esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
It is expected that metabolically active cells such as the culturable cells are more likely to be eGFP-producing cells, as they have more biosynthetic precursors, energy, and other cellular components available for the synthesis of the foreign protein. In the particular case of biofilms grown in LB medium, the higher levels of expressing cells on days 5-7 may also be related to the simultaneous decrease in biofilm thickness, since the transfer of oxygen for the maturation of the protein under study is facilitated in thinner biofilms [9]. Plasmid stability is an important factor in heterologous protein production [5]. In this work, high specific eGFP concentration was obtained in biofilm cells, although on average higher copies of plasmid were found in planktonic cells. These results suggest that the gene dosage was not responsible for the expression of eGFP. The same phenomenon was observed by [PERSON] et al. [6], where biofilm cells had more eGFP concentration even though they presented seven-fold lower PCN than planktonic cells. In the present work, the low eGFP expression levels in planktonic cells for both media may be caused by the high metabolic burden linked to plasmid replication. This added metabolic burden usually causes structural and segregational plasmid instability as well as several metabolic changes in the host cell, which may reduce the yield and activity of the heterologous protein [29, 50]. Although a small number of plasmids were found in biofilm cells in LB, the biofilm environment seems to be advantageous for plasmid retention, since the PCN decrease was more accentuated in suspended cells than in the biofilms formed in the flow cell system (89% versus 59% reduction). Similar to our results, [PERSON] et al. [16] demonstrated that _E. coli_ cultivation as a biofilm has a positive effect on high copy number plasmid maintenance compared to chemostats. The reason for the higher plasmid maintenance in biofilms is that sessile cells tend to grow more slowly than planktonic cells [51], leading to fewer cell divisions and consequently less plasmid partitioning. Furthermore, it was reported that the use of yeast extract and tryptone as nitrogen sources may result in higher plasmid stability [28]. The results obtained in this optimization work are essential to define the operating conditions to be used in further studies focused on the synthesis of other heterologous proteins of different sizes and involved in synthetic pathways for the production of metabolites of high market value.
## 4 Results
### Theoretical Desorption Rate  
The experimental and theoretical desorption rates are compared in Figure 5. The maximum deviation of the mathematical model with respect to the experimental data was 6.89%. This mathematical model has been reported and validated in a previous paper [16], and it was based on the AGMD separation process [36; 37]; however, the H\({}_{2}\)O/LiBr solution transport properties, such as viscosity, limited the mass transfer process. Therefore, the membrane desorption rate can be improved by renewing the solution-membrane boundary layer [19].  
The validation of the mathematical model with the experimental data allowed us to calculate the thermal heat load required for a large-scale membrane desorber/condenser. As mentioned in Section 2, the driving force of the AGMD process is the temperature difference across the membrane. Figure 6 depicts the desorption rate as a function of the average temperature difference between the H\({}_{2}\)O/LiBr solution and the cooling water (\(T_{LiBr,ap}-T_{cw,ap}\)). These temperatures were defined as follows:  
\[T_{LiBr,ap} = \frac{T_{LiBr,in}-T_{LiBr,out}}{2} \tag{4}\]  
\[T_{cw,ap} = \frac{T_{cw,in}-T_{cw,out}}{2} \tag{5}\]  
In this figure, the discontinued line represents the theoretical trend and is compared with the experimental data; it can be seen that the desorption rate increased with the temperature difference. For the mathematical model, we assumed all refrigerant vapor (water vapor) diffused through the stagnant air gap was totally condensed (distillate water) on the condense plate, meaning that the distillate water in the vapor phase could not leave the device, even by the only small drain at the bottom because it was sealed continuously by a water drop. For the output temperatures, the deviation was less than 5% for the cooling water and 1.8% for the H\({}_{2}\)O/LiBr solution on average.  
Figure 5: Theoretical versus experimental desorption rate.
## 3 Iterative Wiener Filter
From the minimization of \(\mathcal{J}\left(\underline{\mathbf{g}}_{1}|\underline{\mathbf{g}}_{2}\right)\), we easily get the Wiener filter for \(\underline{\mathbf{g}}_{1}\), i.e.,  
\[\underline{\mathbf{g}}_{1,\text{W}}=\left(\underline{\mathbf{G}}_{2}^{T} \mathbf{Q}^{-T}\mathbf{R}_{\mathbf{x}}\mathbf{Q}^{-1}\underline{\mathbf{G}}_{ 2}\right)^{-1}\underline{\mathbf{G}}_{2}^{T}\mathbf{Q}^{-T}\mathbf{r}_{\text{ x}d}. \tag{21}\]  
In the same way, from (19) and assuming that \(\underline{\mathbf{g}}_{1}\) is fixed, we can write the MSE criterion as  
\[\mathcal{J}\left(\underline{\mathbf{g}}_{2}|\underline{\mathbf{ g}}_{1}\right)=E\left[\left|d(t)-\underline{\mathbf{g}}_{2}^{T}\underline{ \mathbf{G}}_{1}^{T}\mathbf{Q}^{-T}\mathbf{x}(t)\right|^{2}\right] \tag{22}\] \[=\sigma_{d}^{2}-2\underline{\mathbf{g}}_{2}^{T}\underline{ \mathbf{G}}_{1}^{T}\mathbf{Q}^{-T}\mathbf{r}_{\text{w}d}+\underline{\mathbf{g }}_{2}^{T}\underline{\mathbf{G}}_{1}^{T}\mathbf{Q}^{-T}\mathbf{R}_{\mathbf{x }}\mathbf{Q}^{-1}\underline{\mathbf{G}}_{1}\underline{\mathbf{g}}_{2},\]  
whose minimization leads to the Wiener filter for \(\underline{\mathbf{g}}_{2}\), i.e.,  
\[\underline{\mathbf{g}}_{2,\text{W}}=\left(\underline{\mathbf{G}}_{1}^{T} \mathbf{Q}^{-T}\mathbf{R}_{\mathbf{x}}\mathbf{Q}^{-1}\underline{\mathbf{G}}_{ 1}\right)^{-1}\underline{\mathbf{G}}_{1}^{T}\mathbf{Q}^{-T}\mathbf{r}_{\text{ x}d}. \tag{23}\]  
By alternatively iterating between \(\underline{\mathbf{g}}_{1,\text{W}}\) and \(\underline{\mathbf{g}}_{2,\text{W}}\) as in [33; 16], these two optimal filters will converge to the Wiener solution just after a couple of iterations. The optimal symmetric filter \(\mathbf{g}_{\text{W}}^{+}\) can be easily constructed and then the Wiener filter \(\mathbf{g}_{\text{W}}\), which is the optimal estimate of \(\mathbf{h}\), follows immediately. As compared to the conventional Wiener filter, which obtains its solution by solving a linear system of \(L\) equations that involves large data structures (e.g., the matrix \(\mathbf{R}_{\mathbf{x}}\) of size \(L\times L\)), the proposed iterative Wiener filter combines the solutions of two shorter filters of lengths \(PL_{1}\) and \(PL_{2}\), with \(P\ll L_{2}\). Thus, it would be more robust and accurate, especially when the estimates of the statistics (i.e., \(\mathbf{R_{x}}\) and \(\mathbf{r_{xd}}\)) are less accurate. Such scenarios happen when only a small amount of data samples is available and/or in noisy conditions (i.e., low SNRs), which raise very challenging practical issues.
# Distance Assessment by Object Detection--For Visually Impaired Assistive Mechatronic System
## 5 Discussion
### Low-Cost Camera Subsystem  
The successful distance assessment through object detection using the disparity method depends on many factors:  
* The quality of the images received from the camera (photo or video);
* The existing colors in the image;
* The most-requested color channels (R, G, or B).  
The non-uniformity of the colors in the image, obvious contrasts, contrasting colors, well-defined color distribution, well-defined color groups, and large disparity are all factors that influence the success of this method of distance assessment through object detection.  
In accordance with the research results, the proposals for marking the areas of interest with special signs are mentioned next.  
1. The use of signaling elements in the case of obvious areas to avoid: stairs, windows, cabinets, doors, corners of buildings, etc.
2. The use of specially designed signs to improve the mechatronic system's detection of distances from objects within its environment.
3. The decision to use clear signaling elements will lead, in time, to the formation of a social education more correlated with the problems of people with special needs as well as methods of sensitization and education of the rest of the population to the problems of visually impaired people.
4. Medium-intensity light sources, warm colors, and even scattering are recommended for lighting objects and scenes that need to be monitored using cameras.
5. If necessary, additional lighting can be added to the areas in front of the device for the visually impaired (VIPs-Walker) or in situations in which a mechatronic system used for robotically controlled laparoscopic operations collaborates with a medical team.
## 6 Time Complexity of Proposed Algorithms
### Time Complexity Clustering Algorithm  
The symbols used for calculation is given below:  
\(N\): Number of nodes.  
\(n_{1},n_{2}:\) Nodes among N.  
\(U_{t}\): Node utility function.  
\(p(n_{1})\): \(n_{1}\) node probability to become a cluster head (CH).  
\(Count\): Total number of times as to be a cluster head (CH).  
\(C_{k}\): Number of optimal clusters.  
The time complexity can be calculated step-by-step and then combined to show the algorithm's overall complexity.  
Solution:  
Use the if-else statement from lines 18-23 to check count = \(\frac{C_{k}}{N}\) and count \(\leftarrow\frac{N}{C_{k}}\), taking a constant time due independent of data length. We denote this constant with \(C_{1}\).  
\(p\leftarrow(c_{n},\) z) taking a constant time and denoting with \(C_{2}\). It executes once by receiving a pre-calculated value.  
The statement if-else from lines 28-43 repeats for N number of nodes. This means that the probability p(\(n_{1}\)) will repeat n times and probability p(\(n_{2}\)) will also repeat n times. The time taken will be \(n\cdot n\) times.  
Count = count + 1 will take n times to execute.  
The if-else statement from lines 46-53 takes a constant time to execute. We denote it as \(C_{4}\) and \(C_{5}\).  
From the above time complexity at each step, we can then calculate  
\[C_{1}+C_{2}+C_{3}+n\cdot n+n+C_{4}+C_{5}.\]By dropping constants and n, we are left with \(n^{2}\). The conclusion shows that the time complexity of the clustering algorithm is \(\mathrm{O}(n^{2})\)
## 1 Introduction
### Paper Contributions  
This paper presents a novel hybrid intrusion detection framework tailored for the complex and dynamic landscape of IoT network security. By integrating transformer networks with GANs and optimizing the architecture through an INSBBO algorithm, the proposed model addresses key challenges in modern IDS design, namely, class imbalance, feature diversity, and learning instability. Unlike prior works that focus on standalone deep models or rely on outdated datasets, our approach combines robust temporal-contextual modeling with adversarial data synthesis and multi-objective optimization, leading to a system that generalizes well to both binary and multi-class intrusion detection tasks. The main contributions of this paper can be summarized as follows.  
* We propose a novel IDS model that synergistically combines transformer and GAN components to enhance feature extraction, capture temporal dependencies, and mitigate data imbalance in IoT traffic;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Dataset** & **Classification** & **Method/Model** & **Accuracy** \\ \hline
[24] & CIC-IoT-2023 & Multiclass & DRL (TA-NIDS) & 92.00\% \\ \hline
[25] & CIC-IoT-2023 & Binary/Multiclass & LSTM & 98.75\% \\ \hline
[26] & Web attack data & Binary & Gradient Boosting & 95.00\% \\ \hline
[27] & ToN-IoT & Binary/Multiclass & Stacked Ensemble & 99.00\% \\ \hline
[29] & CIC-IoT-2023 & Binary/Multiclass & Transformer & 99.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._* A novel INSBBO algorithm is introduced to fine-tune both network weights and hyper-parameters of the proposed model, improving model stability, convergence, and classification accuracy;
* The framework integrates adversarial learning and reconstruction-based evaluation to detect both subtle and overt anomalies in IoT network traffic;
* We rigorously evaluate the proposed model using two complementary real-world datasets to ensure practical relevance and generalizability: the CIC-IoT-2023 dataset, which includes 33 diverse attack types across 105 IoT devices, and the TON_IoT dataset, which provides heterogeneous telemetry and network data reflecting a wide range of cyber threats across smart and industrial environments. This dual-dataset evaluation validates the model's robustness across distinct data distributions, device types, and threat scenarios;
* Experimental results show that our model outperforms traditional methods (e.g., CNN, LSTM, and SVM) and recent baselines (e.g., Transformer-only or GAN-only IDS) in both binary and multi-class settings, achieving accuracy up to 99.76% with minimal variance and low false positive rates.
## 2 Methodology
### Method for Extracting Trend Component Based on DWT  
Common trend component extraction methods include discrete wavelet transform (DWT), Hilbert-Huang Transform (HHT), and others. DWT is a multilevel decomposition process, and its algorithmic diagram is shown in Figure 4: a signal x of length N is filtered separately by a high-pass filter g and a low-pass filter h, resulting in the high-frequency component G1 and the low-frequency component H1. Subsequently, every two points in G1 and H1 are extracted to obtain downsampled high-frequency component D1 and low-frequency component A1 (both of length N/2). The same filtering and downsampling process is then applied to A1 to obtain D2 and A2 (both of length N/4), and
## 2 Project Overview and In-Situ Monitoring  
### Badaling Great Wall Station  
The Beijing-Zhangjiakou High-speed railway is about 174 km long, it has ten stations, and it is an important passenger transport project for the 2022 Beijing Winter Olympic Games (Figure 1). It shortens the travel time from Beijing to Zhangjiakou in Hebei Province to less than one hour. The Badaling Great Wall station, at a maximum depth of 102 m, is the deepest high-speed underground railway station in China, with a total length of 470 m. Figure 2 has shown the main structure of the station, the 163 m long super-span tunnels are located at either end of the station. Figure 3 has shown the layout of the super-span tunnel in the Zhangjiakou direction with five cross-section sizes from DK68+285 to DK68+448.The excavation span of the tunnel within the range of DK68+285 to DK68+305 reaches 32.7 m with an excavation area of 494.4 m\({}^{2}\). As the underground high-speed railway tunnel with the largest span in the world, the construction process was very risky.  
Figure 1: Map of Beijing-Zhangjiakou high-speed railway.
## 3 Shield Pitch Angle and Yawing Angle Calculation Method  
### Solution Process of Yawing Angle and Pitch Angle  
According to the above calculation theory, the calculation method of the pitch angle and yawing angle of the shield can be summarized. The specific calculation process is shown in Figure 11.  
The first stage is to input the basic parameters such as soil parameters and shield parameters, and establish the shield-soil interaction model. The relationship curve between the vertical displacement and vertical force on the shield (RCDV) is obtained. Through the RCDV, the vertical displacement \(\mathbf{\Delta s_{v}}\) under gravity is obtained. In the second stage, the \(\mathbf{\Delta s_{v}}\) obtained in the first stage and the unknown pitch angle are set into Equation (18), and the relationship curve between the pitch angle and moment on the vertical plane (RCPV) is obtained, which is obtained under the action of gravity eccentric bending moment. Through the RCPV, the pitch angle \(\beta_{G}\) generated by the \(M_{G}\) and the \(\beta_{A}\) generated by the \(M_{A}^{\beta}\) can be obtained. In the third stage, \(\mathbf{\Delta s_{v}}\), \(\beta_{G}\), and \(\beta_{A}\) are brought into the calculation program, and the unknown yawing angle value is continuously given. The relationship curve between the yawing angle and horizontal bending moment applied by the shield (RCPH) is obtained. Through the RCPH, the \(M_{A}^{\alpha}\) is calculated, then the yawing angle \(\alpha\) is obtained. Finally, \(\mathbf{\Delta s_{v}}\), \(\beta_{G}\), \(\beta_{A}\), and \(\alpha\) are output.
## 4 Promising Neuropeptides Inhibitors for the Development of Effective Cancer Therapy
### Emerging Neuropeptides Inhibitors in Colorectal Cancer
#### 4.1.6 Orexins  
Orexin-A and orexin-B are two new NPs that interact with their respective receptors (OX1R and OX2R) and are involved in different pathophysiologic processes, including inflammation, ulcerative colitis, and cancer [133; 134]. Previous studies have demonstrateda strong and consistent link between OX1R and OX2R expression in cancer. The high prevalence of OX1R in GI cancer is responsible for metastasis to the lung and liver from the colon [135]. It is noteworthy that orexins and their receptors' antagonists were found to exert an antitumor effect on the growth of colon cancer cells, possibly due to the induction of mitochondrial apoptosis [136]. Circumstantial evidence supported that orexin treatment reduces the cell proliferation in colon cancer cell lines (HT-29, Caco-2, and LoVo) by promoting apoptosis, and diminishes the tumor growth in tumor-bearing mice and xenografted tumor in nude mice. However, the antitumor activity of orexin can be reversed by using suitable inhibitors, such as NSC-87877 and PD169316 [137].
## 3 Results and Discussions
### Shale Inhibition Properties of Water-Based Drilling Fluid with SDCap  
#### 3.3.1 Inhibition on Shale Hydration Dispersion  
The shale recovery rate test can reflect the encapsulation effect of encapsulators on clay particles. A high recovery rate implies the strong shale inhibition property of drilling fluid. Figure 5 shows the results of shale recovery rate tests with the five WBDFs. The recovery rates of shales from the South China Sea (shale #1) and Bohai Bay (shale #2) were only 4.2% and 15.7% in pure water, respectively, indicating that the shales were highly prone to hydration dispersion and were hard recover, particularly for deepwater wells. Therefore, wellbore instability occurred easily, and the rheological and filtration properties of drilling fluid were also impacted due to the dispersed clay particles. The WBDFs significantly increased the recovery rate. For shale #1 from deepwater wells, the recovery rates were 75.4%, 68.2%, 77.5%, and 72.0% for WBDF-1, WBDF-2, WBDF-3, and WBDF-4, respectively. For shale #2, the recovery rates were 83.5%, 79.3%, 87.9%, and 83.8%, respectively. For WBDF-5, the recovery rates for shale #1 and shale #2 were 82.2% and 89.3%, respectively, indicating its strong shale inhibition property. The results showed that SDCap was compatible with the WBDFs, and the recovery rate was increased when using SDCap instead of conventional encapsulator in WBDFs. Therefore, it could effectively inhibit the hydration dispersion of shales.
## 4 Materials and Methods
### Gas Chromatography
#### 4.3.3 Gas Chromatography Conditions  
Separations of the obtained extracts were performed by gas chromatography. A GC2010 gas chromatograph (Shimadzu, Kyoto, Japan) equipped with a CombiPAL autosampler (CTC Analytics, AG, Zwingen, Switzerland) and an SLB-5 MS capillary column (length 30 m, inner diameter 0.25 mm, film thickness 0.25 \(\mu\)m; Supelco, Bellefonte, PA, USA) was coupled to a QP 2010 Plus mass spectrometer (MS) and a flame ionization detector (FID), both from Shimadzu (Kyoto, Japan). Helium 4.6 was used as the carrier gas at a constant linear velocity of 30 cm\(\cdot\)s\({}^{-1}\). The split ratio was set to 1:5. The temperature of the injector was maintained at 230 \({}^{\circ}\)C. The temperature gradient was programmed as follows: the initial temperature was 40 \({}^{\circ}\)C (3 min) and then increased at 2 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) to 250 \({}^{\circ}\)C (5.5 min) with a total analysis time of 60 min.  
FID conditions: The detector temperature was set to 270 \({}^{\circ}\)C.  
MS conditions: The interface temperature and the ion source temperature were maintained both at 200 \({}^{\circ}\)C. The mass spectrometer was operated in electron ionization mode (70 eV), and the detection of ions was performed in full scan mode at a range of 33-450 \(m/z\)
## 1 Introduction  
### The Development of the Wireless Sensor Network  
Since the 1990s, wireless sensor networks (WSNs) have attracted attention from academics and industries because of their applications in environmental monitoring, healthcare, automation and the Internet of Things (IoT). According to ONWorld, the world market for wireless sensor network (WSN) systems and services will increase to approximately 46 billion US dollars, which is 500 million US dollars more than the present level [1]. In wireless sensor networks, a large number of nodes are typically deployed in a region. Each node is capable of sensing and delivering measurement data. The deployed nodes are self-configured and communicate by radio to form a network topology. The nodes directly or indirectly transmit data to data sinks. In a wide-deployed WSN, direct delivery is not always suitable because battery-powered nodes cannot establish a direct link to data sinks. As a result, indirect delivery is typical in WSNs. In such an environment, an energy-efficient network topology with full reachability is desirable. However, there are certain restrictions on the design and operation of nodes in WSNs. For example, each node must be tiny, low cost, long-life, robust and reachable in a topology that can operate in a harsh environment. If equipped with these features, WSNs will be more broadly applicable. WSNs are a fundamental network as a part of IoT. Thus, wireless sensor networks are regarded as significant to the future IoT to be developed.
## 4 Results and Discussion
### Comparison of the Present Results with the Earlier Numerical and Experimental Results
However, when the Bond numbers measured by [PERSON] et al. [36] were used in the present computations, the resulting numerical profiles for large bubbles were quite different from the experimental photographs. Hence, the \(Bo\) values used in the present work were adjusted in order to provide a better match between the predicted and measured profiles. These adjusted Bond numbers are compared with those measured from Figure 8b in Table 3. Here, similar Bond numbers are predicted and measured for the two tiny bubbles, but the present computations predict much higher Bond numbers than those measured for the two large bubbles. While [PERSON] et al. [37] measured the bubble cap radius and the junction angles to obtain an alternative form of Bond number (\(Bo_{0}\)), [PERSON] et al. [36] calculated \(Bo\) by measuring the volume of a bubble from the experimental image. It is expected that obtaining \(Bo\) is more complicated than obtaining \(Bo_{0}\) because the lower part of the floating bubble is immersed in the liquid, whereas the upper part is not. Therefore, the discrepancy in Table 3 is thought to be due to experimental errors or the limitations of the current theoretical model. Figure 7: Comparison of the present numerical results with the experimental results of (**a**) [PERSON] et al. [37] and (**b**) [PERSON] et al. [36] for selected bubble parameters, where \(H^{*}=\sqrt{2H}\).