esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiemental Result and Analysis
### Ablation Study  
To explore the role of each component in multimodal sarcasm detection, we conduct a series of ablation experiments and provide a detailed analysis. The ablation experiment removed the multi-view interaction module and the late-fusion module, respectively. Table 4 reports the results of the ablation experiment. (Legend: w/o V denotes without employing multi-view interaction. w/o L denotes that without employing late attention fusion, it concatenates the multimodal representation after multi-view interaction. w/o denotes without employing any interaction and fusion action.)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Acc (\%)** & **F1 (\%)** \\ \hline MVK & **86.68** & **83.92** \\ \hline w/o V & 85.22 & 82.15 \\ w/o L & 84.25 & 82.04 \\ w/o V-L & 81.61 & 76.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experimental result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Modality**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Acc (\%)**} & \multicolumn{4}{c}{**F1-Score**} & \multirow{2}{*}{**Parameters**} \\ \cline{3-4} \cline{6-7}  & & & **Pre (\%)** & & **Rec (\%)** & & **F1 (\%)** \\ \hline \multirow{4}{*}{_Text_} & TextCNN & 79.54 & 73.57 & 78.78 & 76.09 & 6.0 M \\  & TextCNN-RNN & 82.30 & 78.86 & 78.09 & 78.48 & 6.1 M \\  & BERT & 83.85 & 78.72 & 82.27 & 80.22 & 112.8 M \\  & MVK(ours) & 83.36 & 80.09 & 79.46 & 79.78 & 19.5 M \\ \hline \multirow{4}{*}{_Image_} & Image & 64.76 & 54.41 & 70.80 & 61.53 & 14.9 M \\  & ViT & 67.83 & 57.93 & 70.07 & 63.43 & 86.6 M \\  & MVK (ours) & 72.44 & 67.24 & 64.93 & 66.06 & 19.5 M \\ \hline \multirow{4}{*}{_Text + Image_} & HFN & 83.44 & 76.57 & 84.15 & 80.18 & 14.9 M \\  & Attr-BERT & 86.05 & 78.63 & 83.31 & 80.90 & 36.1 M \\ \cline{1-1}  & HKE & 87.02 & 82.97 & 84.90 & 83.92 & 112.5 M \\ \cline{1-1} \cline{2-6}  & **MVK (ours)** & **86.68** & **83.75** & **84.06** & **83.92** & **19.5 M** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main results of the experiment.  
Upon observing the results presented in Table 4, it becomes evident that the model's performance decreases significantly when adopting no actions, indicating that in multimodal sarcasm detection, knowledge enhancement, multi-view interaction, and attention fusion mechanisms are crucial for learning the relationships between multimodal data. Specifically, when the multi-view interaction module is removed (w/o V), the experimental results show a noticeable decline, which indicates that interacting information through different modal perspectives can improve the model's ability to learn dependency relationships between modalities. When removing attention mechanism fusion, the model's performance is slightly worse, indicating that a simple fusion method cannot make the model focus on important information in multimodal representations. It is worth noting that the model achieves excellent performance in multimodal sarcasm detection when employing these two components together, especially when utilizing visual commonsense knowledge to enrich the semantic information of representation.
## 3 Proposed Solution
### Recovery from Attacks
In the opposite case, its reputation will not change significantly. This is implemented in the following way:if (value_rep < threshold) {  if (space_rep < threshold) {  result = value_rep;  } else { result = 1 - value_rep; }  } else {  result = value_rep; } where value rep is the reputation assigned by the algorithms for temporal characterization and space rep is the reputation assigned by the algorithms for spatial characterization. On the other hand, as mentioned in the previous text, in the situations such as the data coming from a node exhibits large variations, temporal inconsistencies are not likely to be detected. However, spatial inconsistencies are very likely to be detected. Thus, spatial inconsistence is sufficient in order to raise an alarm. Concerning the detection of routing protocol anomalies, the explained approach can tell us if there is something suspicious in routing paths of a certain node. Yet, in order to find out the nodes that are the origin of the attack, we need to add one more step. In this second step, if the reputation of the routes calculated in the previous step is lower than the established threshold, the hops that participated in the bad routes will be added to the global list of bad nodes, or if they already exist, the number of their appearance in bad routes is increased. The similar principle is performed for the correct nodes. For each node, let the number of its appearances in bad routes be \(nBad\) and the number of its appearances in good routes be \(nGood\). Finally, if \(nGood\) is greater than \(nBad\), the node keeps its reputation value, and in the opposite case, it is assigned the following reputation value:  
\[\frac{nGood}{nGood+nBad} \tag{10}\]  
In this way, as the bad node spreads its malicious behavior, its reputation will gradually decrease.
## 3 Results and Discussion  
### Descriptive Analysis  
A total of 461 completed questionnaires were processed from the database, using the SPSS software package for Windows (version 19.0, SPSS, Inc., Chicago, IL, USA). According to reports issued by SPSS, the sample consisted of 21% men and 79% women. With regard to the ages of the students in the sample, 35.8% were 17-19 years old; 30.4% were 20-21 years old; and 33.8% were 22 years old and above. The mean and standard deviation of age was 20.81 \(\pm\) 0.12 with an age range of 17 to 32 years. See Table 2 for these results.  
With regard to how long they had used a smartphone, 31.9% of students had one in use for more than five years; 30.2% had one in use for 3-4 years; and 37.9% had one in use for less than two years. See Table 3 for these results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Age** & **Frequency** & **Percentage (\%)** \\ \hline \([17,19]\) & 165 & 35.8 \\ \([20,21]\) & 140 & 30.4 \\ \([22,\text{or more}]\) & 156 & 33.8 \\ \hline
**Total** & **461** & **100** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage distribution of respondents by age.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Frequency** & **Percentage (\%)** \\ \hline Less than a year & 37 & 8 \\ More than 1 year but less than 2 & 72 & 15.6 \\ More than 2 years but less than 3 & 66 & 14.3 \\ More than 3 years but less than 4 & 77 & 16.7 \\ More than 4 years but less than 5 & 62 & 13.5 \\ More than 5 years & 147 & 31.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: How long the respondent has had a smartphone.  
When asked whether the students had a data plan that would allow them to access the Internet, 68.3% of the respondents answered yes and 31.7% answered that they did not have a data plan.  
With regard to the total time dedicated to smartphone use per day, 26.8% answered that they used their smartphones for a total of 1-3 h a day. The majority (34.1%) answered that they used their smartphones for 4-5 h; 19.1% answered saying 5-10 h; and 20% answered saying 10 or more hours a day. Table 4 shows these results.  
With regard to the number of times they usually checked their smartphones in a day, 25.8% of the respondents answered that they checked 1-8 times; 24.5% checked 9-16 times; 29.7% checked 17-30 times; and 20% checked 31 or more times. Table 5 shows these results.  
We can note that 7.6% of students responded to the survey stating that they checked their smartphones every 5 min; 33.2% checked every 10-20 min; 38.2% checked every 30-60 min; and 21% checked every 2 h or less. Table 6 shows these results.
# On Fuzzy and Case-Based Dynamic Software Development Process Modeling and Simulation Approach
## 4 Materials and Methods--On the Fuzzy CMMN Model for SDP Simulation
### The Proposed Approach  
The SDP CMMN model is developed according to the following aspects:  
* CMMN requirements and notation.
* Agile methodology.  
The developed SDP model has input parameters from task consistency and dependency analysis, role analysis, and task time estimates. A business case simulation tool is a tool that simulates a real software development case with selected input data. The simulation tool consists of three separate modules: simulation scenario specification, simulation scenario execution, and report generation. In the report generation module, simulation scenario configurations and historical data may be useful in the future to evaluate performance or progress based on specific data, simulation execution data, and report templates. The input data of the business case simulator tool are formed from the specifications of the tasks and resources. The more accurate and detailed these specifications are, the more accurate the input data will be and the more consistent the simulation results will be with the model. The output of this is a case execution report, generated according to report templates and to the needs of the business or SDP team.  
Using the proposed CMMN model SDP, software development modeling and simulation is performed at the initial state of SDP execution, when the user's needs are defined. These needs are used to define the initial variables for the proposed CMMN model input. After the successful verification of all input data, the simulation can begin. The accuracy of the simulation results depends on the level of detail and accuracy of the initial data. The obtained results show the potential risks to the SDP, such as possible delays, working outages, places where resources are not fully utilized, and so on. Thus, the analysis of the simulation results is important as it identifies problems and possible future challenges of the SDP. Based on the analysis of the obtained results, a new assigned resources plan can be developed before re-simulation. Plan refinement and simulation are performed as many times as possible until a suitable plan meets the user's needs.  
If necessary, SDP simulation can be done during the execution of the SDP. In most cases, this is necessary when unpredictable changes occur, such as changes to the user's needs.
## 3 Defining a Haptic Icon Set
### _Haptic Scales_  
Musicians order the auditory frequencies in musical scales composed of elements called musical notes. According to the Weber-Fechner law [16], our senses work in a logarithmic manner; likewise, the musical notes follow a logarithmic scale so that users can discriminate between them. A haptic scale would consist of haptic notes, each note corresponding to a particular haptic icon. Hence, different haptic scales can be defined by varying different parameters of the haptic icons. Since we have complex synthesised semi-passively presented haptic icons, several haptic scales can be defined, depending on which parameters remain fixed and which change. We could order the haptic icons at least in three different types of scales: frequency-based haptic scales, where each note has an increased frequency with regards to the previous note (keeping constant the amplitude and the shape); shape-based haptic scales, where each different shape would correspond to a different note; and amplitude-based scales, where, keeping the same shape and frequency, each note would have a greater amplitude than its precedent note. Alternatively, we could also create hybrid haptic scales by simultaneously varying several parameters of the haptic icons, allowing us to represent multidimensional elements or a wider set of elements.  
Each haptic scale will be composed of a set of ordered notes or haptic icons. For each haptic icon, we need to specify its shape, amplitude, and frequency. Previous work [PERSON] et al. [32] showed that the best discriminable feature is the frequency so, as a use case, in this paper, we explain and apply the procedure to obtain frequency-based haptic scales. If the haptic scales were based on a different parameter, the procedure will be analogous and similar experiments would need to be carried out.  
In frequency-based haptic scales, the shape and the amplitude remain constant for the different haptic icons, whereas the different notes will have different frequencies. Previous works [32; 45] showed that the more discriminable forms are: sine, sawtooth and square. Experiment 1 (described in Section 4.1) aims to obtain a suitable amplitude for each wave and the average amplitude of those three waves. Then, we need to obtain the range of discriminable frequencies (the minimum and the maximum value). Section 4.2 explains how we experimentally obtained those values. Finally, Section 4.3, explains how we calculate the values of frequency for each haptic icon within the previously calculated range, obeying the Weber-Fechner law [16] to achieve that the differences in the frequency between contiguous notes can be noticeable by users.
# Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads
###### Abstract  
With the development of internet technology, email has become the formal communication method in modern society. Email often contains a large amount of personal privacy information, possible business agreements, and sensitive attachments, which make emails a good target for hackers. One of the most common attack method used by hackers is email XSS (Cross-site scripting). Through exploiting XSS vulnerabilities, hackers can steal identities, logging into the victim's mailbox and stealing content directly. Therefore, this paper proposes an email XSS detection model based on deep learning technology, which can identify whether the XSS payload is carried in the email or not. Firstly, the model could extract the Sender, Receiver, Subject, Content, Attachment field information from the original email. Secondly, the email XSS corpus is formed after data processing. The Word2 Vec algorithm is introduced to train the corpus and extract features for each email sample. Finally, the model uses the Bidirectional-RNN algorithm and Attention mechanism to train the email XSS detection model. In the experiment, the AUC (area under curve) value of the Bidirectional-RNN model reached 0.9979. When the Attention mechanism was added, the accuracy upper limit of the Bidirectional-RNN model was raised to 0.9936, and the loss value was reduced to 0.03.  
Webmail; XSS; Word2 Vec; deep learning; Attention mechanism +
Footnote †: journal: Applied sciences
## 3 The Experimental Sailboat ECO40  
The considered sailboat is a small oceanic race boat length overall of 12.0 m) characterized by a very light displacement. She is a Class 40 oceanic vessel named ECO40, which left from the port of \"Riva di Traiano\" located close to Rome (Italy) on 19 October 2014 to perform a non-stop sailing alone around the world in energy and food self-sufficiency. The boat route goes through the Gibraltar Strait, then descends the Atlantic Ocean and sailing around the Antarctic, at a mean latitude of 50\({}^{\circ}\) S, from west to east, rounding the famous capes of the world: Cape of Good Hope, Cape Leeuwin and Cape Horn. Finally, it ascends the Atlantic Ocean, passing again the Strait of Gibraltar and coming back to homeport [24; 25].  
The boat was equipped with three high precision GPS receivers (rovers), provided by Leica Geosystem, for measuring the movements of the boat. An ad hoc survey was performed just after their installation in order to know the positions of the GPS antennas needed to infer the three-dimensional position and attitude of the boat as a rigid body.  
The three geodetic class GPS antenna/receiver systems are able to acquire code and phase observations on both L1 and L2 frequencies. The GPS antennas were installed on board ECO40, as shown in Figure 1. Two antennas were mounted on the stern roll bar (they are indicated as GPS Stern-right and GPS Stern-left in the figure) and one antenna (GPS Bow) was installed along the main axis of the boat, close to the entrance and protected by a small fiberglass structure. The positions of the three GPS antenna were at the vertices of an isosceles triangle, as described in the Figure 1. The proposed method was applied to three couples of receivers deployed on board (Figure 1): GPS Bow-GPS Stern-right; GPS Bow-GPS Stern-left; and GPS Stern-right-GPS Stern-left.
## 1 Introduction
As mentioned above, the research on seismic performance evaluation of URM walls has made great progress. However, few studies have theoretically clarified the hysteresis characteristics of the rocking behavior, which is the main behavior of URM walls, e.g., [19; 20; 21; 24; 25; 26; 29]. Therefore, in this study, in-plane cyclic loading tests on the rocking behavior of URM walls were conducted, and a nonlinear performance curve evaluation procedure is suggested. In this test, three URM wall specimens were designed and fabricated based on a typical masonry house in Korea. The experimental parameters were the layout of openings (presence or absence) and the configuration of openings (window or door). Static cyclic loading tests were conducted to investigate nonlinear performance curves of masonry walls subjected to a rocking behavior in the in-plane direction. In this paper, the mortar-joint tensile crack strength and rocking strength of masonry walls (i.e., peak and residual strengths) were evaluated by the proposed procedure, the test results were compared, and the effects of opening configurations on the masonry wall strength were examined. The deformation capacity of a rocking behavior was also identified by the procedure. This paper is organized as follows: Section 1 is the introduction; Section 2 is the experimental program, including reference building, test specimen, material characteristics, and test program; Section 3 is the experimental results, including failure patterns and the relationship between lateral load force and drift angle of each specimen; Section 4 is the evaluation of nonlinear performance curve subjected to in-plane rocking behavior, including mortar-joint tensile crack strength and nonlinear performance curve after mortar-joint cracking; and Section 5 is the conclusions.
# Data-Driven Damage Classification Using Guided Waves in Pipe Structures
## 1 Introduction
The high-fidelity finite element method was used to establish a comprehensive database, and a neural network-based surrogate model was developed to relate the damage status with key features from these responses. The results indicate that the proposed approach could lead to an efficient damage detection. The data-driven model was also useful to predict waveform instead of the analytical model [22]. Moreover, data-driven methods based on deep neural networks (DNNs) show great ability for damage classification, because of the powerful data computing and analysis capability reported in recent research. Therefore, DNN is employed in this work for damage classification using guided wave signals. In the following part, the mode selection, excitation, and reception of ultrasonic guided wave (UGW) in the pipe are introduced first. Additionally, the damage types in a pipe and their interactions with UGW are then analyzed. In Section 4, the experimental procedures are all illustrated in detail. Then, the multilayer neural network is built to perform damage classification, in which the effects of the number of hidden layers, the activation function and the noise level are all analyzed, followed by discussions and conclusions.
## 4 Distribution Method for Observation Scheduling in the AEOS Constellation
### Convergence Analysis  
The proposed PIDSM is essentially working on the iterative optimization principle with each satellite aiming to increase the global fitness at each iteration, and thereby the convergence is guaranteed. In particular, the significance values of all targets are exchanged among satellites via local ISL communication, and the global fitness value is increased by recursively adding/removing a target into/from the target sequence of some satellites according to Proposition 1. Meanwhile, the first phase adds as many targets as possible into the target sequence of a satellite, while the second phase ensures that a consensus significance values list can be reached for all satellites at each iteration. When no changes can be made in the obtained solution after executing the first and second phases for a period of time, the whole algorithm is converged.  
Formally, the entire procedure of the PIDSM running on AEOS \(s\) is expressed in detail as follows.  
Step 1: Running Algorithm 4 to include targets. First, add targets to \(\mathbf{a}_{s}\) according to Criterion (20) and update the related information \(\mathbf{Z}_{s}\). When no target can be scheduled to \(\mathbf{a}_{s}\), recalculate \(Q_{sg}\) for each target \(g\) in \(\mathbf{a}_{i}\).  
Step 2: Sending the information lists \(\mathbf{Q}_{s}\), \(\mathbf{Z}_{i}\), and \(\mathbf{u}_{s}\) stored on satellite \(s\) to its neighbor \(c\) with \(H[s,c]=1\).  
Step 3: Receiving the information lists \(\mathbf{Q}_{c}\), \(\mathbf{Z}_{c}\), and \(\mathbf{u}_{c}\) from satellite \(c\) with \(H[s,c]=1\).  
Step 4: Conducting the consensus procedure. Based on the received message \(\mathbf{Q}_{c}\), \(\mathbf{Z}_{c}\), and \(\mathbf{u}_{c}\) from each neighbor \(c\), update the message \(\mathbf{Q}_{s}\) and \(\mathbf{Z}_{s}\) stored on satellite \(s\) itself.  
Step 5: Removing the problematic targets. According to Criterion (21), remove tasks in \(D\epsilon=\{g\in\mathbf{a}_{s}\,\,|\,\,Z_{sg}\
eq s\}\) from \(\mathbf{a}_{i}\) and \(D\epsilon\) until \(D\epsilon=\emptyset\) or that criterion is not satisfied. Then, update \(\mathbf{Q}_{s}\) and place each remaining target \(g\) in \(D\epsilon\) back in \(\mathbf{a}_{s}\) by setting \(Z_{sg}=s\).  
Step 6: Repeat steps 1-7 until no further changes are made to the scheduled solution for a specified period of time.
###### Abstract  
Because of the ongoing changing climate, extreme rainfall events' frequency at the global scale is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic forecasting chain combining heterogeneous observational data sources is a crucial component for an Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict these events, their effects, and put in place anticipatory actions. During the last week of October 2021 an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria, Mala, and Italy) with a death toll of seven people. The CIMa Meteo/Hydro/Hydraulic forecasting chain, including the WRF model, the hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily, thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of the flooded area with water depth and water velocity.  
meteo-hydro chain; data assimilation; extreme rainfall events 2022146348