esa-sceva/satcom-chunk-collection huggingface dataset:
# Trends and Challenges in Network Covert Channels Countermeasures
## 5 Covert Channel Limitation, Elimination and Prevention  
As discussed, upon detecting the covert channel, further actions to be performed can have different functional goals (e.g., limitation or elimination). Usually, applying a countermeasure to a class of covert channels requires to choose among different tradeoffs. Specifically:  
* **security vs. quality**: the limitation or elimination capacity could require reducing the quality perceived by users. For instance, to prevent an attack, the entity in charge of performing the sanitization of the network traffic may delay or drop packets in an unacceptable manner;
* **accuracy vs. performance**: despite the used hardware and software architecture, the processing capacity of the warden (including various middleboxes for enforcing network security) could impact on the behavior of the traffic (i.e., by altering its quality parameters) or require an amount of resources not feasible (e.g., computing and storage capacity). Therefore, the accuracy of the limitation and elimination process should be carefully evaluated especially in terms of overheads;
* **complexity vs. cost**: the degree of sophistication of a covert channel may account for complex mitigation methods. This usually leads to software being developed, hardware resources, or alteration of the network infrastructure, for instance to duplicate traffic flows or to avoid performance bottlenecks. Clearly, such requirements lead to economical expenditures and maintenance costs, which should be carefully evaluated;
* **blockage vs. functionalities**: with the term blockage, we intend the ability of impeding that a given network protocol is used as a carrier. This typically requires to block a protocol for a network or host, or reduce some functionalities. Even if \"mutilating\" the protocol stack can be admissible in scenarios with high security constraints, this tradeoff should be carefully evaluated, especially for networks operating not in a local manner;
* **security vs. risk**: being able to completely eliminate a channel could not be mandatory or possible in terms of the aforementioned tradeoffs. Thus, according to the considered scenario, it could be more convenient to only limit the steganographic bandwidth of the channel. For instance, a channel of few bit/s could be enough to activate or configure a backdoor, but not sufficient to exfiltrate an industrial secret. Usually, this is at the basis of the \"arms race\" between defender and attacker, trying to slow the data exfiltration rate or the steganographic bandwidth to remain under the radar [52].  
We point out that the aforementioned tradeoffs also apply in the case of detection, but the limitation of a channel and the sanitization of network traffic usually require greater amounts of resources to avoid performance bottlenecks or impair some network functionalities.
## 3 Result and Discussion
### Composition of Liquid Smoke of the HDO Product  
GC-MS analysis of liquid smoke of the HDO product using Z3, Z5, Z7, Ni-Z3, Ni-Z5, and Ni-Z7 catalysts, as well as their compound-shifting data of liquid smoke, are listed in Table 9.  
Those compounds can be categorized into groups of categories, such as phenol, alkoxy, carboxylic acid, cycloalkane, and alcohol groups. In this research, the observation is on alteration of the phenol group and its derivatives, such as phenol and methyl phenol and alkoxy groups, into oxygenate compounds such as guaiaicol and methyl guaiaicol. Based on Table 9 lists, the main components of untreated liquid smoke are phenol groups. Generally, phenol and its derivative content increase after HDO, either with acid-activated catalysts or Ni-loaded catalysts. Figure 9 displays the comparison of phenol and alkoxy contents related to respective catalysts. Phenol and its derivatives increased to 62.93% and 60.93% using Z3 and Z5 and decreased to 36.21% using Z7. Compared to initial content, 55.46%, Z3 and Z5 increased phenol and its derivatives 14% and 9%, while Z7 decreased it significantly by 35%. By using Ni-loaded catalysts, phenol content increased 57.88%, 60.06%, and 56.52% for Ni-Z3, Ni-Z5, and Ni-Z7. Another shifted compound that was being particularly observed is the alkoxy group. The Alkoxy group, the initial content of which was 28.18%, decreased to 11.93%, 12.27%, and 6.91% after HDO using Z3, Z5, and Z7, respectively. The same impact was also shown by Ni-Z3, Ni-Z5, and Ni-Z7 as well, decreasing the alkoxy content to 11.61%, 11.49%, and 11.66%, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Catalysts** & **pH** & **Density (g/mL)** & **Water Content (\%)** \\ \hline Commercial Liquid Smoke & 4.0 & 1.049 & 89.97 \\ Z3 & 4.0 & 1.048 & 68.71 \\ Z5 & 3.9 & 1.047 & 84.46 \\ Z7 & 3.8 & 1.047 & 82.47 \\ Ni-Z3 & 3.4 & 1.050 & 78.86 \\ Ni-Z5 & 3.6 & 1.048 & 68.06 \\ Ni-Z7 & 3.4 & 1.046 & 77.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Characteristics of commercial liquid smoke and HDO product. Figure 8: Carboxylic acid percentage in liquid smoke HDO product. Guaiacol (2-methoxyphenol), one of the main components shown in Table 9 decreased significantly after HDO. It might be caused by guaiacol conversion into methane and catechol. Guaiacol also can be directly converted to phenol through the hydrogenolysis of the methoxy group [32; 33; 34]. [PERSON] and [PERSON] [35] reported that catechol and phenol were the primary products, with high composition from guaiacol conversion. Then, catechol can be converted to phenol through the hydrogenolysis of the hydroxyl group. Guaiacol, with content of 12.37%, decreased after HDO, while catechol as a new detected compound has high content either with Z or Ni-Z catalysts.
# Interpolation-Filtering Method for Image Improvement in Digital Holography
## 6 Discussion  
The addition of only two interpolation operations provided an increase in the efficiency of different types of filtering methods. Since only a software upgrade of the filter is needed, it can be easily integrated into existing processing systems where additional enhancement of the reconstruction quality is important. It is known that even some of the best modern numerical algorithms--BM3D and NLM have low efficiency at high noise levels [35; 36]. In this case, the high level of speckle noise in digital holography is the primary concern. For example, Figure 10i shows a significant smearing of parts of the object, while Figure 10k shows a high level of speckle noise. The proposed method allows us to extend the capabilities of these powerful digital filters as well. In the corresponding Figure 10j,l the method allowed to reduce both the level of non-uniformity due to noise and to leave the boundaries of object elements.  
The use of interpolation to increase the statistical base of pixels can be considered universal. It can be applied both before applying filtering and another operation with, further returning the image to its initial size. Here, we used the common case of off-axis Fresnel hologram. However, the proposed approach can be effectively applied to reconstructed images from other types of digital holograms as well as to images with large noise levels (e.g., registered with extremely low exposure). The quality enhancement operations in the image processing chain relying on statistical analysis can probably be improved by adding the described interpolation steps as well. The proposed method can be successfully used in practice, for example, for particle tracking and analysis [19; 102], as well as other medical applications that do not require high temporal resolutions, such as the study of tooth erosion [21].  
Among possible directions for further development of the method is its combination with optical [28; 31; 32; 33] and hybrid [29; 30; 34; 35; 36] approaches to increase the quality of reconstructed images. These approaches typically require the registration of ten [30] or hundreds of [35; 36] holograms to increase the statistical database. The proposed method further increases the statistical base by interpolation. Therefore, it is possible to reduce the number of registered holograms, which will provide an increase in the speed of data analysis of objects and 3D-scenes.
# Segmentation of Stone Slab Cracks Based on an Improved YOLOv8 Algorithm
## 3 Experimental Results Analysis
### Comparison Experiment
#### 3.3.2 Training Effect of Different Kinds of Stones in the ST Dataset  
As shown in Table 6, the improved YOLOv8-Seg model demonstrates clear advantages across different stone types. Overall, the model maintains stable performance on various evaluation metrics, indicating strong robustness across IoU thresholds. In some cases, a gap between recall and precision suggests that complex textures in certain stones increase detection difficulty. This may be addressed by multi-scale training or dynamic weight adjustment. In general, the structural enhancements in YOLOv8-Seg lead to significant performance gains, particularly in complex texture segmentation and detail preservation. The segmentation metrics in Table 6 exhibit systematic variations across stone types that can be traced to measurable surface properties. Giallo Fiorito yields the lowest MIoU (69.8%), whereas Pigges White achieves the highest (85.5%). Post hoc image analysis attributes this gap to two independent factors. First, Giallo Fiorito presents a vein density of 2.7 veins cm\({}^{-2}\)--more than three times that of Pigges White (0.8 veins cm\({}^{-2}\))--introducing high-frequency texture that competes with thin cracks for the model's receptive field. Second, the mean luminance contrast between crack pixels and background falls to 12.3 in Giallo Fiorito versus 24.7 in Pigges White, reducing the effective signal-to-noise ratio. Comparable trends were observed across the remaining categories: stones with either dense mineral banding by 4-7 MIoU points on average, confirming that texture complexity and lighting contrast--not architectural bias--are the dominant drivers of segmentation variability. Table 7 shows that YOLOv8-Seg keeps the computational burden low--only 3.2 M parameters and 8.9 G FLOPs, marginal increases over the 3.1 M/8.2 G of YOLOv8n--yet it yields a 3.8-point mAP boost. The forward pass completes in 32 ms, comfortably below the production line's 40 ms real-time threshold. In comparison, AttentionUNet requires 34.9 M parameters, 256.3 G FLOPs, and processes at 186 ms. These efficiency metrics position YOLOv8-Seg as a deployment-ready solution for on-site stone-crack inspection.
# Frequency-Dependent Schroeder Allpass Filters
## 3 Proposed Frequency-Dependent Schroeder Allpass
### Poles and Group Delay  
Given a feedforward/-back gain \(g(z)=b(z)/a(z)\), the poles of the proposed allpass filter are given by the roots of the polynomial \(a(z)+b(z)z^{-m}\) in (20). For a scalar gain \(g\), (14) gives the pole locations on a circle around the origin with a radius depending the delay length \(m\) and gain \(g\). Based on [PERSON]'s formulation, the system poles \(p_{i}\) satisfy  
\[1+g(p_{i})p_{i}^{-m}=0. \tag{22}\]  
The filter \(g(z)\) is commonly idealized in having zero-phase and \(\left|g(p_{i})\right|\approx\left|g(e^{\text{iarg}(p_{i})})\right|\) such that all system poles lie on the line specified by \(\left|g(e^{\mu\omega})\right|^{1/m}\)[21]. Although this is often not satisfied strictly, in many designs the filter delay of \(g(z)\) is small compared to the delay \(m\) and can be neglected. Therefore, stability of the complete allpass filter requires that \(g(z)\) is dampening, i.e., \(\left|g(e^{\mu\omega})\right|\leq 1\). As a further consequence, poles are also approximately equidistributed along the frequencies. To illustrate this further, we next present two numerical examples.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Latency Aware and Energy-Efficient Routing
#### 3.9.3 Location-Based  
These protocols can be used when nodes know to become aware of their location. They are used to ensure network latency and to avoid congestion to achieve energy efficiency, as explained earlier in this survey.
# ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network
## 4 Experiments  
In this section, the performance of ClipQ on convolutional neural networks will be demonstrated, including image classification, object detection, and semantic segmentation applications. At the same time, ClipQ is compared with other quantization methods. Next, the ablation study will be conducted to reflect the role of parameter attention in weight clipping and activation clipping. Finally, we deploy part of the convolutional neural network on the FPGA to reflect the practical application capabilities of ClipQ.  
Our code implementation mainly refers to the open-source project ZeroQ. The experimental hardware platforms mainly include TiTan XP GPU and Intel Xeon Silver 4116 CPU. The software environment mainly consists of Python 3.7 and Pytorch1.8. The number of particles and iterations in the particle swarm are both 100. \(\Delta\) is 0.1 \(\cdot\)\(L\) for 8-bit quantization and 0.2 \(\cdot\)\(L\) for 6 bits. \(\alpha_{1}\) is equal to 0.2 and \(\alpha_{2}\) is 0.8. Fifty random training samples are selected as calibration data for activation quantization in the image classification task. The calibration data in the object detection and semantic segmentation tasks include just ten random training samples. The pretrained model in the image recognition task comes from Torchvision. The pretrained object detection model and semantic segmentation model come from Openmmlab.  
ImageNet2012 [40], MS COCO 2017 [41], and Cityscapes [42] are used as experimental datasets for image classification, object detection, and semantic segmentation, respectively. Among them, ImageNet2012 is the most mainstream dataset in image classification, including 1.2 million training images and 50,000 test images, with a total of 1000 object categories. The MS COCO 2017 dataset is a multi-object detection dataset, containing nearly 120,000 training images and 5000 test images, with an average of 3.5 categories and 7.7 detection objects per image. The Cityscapes dataset is frequently used in semantic segmentation research, which contains 34 urban street categories. The performance of the semantic segmentation model is evaluated by 1525 testing data from it.
###### Abstract  
Due to increasing concerns about the contamination of animal food products with antibiotic-resistant bacteria and their byproducts, phytogenic feed additives in animal diets have been explored as antibiotic alternatives. In this study, we investigated the effect of ginger root extract (GRE), green tea extract (GTEC caffeinated and GTED decaffeinated), and onion peel combined (OPEC) on the activity of C. _perfringens_ toxin genes and _Emericia tenella_ sporozoites. To this end, two _Clostridium perfringens_ strains, CP19 and CP240 (Rollins Diagnostic Lab, Raleigh, NC, USA), were cultured (three replicates per treatment) as follows: without additives (Control), with Bacifracin Methylene Disalicylate (BMD), with GRE, with GTEC, with GTED, and, finally, with OPEC for 0, 2, 4, 6, 8, and 24 h. RNA was extracted to determine the expression of tpe., alpha toxin (\(\alpha\)-toxin), and NetB and we measured the protein concentration of NetB-positive _C. perfringens_ toxin. Also, we evaluated the cytotoxic effect of green tea and ginger extracts on _E. tenella_ sporozoites. Results show that phytogenic extracts, GRE, GTEC, and GTED, significantly reduced (\(p<0.05\)) the level of expression of \(\alpha\)-toxin gene compared to control; however, BMD treatment showed much less effect. Furthermore, NetB and tpe. encoding gene expression was significantly (\(p<0.05\)) reduced by GRE and GTED, as well as BMD treatment, compared to the control. In contrast, GTEC treatment did not change the expression levels of these genes and was similar to control. With the CP240 strain, all the selected phytogenic extracts significantly reduced (\(p<0.05\)) the expression of selected genes, except for OPEC, which was similar to control. GRE, GTEC, and GTED all reduced the viability of concentration of _E. tenella_ sporozoites. Overall, our data show that these selected phytogenic extracts reduced the level of expression of toxin encoding genes associated with necrotic enteritis and decreased the viability of sporozoites which cause coccidiosis in broiler chicken.  
cocidiosis; necrotic enteritis; plant extracts; green tea; onion peel; ginger root; NetB toxin 4.0/. +
Footnote †: journal: Applied
# Preparation and Chemical Protective Clothing Application of PVDF Based Sodium Sulfonate Membrane
## Materials and Methods
### Evaluation of Permeations and Selectivities  
The American Society for Testing and Materials (ASTM) E96-95 procedure was used to measure the vapor permeation of obtained membranes with a slight modification. Samples to be tested were located over the open-top testing vials with certain mass permeants (water, DMMP, or CEPS) and fixed with fixtures to constitute permeation cells after dried at 60 \({}^{\circ}\)C for 24 h, and the thickness was measured with a digital caliper (Figure 2). The effective test area is 6.28 cm\({}^{2}\). The obtained permeation cells were placed in a convection oven at 35 \({}^{\circ}\)C and 10% RH and weighed at certain times. After the weight losses (\(W\)) became constant, they were calculated from the slopes of the weight-time curves obtained from measuring the weights of permeation cells. Vapor transfer rates (g m\({}^{-2}\) day\({}^{-1}\)) (\(VTR\)) and vapor permeation (\(VP\), mol m\({}^{-1}\) s\({}^{-1}\)) were calculated by Equation (4) and Equation (5). The ratio of water to DMMP or CEPS vapor permeation was defined as selectivity and calculated by Equation (6). Three parallel tests of each membrane were conducted to ensure the accuracy of the results [14].  
\[VTR=W/(t\times A) \tag{4}\]  
\[VP=VTR\times L \tag{5}\]  
\[\mathrm{S}=VP_{water}/VP_{simulants} \tag{6}\]  
where \(t\) represents the time, \(A\) represents the samples area to be tested, and \(L\) is the thickness of sample.  
Figure 2: Schematic drawing of the permeation cell.
## 6 Conclusions  
1. Three distinct deformation phases are identified within the Yeba Group: the D\({}_{1}\) phase--characterized by E-W-trending regional shear foliation (S\({}_{1}\)) formed through brittle-ductile shearing, associated with steeply plunging stretching lineations (plunge directions: 85-105\({}^{\circ}\)); coexisting hanging walls south-directed and north-directed shear-rotated porphyroclasts are observed; the D\({}_{2}\) phase--marked by axial planar cleavage (S\({}_{2}\)) resulting from the folding of S\({}_{1}\); S\({}_{2}\) planes dip north or south with angles of 40-70\({}^{\circ}\), and fold hinges plunge westward or NWW, indicating N-S-directed compression; and the D\({}_{3}\) phase--represented by E-W-striking ductile shear zones that overprint both D\({}_{1}\) and D\({}_{2}\) structures, displaying hanging wall movement toward the NW.
2. During the Late Cretaceous (\(\sim\)90-79 Ma), driven by the northward subduction of the Neo-Tethys Ocean, the Cretaceous Gangdese retroarc basin experienced N-S compression. This tectonic regime triggered the development of a transpressional ductile shear zone within the Yeba Group at \(\sim\)79 Ma, corresponding to the D\({}_{1}\)-phase deformation event. During the Late Cretaceous (\(\sim\)79-70 Ma), under a nearly N-S-oriented compressional stress regime, the subduction of the Neo-Tethyan oceanic lithosphere induced the overall upward extrusion of the Southern Gangdese back-arc basin, includingthe Yeba Group and its overlying strata. This process resulted in crustal shortening and the thickening of the upper crust in the Southern Gangdese, ultimately leading to the D\({}_{2}\)-phase folding deformation within the Yeba Group. During the Oligocene to Miocene (~31-15 Ma), the D\({}_{3}\)-phase deformation was a product of the tectonic transition of the Lhasa terrane from compression to lateral extension, driven by the combined effects of activity along the Gangdese thrust-reverse fault system (GCT) and the uplift of the Gangdese basement.  
Conceptualization, Z.F. and Z.Y.; methodology, Z.Y.; software, M.C.; validation, Z.F., Z.Y. and M.C.; formal analysis, Z.F.; investigation, M.C.; resources, G.W.; data curation, G.W.; writing--original draft preparation, Z.F., Z.Y. and M.C.; writing--review and editing, Z.F., Z.Y. and M.C.; visualization, Z.F.; supervision, G.W.; project administration, Z.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
We are very grateful to the reviewers and editors for their contributions to improving this paper.  
Author [PERSON] was employed by the company Daqing Oilfield Limited. The remaining authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results and Discussion
### Effects of AM Constraints and Objective Functions
#### 3.2.1 Compliance Minimization  
Figure 10 shows a comparison plot of the compliance minimization results obtained from the topology optimization process, comparing the results with and without the inclusion of AM constraints. It is noteworthy that the introduction of AM constraints leads to an increase in compliance for all three designs analyzed. The increase in compliance when adding AM constraints is due to the increase in design change requirements to meet printing limitations. On average, compliance increases by 16.19% when AM constraints are considered, indicating the need to adapt the designs to meet the requirements of AM processes.  
While under the influence of AM constraints, the average support structure volume decreases by 82.96%. This reduction in the support structure volume contributes to the overall decrease in the total volume. The reduction in the total volume achieved using the application of AM constraints demonstrates the potential for material efficiency and cost savings in AM.
## 2 Samples and Methods
### Experimental Procedure
#### 2.2.2 CT Scanning Experiment  
To observe the internal microstructure of the lamellar shale samples under the dry condition, after hydration, triaxial fracturing after hydration, and to analyse the damage of the shale caused by the interaction of hydration, force and lamellar structure CT scanning was performed.  
Firstly, a dry sample for \(\beta\) = 0\({}^{\circ}\) was hydrated in 80 \({}^{\circ}\)C distilled water for 48 h, and then triaxial tests were performed on the sample under confining pressure of 30 MPa. CT tests were carried out on the conditions of this sample when it was dry, after hydration and after triaxial compression. These experiments were conducted using the MICROXCT-400 three-dimensional reconstruction imaging X-ray microscope. The method of CT scanning experiment is based on using X-rays to create a three-dimensional data set image of a sampleby superimposing continuous two-dimensional cross-sections; in this case, the internal pore and fracture structure information can be clearly and intuitively reflected [34; 35].