esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The ARCOMEM Approach
### Intelligently Harvesting and Sampling the Web  
ARCOMEM has developed web crawlers specifically designed for harvesting multimedia data from both standard web pages (the large scale crawler) as well as social media sources via their APIs (the API crawler). In addition, the web crawler is able to crawl the deep web through a set of modules that know how to extract information from certain kinds of web sites. Both crawlers run in a distributed fashion across a cluster of machines and store the raw content in an Apache HBase column-oriented database [27].  
The crawlers can operate in a number of different ways, depending on the requirements of the user, who defines what they want in their dataset. There are three common crawling strategies that can be used individually or together:  
* Standard crawling. A standard web crawl starts with a seed list of URLs, and crawls outwards from the seed pages by following the outlinks. Constraints might be added to limit the number of hops the crawler is allowed to make from a seed page or to limit the crawler to specific Internet domains or IP addresses.
* API-directed crawling. In an API-directed crawl, the user provides keywords that describe the domain of the dataset they want to create. These keywords are then fed to the search APIs of common social media sources (e.g., Twitter, Facebook, YouTube, _etc._), and the returned posts are examined for outlinks that are then used as seeds for a web crawl.
* Intelligent crawling. In an intelligent crawl, the user provides a detailed intelligent crawl specification (ICS) consisting of keywords, topics, events and entities that describe the target domain. A standard crawl and/or API-directed crawl is then started, and as new resourcesare harvested, they are scored against the ICS (using pattern matching and machine learning techniques). Scores for the outlinks of each resource are then created (combining the resource score with specific scores computed based on the link), and these scores are fed back to the large scale crawler, which prioritises the next URL to crawl based on the score. URLs with low scores will not be crawled.
## 4 Design Analysis
### Power Analysis
Furthermore, since the components might have different power consumption values, the highest power consumption value was selected to account for the worst case when calculating the power budget. Additionally, before computing the energy consumption, the components power consumption was increased by 10% to account for the EPS losses that occur due to the converter's inefficiencies [4]. Table 4 represents the power budget of Light-1 during different modes of operations with an assumption that the payload will operate throughout the orbit, to account for the worst-case scenario. The power consumption during different modes of operation is less than the energy generated by the solar panels. Thus, the EPS is capable of sufficiently providing power for the mission.
## 3 Materials and Methods
### Size Measurement Method
#### 3.2.3 Sub-Pixel Edge Detection
In Figure 8, \(\mid\)**lg(A)\(\mid\)**\(\mid\)**, \(\mid\)**lg(B)\(\mid\)**\(\mid\)**, and \(\mid\mid\)**lg(C)\(\mid\)**\(\mid\)** are the three gradient modulus values in the vertical direction in the entire region from point **A** to point **C**. The Canny algorithm selects point **B** with the largest gradient modulus as the edge point. However, there may be a point \(\eta\) between points **A** and **C** whose modulus is the extreme point of this region. At this time, \(\eta\) is more suitable as an edge point. [PERSON] gave a method for estimating n-point and calculating the offset of n-point. Additionally, the sub-pixel position of \(\eta\) is given, as shown in Equation (9):  
\[\eta=\frac{\mathbf{1}}{\mathbf{2}}\frac{\left|\left|\mathbf{g}(\mathbf{A}) \right|\right|-\left|\left|\mathbf{g}(\mathbf{C})\right|\right|}{\left| \left|\mathbf{g}(\mathbf{A})\right|\right|+\left|\left|\mathbf{g}(\mathbf{C}) \right|\right|-\left|\left|\mathbf{g}(\mathbf{B})\right|\right|} \tag{9}\]  
Directly following [PERSON]'s method, nevertheless, will still result in numerous mistakes. The following four sources can go wrong: (1) The error caused by presuming that the gradient satisfies the quadratic curve law; (2) using a finite term, determine the error caused by the gradient value; (3) the gradient of some locations in the interpolation calculation is not calculated using a clever operation; (4) data errors. Here is a more effective way to prevent these error sources from affecting edge detection: Point \((x,\,y)\) is an edge point in the horizontal direction when the gradient at that point satisfies the following criteria:  
\[\left\{ \begin{array}{l}\left|\left|\left|\mathbf{g}(x-1,y)\right|\right|< \left|\left|\mathbf{g}(x,y)\right|\right|\\ \left|\left|\mathbf{g}(x,y)\right|\right|>\left|\left|\mathbf{g}(x+1,y)\right| \right|\\ \left|\left|\mathbf{g}_{x}(x,y)\right|>\left|\mathbf{g}_{y}(x,y)\right|\right| \end{array} \right. \tag{10}\]  
Similarly, the vertical edge points are defined as follows:  
\[\left\{ \begin{array}{l}\left|\left|\mathbf{g}(x,y-1)\right|\right|<\left| \left|\mathbf{g}(x,y)\right|\right|\\ \left|\left|\mathbf{g}(x,y)\right|\right|>\left|\left|\mathbf{g}(x,y+1) \right|\\ \left|\left|\mathbf{g}_{x}(x,y)\right|<\left|\mathbf{g}_{y}(x,y)\right| \right.\end{array} \right. \tag{11}\]  
When the gradients in the \(x\) and \(y\) directions are equal, it defaults to the horizontal edge point.
## 3 Results
### One-Way Analysis of Variance (ANOVA)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Accidents Type (F = 17.423, p < 0.001)**} \\ \cline{2-11}  & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & **Mean (SD\(\pm\))** & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**5**} & \multicolumn{2}{c}{**6**} & \multicolumn{2}{c}{**7**} & \multicolumn{2}{c}{**8**} & \multicolumn{2}{c}{**9**} \\ \hline \multirow{2}{*}{1} & **Fall of person** & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 11.959 & *** & *** & *** & *** & *** & *** & *** & *** & *** \\  & **from height** & (5.094) & & & & & & & & & & & \\ \hline \multirow{2}{*}{2} & **Slip, trip or fall** & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 0.0000 & *** & & ns & ns & * & ns & ns & * & * \\  & **on same level** & & & & & & & & & & & & \\ \hline \multirow{3}{*}{3} & **Sriking against** & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 0.201 & *** & ns & & ns & * & ns & ns & * & * \\  & **object** & & & & & & & & & & ns & * & * \\ \hline \multirow{3}{*}{4} & **Sriking against** & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 2.434 & *** & ns & ns & & ns & ns & ns & ns & ns \\  & **moving object** & & & & & & & & & & & ns & ns & ns \\ \hline \multirow{3}{*}{5} & **Contact with** & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 2.919 & *** & * & * & ns & & * & ns & ns & ns & ns \\  & **electric discharge** & & & & & & & & & & & ns & ns \\ \hline \multirow{3}{*}{6} & **Injured while** & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 0.0000 & *** & ns & ns & ns & * & & ns & * & ns & * \\  & **between objects** & & & & & & & & & & ns & ns & ns \\ \hline \multirow{3}{*}{7} & **Trapped in or** & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & 1.285 & *** & ns & ns & ns & ns & ns & & ns & ns \\  & **between objects** & & & & & & & & & & ns & ns \\ \hline \multirow{3}{*}{8} & **Struck by falling** & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & 2.879 & *** & * & * & ns & ns & * & ns & & ns \\  & **object** & & (2.093) & & & & & & ns & * & ns & & ns \\ \hline \multirow{3}{*}{9} & **Others** & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & 3.021 & *** & * & * & ns & ns & * & ns & ns \\  & **(2.199) & & & & & & & & & ns & ns \\ \hline \hline \end{tabular}  
* Others: includes all accidents types other than above mentioned in occupational safety and health statics, Labour department of Hong Kong. Results are significant as:
* indicates p-value < 0.05; *
* indicates p-value < 0.01; **
* indicates p-value < 0.001 and \"ns” indicates insignificant difference results.  
\end{table} 
Table 5: Result of ANOVA analysis based on mortality rates (MRs). SD = 5.084) was the only accident type which manifested the higher significant results (***) with all other accident types during the analysis period. From the analysis of results, it was found that all other accident types have shown the significant effect with \"fall of person from height\" throughout the analysis period as shown in Table 5 below.
## 4 Discussion
### Factor Analysis for Spatial Variation of WUE  
The results of factor detection and interaction detection were analyzed jointly. In all four sub-areas, temperature and its interaction with other factors show strong explanatory power. Temperature is the dominant factor in the spatial distribution of WUE. It is consistent with the previous study [62]. [PERSON] et al. [10] also found that temperature was the most crucial driver of forest WUE change in India. Moreover, precipitation, LAI, and specific humidity also have strong explanatory power, but exhibit significant spatial heterogeneity. In the eastern QM, the top three interactions in terms of explanatory power were PR\(\cap\)LAI (0.67), AT\(\cap\)LAI (0.62), and HUM\(\cap\)LAI (0.57). In the central QM, the top three interactions were AT\(\cap\)LAI (0.65), PR\(\cap\)LAI (0.64), and SR\(\cap\)LAI (0.61), in that order. Although the explanatory power of LAI is not great when acting alone, its combination with temperature and precipitation can enhance the explanatory power. It suggested that the effect of temperature and precipitation on the spatial variation of WUE may be achieved by changing LAI [58]. The explanatory power of precipitation is considerably greater in the sparsely vegetated and arid western QM and HC than that in the relatively humid eastern and central QM. Namely, precipitation shows a stronger effect on the spatial variation of WUE in arid lands compared to relatively humid areas. Interaction detection also further corroborated the above conclusions. The top three interactions in the western QM and HC are all precipitation related.
## 4 Results
### Development of an Artificial Neural Network-Based Equation  
The FEA results of the corroded X65 pipe provided insights for suitable parameters and their range in the development of the ANN model. The parameter that had the most impact on failure pressure was found to be defect depth, followed by longitudinal compressive stress, defect length, and defect width. The average reduction rates in failure pressure due to defect depth, longitudinal compressive stress, and defect length were \(-0.54\), \(-0.41\), and \(-0.06\), respectively. The effect of defect width was negligible, as its average rate of decrement in failure pressure was \(-0.002\). Therefore, the defect width was fixed at 10 w/t in the FEA of X65 for the ANN training dataset. The FEA results for the ANN training dataset are tabulated in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Defect Parameters**} & \multicolumn{2}{c}{**External Load**} & \multicolumn{2}{c}{**Normalised**} & \multicolumn{2}{c}{**Defect Parameters**} & \multicolumn{2}{c}{**External Load**} & \multicolumn{2}{c}{**Normalised**} \\ \cline{2-9} _d/t_ & _I/D_ & _w/t_ & \(\sigma_{c}\)/\(\sigma_{y}\)_ & **Failure Pressure** & _d/t_ & _I/D_ & _w/t_ & \(\sigma_{c}\)/\(\sigma_{y}\)_ & **Failure Pressure** \\ \hline
0.2 & 0.8 & 10 & - & 0.82 & 0.5 & 0.8 & 6 & 0.5 & 0.57 \\
0.4 & 0.8 & 10 & - & 0.68 & 0.5 & 0.8 & 14 & 0.5 & 0.5  
For the formulation of the empirical equation, 176 sets of X52 FEA results from the work of [PERSON] et al. (2019) and 81 sets of X80 FEA results from the work of [PERSON] et al. (2020) were used to train the ANN ([PERSON] et al., 2020, 2011), in addition to the FEA results of corroded X65 pipelines. The ANN model was developed using MathWorks MATLAB. The architecture of the ANN is illustrated in Figure 7.  
The ANN was based on a feedforward neural network (FFNN), with Levenberg-Marquardt backpropagation as its training algorithm. The inputs were the true UTS of the pipeline, normalised corrosion defect depth (\(d/t\)), normalised corrosion defect length (\(l/D\)), normalised corrosion defect width (\(w/t\)), and normalised axial compressive stress, \(\sigma_{c}/\sigma_{y}\). The output was normalised failure pressure, \(P_{f}/P_{i}\). The ANN had two hidden layers, with six hidden neurons in each layer. The number of hidden neurons was determined with trial and error to find the best performing configuration. The activation functions for the hidden layers were a hyperbolic tangent sigmoid transfer function and a linear transfer function for the output layer. The input and output neurons were normalised, and the values of true UTS, \(d/t\), \(l/D\), \(w/t\), \(\sigma_{c}/\sigma_{y}\), and \(P_{f}/P_{i}\) were set to be between the range from \(-1\) to \(1\). Equation (5) was used to normalise the values to prevent inputs with large values from dominating other inputs.
## 4 Role of URLLC in Operating IoT
### URLLC and Vehicle-to-Vehicle (V2V)  
One of the most promising and important applications of the future 5G network is V2V communication. V2V communication is one of the technologies that can lead to an intelligent transport system [39]. Naturally, for V2V, road safety (distance awareness to avoid any collision, speed limits, location-based traveling, environment information, road condition) plays a vital role and is extremely time-critical, as shown in Figure 10.  
Because of the safety concern, European Telecommunications Standard Institute (ETSI) has standardized safety protocols based on two awareness-based messages: decentralized environmental notification message (DENM) and cooperative awareness message (CAM) [40]. To reflect vehicles based on the mentioned safety standards, V2V communication should have the low latency characteristic of URLLC. As discussed in Section 3.6, handover is still an issue in URLLC implementation owing to the mobility of vehicles (as shown in Table 4) delivering safety standard messages.  
Figure 9: Traditional machine learning (ML) concept (centralized). AI, artificial intelligence.
# Intrusion Detection for Industrial Control Systems Based on Improved Contrastive Learning SimCLR
## 4 Experiments and Results Analysis
### Model Evaluation Metrics  
In this experiment, four metrics were adopted to evaluate the model: Accuracy, Precision, Recall, and F1, in which several basic concepts need to be introduced:  
TP (True Positive): The prediction is positive, and the actual case is positive.  
FP (False Positive): Predicted positive case, actual negative case.  
FN (False Negative): Predicted negative case, actual positive case.  
TN (True Negative): The predicted case is negative, and the actual case is negative.  
Accuracy rate: It indicates the percentage of all correctly classified samples in the total number of samples. The specific calculation formula is shown in Equation (12):  
\[Acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{12}\]  
Precision rate: indicates the percentage of correct predictions that are positive among all predictions that are positive. The specific calculation formula is shown in Equation (13):  
\[\text{Precision }=\frac{TP}{TP+FP} \tag{13}\]  
Recall: indicates the percentage of correct predictions that are positive in all actual positive. The specific calculation formula is shown in Equation (14):  
\[\text{Recall }=\frac{TP}{TP+FN} \tag{14}\]  
\(F_{1}\): The calculation formula is shown in Equation (15):  
\[F_{1}=\frac{2}{1/\text{ precision }+1/\text{ recall}} \tag{15}\]
_Appl. Sci._**2012**, \(2\), 13-23; doi:10.3390/app2010013  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Surface Sampling of a Dry Aerosol Deposited Ricin**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 Edgewood Chemical Biological Center, United States Army, Department of Defense,  
5183 Blackhawk Road, Aberdeen Proving Ground, MD 21010, USA;  
E-Mails: [EMAIL_ADDRESS] (P.J.C.); [EMAIL_ADDRESS] (R.L.B.)  
2 Science Applications International Corporation, 1710 SAIC Drive, McLean, VA 22102, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-410-436-7348; Fax: +1-410-436-2165.  
_Received: 8 December 2011; in revised form: 28 December 2011 / Accepted: 28 December 2011 / Published: 13 January 2012_  
**Abstract:** Sampling of small molecules from both porous and non-porous surfaces poses a significant challenge across biological agents. Particle sizes of toxins are smaller than living organisms and can be extremely toxic at low level concentrations. A small number of studies evaluating sampling efficiencies of commercial off the shelf (COTS) materials have been performed with toxins and proteins. However, they have been limited to non-ricin stimulants with drastically different physical properties than their native counterparts. We have identified a commercially available non-toxic recombinant ricin, complete with both A and B subunits present, which can be recognized by antibodies commonly used to assay native ricin. In evaluating recovery efficiency, we deposited the recombinant ricin by both liquid deposition, and as a dry aerosol. Our studies demonstrated a significant difference in recovery efficiencies from liquid deposited ricin, ranging between 30% and 70%, than from an aerosol generated deposition ranging from below detectable levels to 22%, depending on the contaminated surface and swab material being used. This study demonstrates the necessity for accurate dissemination techniques of sampling technologies for the consideration of use in an environment where suspected toxin contamination is being evaluated.  
**Keywords:** surface sampling; recovery efficiency; bioterrorism; detection; ricin; toxin
## 2 Materials and Methods
### FTIR Measurements  
FTIR spectra were measured using the background-corrected ATR-FTIR technique (i.e., Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy) (16 scans per sample), with an HATR Ge crystal attachment (crystal truncated at 45\({}^{\circ}\) to allow for 10 internal reflections) in the form of a crystal plate, at room temperature T = 23 \({}^{\circ}\)C. All measurements were performed using a 670-Varian Spectrometer IR (Agilent, Santa Clara, CA, USA). Prior to each measurement, the crystal was cleaned using ultrapure solvents (Sigma-Aldrich, St. Louis, MO, USA). The apparatus was constantly purged with argon approximately 60 min prior to and during all measurements. The obtained infrared spectra were measured at the resolution of one 2 cm\({}^{-1}\) within the spectral range between 3200 and 800 cm\({}^{-1}\). The Fourier-transformed and averaged spectra were analysed and processed using Grams/AI 8.0 software (Thermo Fisher Scientific, Waltham, MA, USA).
## 5 Experiment and Evaluation
### Evaluation  
We insist that the proposed intrusion detection system achieves all design goals presented in Section 3.  
**Universality.** Since internal states of a target and an emulated device are synchronized, all sort of remote code attacks that cause memory modification in a target device will be detected by memory integrity monitor.  
**Generality.** Major network vendors such as Cisco and Huawei provide VM images of their virtualized routers in standardized format (e.g., OVA) to support various VMMs including VMWare ESXi, Citrix Xen, or KVM. Since the proposed system has no dependency on any specific VMMs, generality can be achieved.  
Figure 4: Experimental result.  
Runtime support.Memory integrity monitor periodically takes snapshots from an emulated device. As shown in the experiment in the previous section, the attack can be detected at least within around 2 s. The detection time is mainly determined from the delay of memory dump. Recent studies show that such delay is reasonable (e.g., dumping 1 GB memory via GCORE takes about 1.5 s [12]), thus an event of remote code attack can be detected in time to sufficiently mitigate the attack.  
Availability.Unlike previous works [15], the proposed system requires no modification to firmware of a target device. Besides, mirroring ingress traffic also does not affect the existing network topology. Therefore, the proposed system achieves availability.
## 3 Results and Discussion
### Practical and Economic Feasibility  
The practical and economic aspects of the proposed double-strap joint method for FRP bar splicing have been analyzed relative to the classic lap splice method. One hypothetic RC beam with a length of 6 m, strengthened with one FRP bar of \(\mathcal{O}\)10, is chosen for this analysis. Required groove sizes and the consumption of materials are compared in Table 2 for both cases of FRP splicing. The required groove cross-section for the double-strap joint method is 20 \(\times\) 20 mm, with the grove widening to 50 \(\times\) 20 \(\times\) 600 mm in the cut-off zone. In the case of the classic lap splice method, it is assumed that the groove width is equal to the double value of the required groove width for one FRP bar, i.e., the groove cross-section is 40 \(\times\) 20 mm, considering that such a solution provides better results regarding the beam strength compared to the solutions with two grooves (one groove for each FRP bar) [28]. It is determined that the required overlapping length is 60\(\mathcal{O}\) and 110\(\mathcal{O}\) for the double-strap joint and classic lap splice method, respectively, considering that these overlapping lengths provide beam strengths equal to cases of beams with continual additional FRP bars. It should be mentioned that this analysis covers required quantities of FRP bars, epoxy, and primer for concrete surface preparation. The consumption of epoxy is 1.55 kg/m\({}^{2}\) per mm of thickness, and the consumption of primer is 300 g/m\({}^{2}\), where both values have been adopted according to the manufacturer's data [20]. Based on the conducted analysis, one may conclude that the double-strap joint method requires about 40% less groove volume/epoxy and about 25% less groove surface area/primer compared to the classic lap splice method for the analyzed case. In addition to savings in material, the required execution time is less in the case of the double-strap joint method, which is also a very important factor in such an analysis. These results should provide more benefits regardless of the negligibly higher required FRP length (less than 1.50% in the analyzed case). It is worth mentioning that a cost analysis has not been provided here, because the costs of the material and construction work are different in different countries.