esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The study included 86 patients with quantified mitral regurgitation evaluated through 3D ultrasound. A total of 53 patients had nonvalvular persistent AF without LV dysfunction or dilation, and 33 were normal controls [16]. The relationship between LA volume and MA size is likely bidirectional, as mitral valve dysfunction causes atrial stretch, with cellular and tissue alterations that can lead to fibrosis [17]. Atrial stretch leads to conduction slowing across the pulmonary veins-left atrial junction, predisposing to local reentry [18]. The effective refractory period of the left atrium is affected by heterogeneous tissue changes, which further increase the risk of atrial fibrillation. Subsequently, AF increases the LA size and mitral annulus diameter, and leads to MV dysfunction and regurgitation. All things considered, the main mechanism of mitral annular dilation remains to be left ventricular dilation. The mechanism of mitral annulus dilation, due to left atrial dilation, is primarily biomechanical; the mitral annulus is a narrow fibrous tissue that connects the atrioventricular junction to the fibrous trigone anteromerdially. Because it lacks its own contractile fibers, it is possible that it will alter its size in response to the numerous dilator and constrictor forces operating on it from both the atrium and the ventricle [19]. The constrictor forces include contraction of the supra-annular portion of the left atrium during atrial contraction, contraction of the base of the left ventricle during ventricular contraction, and papillary muscle forces acting centripetally when the mitral valve is closed, through chordae tendineae and mitral leaflets. The posterior annulus is vulnerable to traction. Enlargement of the left atrium exerts tension on the posterior mitral leaflet by displacing the posterior left atrial wall, altering the mitral annular planarity and interfering with normal mitral leaflet coaptation [20,21]. This finding is supported by a study conducted by [PERSON] et al., which suggests that isolated atrial fibrillation is generally not an etiology for mitral regurgitation, despite a few cases of such an association. There is a subset of patients with AF who develop MR without LV dysfunction; in this case, the pathology is termed atrial functional mitral regurgitation. The etiologies are considered to be left atrial and mitral annular dilation associated with reduced leaflet coaptation in the context of abnormal leaflet remodeling, derangement of the MA saddle shape, or atriogenic tethering of the posterior mitral leaflet [22, 23, 24, 25].
# Exploiting Outage and Error Probability
## 4 Simulation Results and Discussions
### Network Performance Parameters--Discussion  
#### 4.2.1 Network Lifetime  
Figure 8 shows network lifetime comparison of the proposed protocols with EEDBR and CARQ. Network lifetime of ACE, E-ACE and CARQ is less than EEDBR because they consume more energy in retransmissions in case of erroneous data reception. Difference in lifetime of EEDBR and ACE is greater, whereas, the difference in lifetime of ACE, E-ACE an CARQ is small. This is due to the fact that ACE and CARQ allows only two relays and hence, maximum two retransmissions can be done, whereas, E-ACE allows \(m\) relays to retransmit data packet. However, most of the time two retransmissions are enough to achieve acceptable BER at destination and in some cases (low SNR region, obstacle, etc.), there is requirement for three or more retransmissions. This also validates our assumption of limiting \(m\) to 3.  
Although, both CARQ and ACE are using two retransmission nodes, however, lifetime of ACE is less than CARQ because, ACE has more throughput.The reason behind is that ACE use MRC technique to combine data from direct transmission and relayed transmissions. MRC significantly reduces the BER introduced during individual transmissions. Signal combining technique is not incorporated in CARQ, therefore, data does not achieve acceptable BER. Hence packet is forcefully dropped at the same hop even after 2 retransmissions and protocol continues with the next node packet.
# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 3 Analysis and Procedure of the Development Model
### Analysis of the Development Model
The information is sequentially relayed throughout the network over time, with each prior input stored as memory, thus enhancing the processing of incoming data. (4) LSTM  
LSTM networks, a type of traditional RNN, are designed to address the gradient vanishing problem that occurs during the training processes, particularly when there is a significant time gap between the relevant information and its point of use in long-term trend data. This issue causes the gradient to diminish progressively during backpropagation, substantially reducing the network's learning capability. In contrast to that of the standard RNNs, the LSTM architecture incorporates a cell state along with the hidden state, which helps in preserving information over longer sequences. In this study, the LSTM model was employed to compare its predictive power against that of the newly developed power-function-based LSTM model. The standard LSTM model is structured as a relatively simple multi-layered neural network optimized for predicting short-term time steps (e.g., one hour ahead) using a single window. The predictive accuracy of the LSTM model varies with the time steps. Structurally, a linear transformation layer is inserted between the data input and prediction layers owing to limitations of the algorithm. While enabling fast computation, this layer reduces the model's predictive performance. The model relies primarily on the values and features of the immediately preceding time step (t = 0) to predict the next time step, often failing to capture long-term trends and characteristics effectively. (5) Proposed Power-Function-Based LSTM Model  
This study introduces a power-function-based LSTM model to address the challenges presented by the forget gate in the conventional LSTM models. The employed LSTM  
Figure 1: Basic recursive structure of recurrent neural network. model is an RNN that includes an LSTM hidden layer. When dealing with lengthy input streams without explicitly identified start and end points, the state of the LSTM can expand indefinitely, thus potentially destabilizing the network. To overcome this issue, we implemented a method of resetting the memory cell contents before initiating a new sequence. The use of an LSTM with a forget gate aims to prevent instability or interruptions during the learning process.
## 2 Prototype Bridge and the Scaled Model
### Scaled Model  
#### 2.2.1 Shake Table System  
This test is carried out on the shake tables system of Fuzhou University (China), shown in Figure 2. The shake tables system is capable of 3-freedom motions with maximum acceleration is up to 1.5 g. The effective operation frequency band is 0.1-50 Hz. The shake tables system consists of one middle tables and two side tables. The middle table is 4 m \(\times\) 4 m, while the side table is 2.5 m \(\times\) 2.5 m. The maximum payload of middle table and side table are 25 t and 10 t respectively. The two side tables are movable in longitudinal direction with the maximum length of 24 m. All three tables can work together or independently, which can simulate uniform excitation and non-uniform excitation separately. Other parameters of the shake tables of Fuzhou University are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Table NO.** & **Table Size** & **Self-Weight** & **Payload** & \begin{tabular}{c} **Degree of** \\ **Freedom** \\ \end{tabular} & \
\begin{tabular}{c} **Stroke** \\ **Length** \\ \end{tabular} & **Velocity** & **Acceleration** &
\begin{tabular}{c} **Operation** \\ **Frequency** \\ \end{tabular} \\ \hline
1\# & 4 m \(\times\) 4 m & 9.65 t & 22 t & 3 & \(\pm\)250 mm & 75 cm/s & 1.5 g(Ux)/1.2 g(Uy) & 0.1-50 Hz \\
2\# (3\#) & 2.5 \(\times\) 2.5 m & 3.0 t & 10 t & 3 & \(\pm\)250 mm & 150 cm/s & 1.5 g(Ux)/1.2 g(Uy) & 0.1-50 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance specifications of the shake tables system.  
Figure 1: (**a**) Elevation of the CSB; (**b**) the cross section of girder; (**c**) the main tower.  
Figure 2: Shake tables system of Fuzhou University.
## 1 Introduction  
The construction industry has a relatively low digitisation level compared to other sectors [1, 2]. Although it is seen as a major factor in the innovation of the Architecture, Engineering and Construction and Operations (AECO) sector, digitisation in the construction industry still shows a slow growth rate [3]. However, improvements in methodologies and technologies are under development to better manage AECO processes [4].  
This article presents a literature review on the integration of Building Information Modelling (BIM) and Internet of Things (IoT) for the Facility Management (FM) of the constructed asset. It is divided into four main parts: (a) an overview of FM and the impact of digitisation in the sector; (b) the description of the research method; (c) an in-depth content analysis of 99 selected journals' articles on BIM-IOT integration for FM, which allows identifying both benefits/opportunities and issues/limits at technical and operational levels; (d) conclusions and a description of a possible future research agenda.  
The context is the fourth industrial revolution (Industry 4.0), where several technological changes in many sectors have been made, including in the AECO one [5, 6].  
There are many studies on the application of digital technologies aiming to promote digitisation in the built environment. However, compared to the design and construction stages, there is a lack of research on applying these new technologies in the operation and use stage of the building life cycle [7], particularly for the FM sector. FM represents up to 85% of the whole life cycle cost of the building [8]. Even though the life cycle cost of a building can and should be controlled in the design phase the adoption of innovative tools and technologies to improve FM in existing buildings is continually increasing. [PERSON] et al. [7] identified and discussed several possibilities for future research into digital technologies like integrating FM with BIM, reality capture technology, IoT, Radio Frequency Identification (RFID), and Geographic Information System.  
Among several studies on applying new technologies, a significant solution taken into consideration in the last years by the AECO sector has emerged: the Cyber-Physical Systems (CPS) [6]. CPS, also known as Digital Twins (DT), are systems based on the combination of physical and digital objects. Through simulation of an as-built component (or system), using digital models and several types of data, DT allows mirroring the life of its corresponding real twin to forecast the health of building components, their service life, faults [9] and, in general, the building performances [10].  
Even if not risk-free, these digital innovations will enable new dynamics and allow new services that will improve efficiency and sustainability in building management processes [11].
## 6 Multi-Slab Laser Amplifier
### Numerical Modelling of Laser Amplifiers
The slab was placed in the eight-slab amplifier head, and the cooling was provided by forced-flow gaseous helium cooled down to 160 K. The velocity of the helium in the channels between two neighboring slabs was up to 30 m\(\cdot\)s\({}^{-1}\). The typical value of the heat transfer coefficient from the gas to the slab surface was evaluated as 1600 W\(\cdot\)m\({}^{-2}\).K\({}^{-1}\). The heat load and temperature distributions calculated from the energetic calculations are shown in Figure 17. Note that the temperature distribution is no longer left-right symmetrical, because the cooling provided by the helium flow is from left to right. The wavefront deformation expressed by the optical path difference (OPD) and the laser beam depolarization, which quantifies the ratio of the laser power transferred from x-oriented linear polarization to the perpendicular polarization direction, are shown in Figure 18.  
Figure 17: Calculated **(a)** heat load and **(b)** temperature on the central cut plane. The squares marked by the black line show the border between Yb:YAG and Cr:YAG and the pumped and unpumped regions. Figure 18: Calculations of the **(a)** optical path difference and **(b)** depolarization of a multi-pass multi-slab amplifier. It should be noted that OPD and depolarization have been evaluated after 64 passes (four passes through two amplifier heads, eight slabs each). This represents the worst case, and the resulting OPD peak-to-valley (P-V) was found to be 8.65 waves at 1030 nm, while the average depolarization ratio was equal to 1.1%. The calculations show that the depolarization losses are relatively low, and the OPD could be compensated by a deformable mirror. In the optimized case, the ratio between the active part and absorber was slightly changed. The best results were obtained for a size of Yb:YAG of 50 mm and of Cr:YAG of 17.5 mm. The overall size of the slab was kept unchanged. The OPD P-V dropped to 6.65 waves at 1030 nm. Because the beam is imaged between the amplifier heads, it is also rotated between them. If this rotation is considered during OPD calculations, the OPD further drops to 3.16 waves (Figure 19). Note that the residual depolarization is not affected by the optimization of the slab geometry or the beam rotation between amplifier heads and remains close to 1%. The total OPD that includes beam rotation between amplifiers, aberrations from optical elements, including the Seidel aberrations and manufacturing defects, the output beam shape and wavefront correction can be found in [61].
### Theoretical Framework
#### 3.3.2 The Impact of Homophily on User Thresholds  
In real-world social settings, the decision as to whether a user accepts or propagates information is influenced by a variety of factors. One crucial factor is homophily, which refers to the tendency for individuals with similar characteristics, such as behaviors, interests, or beliefs, to form connections. Homophily significantly shapes group behavior and influences how information spreads within social networks. People are naturally inclined to connect with others who resemble them, and as a result, the more similar individuals are, the more likely they are to influence each other's decisions. To account for this phenomenon, a homophily coefficient is introduced into the model to adjust the activation threshold of each node. The activation threshold reflects the minimum influence required for a user to adopt and share a piece of information. By incorporating homophily, the threshold is lowered when there is a high degree of similarity between a node and its neighbors, making it easier for the node to be influenced and activated.  
To better represent the dynamics of user behavior in the network, nodes are divided into the following two categories: activated (those who have adopted the information) and non-activated (those who have not). A higher proportion of a node's neighbors sharing the same activated or non-activated state signals stronger similarity or homophily within that local network. In this context, the similarity between nodes is used as a proxy for homophily, and this similarity is factored into the calculation of each node's activation threshold. Nodes with higher similarity to their neighbors are more likely to be activated, as the threshold is adjusted to reflect the greater likelihood of influence in such scenarios. The degree of homophily is quantified by examining the proportion of edges in the network that connect nodes sharing the same activation state. Let \(p\) represent the probability that a node is activated and \(q\) represent the probability that it is not, where \(q=1-p\). The probability that two connected nodes share the same state is \(p^{2}+q^{2}\), which measures the similarity in the network.  
To calculate homophily more formally, Let \(S\) represent the number of edges that connect two nodes in the same state (both activated or both non-activated) and \(E\) represent the total number of edges in the network. An \(S/E\) ratio exceeding \(p^{2}+q^{2}\) indicates the presence of homophily in the network, meaning that users are more likely to be connected to others who are in the same state, whether activated or non-activated.
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 3 Explanation Methods for Deep Learning Cancer Detection Models
### Post-Hoc
#### 3.1.4 Prostate Cancer  
Deep learning has also shown great success for prostate cancer [114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126]. The authors of [127] implement a convolution feature extractor followed by a SVM classifier for the task of detecting prostate cancer. They report an AUC of 0.86 for T2W images and 0.93 for ADC images. After prediction the authors generate CAM heatmaps for the MRI images to show localization of prostate lesions.  
The authors of [32] demonstrated the ability to localize prostate lesions in T2W transverse images using Grad-CAM. The authors used MRI images for classification as well as tabular data such as weight, height, and age from ProstateX dataset. Deep learning techniques were used for classification. After classification GradCAM, Saliency Maps, LIME and Deep-SHAP were used to provide post-hoc explanations. They reports results  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Results** & **Perturbation or Gradient** & **Local or Global** & **Model-Agnostic or Model-Specific** \\ \hline SHAP & Perturbation & Both & Model-Agnostic \\ \hline CAM & Gradient & Local & Model-Specific \\ \hline PG-CAM & Gradient & Local & Model-Specific \\ \hline Occlusion & Perturbation & Local & Model-Specific \\ \hline Saliency Map & Gradient & Local & Model-Agnostic \\ \hline Integrated Gradients & Gradient & Local & Model-Agnostic \\ \hline \hline \end{tabular}
\end{table}
Table 1: Post-Hoc explanation techniques for cancer classification.  
of an average error of 6.93 pixels. The authors also made the qualitative observation that different explanation methods highlight different aspects of the resulting classification.
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
## 2 Materials and Methods
### Experimentation  
#### 2.2.1 MATB Task  
The Multi-Attribute Task Battery (MATB) was adopted as the experimental task for this research [19]. Three components of the MATB were utilized: the Tracking Task (TRA), System Monitoring Task (SYS), and Resource Management Task (RES) [20], as shown in Figure 1. Table 1 describes the operation steps of each subtask [21].  
In this research, each subtask set the trigger number to 2, 12, and 36 at low mental workload (LMW), medium mental workload (MMW), and high mental workload (HMW), respectively, every 12 min. The MATB tasks with LMW, MMW, and HMW were separately carried out in the order designed by the Latin square method at each noise exposure. The subtask performance of accuracy and response time were automatically recorded by the computer. The ratio of accuracy to average response time (Equation (1)) was used to evaluate the weighted MATB task performance for each subject.  
\[\textit{Weighted task performance}=\textit{Average}(\frac{ACC_{i}}{RT_{i}}),\ i=1,2,3 \tag{1}\]  
where _Weighted task performance_ is the ratio of accuracy to the average response time of the MATB task; \(ACC_{i}\) and \(RT_{i}\) are the accuracy and response times of subtask \(i\) for each subject, respectively; and the corresponding subtasks are SYS, TRA, and RES with subscript \(i\) as 1, 2, and 3, respectively.
## 3 Results and Discussion  
### Beam Based on the Menger Sponge  
In order to evaluate the fractal generation and static analysis of beams, a prismatic solid beam was first studied. Next, the box-counting algorithm was also used to estimate the fractal dimension of the set of beams based on the Menger sponge. The estimation was carried out for iterations \(N=1\), 2 and 3 (Table 1). The original solid beam was represented for iteration \(N=0\). As expected, the fractal dimension of this solid beam was equal to 3, which is in agreement with the topological dimension of a 3D volume.  
According to Table 1, the greater the iteration, the closer the estimation to the fractal dimension of the Menger sponge (\(F_{D}=2.727\)). Note that the analytical value cannot be exactly estimated for two reasons. First, the largest iteration reached is \(N=3\), which is far from the theoretical infinite iteration which fits with Equation (1). Second, the beam has a prismatic shape and not a cubic geometry, as in the case of the Menger sponge. Therefore, the obtained fractal dimension is accepted as a proper approximation. This also makes it possible to validate the box-counting algorithm used in this work.  
Table 1 shows the mechanical resistance parameters obtained from the static simulation. The maximum displacement of the beam (\(Disp_{max}\)) increased with increasing number of iterations. This was expected since the beam was being lightened as the construction of the fractal geometry of the Menger sponge progressed. In order to link the results of the static analysis with the fractal geometry of the beam, fractal dimension versus maximum displacement is depicted in Figure 7. A decreasing relationship between both variables was found, which fit fairly well to a linear fit (\(R^{2}>0.95\)).