esa-sceva/satcom-chunk-collection huggingface dataset:
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 4 Method to Identify Activity Executions
### Then Details-on-Demand: Determine Start and End Patterns and Activity Signatures
#### 4.4.2 Activity Signatures  
Once the time-related borders of activity \(A\) were clearly identified, we derive its _activity signature_\(AS(A)\). We define the activity signature \(AS(A)\) of activity \(A\) as: the sequence of all sensor and actuator data represented as time series for the particular CPS component within the identified activity boundaries relative to its start time \(t_{As}\) (with \(t_{As}=0\)) and its end time \(t_{Ac}\) relative to \(t_{As}\). This sequence of sensor and actuator data is associated with a unique _activity label_\(L_{A}\)[5]. Following the idea of the _digital twin_ being a digital representation of a physical component or object [65; 66], we regard a processing activity as an abstract digital concept and its activity signature belonging to the activity's _physical twin_. The activity signature represents the physical manifestation of the execution of the corresponding process activity in the sensors and actuators of the IoT environment [36].  
Figure 13 shows the activity signature for the _Transport from Oven to Milling_ activity (cf. Figure 10) executed by the WT_1 component (relative time in seconds on x-axis, values of sensors and actuators on y-axis). Figure 14 shows the more complex activity signature for the _Get Workpiece from Pickup_ activity (cf. Figure 12) executed by the VGR_1 component.  
Figure 13: Activity Signature for _Transport from Oven to Milling_ executed by WT_1.
## 4 Case Studies
### Energy Reduction Schemes  
Greenhouse gas (GHG) reduction is a global challenge. In the EU passenger vehicles contribute 60.6% of the total CO2 emissions from road transportation [63]. Telematic devices can assist in promoting efficient driver behavior, thereby increasing fuel efficiency, and reducing emissions. Reductions in emissions is also possible through optimal vehicle routing. GPS and speed information can be broadcasted to a neighborhood of vehicles, diverting traffic from congested areas or for efficient arrival at intersections. EVs can also benefit from the usage of telematic devices as they provide accurate recordings of the current vehicle state.  
The technical limitations of battery performance monitoring systems reduce the accurate readings of SOC and battery health. Common SOC estimation methods such as EMs, ECMs, and EIMs cannot provide a comprehensive description of battery degeneration and subsequently provide inaccurate readings of SOC [40]. High SOC levels also contribute to battery degeneration, reducing the lifetime of the battery [46]. The battery is considered at the end-of-life when the charging state is reduced by 20% [64]. Inaccurate readings of the battery and a high state of charge can contribute to 52% reduction in battery lifetime [46]. The average lifetime of a battery is 10 years [46; 64], so inaccurate readings of SOC could reduce the battery lifetime to 5 years.  
Stringent regulation on data access can have an adverse effect on the optimal fuel efficiency recommendations that will be present in EVs. Methods that model driving behavior for fuel efficiency optimization require access to a driver's telematic data [65]. Access to raw telematic data will not be freely available, as regulatory requirements for host vehicle processing or transmitting data to a third party is required [1].  
Battery health monitoring is a key factor in efficiently charging an EV and prolonging the battery lifetime. If the battery health degenerates significantly, the range that the EV can travel will be reduced. The cost of replacement of the battery within an EV will be significant for the driver, typically the battery is valued at 25-30% of the price of the vehicle [66]. The combination of reduced range and high battery replacement costs may contribute to poor market acceptance of EVs.  
Battery performance and fuel optimization methods that provide customized behavioral analysis of driving behavior will also be limited by the restricted access to telematic data. Access to personal data will require explicit definitions of how the data is intended to be used and requires consent before processing. The user has the right to deny access to their telematic data or in the case of data transmission, can terminate the process. Data transmission from the vehicle will also be subject to the processing restrictions determined by the EDBP. Models which provide real-time improvements through the transmission of telematic data will become inefficient due to the limitations of data access.
# Congestion Control and Traffic Differentiation for Heterogeneous 6 TiSCH Networks in IIoT
## 4 Multi-Queue Model and Priority-Based Transmission
### Mathematical Analysis
#### 4.2.3 Average Queue Waiting Time in Q\({}_{3}\)  
According to the FIFO approach in Q\({}_{3}\), an arbitrary tagged T\({}_{3}\) packet has to wait for the transmission of all packets that arrived ahead. Since Q\({}_{3}\) has the lowest priority, \(\overline{W_{Q_{3}}}\) is directly affected by the arrivals of T\({}_{1}\) and T\({}_{2}\). In addition to the average number of packets already waiting in Q\({}_{1}\), Q\({}_{2}\), and Q\({}_{3}\), which are \(\overline{\alpha_{1}}\overline{W_{Q_{1}}}\), \(\overline{\alpha_{2}}\overline{W_{Q_{2}}}\), and \(\overline{\alpha_{3}}\overline{W_{Q_{3}}}\), respectively, a tagged packet that arrives at Q\({}_{3}\) has to wait for higher priority packets to arrive at Q\({}_{1}\) and Q\({}_{2}\), which are on average \(\overline{\alpha_{1}}\overline{W_{Q_{3}}}+\overline{\alpha_{2}}\overline{W_{Q _{3}}}\) packets. Accordingly, \(\overline{W_{Q_{3}}}\) is given as:  
\[\overline{W_{Q_{3}}} =\frac{T_{c}}{2}\left(3\left(\overline{\alpha_{1}}\overline{W_{Q _{1}}}+\overline{\alpha_{2}}\overline{W_{Q_{2}}}+\overline{\alpha_{3}} \overline{W_{Q_{3}}}+\overline{\alpha_{1}}\overline{W_{Q_{3}}}+\overline{ \alpha_{2}}\overline{W_{Q_{3}}}\right)+1\right) \tag{25}\] \[=\frac{T_{c}}{2}\left(3\left(\left(\alpha_{1}\frac{1-\left(\alpha _{1}T_{c}\right)^{K}}{1-\left(\alpha_{1}T_{c}\right)^{(K+1)}}\right)\left( \overline{W_{Q_{1}}}+\overline{W_{Q_{3}}}\right)+\left(\alpha_{2}\frac{1- \left(\alpha_{2}T_{c}\right)^{K}}{1-\left(\alpha_{2}T_{c}\right)^{(K+1)}} \right)\left(\overline{W_{Q_{2}}}+\overline{W_{Q_{3}}}\right)\right.\] \[+\left.\left(\alpha_{3}\frac{1-\left(\alpha_{3}T_{c}\right)^{K}}{ 1-\left(\alpha_{3}T_{c}\right)^{(K+1)}}\right)\overline{W_{Q_{3}}}\right)+1 \right).\]  
The average queue waiting times given in Equations (22), (24), and (25) must satisfy the Kleinrock conservation law [40]:  
\[\sum_{i=1}^{3}\rho_{i}\overline{W_{Q_{i}}}=\frac{\sum_{i=1}^{3}\rho_{i} \overline{W_{0}}}{1-\sum_{i=1}^{3}\rho_{i}}, \tag{26}\]  
where \(\rho_{i}=\overline{S_{i}}\overline{\alpha_{i}}\) is the utilization factor of Q\({}_{i}\), \(\overline{S_{i}}\) is the average service time of packets in Q\({}_{i}\), and \(\overline{W_{0}}\) is the average residual service time of the packet whose transmission is in progress. According to the mean residual life formula [40], \(\overline{W_{0}}\) is given as:  
\[\overline{W_{0}}=\sum_{i=1}^{3}\rho_{i}\frac{\overline{S_{i}}^{2}}{2S_{i}}= \frac{T_{c}^{2}}{2}\sum_{i=1}^{3}\overline{\alpha_{i}}. \tag{27}\]  
Since we have a deterministic service time for Q\({}_{1}\), Q\({}_{2}\) and Q\({}_{3}\), i.e., \(\overline{S_{i}}^{2}=T_{c}^{2}\), \(\overline{W_{0}}\) in Equation (27) can be obtained as follows:  
\[\overline{W_{0}}=\frac{T_{c}^{2}}{2}\left(\alpha_{1}\frac{1-\left(\alpha_{1}T_ {c}\right)^{K}}{1-\left(\alpha_{1}T_{c}\right)^{(K+1)}}+\alpha_{2}\frac{1- \left(\alpha_{2}T_{c}\right)^{K}}{1-\left(\alpha_{2}T_{c}\right)^{(K+1
## 4 Results and Findings
### Most-Cited Countries and Their Publications  
Australia ranks with 19,977 citations, a very slight distance from the USA with 19,051 citations. This implies that Australia is well represented in the field and participates in several kinds of research. However, it is always good to note that although there are more publications from Australia, it is visible that research from the USA is potentially having a more significant impact, as revealed by the citation data. The citation represents the degree of visibility and the utility of research by scholars from various countries.  
Figure 7: Country co-authorship network of top 10 countries.  
China is the third on the list, with 12,094 citations. However, as presented here, China has fewer ML/DL and cybersecurity citations than Australia and the USA. Still, it is growing, and the citation records suggest that the research is making a growing impact. The total link strength, which tends to give insight into the connection between a country's research output and other nations, displays the degree of collaboration. These collaborations, together with China's increased participation and its place within these international co-authorship networks, highlight the fact that more members of the academic community are recognising and engaging with Chinese research.  
The United Kingdom is fourth with 10,994 citations, which indicates European countries' dominance. However, the number of such publications differs significantly between the countries, with the USA and Australia boasting more prominent citation scores than their publications. This indicates that even though the UK produced numerous publications in human resource management, its research might not have been as impactful internationally.  
Saudi Arabia is in fifth position with 9281 citations, while India stands in sixth with 8072. A particular focus should be placed on Saudi Arabia as it underlines its increased participation in exploring cybersecurity and ML/DL research with support from the institutional framework and funding of advanced technologies. India, although lagging slightly, is emerging as a force in the research area, emphasising the cost-effectiveness of solutions that address cybersecurity issues, particularly in the context of developing countries. Other countries with appreciable contributions include Spain, Canada, Pakistan, and Italy, ranked seventh to tenth. For example, Spain comes seventh with 6952 citations, showing its moderate activity within the field. However, it still does not reach countries such as the USA and Australia in terms of citations. Canada and Pakistan show considerable engagement, but their research influence, as reflected in citations, might not be as widespread as that of the USA or China (see Table 8).
# Analysis and Optimization of the Robotic Mobile Fulfillment Systems Considering Congestion
## 3 Modeling and Solution
### Solution
#### 3.3.5 Performance Index Estimation  
The solution of the SOQN model gives the average queue length \(L_{i}(i\in\{1,\ldots,N+4\})\) of vehicles at node \(i\). The formula of \(Q_{i}\) is as follows:  
\[L_{i}=(L_{i}|y\leq 0)(\pi(y\leq 0))+(L_{i}|y>0)\rho(\pi(y\geq 0)) \tag{26}\]  
The main performance index of the system is the utilization rate of the vehicle \(U_{R}\), task cycle time or throughput time \(E[CT]\), the average length of tasks waiting for service in external queue \(L_{\mathbb{Q}_{o}}\), the expected aisle blocking delay time \(E[ABD]\). The number of idle vehicles in the internal queue can be obtained by calculating the average number of customers in the synchronization node.  
\[L_{\mathbb{Q}_{R}}=(L_{A+6}|y\leq 0)\pi(y\leq 0) \tag{27}\]  
The proportion of idle vehicles is expressed as \(\frac{L_{\mathbb{Q}_{R}}}{R}\). Then according to the Utilization Law, the utilization rate of the vehicle is expressed as \(U_{R}=1-\frac{L_{\mathbb{Q}_{R}}}{R}\). According to the Pollaczek-Khinchin average queue length equation of the M/D/1 queuing system, the average queue length \(L_{Q_{o}}\) of the external queue can be obtained. The external queue only exists when \(y\geq 0\), that is, \(\left(L_{Q_{o}}\middle|y\geq 0\right)\). The unconditional queue \(L_{Q_{o}}\) is obtained by multiplying the conditional value by the steady-state probability, the formula is as follows:  
\[L_{Q_{o}}=\bigg{(}\rho+\frac{\rho^{2}}{2(1-\rho)}\bigg{)}\pi(y\geq 0) \tag{28}\]  
According to the average queue length and Little's Law, the expected task cycle time \(E[CT]\) can be obtained. Task cycle time includes two parts, one is the average waiting time of task matching idle vehicle, the other is the task execution time. Therefore, the task cycle time is expressed as the following formula:  
\[E[CT]=\frac{L_{Q_{o}}}{\lambda}+\frac{\sum_{i=1}^{A+5}L_{i}}{\lambda} \tag{29}\]  
The delay time due to blockage can be calculated by calculating the waiting time in the aisle. By using Little's Law to calculate the running time of the vehicle in the aisle, the expected aisle blocking delay time \(E[ABD]\) can be obtained as follows:  
\[E[ABD]=\frac{\sum_{i=1}^{A}L_{i}}{\lambda}-E[t_{A}] \tag{30}\]  
After the delay time of aisle blockage and the cycle time of expected tasks are obtained, the ratio of the delay time of aisle blockage can be obtained, and the influence of delay factors on system performance can be analyzed. The ratio is \(\frac{E[ABD]}{E[CT]}\). Then the accuracy of the solution and the influence of blocking delay factors on the system performance is analyzed through specific simulation and numerical experiments.
## 5 Results and Evaluation
### Comparative Analysis
#### 5.2.2 Performance Comparison with DupPR-Complementary Data
The recall of proposed GDN-GS technique is 8.42%, 13.86%, 19.31%, 24.75%, 30.19%, 35.63%, 41.07%, and 46.51% more efficient than the existing techniques, which include PRs-text, PRs-text-similarity, PRs-improved, support vector machine, k-nearest neighbour, random forest, hierarchical clustering, and K-means clustering. The F-measure of proposed GDN-GS technique outperforms existing techniques such as PRs-text, PRs-text-similarity, PRs-Improved, support vector machine, k-nearest neighbour, random forest, hierarchical clustering, and K-means clustering by a factor of 8.54%, 14%, 19.47%, 24.94%, 30.40%, 35.87%, and 46.80%, respectively. The SRE of our proposed GDN-GS technique outperforms the state-of-the-art PRs-text, PRs-text-similarity, PRs-improved, support vector machine, k-nearest neighbour, random forest, hierarchical clustering, and K-means clustering techniques by 7.96%, 13.55%, 19.14%, 24.73%, 30.32%, 35.91%, and 47.09%, respectively.
# Effect of Cation Influx on the Viability of Freeze-Dried _Lactobacillus brevis_ WiKim0069
## 3 Results
### Cold Adaption Enhances Cation Influx  
To investigate the correlation between viability and cations inside _L. brevis_ WiKim0069 during freeze-drying, the cation influx was measured by confocal microscopy using Fluo-4 AM (to visualize Ca\({}^{2+}\)) and Mag-Fluo-4 AM (to visualize Mg\({}^{2+}\)). Intracellular Ca\({}^{2+}\) influx was observed in the presence of soy powder (Figure 3a). Ca\({}^{2+}\) influx was further increased when _L. brevis_ WiKim0069 was cold-adapted prior to the freeze-drying process. However, when the soy powder was treated with EDTA, a cationic resin filter, or EGTA, Ca\({}^{2+}\) influx was not observed. Similar to Ca\({}^{2+}\) influx, Mg\({}^{2+}\) influx was observed in the presence of soy powder (Figure 3b). Pretreatment with EDTA or a cationic resin filter caused a reduction in Mg\({}^{2+}\) influx, whereas treatment with EGTA had no effect on Mg\({}^{2+}\) influx. These results  
Figure 2: Cold adaption enhances the storage stability of _L. brevis_ WiKim0069 freeze-dried in the presence of soy powder. Storage stability was assessed as the viability of freeze-dried _L. brevis_ WiKim0069 at the indicated time points during storage for 58 days. w/o: Without; w/: With.  
indicated that cold adaptation at 10 \({}^{\circ}\)C prior to freeze-drying increases the intracellular cation influx, resulting in enhanced viability of freeze-dried _L. brevis_ WiKim0069.  
Figure 3: Cold adaption enhances Cation influx into _L. brevis_ WiKim0069 cells in the presence of soy powder. Confocal micrographs revealing Ca\({}^{2+}\) (**a**) and Mg\({}^{2+}\) (**b**) influx into _L. brevis_ WiKim0069 cells under different cryoprotective conditions. SP: Soy powder extract; SP-EDTA: Soy powder pretreated with EDTA; SP-EGTA: Soy powder pretreated with EGTA; SP-Filter: Soy powder pretreated with a cation filter.
## 4 Results
### Methods Comparison Analysis  
In this section, we compare the proposed method SemiPolypSeg with other current SOTA semi-supervised algorithms under different partition protocols on Kvasir-SEG and CVC-ClinicDB, including mean teacher (MT) [20], Cross-Consistency Training (CCT) [21], CMT [19], and UCMT [19]. HTCSNet's fully-supervised training, using only the specified proportion of labeled data, produced the baseline model. To establish a performance benchmark, HTCSNet underwent fully supervised training with 100% labeled data, delineating the upper limit of its capabilities. For the performance of alternative methods, we utilized the findings presented in the paper [19]. To ensure fair comparisons, our method was implemented using two segmentation networks: DeepLabv3+ and HTCSNet, respectively.  
Table 4 presents the outcomes of comparative experiments on Kvasir-SEG under 15% and 30% labeled images. With 15% labeled images, the proposed SemiPolypSeg method with DeepLabv3+ architecture demonstrated consistent and significant advancements compared to other SOTA semi-supervised learning methods in terms of the DSC. By using 15% labeled images and 85% unlabeled images, the DSC increased by 1.59%, 7.89%, 0.95%, and 0.35% compared to MT, CCT, CMT, and UCMT, respectively. When using 30% labeled images, all semi-supervised learning methods showed improvement. Our approach still exhibited substantial performance gains, with the DSC increasing by 1.19%, 5.24%, 1.3%, and 0.85% compared to MT, CCT, CMT, and UCMT, respectively.  
In addition, comparing the DeepLabv3+ architecture, we conducted experiments on Kvasir-SEG under 15% and 30% labeled images using the SemiPolypSeg framework with the HTCSNet architecture that is proposed in this paper. The results demonstrate our proposed new architecture (HTCSNet)'s effectiveness. Our method with HTCSNet obtained the highest DSC of 89.68% and 90.62%, outperforming the SemiPolypSeg framework with DeepLabv3+ architecture by 0.65% and 0.71%, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lr** & \(\beta_{1}\) & \(\beta_{2}\) & **Weight Decay** \\ \hline
0.0001 & 0.9 & 0.999 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters of the AdamW optimizer.  
Table 5 presents the performance of our proposed method compared to other SOTA methods using 15% and 30% labeled images on CVC-ClinicDB. Similarly, our method achieved the top performance with the highest DSC of 89.72% and 90.06% for 15% and 30% labeled data, respectively. It outperformed other approaches (MT, CCT, CMT, and UCMT) by 5.21% (**5.28%**), 11.15% (**15.27%**), 2.78% (**3.59%**), and 2.1% (**2.17%**) for 15% (**30%**) labeled data, respectively. Through experiments on these two datasets, we can find that our method significantly improves performance with limited labeled data compared to other methods. This indicates that it can more efficiently utilize unlabeled images than other semi-supervised methods.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 1 Introduction
Then, the SMS classification is a typical decoding problem in HMM by using the Viterbi algorithm. To the best of our knowledge, this is the first research on applying HMM for SMS spam detection based on word order. The main contributions of this research are threefold:  
1. We first propose to use a hidden Markov model for spam SMS detection based on word order. This method uses the word order information that consists of the key importance for human language, but it has been ignored by many traditional methods based on the BoW model. 2. This research solves the issue where the TF.IDF algorithm for word weighting does not work well in SMS spam detection, due to the extremely low term frequency. 3. The proposed method can be applied to alphabetic text (e.g., English) and hieroglyphic text (e.g., Chinese). It is not language-sensitive. The rest of the paper is organized as follows. Related work is discussed in Section 2. The problem formulation and the proposed SMS spam detection method based on the discrete HMM are presented in Section 3. The experimental results and performance comparisons with well-known models are outlined in Section 4. The conclusions are drawn and future work is discussed in Section 5.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 3 Countermeasures against Web Tracking
### Challenges
#### 3.3.2 Bot Detection  
[PERSON] et al. [66] analyzed the reliability of OpenWPM and found that scripts detecting the presence of display and wrapper functions could detect bots on OpenWPM, and at least 16.7% of sites on Tranco Top 100k recognized Selenium and OpenWPM. [PERSON] et al. [13] suggested that crawler detection could use fingerprinting. Accordingly, researchers conducted a study to analyze the script of the Alexa 10K site, modified the properties of the Web bot step by step, and checked the bot detection rate of the website. As a result, researchers confirmed that changing the attribute to match an actual browser resulted in a decrease in the detection rate. [PERSON] et al. [67] introduced the concept of JavaScript template attack, which allowed trackers to detect bots and create a healthy crawler. JavaScript accesses all child and parent objects through a prototype chain. In the DOM structure, window objects exist at the top, and the concept of JavaScript template attack is to explore all the properties that exist by traversing the window. By comparing the differences between the general browser environment and the Web bot environment, the attack can identify potential bots. However, the challenge of increased execution timebetween crawling and detecting sophisticated bot detection codes remains, despite solutions proposed by researchers. To address the limitations of the previously mentioned bot detection methods, several improvements can be proposed. First, AI-driven behavioral analytics: Advanced machine learning models that analyze subtle user behaviors (e.g., mouse movements, interaction timing) can be used to detect bots. AI can identify patterns that differentiate legitimate users from bots, while minimizing false positives. Second, botnet fingerprinting: A botnet detection system can be employed to identify common infrastructures or behaviors shared by known tracking bots or Web crawlers. This would enhance the detection of coordinated tracking attempts across multiple sites. Lastly, CAPTCHA alternatives: less intrusive and passive bot detection methods, such as behavioral biometrics that measure unique human behaviors (e.g., typing rhythm), can be implemented without requiring explicit user interaction.