esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Case studies
### Seismic Input  
According to the Italian Seismic Code [28], the elastic response spectra of the selected case study structures are referred to design actions with return periods of 50 years (Damage Limit State DLS), 475 years (Life safety Limit State LLS) and 975 years (Collapse Limit State CLS) and to the soil type C. Reference to [20] can be made for further details about the seismic input.  
This study aims to estimate the probability of reaching the collapse and usability-preventing performance levels supported by annual rates of failure computed by non-linear dynamic analyses considering advanced modelling of the seismic isolation systems compared to that of previous studies [17, 18]. The results of NTHAs, have been used to estimate the mean rates of failure \(\lambda_{f}\) (using the R2R-EU software [31]) for both the GC and UPD performance levels implementing the Performance-based Earthquake Engineering Framework [32].  
Multi-stripe non-linear dynamic analyses have been carried out considering 20 ground motions per 10 intensity measure levels (IMs), as reported Table 2.; IM2, IM5, and IM6 correspond to the return periods of the DLS, LLS, and CLS. Due to base-isolated structures vibration period, generally assumed as T = 3 sec for this kind of isolation system, the attenuation law of [PERSON] and Bommer [33] has been implemented to compute hazard and disaggregation. The elastic spectra of seismic inputs for IM6, as an example compared with design spectra provided by code for CLS, are reported in Figure 3a,b for the sites of L'Aquila and Naples, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case Study**} & **M** & **W** & **N\({}_{\mathbf{Sd}}\)** \\ \cline{2-4}  & **[ton]** & **[kN]** & **[kN]** \\ \hline New Construction & 2178 & 22,330 & 890 \\ Existing LSD & 1582 & 18,568 & 647 \\ Existing GLD & 1546 & 18,040 & 683 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total mass and axial loads of archetype buildings.  
Figure 2: Plan views of existing buildings for the (a) L’Aquila and (b) Napoli.
# Co-Simulation of Multiple Vehicle Routing Problem Models
## 3 The Designed Model
### Description of Atomic VRP Models  
The routing and scheduling of a service correspond to the creation of vehicle routes for the depot. By assigning each client to a unique route and respecting the capacity of the vehicle the order of the clients on every vehicle route will be determined. To this end, we develop two models to solve VRP such that each model is to satisfy a different objective.  
The first is to try to minimize the total traveling distance and the second is to minimize the number of vehicles.
# Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks
###### Abstract  
Accurately identifying the pixels of small organs or lesions from magnetic resonance imaging (MRI) has a critical impact on clinical diagnosis. U-net is the most well-known and commonly used neural network for image segmentation. However, the small anatomical structures in medical images cannot be well recognised by U-net. This paper explores the performance of the U-net architectures in knee MRI segmentation to find a relative structure that can obtain high accuracies for both small and large anatomical structures. To maximise the utilities of U-net architecture, we apply three types of components, residual blocks, squeeze-and-excitation (SE) blocks, and dense blocks, to construct four variants of U-net, namely U-net variants. Among these variants, our experiments show that SE blocks can improve the segmentation accuracies of small labels. We adopt DeepLabv3 plus architecture for 3D medical image segmentation by equipping SE blocks based on this discovery. The experimental results show that U-net with SE block achieves higher accuracy in parts of small anatomical structures. In contrast, DeepLabv3 plus with SE block performs better on the average dice coefficient of small and large labels.  
medical image segmentation; convolutional neural networks; SE block; U-net; DeepLabv3 plus 2022
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Prediction Intervals Formulation and Evaluation Metrics
#### 2.5.1 Prediction Interval Formulation  
Given a dataset containing point forecasts from different models at certain quantiles (e.g., 0.90, 0.95 and 0.99) along with the actual prediction value such that \(D=\{(x_{i},y_{i}),i=1,2,\cdots m\}\). \(x_{i}\) represents an input vector corresponding to a particular variable in \(D\) and \(y_{i}\) represents the actual value. The PI with nominal confidence (PINC) \(100(1-\alpha)\%\) for the \(y_{i}\) is given in Equation (18)  
\[Q^{\alpha}(x_{i})=\big{[}L^{\alpha}(x_{i}),U^{\alpha}(x_{i})\big{]}, \tag{18}\]  
where \(Q^{\alpha}(x_{i})\) represents the range of PI values within the actual \(y_{i}\) having \(L^{\alpha}(x_{i})\) and \(U^{\alpha}(x_{i})\) as its lower and upper bound estimate (LUBE) values, respectively. Thus, the probability that \(Q^{\alpha}(x_{i})\) lies within \(y_{i}\) is expected to be \(100(1-\alpha)\%\) can be expressed in Equation (19) [20].  
\[PINC=Pr(Q^{\alpha}(x_{i})\in y_{i})=100(1-\alpha)\% \tag{19}\]
# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 1 Introduction
In such an environment, developing efficient QoE-aware service provisioning policies while considering storage and computation resource limitations of MEC remains a challenging task, which is yet to be investigated extensively and holistically. Motivated by the above facts, in this paper, we investigate a utility-centric approach to the problem of service provisioning in MEC, using utility score as QoE assessment [26]. Specifically, we take measurements of an objective QoS parameter (e.g., service latency), which is then mapped onto subjective mean opinion score (MOS) using a utility function. The utility function is derived from [27; 28] based on the observation that user perception of service latency does not advance beyond a certain threshold. For example, experimental results in [29] show that in VR services, service latency values smaller than 20 ms are not perceived by the users, and thus no difference in terms of user experience while in some cloud-based interactive games [30], only latencies above 50 ms affect the gaming experience. In brief, the main contributions of this paper are summarized as follows. * We formulate the problem of service provisioning as an Integer Nonlinear Programming (INLP) problem that jointly optimizes the service placement decisions and request scheduling decisions so as to maximize the total utility (or satisfaction) of all users within both the storage and computation resource constraints in MEC systems. * We then propose a Nested-Genetic algorithm (Nested-GA) that consists of two genetic algorithms (outer and inner), each of whom solves a sub-problem (i.e., service placement or request scheduling). * We justify the efficiency of our proposed algorithm by extensive simulations. The experimental results show that our proposal consistently outperforms baselines in terms of the total utility and can provide close-to-optimal solutions. The remainder of this paper is structured as follows. We start by reviewing some relevant research works in Section 2. In Section 3, the system model and problem formulation are presented. We then introduce our proposed algorithm in Section 4. Simulation results are shown and discussed in Section 5, followed by our conclusions in Section 6.
## 3 Results and Discussion
### Penetration  
According to ASTM C403, a CLSM can be used in construction works only when it has a penetration value of 2.75 MPa. Figure 3 presents the penetration result. When the WFS replacement was 10% and the replacement of MSWI bottom ash was 25%, 50%, 75%, and 100%, a penetration value of 2.75 MPa was achieved in 14.33-16.87 h, satisfying the time requirement of 12-36 h for ordinary CLSMs.  
In the present study, the time required to achieve a penetration value of 2.75 MPa extended as the replacement of MSWI bottom ash was increased, because the bottom ash had large structure pores, which facilitated water absorption and delayed the hydration effects, thereby prolonging the initial setting time. The uneven combustion of the incinerator bottom ash also resulted in excess organic matter in the bottom ash, thereby delaying the setting of the CLSM.
## 1 Introduction
Although the above models can be trained on large sequences with lower complexity, this does not increase autoregressive inference speed. In this work, we propose a Gaussian-linearized transformer with tranquilized time-series decomposition methods with the computational complexity of \(O\) (\(N\)) for fault diagnosis and forecasting tasks of methane sensors. The proposed attention block of the model can automatically capture features of the signal of gas sensor by the query matrix and compute the autocorrelation between gas signals at different times by the key matrix and value matrix. Furthermore, the model can increase the accuracy of fault diagnosis and forecasting and effectively decrease the training time. The main contributions of the work are as follows:  
* The traditional self-attention mechanism was changed to reduce the time complexity to \(O\) (\(L\)). Firstly, \(Q\) and \(K\) were mapped to the Gelu function to obtain a Gaussian distribution, which was inspired by the kernel method. Secondly, in contrast to the traditional softmax calculation, we performed a softmax operation on Gelu (\(Q\)) and Gelu (\(K\)), and then multiplied the product of softmax (Gelu (\(K\))) and \(V\) with Gelu (\(Q\)) to complete the calculation of the Gaussian-linearized attention. Finally, the time complexity of the model was reduced to \(O\) (\(L\));
* The traditional embedding block was changed into a tranquilized time-series decomposition block in the fault forecasting task. After the decomposition block, we obtained multivariate time-series sequences and transformed them into the Gaussian-linearized transformer model with improved accuracy of fault forecast data;
* The complex fault environment was taken into account in the actual use of the methane sensor array. In practice, the number of sensor array faults occurs randomly and the fault models may be different at the same time. Therefore, combined with the above actual situation, the data were made closer to the actual situation.
# MobiDabet: Mobile Telemonitoring System for Patients with Type 2 Diabetes Mellitus (T2 DM)
## 4 Preliminary Usability Assessment of the Proposed System
### Description of the Preliminary Usability Assessment of the System Based on a Field Study  
In order to measure the quality in the use of both proposed system applications, in this work, a preliminary usability assessment was carried out on the system based on a field study. According to the guidelines of quality in use model of a software product defined in the standards ISO/IEC 25,010 and ISO/IEC 25,022 [16], the following aspects of our system were evaluated: efficiency, effectiveness, satisfaction, presentation, content, and navigation. In order to accomplish the evaluation, the following usability measurement instruments were developed and/or used:  
* Instrument 1: List of tasks to measure effectiveness and efficiency.
* Instrument 2: User interface satisfaction questionnaire.
* Instrument 3: Questionnaire of product quality metrics to measure presentation, content, and navigation.  
In evaluating and obtaining results, the formulas of the metrics defined in the ISO/IEC 25,022 standard [16] were applied.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Module** & **Sub-Module/Services** & **EF** & **ES** & **PE** & **I** \\ \hline Authentication & & 0 & 0 & 0 & 3 \\ \hline Health Education & & 0 & 0 & 0 & 3 \\ \hline \multirow{4}{*}{Nutrition} & Patterns & 0 & 0 & 0 & 3 \\  & Menus & 0 & 0 & 0 & 3 \\  & Intake frequency questionnaire & 0 & 0 & 0 & 3 \\  & Feedback & 1 & 1 & 0 & 3 \\ \hline \multirow{2}{*}{Glucose record} & Record & 0 & 0 & 0 & 3 \\  & History & 1 & 1 & 0 & 3 \\ \hline \multirow{4}{*}{Laboratory results} & Blood chemistry test & 1 & 1 & 0 & 3 \\  & Complete blood count & 0 & 0 & 0 & 3 \\ \cline{1-1}  & General urine test & 0 & 0 & 0 & 3 \\ \cline{1-1}  & Liver function test & 0 & 0 & 0 & 3 \\ \cline{1-1}  & Thyroid profile test & 1 & 1 & 0 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test of the services of the web application for patients. (EF) errors found, (ES) errors solved, (PE) pending errors, and (I) iterations.
# Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection
## 4 Result  
### Quantitative Result  
#### 4.1.1 Quantitative Result of Image Captioning Model  
The overall training process of the Image Captioning model was done by extracting the features of train images from InceptionV3 and caching them to disk with the '.npy' extension. Meanwhile, we used five captions for every feature as an input of the Transformer.  
The training was conducted with setting the epoch to 30 with the Adam optimizer. The loss function was Sparse Category Cross-Entropy with the drop rate at 0.2 to prevent overfitting.  
Figure 10: Structural Diagram of Crop Disease Diagnosis Solution.  
Figure 9: Structure of YOLOv5.  
Hyperparameters are the default values published in the 'Attention all you need' paper, which are set with four layers of Encoder-Decoder, 2048 hidden layers of Encoder-Decoder Position-Wise-Feed Forward neural networks, 512 input/output dimensions of Encoder-Decoder, and eight heads for parallel use in Multi-Head-Attention.  
Caption generation performance of the Image Captioning model was evaluated using BLEU score [32]. The BLEU score is a representative sentence generation performance indicator frequently used in the field of machine translation, yielding a mathematically calculated score of similarity between human-made sentences and model-generated sentences. The BLEU score has a value between 0 and 1, which means that the closer it is to 1, the higher the sentence generation performance. The BLEU score is calculated by tokenizing each sentence in a word unit and comparing the number of tokens shared between the tokens of the human-made sentence and the tokens of the sentence generated by the model. Meanwhile, the N-gram technique can be applied when calculating the BLEU score. Similarities in sentence structure can be analyzed by grouping tokens by the number of words in N-gram units to compare pairs of words with each other. In this study, the performance of the Image Captioning model was analyzed using BLEU_1, BLEU_2, BLEU_3, and BLEU_4, which are BLEU scores applied with 1-g, 2-g, 3-g, and 4-g, respectively, as well as BLEU_AVG, which is the average of these four BLEU scores. The performance of the model was analyzed with 306 images evenly containing disease-infected and normal crops, and each score was multiplied by 100 so that the final score could be expressed as a percentage. Each calculated BLEU score is shown in Table 2.  
As shown in Table 2, all five BLEU scores were more than 50 points, and in particular, the average score of each N-gram-based BLEU score was calculated to be about 64.96 points. According to the 'AutoML Translation Guide' on the Google Cloud website, the quality of the sentence is considered very high if the BLEU score of the generated sentence is over 50 points [33]. Evaluating the model's ability to generate captions based on this criterion, it can be interpreted that the Image Captioning model in this study produces very high quality captions since all measured BLEU scores are over 50 points.
# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 6 Conclusions  
This paper proposes an abnormal event prediction model CDSTAEP, which is based on an event association graph and mixed-curvature space representation. Most of the existing network abnormal event prediction methods are based on time features or the spatiotemporal features associated with hosts, embedding events in Euclidean space, using the LSTM model and its improved model, hidden Markov model, etc., to predict future events. However, this approach, which relies on the relationship between event time and hosts, does not consider the correlation between events. To simultaneously capture, characterize, and fuse the time, inter-domain (between hosts), and inter-space dependencies of events, the prediction model proposed in this paper captures the relationship between cross-domain events in the Riemannian space.  
First, we used a data set of a national key research and development plan, used the entity as the anchor, and referred to the three dimensions of the domain, time, and event to generate entity-level cross-domain event sequences, and then constructed a cross-domain event association graph. On this basis, we took advantage of the mixed-curvature space to better characterize complex network relationships and introduce event relationships into the final expression of event features. We use the mixed-curvature GNN to obtain the representation of the entity event vector sequence to capture the spatial relationship between events. At the same time, combined with the mixed-curvature vector representation of the events, LSTM-ATT was used to capture the temporal relationship characteristics of the events. Then, combined with the spatiotemporal vector representation of the events, self-supervised learning was used to obtain the fusion representation X of the spatiotemporal features of the events. Sending X into MLP, by optimizing the overall objective function, a better prediction effect was obtained. The experimental results show that the CDSTAEP model can obtain better event prediction results than LSTM, LSTM-ATT.  
Conceptualization, M.G.; methodology, M.G.; software, M.G. and R.W.; validation, M.G., H.Z. and R.W.; formal analysis, M.G., H.Z. and R.W.; investigation, M.G., H.Z., R.W. and Y.X.; resources, H.Z. and Y.X.; data curation, H.Z. and R.W.; writing--original draft preparation, M.G.; writing--review and editing, M.G., H.Z., R.W. and Y.X.; visualization, M.G. and R.W.; supervision, H.Z. and Y.X.; project administration, M.G. and Y.X.; funding acquisition, H.Z. and Y.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China under Grant 2020 YFB1708600, 2018 YFB0804500.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: For reasons related to the project, we do not provide the dataset for now.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Statistical Analysis  
The results obtained during the research were analyzed statistically (STATISTICA StatSoft, Inc. USA 2014; version 12. StatSoft Poland). The normality of the data distribution was verified with the Shapiro-Wilk test. Due to the normal distribution, the characteristics of the examined variables were presented in the form of means and standard deviation (Mean \(\pm\) SD). To estimate the significance of differences in the temperature of selected body surface areas and between contralateral areas in football players, Student's t-test was used. To test differences in biochemical parameters between different time points, a one-way ANOVA with repeated measures was used with [PERSON]'s HSD post hoc tests. Correlations between the values of the skin temperatures of selected body areas as well as between temperature changes after PBC and other analyzed variables (blood pressure, BMI, FAT, FAT%, MM, TBW, and the post-match blood concentration of the LA, CK, LDH, and AST) were estimated by calculating the Pearson correlation coefficient. The \(p\) value \(<\) 0.05 was considered statistically significant.  
Figure 2: Sample thermographic photos before (1 and 3) and after (2 and 4) partial body cryostimulation for the back view. Legend: Ch—chest; Ab-abdominal; AL—left arm; AR—right arm; FrL—left forearm; FrR—right forearm; ThL—left thigh; ThR—right thigh; SL—left shank; SR—right shank; KL—left knee; KR-right knee.
### Digital Transformation and Human-Machine Cooperation in CPPS  
A central aspect of Industry 4.0 and CPPS is the merging of the physical world with the virtual world: CPPS are characterized by intensive connections between collaborating computational entities and physical objects, relying on large quantities of data and using a variety of services to access and process these data [11]. In this context, the exchange of information plays a significant role [11; 12]. For instance, planning data from different disciplines are made available throughout the entire plant lifecycle, a close communication between different machines or devices is established, and current sensor data are continuously analyzed and applied for control purposes. This technological innovation of CPPS rests on a wide variety of concepts (e.g., formal semantics, information modelling standards) and specific technologies (e.g., ontologies and Linked Data, OPC UA). For instance, semantic networks of various planning data [13] can be used to model system relations, and semantic communication standards for the processing of live data [14] can be used to integrate data from different sources. As a result, the boundaries within the classic automation pyramid are increasingly dissolved, and information systems are loosely coupled in order to respond more flexibly to new requirements.  
Such digital transformation technologies for information modelling, processing, exchange, and integration in CPPS have the potential to make relevant context information available wherever it is needed in the system. On the one hand, in this context, information can enhance system control and automatic failure detection within the system [15]. On the other hand, it can be provided to human operators to support their context-dependent cognitive activities, and thereby enhance human reasoning in unprecedented ways.
# Corrosion Behavior of Corrosion-Resistant Spring Steel Used in High Speed Railway
## 3 Results and Discussion
### Phase Analysis of Corroded Rust Layer  
Figure 2 shows the XRD patterns of 60 Si2 Mn-B and 60 Si2 Mn-A at different corrosion cycles of 24 h, 72 h, and 360 h. Figure 2 shows that in the same corrosion period, 60 Si2 Mn-B and 60 Si2 Mn-A corrosion rust layer products are the same. The addition of corrosion resistant elements Cu, Ni, and Cr has no effect on the type of phase in the spring steel rust layer. After 24 h of corrosion, the surface corrosion rust layer is mainly composed of Fe\({}_{3}\)O\({}_{4}\) and \(\gamma\)-FeOOH, and the Fe peak is caused by the thin surface rust layer and partially exposed matrix in the early corrosion stage. The corroded rust layers are \(\gamma\)-FeOOH and \(\alpha\)-FeOOH at 72 h and 360 h, respectively, and the phase diffraction peak intensity at 360 h is higher than that at 72 h, indicating that the number of corroded rust layers increases with the extension of the corrosion time.  
Figure 1: Curves of corrosion miss-loss rate of tested steels as a function of cycling time.  
\(\gamma\)-FeOOH is the first metastable product with a tendency of spontaneous transition to thermodynamic stable phase. As the corrosion cycle increases, \(\gamma\)-FeOOH first dissolves and precipitates to form amorphous ferric hydroxide, which is then deprotonated by hydroxyl ions supplied by water to form \(\alpha\)-FeOOH [2], the rust layer at the later stage of corrosion. \(\alpha\)-FeOOH is the most stable insulator phase in the rust layer, with fine grain and strong corrosion resistance, which plays a good protective role on the matrix. When simulating the corrosion experiment of industrial atmosphere environment, the corrosion solution is slightly acidic, and the acidic solution will also promote the transition from \(\gamma\)-FeOOH to \(\alpha\)-FeOOH [25].