esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Accident Type (The Number of Fatal Incidents)** & \
\begin{tabular}{c} **Electrical** \\ **Shock** \\ \end{tabular} & **Slip** & **Fall** & **Hit** & **Collision** & **Jammed** &
\begin{tabular}{c} **Chemical Spill** \\ **or Contact** \\ \end{tabular} & **Fire** & **Bumped** & **Drowned** \\ \hline Interior construction work & - & 2 & 23 & 3 & 1 & 2 & - & 1 & - & - \\ Earth work & 5 & 8 & 48 & 45 & 85 & 29 & 1 & - & 50 & 1 \\ Paster, waterproof, and masonry construction work & 6 & 9 & 237 & 10 & 7 & 5 & 3 & 7 & 3 & 2 \\ Stone work & - & - & 33 & 13 & 6 & 2 & - & - & 7 & - \\ Painting work & 7 & 3 & 188 & 2 & 1 & 7 & 1 & 1 & 2 & 4 \\ Scaffold and demolition work & 14 & 12 & 357 & 55 & 64 & 22 & 1 & 4 & 14 & 5 \\ Metal structure and window construction work & 6 & 3 & 173 & 9 & 4 & 3 & - & 2 & 1 & - \\ Roofing, sheet metal, and fabrication construction work & 5 & 4 & 335 & 7 & 5 & 2 & - & 2 & - & - \\ Reinforced concrete construction work & 14 & 20 & 408 & 62 & 82 & 20 & 7 & 2 & 27 & 9 \\ Mechanical construction work & 18 & 3 & 162 & 13 & 5 & 28 & 16 & 22 & 8 & 5 \\ Water and sewage work & 2 & 2 & 10 & 19 & 68 & 9 & 4 & 5 & 15 & 10 \\ Boring and growing work & 1 & - & 1 & - & 1 & - & - & - & 1 \\ Railway and track work & - & - & 4 & - & 1 & - & - & 11 & 7 \\ Road pavement work & 2 & 5 & 9 & 8 & 2 & 11 & - & - & 11 & - \\ Subaqueous work & - & 1 & 1 & 2 & 1 & 3 & - & - & 1 & 13 \\ Landscapes problem & 2 & 3 & 21 & 7 & 1 & 1 & - & - & 10 & - \\ Landscape architectural facilities setting work & - & 2 & 2 & 1 & 1 & - & - & 1 & - \\ Structural leaf work & 2 & 5 & 259 & 28 & 18 & 6 & - & - & 6 & - \\ Cubeubey installation work & - & - & - & 1 & - & - & - & - & - & - \\ Dreging work & 1 & - & 2 & 1 & 3 & - & - & - & 1 & 4 \\ Elevator and escalator installation work & - & - & 26 & 4 & - & - & - & 1 & - & - \\ Gas facility construction work & - & - & 10 & 1 & 3 & 1 & 3 & 5 & 3 & 1 \\ Heating system construction work & 1 & 1 & 5 & 1 & - & 2 & - & - & 1 & - \\ Facilities maintenance work & 1 & - & 20 & 2 & 4 & 1 & - & 1 & - & 1 \\ Electrical installation work & 99 & 6 & 128 & 6 & 6 & 8 & 2 & 3 & 13 & - \\ Information and communication construction work & 11 & 1 & 50 & 5 & 2 & 1 & 2 & - & 5 & 1 \\ Firefighting system installation work & 1 & - & 3 & - & 1 & 1 & - & - & - & - \\ \hline Sum & 199 & 90 & 2515 & 303 & 372 & 164 & 41 & 56 & 240 & 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Analyzed accident type for 27 work types in consideration of frequency of fatal incident.
# Scour Detection with Monitoring Methods and Machine Learning Algorithms--A Critical Review
## 2 Conventional Monitoring-Based and Machine Learning-Based Methods to Identify Scour
The following sections explain the methods, properties, and main outcomes of conventional monitoring-based and machine learning-based studies. For conventional monitoring-based studies, the databases searched were Google Scholar and Scopus and a total of 168 studies were screened. After applying the exclusion criteria, 22 studies remained. For machine learning-based studies, the databases searched were Web of Science and Scopus. A total of 38 elements were screened. Applying the exclusion criteria, this resulted in 14 studies. The eligibility criteria applied were:  
* Academic papers published in the recent years;
* Written in English;
* Aiming to detect bridge scour, not other types of damage;
* Scour detection methods were monitoring or ML-based.
## 2 Materials and Methods
### Study Area  
The study area is located in the north subarctic region of the Seward Peninsula, Council, Alaska (64\({}^{\circ}\) 50.611 N, 163\({}^{\circ}\) 42.709 W), as shown in Figure 1. The altitude is approximately 30 m, and the annual mean temperature and precipitation are \(-\)3.1 \({}^{\circ}\)C \(\pm\) 14 \({}^{\circ}\)C and 258 mm, respectively [48]. According to the literature [48], the soil property of the site was mostly acidic (i.e., the soil pH ranges from 3.90 to 5.02) and most of the soils are fully saturated (i.e., soil moisture content of 100%). The average total carbon and total nitrogen contents of the soil within a depth of 30 cm are 40% and 1.5%, respectively. Ammonium cation (NH4+) concentrations were measured to be much higher than those of nitrate (NO3) at every measured point and depth. The concentration of ammonium (NH4+) ranges from 8.6 \(\upmu\)g to 93.1 \(\upmu\)g per unit soil mass (1 g), while that of NO3 is negligibly small [49].  
Figure 1: Measurement site in Council, the Seward Peninsula, Alaska.  
From the site investigation of this study, soil colors are grayish brown, black, and reddish brown. Sieve analysis results showed that the fraction ranges of clays, salts, and sands are 6.4%-20.3%, 55.1%-77.3%, and 15.2%-38.6%, respectively, depending on the sampling depth and location. It was noted that the fraction of silt is dominant.  
Within the test site in Figure 2, automatic remote sensors of soil temperature measurements were installed at three sites (A, B, and C). The distance between the sites A and B was approximately 70 m. The soil temperatures at each site were measured at different depths of 5, 10, 15, 20, and 30 cm. The soil temperature estimation model was developed based on the data from the sites A and B, while the soil depth profile at the site C was used to validate the developed model. Each point consisted of battery-powered sensor nodes and a flash memory-installed sensor interface circuit (sink node) to remotely store and transmit sensor read data to a receiver (Campbell Scientific 107 Temperature Probe). The air temperature was also measured above 1.2 m from the ground surface within the test sites (the air temperature measurement location is marked \"X\" in red in Figure 2).
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 4 Design of the PAUM System
### User Utility Maximization  
This section delineates user utility and develops an optimization framework to optimally allocate resources based on the desired user utility. We want to allocate bandwidth resources to the UEs so that the user data rate is maximized, the interference is minimized, and the high-priority users obtain access to good quality resources.  
Thus, the Utility function \(U_{z}\) for an _uplink user_, \(z\in Z\) is defined as:  
\[U_{z}=\frac{r_{z}}{P_{z}XI_{z}};\qquad\forall z\in Z \tag{4}\]  
and the optimization framework is formulated as:  
\[Maximize:f=\sum_{\forall t\in T}\sum_{\forall z\in Z}\sum_{\forall c\in C}i_{ z,t}\times U_{z} \tag{5}\]  
Subject to:  
\[i_{z,t}^{c}\in\{0,1\};\qquad\forall t\in T,\quad\forall z\in Z,\quad\forall c\in C \tag{6}\]  
\[r_{z,t}^{c}\leq r_{z,max};\qquad\forall t\in T,\quad\forall z\in Z,\quad \forall c\in C \tag{7}\]  
\[\sum_{t\in T}k_{z,t}^{c}=1;\qquad\forall z\in Z,\quad N<T,\quad\forall c\in C \tag{8}\]  
\[\sum_{t\in T}k_{z,t}^{c}\leq 1;\qquad\forall z\in Z,\quad N>T,\quad\forall c\in C \tag{9}\]  
\[\rho_{z,t}^{c}\leq p_{max};\qquad\forall t\in T,\quad\forall z\in Z,\quad \forall c\in C \tag{10}\]  
\[h_{z,t}^{c}\geq\ h_{min};\qquad\forall t\in T,\quad\forall z\in Z,\quad \forall c\in C \tag{11}\]  
Here, the objective function in Equation (5) is formulated as an MNILP (Multiobject Non-Integer Linear Programming) to be solved by the eNB. Maximizing utility with an allocated channel and its time slot for all uplink users resulted in an optimum solution with some effective constraints.  
Allocation ConstraintThe binary variable \(\hat{r}_{z,t}^{c}\), in Equation (6) contains 1 if an uplink user \(z\in Z\) is allocated a time slot \(t\in T\) of channel \(c\in C\), and 0 otherwise.  
Budget ConstraintThe measurement variable \(\hat{r}_{z,t}^{c}\) in Equation (7) enumerates how much data of a UE is possible to be transmitted through a particular channel \(c\in C\) with an allocated time slot.  
Time Slot Allocation ConstraintThe constraint in Equations (8) and (9) creates a restriction that exactly one time slot in a time frame can be allocated to an uplink user \(z\) if the number of users is less than the available time slots; otherwise, the allocation of a time slot to an uplink user \(z\) is not guaranteed but is rather opportunistic following its priority.  
Interference ConstraintConstraints in Equations (10) and (11) mitigate interference issues by keeping power within a certain range while choosing a channel that offers gain above a certain minimum threshold. In the PAUM system, the utility of a patient emphasized the QoS of a successful and reliable smart healthcare system. The maximization of utility for any uplink user upgrades the system's access reliability; under the time slot assignment, its higher priority and its higher data rate and event-influenced delay is achieved.
# Contract-Based Incentive Mechanism for Mobile Crowdsourcing Networks
## 3 Contract-Based Crowdsourcing Incentive Mechanism  
As a result of information asymmetry, the SP may not obtain the MUs' exact crowdsourcing efforts after contracting between the SP and the MUs. Therefore, the designed contract should ensure  
Figure 2: Contract-based incentive mechanism for mobile crowdsourcing.  
that each MU selects the optimal effort \(e_{i}^{*}\) to maximize its own utility. Then, the following incentive compatibility (IC) constraint should be satisfied:  
\[\left(IC\right)\quad\max_{\epsilon_{i}\geq 0}\quad E\left[u\left(W_{M_{i}} \right)\right] \tag{12}\]  
We let \(f_{M_{i}}=\alpha_{i}+\beta_{i}\theta_{i}e_{i}-\frac{c_{i}}{2}e_{i}^{2}-\frac{ \eta_{M}}{2}\beta_{i}^{2}\sigma^{2}\); then, the \(i\)th MU's expected utility \(u\left(W_{M_{i}}\right)\) in Equation (7) can be rewritten as  
\[E\left[u\left(W_{M_{i}}\right)\right]=-e^{-\eta_{M}f_{M_{i}}} \tag{13}\]  
Because \(\frac{\partial E\left[u\left(W_{M_{i}}\right)\right]}{\partial f_{M_{i}}}= \eta_{M}e^{-\eta_{M}f_{M_{i}}}>0\), the IC constraint in Equation (12) can be simplified as  
\[\left(IC\right)\quad\max_{\epsilon_{i}\geq 0}\quad f_{M_{i}}=\alpha_{i}+\beta_{ i}\theta_{i}e_{i}-\frac{c_{i}}{2}e_{i}^{2}-\frac{\eta_{M}}{2}\beta_{i}^{2} \sigma^{2} \tag{14}\]  
Then, in order to ensure that the utility each MU has received is no lower than its _retained utility_\(\bar{U}\), the following individually rational (IR) constraint should be satisfied:  
\[\left(IR\right)\quad\alpha_{i}+\beta_{i}\theta_{i}e_{i}-\frac{c_{i}}{2}e_{i}^{ 2}-\frac{\eta_{M}}{2}\beta_{i}^{2}\sigma^{2}\geq\bar{U},\quad 1\leq i\leq N \tag{15}\]  
Thus, on the basis of the above IC and IR constraints, the optimal contract is designed to achieve the maximum expected utility of the SP, which can be written as  
\[\begin{array}{rl}\max_{\{\{\alpha_{i},\beta_{i}\}\geq 0\}}&E\left[u\left(W_{S} \right)\right],\\ s.t.&\left(IC\right)\quad\max_{\epsilon_{i}\geq 0}\quad\alpha_{i}+\beta_{ i}\theta_{i}e_{i}-\frac{c_{i}}{2}e_{i}^{2}-\frac{\eta_{M}}{2}\beta_{i}^{2} \sigma^  
We note that the SP's optimization problem with \(2N\) variables \((\alpha_{i},\beta_{i})\) in Equation (19) is simplified to the variables \(\beta_{i}\) in Equation (20). Any local optimal solution (denoted as \(\hat{\beta}_{i}\)) to the problem of Equation (19) satisfies  
\[\left.\frac{df_{s}}{d\beta_{i}}\right|_{\beta_{i}=\hat{\beta}_{i}}=\frac{\theta_ {i}^{2}}{c_{i}}-\frac{\theta_{i}^{2}}{c_{i}}\hat{\beta}_{i}-\eta_{M}\sigma^{2} \hat{\beta}_{i}-\eta_{S}\sigma^{2}\left(\hat{\beta}_{i}-1\right)=0 \tag{21}\]  
Then, the second-order derivative of the problem of Equation (19) is  
\[\left.\frac{d^{2}f_{s}}{d\beta_{i}^{2}}\right|_{\beta_{i}=\hat{\beta}_{i}}=- \frac{\theta_{i}^{2}}{c_{i}}-\eta_{M}\sigma^{2}-\eta_{S}\sigma^{2}<0 \tag{22}\]  
Thus, the optimal solution to Equation (19) is achieved as  
\[\beta_{i}^{*}=\frac{\theta_{i}^{2}+\eta_{S}\sigma^{2}c_{i}}{\theta_{i}^{2}+ \eta_{M}\sigma^{2}c_{i}+\eta_{S}\sigma^{2}c_{i}} \tag{23}\]  
Therefore, Table 1 summarizes the optimal contract settings and the two parties' optimal expected utilities.
Congested traffic data are still necessary to calibrate and validate these models [25]. The potential of computer vision and image-processing techniques has been exploited for traffic surveillance for many years [26]. Image/video traffic data are analysed to detect [27; 28] and classify the vehicles [29; 30]. Information can also be found in image/video data on traffic density [31], vehicle speed [32; 33] and driver behaviour [34] for different conditions of weather and traffic. For bridge loading, [PERSON], et al. [19] use video data to analyse the distribution of trucks on multi-lane bridges in congested traffic. The composition of traffic on the roads of the United Kingdom is investigated by [PERSON] and [PERSON] [35] using video data. A microsimulation model is calibrated by [PERSON] et al. [14] based on traffic composition and driver behaviour for different traffic conditions observed in video data. [PERSON], et al. [36] explore the potential of cameras and image processing techniques to collect congested traffic data. The feasibility is established of extracting valuable information from images such as traffic composition, vehicle lengths and bridge load effects.
# A Feature Extraction Method Using Auditory Nerve Response for Collapsing Coal-Gangue Recognition
## 4 Experimental Results
### Comparison with the Recognition Method Based on MF-Score Feature Reduction  
We further compare the proposed method's performance with that of [PERSON] et al. [13] who, recently, proposed a method, which was based on feature selection. The method was conducted on the same datasets and classes as in the proposed method. The primary steps of the comparison include preprocessing, which is followed by traditional feature extraction, and automatic selection of features based on the MF-score feature reduction method and coal-gangue recognition based on the SVM classifier. Such extracted features are generated by analyzing signals in domains of time, frequency, or time frequency. Moreover, a feature reduction method on the basis of MF-score, employed to optimize the feature attributes, is achieved based on coal-gangue samples. Then, the information of feature subsets after feature reduction is inputted as SVM classifier's training samples and test samples.  
Therefore, residual variance and fractal dimensions are selected to obtain the optimum of feature subset. As shown in Figures 12b and 13 and Table 2, the test accuracy and AUC of the compared methods are 91.38% and 0.98, respectively. The proposed method's test accuracy, test accuracy and AUC are 99.23% and 1.00, respectively. The result shows that the features extracted by the AN response model are more effective and advantageous than those obtained by the MF-score feature reduction method.  
On the basis of the above comparison, the superiority of our proposed method can be summarized as follows: The advantageous classification performance has been achieved by using a single type of feature. By imitating the process of classifying coal and gangue in the human ear, the features extracted from the AN response model contained more effective information than those extracted from time, frequency, and time-frequency domains. The limitations of our work include the following: In fully mechanized caving faces, coal mixed with gangue falls down. With the continuous discharge of coal, the proportion of gangue continues to increase up to a certain percentage of gangue. In the experiment, the sample data collected is only divided into coal and gangue, and therefore we do not study the impact properties of different mixing ratios of coal and gangue. Hence, the proposed method is not applicable to all fully mechanized caving faces.
## 4 Experiments
### Measuring Sensitivity to Sub-Concepts  
We finally studied how much the prediction of the model is influenced by the concepts by performing two analyses. The first one, which is derived from the TCAV approach [2], basically measures the percentage of instances that represent a concept among a set of programs that are positive with respect to a classification task. Formally, for each concept \(c\), we considered a sample \(X_{v}\) of 100 methods from the dataset that are both labeled and predicted to be vulnerable by our model. Notice that we considered the vulnerable class, but the same considerations can be made on the other class of safe programs. Then, we  
Figure 6: Average accuracies obtained over 10 runs by the linear SVCs trained on random concepts.  
measured the sensitivity \(S_{c,v}\) by computing, \(\forall x\in X_{v}\), the distance \(d_{c,l}(f_{l}^{*}(x))\), where \(l\) is the layer referred to the best support vector \(V_{c,l}\) trained on the concept \(c\), and by computing the ratio between the number of times that \(d_{c,l}(f_{l}^{*}(x))\) has a positive value, and the cardinality of sample \(X_{v}\):  
\[S_{c,v}=\frac{|\{x\in X_{v}\colon d_{c,l}(f_{l}^{*}(x))>0\}|}{|X_{v}|} \tag{1}\]  
A second analysis was performed on the instances generated via evolutionary search. We measured how accurately a given network classifies the instances generated by evolutionary means in our experiments, where we chose one of our 4 concepts, a single CWE, and a configuration among the following two:  
* Instances that are not vulnerable, and whose fitness value is maximized (the presence of the chosen concept is strong);
* Instances that are vulnerable, and whose fitness value is minimized (the presence of the chosen concept is weak).  
This last choice aims at checking the impact of a strong concept when the network should classify the instance as safe, and conversely in the case of instances known to be vulnerable. We recall that among the four concepts we identified, two are commonly considered relevant when manually looking for CWE-369 or CWE-789 vulnerabilities, while two are higher-level concepts, which we identified as instances of what we can probe for in our classifiers.  
This leads to 16 different experiments, and from each, we eventually obtain a population of source code methods, evolved according to the chosen combination of CWE, concept, and desired presence of the concept. For each experiment, we ask of the network, trained on the chosen CWE, to classify all the individuals generated during the 12 generations of the run, taken without repetitions. We do this separately on two subsets of the total population of an experiment: the half population with the individuals of highest fitness values, and the half with the lowest fitness values. Finally, the relevance for a network of a sub-concept is measured by looking at the absolute accuracy of the classification on each of the two subsets, and by analyzing the relative accuracy between them. This will allow to study how a concept impacts classification, both on safe and on vulnerable source code.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 2 Literature Reviews  
### Vibration-Based Techniques for Caisson-Foundation Systems  
Many researchers have utilized vibration-based techniques to handle the transient interactions between waves, structures, and foundations [3; 8]. For caisson-foundation structures, vital structural information can be obtained from a few sensors strategically placed in the small above-water section [5; 6; 7]. [PERSON] et al. [17] studied the dynamic response of a caisson breakwater using a rigid body on an elastic foundation model under wave action. [PERSON] et al. [8] employed vibration-based methods to detect damage at the interface between the caisson and the foundation. [PERSON] et al. [9; 10] combined simplified analytical approaches with in situ vibration measurements to assess caisson-foundation system integrity. [PERSON] et al. [11; 12] developed a practical scheme to differentiate the impact of foundation conditions and environmental factors on the vibration characteristics of the breakwater system. [PERSON] et al. [13] proposed the pseudo-wave method superposing the vibration responses from several man-made excitations to simulate the wave impact on the caisson system.
## 3 Results
### Activation of MAPK and AMPK Signaling Pathway during Adipogenic Differentiation of hBM-MSCs  
The regulation of MAPK and AMPK signaling pathways are needed during the early stages of MSC adipogenesis. Activation of MAPKs is observed during adipogenesis to increase the adipogenic gene expression, while AMPK activation was shown to be suppressed as AMPK activation induces lipolysis, energy expenditure, and decrease in white adipose tissue [24; 25]. Therefore, these pathways were examined in adipo-induced hBM-MSCs to underly the mechanism behind the anti-adipogenic effect of loliolide. To this end, phosphorylation levels of p38, ERK, and JNK was evaluated by Western blot. The levels of total MAPK expression were not altered with differentiation, while phosphorylation of these MAPKs was notably increased with adipogenesis (Figure 3A). Treatment with loliolide significantly decreased the adipogenesis-induced levels of phosphorylated (p-) p38 and JNK, whereas p-ERK levels were not affected. Next, activation levels of AMPK with or without an AMPK inhibitor (compound C) were analyzed by Western blot. The level of p-AMPK was inhibited by adipogenesis; however, loliolide treatment (10 \(\upmu\)M) significantly elevated the phosphorylation of AMPK, while total protein levels of AMPK were not changed in adipo-induced hBM-MSCs (Figure 3B). Further support for the effect of loliolide on AMPK phosphorylation was obtained from experiments using compound C (a known AMPK inhibitor) treatment along with loliolide. The compound C-only treated group displayed nearly diminished AMPK levels in adipo-induced hBM-MSCs as early as 6 h after administration of adipogenic differentiation medium. Loliolide treatment reverted the inhibitory effect of compound C on AMPK phosphorylation in a time-dependent manner (Figure 3C). Twelve hours after adipogenic inducement, loliolide treatment almost completely reinstated the phosphorylated AMPK levels despite the presence of compound C. These results indicate the involvement of MAPK/AMPK phosphorylation in loliolide-mediated suppression of adipogenesis.  
Figure 3: The effect of loliolide treatment on the protein and phosphorylation levels of p38, ERK, and JNK MAPKs and AMPK pathways. The hBM-MSCs were differentiated into adipocytes in six-well plates with or without loliolide treatment (10 \(\upmu\)M). One group was left untreated and non-differentiated as the negative control. The hBM-MSCs at Day 10 of differentiation were used for the analysis of total and phosphorylated (p-) MAPK and AMPK levels, namely p38, ERK, JNK, (**A**), and AMPK (**B**,**C**), using immunoblotting. \(\upbeta\)-actin was used as housekeeping protein. The inhibitor was compound C,an AMPK activation inhibitor. Different letters (a-d) above the sample groups indicate that the difference between each group is statistically significant (\(p<0.05\)) compared to the untreated differentiated control (compared to 0 h in (**C**)).
## 3 Method
### Basic Theory and Techniques for SOM
#### 3.3.2 Limitations of SOM Method in Fault Diagnosis  
As a clustering and visualization algorithm, SOM is commonly used in the field of fault diagnosis. However, due to the performance limitations of SOM itself, there are some limitations in handling fault diagnosis tasks, especially when dealing with large data samples and high-dimensional data:  
(1) Data volume limitation: The SOM method has certain limitations when dealing with large-scale data. The data sample size of the fault diagnosis task is large, and the SOM method needs more iterations to converge to a stable result, which increases the calculation time and cost. Meanwhile, the SOM method may be affected by data sampling and distribution, resulting in increased instability of model training results.  
(2) Data dimension limitation: The SOM method has limited processing capacity for high-dimensional data. SOM has a good clustering effect when processing low-dimensional data, but when performing fault diagnosis tasks, the data dimensions are high, and the mapped results may lose some feature information of the original data, resulting in errors in the identification of cluster centers and aliasing.