the current use of ICS in subjects even without oral corticosteroid also increased the risk of tuberculosis (TB) in a low-prevalence country (RR 1.33 (95% CI, 1.04-1.71)) [35] as well as in an intermediate-burden setting (OR 1.20 (95% CI 1.08-1.34)) [36]. In addition, the increased risk of TB infection was significantly associated with higher doses of ICS [35; 36]. These studies highlighted the importance of the risk of pulmonary infection among COPD patients who are treated with high dose ICS and provided the clinically relevant question of which subset of COPD subjects would benefit from ICS therapy.
## 2 Related Work  
### Demand Side Management Techniques  
DSM can significantly decrease the peak electricity demand by controlling the operations of end user appliances, such as lighting, heating, and laundry devices, etc. [1; 7]. It makes the grid more sustainable by decreasing the peak to average ratio (PAR) and increasing the base load. DMS techniques can be categorized as direct and indirect load control methodologies [8]. In the direct load control method, the consumers have direct control over their selected loads and are also offered incentives by the utility companies. These reduced loads regulate frequency and minimize PAR, and OPEX in the grids. On the other hand, the indirect load control method employs advanced metering infrastructure to contribute to the optimization process. The information regarding dynamic energy pricing and load prediction is shared by the grid, hence providing distributed decision markers. The consumers also have access to this information and the option to optimize their benefits.  
An efficient DLC scheme for PAR reduction can be executed in a number of ways; the scheduling of direct load control is usually carried out to minimize operation costs and to reduce system peak load [9; 10]. Several optimization techniques can be employed to schedule the loads with targets of reducing costs. In order to limit data sharing by consumers, a multilayered iterative genetic algorithm (GA) method is used to obtain sub-optimal scheduling. A comprehensive review of central DSM is undertaken to examine frequency regulation in SG, where spinning reserve and area generation control are absent. Various solutions to this problem have been proposed, e.g., the introduction of highly self-beneficial strategies [11; 12] and cooperative methods [13; 14] based on gameteoretic framework where consumers can improve load scheduling by sharing their load profile with other agents and a non-cooperative game theoretic approach for energy storage scheduling among a community of prosumers results in good PAR reductions [15]. A GA-based method understands consumer behavior to find a way to increase retailer profit. In [16; 17], ways to lower electricity charges are explored, i.e., the charging and discharging of hybrid electric vehicles. In [15], consumers are given the autonomy to dynamically adjust their power consumption with the control centers and other consumers whereas in [18], the consumers can only interact with the control center. The energy pricing information based on customers' consumption needs and their share of energy requirements is provided by the control center.
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 5 Tensor-Based Approaches for Nonlinear Systems
### Block-Oriented Models  
Nonlinear input-output models constituted by a cascade of linear dynamic subsystems with memoryless (static) nonlinearities, also called block-oriented (or block-structured) nonlinear models, have been extensively studied by many authors during the last three decades. They play an important role in many fields of application owing to their low parametric complexity implying a low computational cost for system identification. Moreover, they often reflect the structure of physical systems. We review hereafter the three most common block-oriented models and their tensor representation. According to the Weierstrass theorem, it is assumed that nonlinear blocks are continuous and therefore can be represented with a polynomial of a given degree \(P\): \(c(x)=\sum\limits_{p=0}^{p}c_{p}x^{p}\).
# Tests of the Influence of DAF (Delayed Auditory Feedback) on Changes in Speech Signal Parameters
## 4 Result and Discussion
Figure 7: Average speech tempo for reading texts by study participants at specific delays in woman (w) and men (m). Importantly, the observed changes in speech tempo may affect text intelligibility, which is critical for effective communication. The results indicate partial adaptation by both women and men to the conditions of disrupted auditory feedback--particularly at the 360 ms delay--when reading texts with higher complexity levels. This adaptation highlights the potential for individuals to adjust to challenging speech conditions, though the process remains influenced by the complexity of the material being read.