esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methodology of the Experiment
This ensures that the sensor system can effectively capture relevant data points while being minimally invasive and seamlessly integrated into the participants' living spaces. First, in order to demonstrate whether the variables changed or not in a statistically significant way with the use of the sensor network, hypothesis tests for paired samples were used. To define the relevant hypothesis test, whether it is a parametric \(t\)-test (see in Appendix A Equation (A1)) or a non-parametric Wilcoxon test (see in Appendix A Equation (A2)), the normality of the difference between each metric (with the sensor network--without the sensor network) is verified using graphical methods. If the graphical methods do not find reliability, the Kolmogorov-Smirnov hypothesis test is executed to verify normality for a sample size greater than 50, or the Shapiro-Wilk test for samples of size 50 or smaller (see in Appendix A Equations (A3) and (A4), respectively). Second, we analyzed the correlations that the variables have with each other for the two scenarios separately (without the use of the sensor network and with the sensor network). To do this, the Pearson correlation coefficient is chosen (see in Appendix A Equation (A5)) for each pair of variables that are normally distributed, or a non-parametric correlation coefficient such as Spearman's (see in Appendix A Equation (A6)) for a pair of variables where at least one is not normally distributed. Therefore, the normality of the metrics is verified separately, and the appropriate correlation coefficient is used. Third, we ran a K-means clustering algorithm on the metrics for the two scenarios (before and after using the sensor network) with the purpose of grouping people with similar performance in their sleep-related vital signs. In Appendix A, Equation (A7) shows the cluster's k-means algorithm. To choose the best grouping number of clusters we used a couple of parameters, the higher silhouette score, and a min of 10 samples per cluster. The silhouette score is a coefficient that goes from \(-1\) to 1, where a number close to 1 shows a better fitness between the elements of the clusters (see in Appendix A Equation (A8)). Also, we decided to work with a min of 10 samples for cluster to make representative groups. Then, we identified which samples changed to a better performance cluster, to a worse performance cluster, and which ones remained in the same cluster after using the sensor network. This classification is important, as it was used to compare the metrics before and after utilizing the sensor network on sample groups with similar performance in their metrics. Furthermore, each sample was labeled based on its performance classification, and this label was used as the target variable in a multinomial multivariable logistic regression with a Logit parameter estimation (see in Appendix A.9) performed to describe the predictive power of each metric in odds ratios and the relationships among them on the potential impact of the sensor network.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 30 cm Depth in Bangladesh  
Similar to Section 5.1, we combined the three types of lead times, five sizes of time windows, eight input scenarios, and five tree-based models to predict soil temperatures at the depth of 30 cm (ST30) in order to show the optimal combination of a tree model, time window, and input scenario in forecasting soil temperature. The model performance was also evaluated using root mean squared error (RMSE) and Pearson correlation coefficient (R).
## 2 Methodology  
The tropospheric height extends from the Earth's surface to 8-18 km; impacted by radiation heating on the ground, the temperature decreases with the increase in altitude. Meanwhile, the water vapor in the troposphere changes greatly, increased by the evaporation of oceans, lakes, and rivers.  
The uneven distribution of meteorological parameters may lead to variation of the tropospheric structure, resulting in the refraction of the electromagnetic wave in the transmission medium. The refractive index \(n\) is used to measure the degree of refraction [14]:  
\[n=\frac{c}{v} \tag{1}\]  
where \(c\) is the velocity in the free space and \(v\) is the velocity in the transmission medium.  
To better reflect the degree of refraction by numerical size, the refractivity \(N\) is defined as \(N=(n-1)\times 10^{6}\) in the microwave frequency band and below [14]. The refractivity close to the Earth's surface mainly varies in the range of 250 N-unit-400 N-unit. The relationship between the refractivity \(N\) and meteorological parameters [14], including temperature \(T\), pressure \(P\), and water vapor pressure \(e\) is:  
\[N=\frac{77.6}{T}\bigg{(}P+\frac{4810}{T}e\bigg{)}=N_{d}+N_{w} \tag{2}\]  
where \(N_{d}\) and \(N_{w}\) correspond to dry air and water vapor contributions, respectively.  
The refractivity structures in the lower troposphere change with the influence of weather processes, including air subsidence, advection, ground heating, and radiative cooling. As a unique phenomenon at the bottom of the troposphere, evaporation ducts arise from a sharp decrease in relative humidity near the sea surface. The electromagnetic wave refracts repeatedly in the trapped layer while signals propagate through the evaporation duct channel, and the wavefront changes from spherical to cylindrical expansion. The path loss is reduced compared with the transmission in a standard atmosphere, so that communication effects are improved.  
The geographical features of the Taiwan Strait, including the low latitude, intense sunlight exposure, etc., and high sea surface temperature (SST) caused by the TSWC, creates favorable meteorological conditions for the evaporation duct. In this paper, the communication effect was modeled and analyzed according to the method in Figure 1. Meteorological parameters, including temperature, relative humidity, pressure, and wind speed, were collected for the modeling database in Section 2.1. Meanwhile, marine radiosonde datasetswere also collected to validate the modeling database. Section 2.2 analyzes the evaporation duct characteristics based on the collected meteorological parameters, and the parabolic equation method is introduced to analyze the communication effect across the Taiwan Strait.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 3 APTs Faced by IIoT and Its Defense Methods
### Transport Layer  
The transmission layer of IIoT, also known as the network layer, primarily provides ubiquitous connectivity for equipment and transmits generated data from the perception layer to the platform layer through a heterogeneous communication network aggregation [55; 56]. In the transmission layer, APT attacks may include intrusion into gateways, eavesdropping or man-in-the-middle attacks on data transmission, and tampering with protocol conversion; the following are some common examples:  
* Distributed denial-of-service (DDoS) attacks [57], where attackers can occupy IIoT network bandwidth and resources by sending a large number of requests, causing system crashes or becoming unusable;
* Phishing attacks [58], where attackers can send deceptive emails, messages, or other information to trick users into providing sensitive information or downloading malware;
* Malware attacks [51; 52], where attackers can infect IIoT systems by sending malware or exploiting vulnerabilities, which may lead to confidential data leaks, system crashes, or other security issues;
* Wireless intrusion attacks [50], where attackers can invade IIoT systems by exploiting wireless network vulnerabilities. For example, they can use attacks against Wi-Fi or Bluetooth protocols to obtain sensitive information in the system.  
To protect their IIoT networks against these threats, enterprises can take multiple measures such as strengthening network security monitoring, using secure authentication technologies, scanning and repairing system vulnerabilities, limiting network access, and using IoT equipment security protocols. Additionally, it is essential to train employees effectively on network security education and awareness.
## 7 Discussion: Pitfalls and Guidelines for Planning and Conducting XAI Evaluations
Key sectors in this regard include healthcare, justice, and finance, where real-user studies typically comprise smaller sample sizes due to the specific conditions of the domain and the unique characteristics of the target group. Conversely, the availability of crowd workers and online panel platforms simplifies the recruitment process for proxy-user studies, enabling larger sample sizes. While recruiting proxy users can be beneficial for achieving a substantial sample size and sometimes essential for gathering valuable insights, researchers must be mindful of the limitations and potential biases this approach introduces. It is crucial to carefully assess how accurately proxy users represent the target audience and to interpret the findings considering these constraints. Relying on proxy users, rather than real users from the target group, can be viewed as a compromise often driven by practical considerations. However, the decision to use proxy users is often made for pragmatic reasons without considering the implications for the study design and the applicability of the research findings for real-world scenarios. **Guidelines:** The sampling method used has a serious impact on the study results. Sometimes, a small sample with real users could lead to more valid results than large sample studies with proxy users. Therefore, the decision sampling method should be carried out intentionally, balancing the statistically required sample size, contextual relevance, and ecological validity, as well as with the practicalities of conducting the study in a time- and cost-efficient manner. In addition, researchers should articulate the rationale behind the sampling decision and the implications for the study design and the limitations of the findings. The final step is the analysis of the gathered data, where guidelines also exist, such as thematic analysis for qualitative data or statistical evaluation for quantitative data. However, these are beyond the scope of this study, as we focused on the methodological aspects of planning and conducting empirical evaluation studies.
# Improving CNN Fish Detection and Classification with Tracking
###### Abstract  
The regular and consistent monitoring of marine ecosystems and fish communities is becoming more and more crucial due to increasing human pressures. To this end, underwater camera technology has become a major tool to collect an important amount of marine data. As the size of the data collected outgrew the ability to process it, new means of automatic processing have been explored. Convolutional neural networks (CNNs) have been the most popular method for automatic underwater video analysis for the last few years. However, such algorithms are rather image-based and do not exploit the potential of video data. In this paper, we propose a method of coupling video tracking and CNN image analysis to perform a robust and accurate fish classification on deep sea videos and improve automatic classification accuracy. Our method fused CNNs and tracking methods, allowing us to detect 12% more individuals compared to CNN alone.  
marine ecosystems; convolutional neural networks (CNNs); BRUVS video data; fish classification; automatic processing; tracking +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Relationships Between APC and Predictors  
The present study not only tried to evaluate the shelf life of pasteurized orange juice as a function of the aerobic plate count (APC) but also from a physicochemical point of view. Thus, at each time point and storage temperature (stress factor), the color (L*, a*, and b*) and pH were measured. However, the microbiological deterioration of orange juice at each temperature level inevitably affects the physicochemical characteristics ([PERSON] et al., 2016). It is thus of great interest to examine the relationship between the APC growth and the physicochemical variables at each storage temperature level. The relationship between the APC and the physicochemical variables was examined by using a reformed spreadsheet  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & **Estimate** & **Std Error** & \(p\)**-Value** \\ \hline
4 & \(-\)0.018118 & 0.0125045 & 0.1473 \\
15 & \(-\)0.039981 & 0.0129765 & 0.0021 \\
20 & \(-\)0.05344 & 0.0129765 & \(<\)0.0001 \\
25 & \(-\)0.201351 & 0.0129765 & \(<\)0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical information on b*during different aging conditions.  
Figure 6: Curve fitting plot of experimental data in the linear model of b* value vs. storage days. (a) 4 \({}^{\circ}\)C; (b) 15 \({}^{\circ}\)C; (c) 20 \({}^{\circ}\)C; (d) 25 \({}^{\circ}\)C.  
from the present data: four temperature levels (5, 15, 20, 25) at 10 specific storage days and mainly from the last days of storage were selected, and the predicted APC values were extrapolated via the equation models (as previously explained) for a greater number days of storage for reasons of data conformation.  
The results of the multiple regression analysis are shown in the table (Table 8), in which the parameters L*, a*, and temperature significantly affect the aerobic plate count. The prediction profiler of the APC versus all the predictors (Figure 7) shows that the increasing APC growth is related to a decrease in lightness (L*), redness (a*), and yellowness (b*) and an increase in the temperature, as expected. As stated by [PERSON] et al. [21], changes in the colorimetric L*, a*, and b* during the storage of orange juice are interrelated due to both oxidative and non-oxidative darkening reactions. However, it was observed that the color degradation was also directly related to the increase in the APC values. This is mainly attributed to the activity of sporoforming spoilage bacterial strains, which produce hydrolytic enzymes that cause cloudiness, off-doors, and deterioration of the juices [23].  
The above APC profiler is extremely useful for applications in the industry because, apart from its predictive value, it presents the possibility of using the color characteristics of pasteurized orange juice as indices of the microbiological deterioration at each time point and thus of the shelf life of the products.
# Three-Dimensional Velocity Field Interpolation Based on Attention Mechanism
## 2 Materials and Methods
### Joint Attention Mechanism
#### 2.2.1 TA Block  
The three-dimensional velocity field data is composed of inline, xline, and depth dimensions. Effectively extracting key information from these three dimensions and allowing interactions between each dimension are crucial for interpolation tasks. Therefore, we introduce a triple attention mechanism [35]. This mechanism aims to realize the interaction between the three dimensions, namely the H, C, and W dimensions. It is implemented through rotation and parallel computing without reducing the data dimensions. Each branch first rotates a C \(\times\) H \(\times\) W input tensor by 90\({}^{\circ}\), changing its shape to W \(\times\) H \(\times\) C. Then, through a pooling module, it is reduced to 2 \(\times\) H \(\times\) C, and then passes through a convolution layer and batch normalization layer, ultimately becoming a 1 \(\times\) H \(\times\) C intermediate representation. This intermediate representation generates an attention weight representation through a sigmoid activation layer, used for weighting the rotated input vector. Finally, the weighted vector is rotated back to its original direction. The pooling module is responsible for reducing the size of one dimension of the tensor to 2 by average-pooling and max-pooling, and then concatenating them.  
Our constructed TA Block, as shown in Figure 2, utilizes the triple attention mechanism to establish connections between the inline, xline, and depth dimensions, enhancing the network model's extraction of spatial information and better integrating underground geological structural features. In Figure 2, taking the first branch as an example, a copy of the input is first made, and then the copied data are rotated 90 degrees along the depth direction. The rotated data are then divided into two branches. The first branch performs global and average pooling in the inline direction, then extracts features through a 7 \(\times\) 7 convolution, and passes the extracted features through a sigmoid layer to become attention weight values. Multiplying the attention weight values with the rotated data generates attention for the depth and xline dimensions, allowing interaction between these two dimensions.  
The remaining two branches follow the same approach, but each is rotated 90 degrees along the \"inline\" and \"xline\" directions before subsequent operations. This completes the joint processing among the three dimensions, namely, our \"inline\"inline\", \"inline\", \"inline\", and \"xline-depth\" joint channels. Next, the outputs of these three joint channels are fed into the CA Block, where weights are learned for each channel individually. These weights are then used for weighted summation, completing end-to-end triple attention weighting. This enables attention interaction between every pair of dimensions, effectively extracting three-dimensional information from the spatial data.
# Damage Identification Based on Adding Mass for Liquid-Solid Coupling Structures
## 4 Damage Identification Experiment of a Submerged Cantilever Steel Beam  
In this section, the additional mass method for damage identification is verified by performing an experiment on a submerged cantilever steel beam. As the cantilever beam is submerged in water, it is highly demanding for the sensor type and experimental conditions when the experiment is conducted underwater. The method of adding mass to the cantilever beam is adopted to address the limitations of the experimental conditions. Mass is added at different positions to construct different structures, thereby increasing the amounts of experimental data and improving the accuracy of damage identification.
# Effect of the Surface Roughness on the Shear Strength of Granular Materials in Ring Shear Tests
## 3 Results
### Shear Characteristics of Gravel in the Ring Shear Tests  
Ring shear tests were initially performed using a classic smooth surface for the aquarium gravel with a normal stress of 0 kPa and a velocity of 0.5 mm/s. The mechanical properties of materials can often be described by their shear stress and shear strain (or displacement) relationship in response to the applied load. Figure 3 shows that in the shear stress-shear time relationship, the particles exhibited a typical strain-hardening behavior, regardless of the drainage condition, which implies that shear stress increases with increasing shear strain. This may be considered a shear resistance (i.e., yield strength) occurring at the beginning of plastic behavior based on the rheology. There was no contractive or dilative behavior apparent during shearing. Because the normal stress remained constant during shearing, very little decrease in the height of the sample was observed (the variation was less than 0.1 mm). According to [PERSON] et al. [31], typical dense sand and over-consolidated clays are dilative, while loose sands and normally consolidated clays are contractive. There was little effect based on the drainage condition. At the end of the test, as shown in Figure 3, the maximum shear strength was approximately 20 kPa. The minor variation in normal stresses with both drained and undrained conditions was caused by particle rearrangement, interlocking, and fragmentation along the artificial sliding plane during the ring shear test (Figure 3c,f). In the drained condition, after 1200 s, there was a slight decrease in shear stress, which may occur because the strain localization due to grain crushing was more significant in the drained condition; the large particles in the drained condition could freely move from the upper part to the middle part (artificial sliding plane) within the ring shear box. Similar results were reported by [PERSON] et al. [26]. The pore water pressure is also an important factor in determining the shear strength in the undrained condition, but in this study, this value was not a major factor because it strongly depends on the drainage and shear velocity. Thus, the measurement of pore water pressure was not considered.  
Figure 3: Shear characteristics of gravels using smooth surface test box: (**a–c**) Drained and (**d–f**) undrained conditions at 0.5 mm/s. \(\tau\) = Shear stress (kPa); H\({}_{\rm v}\) = Height of soil sample (mm); NS = Normal stress (kPa).
## 1 Introduction and Related Works
### Traffic Monitoring  
Traffic monitoring requires remotely sensing the traffic conditions on desired locations on the roads. [PERSON] et al. [9], in their pioneering work with wireless location technology (WLT)-based traffic monitoring, conducted multiple operational tests and concluded that there are many advantages, including the fact that the WLT offers a new platform for collecting traffic data spatially. The authors point out that there are associated sampling challenges that need to be addressed with concerted efforts in the future. [PERSON] et al. [10] developed a GPS and GIS-integrated traffic monitoring system, named GISTT, whose main aim is to replace traditional traffic probes with dedicated data logging personnel with GPS data loggers. The study assumes that the GPS data loggers are always in the auto mode. [PERSON] et al. [11] used GPS data for WLT-based transit performance monitoring services. [PERSON] and [PERSON] [12] attempted to utilize public transit vehicles that voluntarily travel on their routes regularly, for monitoring the general traffic by correlating the behaviors of transit buses to the general traffic. Due to the intrinsic nature of the bus mode that frequently stops even on uncongested roads for serving bus stops, the proposed method, at its best, is an indirect estimation of the actual general traffic conditions. [PERSON] [13] implemented a WLT-based traffic monitoring system by solely using the triangulation of cell phone towers without the use of GPS signals on major highways. This approach results in relatively lower accuracies (100-300 m) and needs sufficiently large sample sizes in order to overcome the accuracy issues. In addition, the accuracy also depends on the density of cell tower distributions, which happen to be sufficiently high only near major roads which limits the method's expandability into arterial roads. A GPS-based WLT can provide more accurate location information especially when AGPS chips are assisted with cell phone signals. ([PERSON], Assisted GPS, A-GPS, AGPS, [[https://www.lifewire.com/assisted-gps-1683306](https://www.lifewire.com/assisted-gps-1683306)]([https://www.lifewire.com/assisted-gps-1683306](https://www.lifewire.com/assisted-gps-1683306))) [PERSON] et al. [14] estimated passenger traffic flows of a transportation hub using mobile phone data using AGPS-enabled phones.