esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, based on the theory of active bearing of FSP and relying on the Yulinzi Tunnel in Qingyang, Gansu Province, the research was carried out. The numerical simulation method was used to analyze the active bearing effect under different parameter conditions, and the specific implementation scheme of active bearing technology in the  
Figure 24: Comparison of measured arch vault settlement curves with and without active bearing.  
field was proposed, and the deformation control effect was verified. The main conclusions are as follows:  
1. The bearing capacity process of the traditional FSP is slow, and the active bearing can provide the bearing capacity of the FSP as early as possible. Only when the surrounding rock settlement is large can the traditional FSP provide a large bearing capacity, while the active bearing can provide the bearing capacity in advance by preloading.
2. The nonlinear spring can be used to simulate the bearing capacity of the steel pipe when simulating the supporting effect of the FSP on the tunnel. The control effect of increasing the diameter of FSP on tunnel deformation is better than that of increasing the number of steel pipes. However, due to the limited bearing capacity of the steel pipe itself, the deformation of the surrounding rock cannot be largely controlled simply by increasing the number or diameter of the steel pipe, and other auxiliary measures need to be taken.
3. The self-designed loading support realizes the active bearing of FSP can completely fit the size of any arch and grid steel frame. After the construction of the arch frame and FSP is completed manually, the preloading process at the end of FSP can be quickly carried out during tunnel construction by selecting a suitable type of loading device.
4. By adopting the active bearing method of FSP, the settlement rate of the arch vault can be significantly reduced within 1-2 days, which can be reduced by about 70%. The settlement of the arch vault can be reduced by more than 50% within 1-3 days. Therefore, the active bearing technology of FSP can provide a large bearing capacity at the initial stage of the surrounding rock deformation. It has a positive effect on reducing the overall deformation of the surrounding rock, slowing down the release of the surrounding rock pressure, and controlling the settlement of the arch vault.  
Conceptualization, Y.W.; methodology, Y.W.; validation, L.W.; investigation, K.H.; resources, Z.Z.; data curation, Z.Z.; writing--original draft preparation, L.W.; writing--review and editing, K.H.; visualization, C.T.; project administration, Z.Z. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51978668) and The First Engineering Company of Shanxi Road and Bridge Group (null).  
Not applicable.  
All data are available from the author.  
The authors declare no conflict of interest.
# A Transformer-Less Buck-Boost Grid-Tied Inverter with Low Leakage-Current and High Voltage-Gain
## 4 Loss Analysis
It is able to be seen from the switching loss curves shown in Figure 9c that since the proposed converter only requires a single stage of energy processing and only one switch performs high-frequency switching in each state, the switching losses are greatly improved. Figure 9d shows the curves of the total conduction loss of the diodes based on Equations (28)-(30). Since the diodes \(D_{1}\) and \(D_{2}\) only conduct in the step-down mode, the total diode conduction loss is lower at low input voltage. When the input voltage increases, the operation time of the step-down mode increases, and the total diode conduction loss increases accordingly. The traditional two-stage system only uses one diode in the boost converter, and the output voltage is a high voltage of 400 V, so it has lower diode conduction loss. Overall, under the condition of 100 V input, the total loss of the investigated inverter is 19.08 W, and the total loss of the traditional two-stage grid-tied system is 24.67 W. Therefore, it is able to be known that the total power loss of the investigated inverter can be reduced to improve conversion efficiency.
## 4 Experiments and Results
### Inter-Class Separability and Intra-Class Consistency Validation Experiments
#### 4.3.2 3D Visualization of Typical Ground Objects and Visualization of Intra-Class Consistency  
Table 11 shows that the 0123-combination image has the best separability of buildings and roads from background ground objects relative to the original image and the TCT image. To analyze the spectral features of the original image, TCT image and 0123-combination image, seven typical ground objects, which are bare soil, buildings, mountains, roads, shadows, vegetation, and water bodies, were selected in region 1, region 2, region 3, region  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Image** & **Accuracy** & **Precision** & **Recall** & **F1** & **IoU** & **Kappa** & **1\_Recall** \\ \hline Region4 & & & & & & \\
0123 & 0.9184 & 0.9124 & 0.8665 & 0.8862 & 0.8010 & 0.7729 & 0.7612 \\ Original & 0.8435 & 0.8593 & 0.7115 & 0.7466 & 0.6210  
5, mountain 1 and mountain 2, respectively. The sampling point locations of the three images are identical, and the duplicate values are excluded, and the results are as follows in Table 3. The 0123-combination image has the least number of sampling points, which also indicates that the 0123-combination image has the best intra-class consistency. The first three components of the original image, the TCT image (the TCT image was also linearly stretched, and percentage truncated to ensure a fair comparison with the pseudo-TCT) and the 0123-combination image were linearly stretched to 0-255 and then mapped to the 3D space, and the results are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Image** & **Bare Soil** & **Building** & **Mountain** & **Road** & **Shadow** & **Vegetation** & **Water Body** & **Total** \\ \hline Original & 48,190 & 18,010 & 24,169 & 12,394 & 8850 & 22,475 & 76,827 & 210,915 \\ TCT & 35,365 & 7389 & 20,578 & 9435 & 2767 & 14,302 & 175 & 90,011 \\
0123 & 37,708 & 7368 & 18,388 & 7304 & 919 & 10,828 & 2648 & 85,163 \\ \hline \end{tabular}
\end{table}
Table 3: Typical ground objects sampling points.  
Figure 6: ISODATA clustering results. (**a**) region 4 clustering result of the original image; (**b**) region 4 clustering result of TCT image; (**c**) region 4 clustering result of 0123 combination image; (**d**) image; B clustering result of the original image; (**e**) image B clustering result of TCT image; (**f**) image B clustering result of 0123 combination image.  
To quantify the effect of image enhancement, we calculated the standard deviations of the intra-class consistency image of the original image, the intra-class consistency image of the TCT image, and the intra-class consistency image of the pseudo-TCT image, which were 8.93, 26.74, and 30.15, respectively.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 5 Analysis
### RQ2: What Types of Malicious Activity and Which Scenarios Involve IoT Botnets?
#### 5.2.3 Denial of Service (DoS/DDoS) Attacks  
The aim of a DDoS attack is to harm, collapse or close down a service or to block the benign users from using said service by employing many previously infected sources. An attacker aims to make the target inaccessible by overwhelming its resources with a large volume of traffic using IoT devices [87]. A large number of DDoS attack botnets are employed to start an attack, once the compromised devices are usable. If the DDoS attack is not discovered and avoided, the DDoS will flood the target with illegitimate requests and reject the requests of the authentic users [88]. DDoS is one of the types of attack that has been a significant challenge in the IoT environment. It comes in different types: flooding attacks (e.g., UDP flood or HTTP flood), amplification and IP spoofing, selective forwarding, hello floods, overloading, sinkholes, wormhole attacks, packet fragmentation attacks, exhaustive attacks and jamming attacks [89]. Figure 9 explains the DDoS attack mechanism. Throughout DDoS attacks, the attacker utilizes a command mechanism to exploit and compromise other devices by imposing malicious code on them and creating a dispersed network of controlled IoT devices. As Figure 8 explains, 16% of the studies concentrated on this type of attack--six studies, as Table 10 displays [46; 47; 63; 66; 74; 77; 80].  
Figure 8: Number of studies selected per malicious activity.
## 3 Results and Discussion  
### Isotropic Consolidation  
Figure 3a-f indicate both the volumetric and pore pressure responses of the unstabilised (or non-stabilised) and stabilised soils with time during the consolidation stage. A decrease in volume as a result of the dissipation of excess pore water are observed. The volumetric reduction was greater for the unstabilised soils when compared to the stabilised soils. For the stabilised soil samples, the addition of calcium compound-rich agents clearly caused a hardening of the soils resulting in a decrease in the water content of these samples. As could be observed from the pore water pressure (pwp) curves, the time required for the expulsion of water from the non-stabilised soils was more than double those for the stabilised soils across all the samples. Figure 3a-f also generally indicate that consolidation under a higher pressure produced greater volumetric strain. Moreover, the volumetric strain curves were observed to have become asymptotic to the horizontal axis with the passage of time, thus indicating the end of primary consolidation under the given pressure.  
In terms of the quantity of sulphates in the non-stabilised soils, there seems to be greater volume change observed for soils containing lower proportion of sulphates (Figure 3g,h). Although gypsum is a soluble salt with relatively less alkalinity when compared to other calcium oxide (or hydroxide) agents, they are still capable of binding particles of soils together [31; 32]. It should also be noted that a higher solubility of gypsum can lead to greater deterioration of the bonding within the soil-gypsum mixture [33]. With regard to this study, the deformation of the soils was less with increasing sulphate content, as noted when the amount of gypsum was raised from 10-30% in the soil. Hence, it could be inferred that the surplus quantity of gypsum left without dissolution contributed to a reduction in the soil's volume.  
On the other hand, it is interesting to also note that, for the stabilised soils, the amount of volume change seemingly increased with the quantity of sulphates in the soil (Figure 3i,j). As observed above, gypsum may have led to a reduction in volume by acting as a stabilising agent [34]. This phenomenon is mostly associated with short-term reaction and curing [35]. However, since gypsum is a lightweight material (relatively low specific gravity), any unreacted gypsum in the lime/GGBS-stabilised soils can result in a decrease in the overall specific weight, thus affecting the texture of the mix through an increment in pore sizes [36]. This can then lead to an increase in the volume of the stabilised soil. Although the quantity of GGBS required to prevent any possible attacks by sulphates should be twice the amount of lime in the mix [11], this may not have been sufficient to prevent volumetric increment as is the case in this study.  
Overall, Figure 3g-j indicate that consolidation of the samples under a higher constant effective pressure did produce greater volumetric strain.  
Figure 3: _Cont_.
## 2 Materials and Methods  
### Flood Foresight System  
The SAR flood footprints are compared to dynamic, event-specific flood inundation maps generated by JBA's Flood Foresight system [50]. Flood Foresight is an operational system developed to rapidly provide broad-scale estimates of flood hazard and impacts before, during, and after major riverine flood events (Figure 1).
## 2 Results
### Effects of Peptides on Gene Expression  
To understand the molecular mechanisms underlying the inhibition of biofilm formation in the EDL933 strain, we monitored the effects of the peptides (at 1/2 MIC) on the expression levels of some genes related to the formation and dispersion of biofilm, as well as to the stress response, through quantitative real-time PCR.  
The results shown in Figure 5 revealed a variable degree of upregulation of the microbial RNA of the _fflC_, _fflD_, _fflC_, _nirB_, _spoT_, _sodC_, _katE_, _csrA_, and _osmB_ genes in cells treated with Esc(1-18) and Esc(1-21) with respect to the untreated EDL933 strain.  
The genes with the highest transcript abundance for both peptides were _fllC_ and _flflD_; however, all genes that we examined showed a fold change greater than 1.5 fold.  
Among the genes correlated with bacterial motility or its regulation, the _fliC_ and _csrA_ genes were upregulated by more than 2.6 and 1.7 fold in cells treated with Esc(1-21) and Esc(1-18), respectively, while the expression level of the _hua_ gene increased by 1.6 fold for both peptides.  
To study the potential effects of the peptides on the stringent response, we examined the expression levels of the _spoT_ and _relA_ genes in the pathogen _E.coli_ O157:H7 grown in a minimal medium. The results showed that both peptides induced an upregulation of _spoT_, but not of _relA_, which was unchanged with respect to the control (data not shown). In addition, we observed an induction by more than two fold of the _nirB_ gene, which encodes for the cytoplasmic nitrite reductase NirB, which is involved in the denitrification pathway of NO produced in the cytoplasm of bacteria. Furthermore, the genes involved in the response to stress conditions--oxidative or environmental stresses--were also upregulated. In fact, the _sodC_ and _katE_ genes, which encode for the superoxide dismutase SodC and the catalase KatE, respectively, were over-expressed by more than 1.7 fold in the presence of the two peptides, indicating that they induced oxidative stress. Finally, the expression level of the _osmB_ gene, which encodes for a lipoprotein involved in the osmotic balance's  
Figure 5: Expression levels of selected genes related to biofilm formation and stress conditions in the _E. coli_ O157:H7 EDL933 strain treated with the peptides at sub-inhibitory concentrations of 2 and 16 \(\mu\)M for Esc(1-21) and Esc(1-18), respectively, compared to the untreated control samples (CTR). Transcriptional profiles were measured with qRT-PCR. \(p<0.01\) for all bars with respect to CTR.  
maintenance, was two or three fold higher in the bacterial cells grown in the presence of Esc(1-21) or Esc(1-18) than in the cells grown in the absence of peptides.
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 2 Applications of FSSs
### IRSs for Cellular Networks
#### 2.2.3 Beamforming Integration  
Beamforming technology is commonly used in modern communication systems to direct signals in specific directions, increasing efficiency and signal strength for targeted receivers. IRSs can complement this technology by acting as \"smart reflectors\" that enhance the beamforming capabilities of base stations. In this setup, the IRS elements adjust their reflection angles and phases to further direct the signal beams, improving communication quality even when users are in highly obstructed environments. This synergy between IRS and beamforming significantly enhances spectral efficiency and reduces energy consumption in the network [102].
# Cognitive Electronic Unit for AI-Guided Real-Time Echocardiographic Imaging
## 5 Conclusions  
This work has demonstrated that lightweight CNN architectures running on embedded hardware can perform near-real-time classification of echocardiographic views and image quality. Our approach reached over 85% accuracy for differentiating apical two-chamber, apical four-chamber, and unrelated views as well as 66% accuracy in distinguishing good-quality vs. poor-quality images on a relatively small dataset. With an average inference time of \(\sim\)13 ms per frame, the proposed system provides immediate visual feedback, aiding sonographers in maintaining consistent scanning protocols and reducing inter-operator variability. Building on these findings, we plan to expand our dataset through extensive clinical trials in order to capture a broader range of patient anatomies, imaging conditions, and device types to further validate and refine our models. Another key step is the direct integration of IMU signals into the CNN pipeline to create a unified spatiotemporal framework capable of capturing more intricate probe movements and cardiac dynamics. Enhancing interpretability is another critical research direction; by adopting explainable AI techniques, we aim to foster greater clinician trust and facilitate smoother adoption in everyday practice. Ultimately, these advancements--larger and more diverse datasets, unified IMU-CNN architectures, and improved interpretability--are all geared toward standardizing echocardiographic imaging while ensuring more accurate and reproducible examinations. By offering real-time guidance on an embedded platform, our system holds promise for streamlining clinical workflows, extending access to high-quality cardiac imaging, and supporting both novice and experienced sonographers in delivering consistent and reliable patient care.  
Conceptualization, E.D.L., E.A., M.L.R. and F.D.; formal analysis, M.L.R., T.M., R.B. and F.D.; funding acquisition, R.B. and F.D.; methodology, E.D.L. and E.A.; software, E.D.L., E.A. and V.V.; supervision, T.M., R.B. and F.D.; validation, M.L.R. and T.M.; writing--original draft, E.D.L.; writing--review and editing, E.D.L., E.A., V.V., M.L.R., T.M., R.B. and F.D. All authors have read and agreed to the published version of the manuscript.  
The authors received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
Not applicable.  
The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest. Author Mr. [PERSON] is employed by the company Predict S.p.-A., Bari, Italy. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Methods
Object-based classification: CVA is sensitive to false changes caused by many factors such as different sensors, the light condition, seasonal changes, etc. [37]. Therefore, the supervised classification was performed separately on the bi-temporal images to further reduce the false alarm. The random-forest (RF) classifier was used in this study. This method utilizes ensemble learning to combine many decision trees and has been reported to achieve a high accuracy [38]. The training objects based on the segmentation results were selected separately on the two images. The mean and standard deviation of pixel values inside each object were used as the object-level features for classification. The selected training objects provide prior knowledge of the object-level features across three spectral bands. In the training process, a five-fold cross-validation was used to automatically determine the optimal parameters in the RF algorithm such as the number of trees, the criterion to measure the quality of a split, the maximum depth of the tree, etc. Based on the selected parameters, the classification was then applied to those potentially changed objects. Figure 3: An illustration of an image at the pixel level (**a**), image segmentation (**b**), and the image at the object level (**c**) (different objects are distinguished by colors), and a segmentation result of a remote-sensing sub-scene (**d**). Refining changed objects: After deriving the classification results, the land-cover classes were compared per object in the bi-temporal images. The object with the same class label in both images was regarded as the false change and thus categorized as the unchanged object. Manual editing may be stepped in to refine the results. Especially, some changes caused by reconstruction work were difficult to be identified using machine-learning methods and thus were delineated manually. * Calculating interference degree: The percentage of changes (Equation (3)) was calculated for the core zone and the buffer zone of a CHS. \[I=\frac{A_{c}}{A},\] (3) where \(A_{c}\) is the area value of all the changed objects in the core/buffer zone, and \(A\) is the area value of the core/buffer zone. We defined this percentage \(I\) as the interference degree, which directly reflects the amount of land-cover changes in cultural landscapes surrounding a CHS. We also obtained the from-to land cover-change information that shows the conversion types of these changes from the classification result.
## 3 Deep Forest Reinforcement Learning and Preventive Strategy
### Deep Forest Reinforcement Learning as a Preventive Strategy for Automatic Generation Control  
In the framework of the DFRL-based controller as a preventive strategy for AGC, the Q-value matrix is split into three submatrices, i.e., \(n_{s}\) = 3. The number of classes of next states is three, depending on the frequency deviation \(\Delta f\), i.e., RL-I for \((-\infty,-0.1]\) Hz, RL-II for \((-0.1,0.1]\) Hz, and RL-III for \((0.1,\infty)\) Hz. The control period of the controller of the preventive strategy for AGC is set to 4 s, i.e., \(\Delta t\) = 4 s. The current state of the environment \(S_{t}\) is defined as the frequency deviation of the power system \(\Delta f_{t}\), i.e., \(S_{t}=\Delta f_{t}\). The action at the \(t\)-th time of the power system \(a_{t}\) is defined as the generation command for the AGC unit at the \(t\)-th time \(\Delta P_{\text{Gf}}\), i.e., \(a_{t}=\Delta P_{\text{Gf}}\) (Figure 5). The reward value \(r_{t}\) for the reward function can be described as  
\[r_{t}=R(s,s^{\prime},a)=\left\{\begin{array}{ll}\lambda_{1},&\left|\Delta f _{t}\right|\leq\Delta f_{50}\\ \lambda_{2}\Delta{f_{t}}^{2},&\text{Otherwise}\end{array}\right., \tag{15}\]  
where the reward value \(r_{t}\) is a positive value \(\lambda_{1}\) when \(\left|\Delta f_{t}\right|\) is less than or equal to \(\Delta f_{50}\); \(\lambda_{1}>0\), \(\lambda_{2}<0\), and \(\Delta f_{50}<0.1\) Hz. The variables \(\lambda_{1}\), \(\lambda_{2}\), \(\Delta f_{50}\) in this work were set to 10, \(-100\), and 0.005 Hz, respectively. The pseudo-code of the proposed DFRL for the preventive strategy for AGC is given in Algorithm 1.  
Figure 4: Structure of deep forest reinforcement learning.  
```
1:Initial parameters of Q learning of the DFRL, i.e., \(\alpha,\gamma,\beta\)
2:Initial system state \(s\)
3:Initial Q-value matrix \(\mathbf{Q}\) and probability distribution matrix \(\mathbf{P}\)
4:while loop: do
5: Obtain the system state \(s\) from the environment as Equations (1), (6), and (7)
6: Save the system state \(s\) to the recorder of the DFRL
7: Calculate the reward value \(R(s,s^{\prime},a)\) as Equation (15)
8: Select Q learning from the next systemic state as Equations (8)-(10)
9: Update Q-value matrix and probability distribution matrix as Equations (11) and (12), respectively
10: Select the output action by \(\mathbf{P}\) and a random probability as Equation (13)
11: Given the selected output action to the AGC unit and the recorder of the DFRL
12:return loop
```  
**Algorithm 1** Pseudo-code of the proposed deep forest reinforcement learning for the preventive strategy for automatic generation control