esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Simulation Analysis  
In this section, MATLAB is used to simulate the transmission process of the whole system. Then, the system is studied and analyzed from three aspects, the length of the reference signal, the category of chaotic mapping, and the number of semi-spread spectrum factors. Finally, the bit error rates of different systems are compared.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 3 Phenomena and Problems Caused by Photodarkening  
The performance degradation of doped fiber is caused by PD. Thus, the stability of the output power and efficiency of doped fiber lasers and amplifiers is affected. The phenomena and problems caused by PD in doped fibers are mainly absorption spectrum change [1; 2; 4], output power reduction [2], temperature rise [38; 39], mode instability threshold reduction [66; 67; 68], RIC [28], fluorescence lifetime reduction [32; 69], etc.
# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 3 Experimental Set-Up
### Active Shape Model (ASM)
#### 3.3.1 Landmarks  
When a video frame is captured, suitable reference points must be assigned to the object's outline. The landmarks must be located in the contour of the pedestrian and must be updated from one image to another. In a two-dimensional image, they are represented as landmarks by a two-dimensional vector given by Equation (11). The configuration of this system consists of 50 manually assigned reference points (\(L=50\)). The main feature of reference points or landmarks is to control the shape of the model contours. Specifically, the initially assigned landmarks are updated by minimizing the deviation from the original profile, which is normal to the boundary at each landmark.  
\[\mathbf{x}=[x_{1},\ldots,x_{L},y_{1},\ldots,y_{L}]^{T} \tag{11}\]
## 4 Results
### Ferruginous Sandstone Chemical Characteristics  
(1) Major element content  
The SiO\({}_{2}\) content of ferruginous sandstone ranges between 6.67 wt.% and 81.35 wt.% with an average value of 56.73 wt.%. The Fe element in the Wajid ferruginous sandstone basically exists in the form of ferric iron, with Fe\({}_{2}\)O\({}_{3}\) content of 12.04-84.97 wt.% and an average content of 28.95 wt.%. The content of FeO is mostly lower than 0.1 wt.% and the highest content is only 0.64 wt.%. The manganese content of some ferruginous sandstone is also high, with an average MnO content of 0.85 wt.% and a maximum of 3.43 wt.% (Table 1).  
(2) Organic carbon and sulfur  
The organic carbon and sulfur analyses showed that the average content of organic carbon was 0.067 wt.%, and the average sulfur content was 0.045 wt.%. The content of the organic carbon in ferruginous sandstone is not different from other sandstones, with an average of 0.0588 wt.%; the content of sulfur increased significantly, with an average of 0.0672 wt.% (Table 2). The content of sulfur displayed a good correlation coefficient with the uranium content. It indicates that sulfur, as one of the reduce matters, plays a significant role in the concretion of uranium, and the sulfur was always disseminated in the hematite matrix.  
(3) pH and Eh  
The pH value of the Wajid sandstone in the study area was between 8.49 and 9.80, with an average value of 8.98. This indicated a weak alkaline environment (Table 2). The Eh value was between 192 and 251 mV, and the average value was 226.45 mV. The average pH of the ferruginous sandstone was 8.61, with a higher Eh of 246.25 mV. It was concluded that the Wajid Sandstone, which included the ferruginous sandstone, was in an alkaline oxidation environment.
## 2 Materials and Methods  
### Experimental Site and Innovative Mulch  
Experimental cultivation was carried out in a private farm, located in Falciano del Massico (Caserta) (41\({}^{\circ}\)140\({}^{\prime}\) N, 13\({}^{\circ}\)944\({}^{\prime}\) E), under a multi-tunnels structure with a semi-circular cross-section (2.5 m high, 5.3 m wide, 38 m long). Tunnels were covered by clear ethylene-vinyl acetate plastic film with a thickness of 18 mm. Soil had a loam texture (USDA) composed of: 10% clay, 53% silt, 37% sand, pH 7.8, 2.2% organic matter, total N 1.3 g kg\({}^{-1}\), C/N 9.8, electrical conductivity 286 dS m\({}^{-1}\), and available p 99 mg kg\({}^{-1}\), as reported in [PERSON] et al. (2022) [8]. Two biodegradable mulching films were compared to the standard black, low-density PE mulching film, 50 \(\upmu\)m thick: (a) the innovative N5 (Mater-Bi, Novamont, Novara, Italy), grade EF08P0, black-coloured, 18 \(\upmu\)m thick, 1.6 m width and (b) the commercial standard N18 (grade EF04P0 Mater-Bi), black-coloured, 18 \(\upmu\)m thick, 1.6 m width. Compared to N18, the N5 film presented a durability optimized for a longer crop cycle [8]. Two strawberry cvs., namely Elide (CIV) and Sabrina (Planasa), were cropped on each type of the three mulching films. The main mechanical properties for a Mater-Bi film, 15 \(\upmu\)m thick, grade EF04P are: tensile strength 35 MPa, parallel elongation at break 380%, Young's modulus 200 MPA; biodegradability, according to UNI EN standard 13432 and 14995, must be \(\geq\)90% conversion into CO\({}_{2}\) within 2 years under ambient soil conditions; water vapour pressure is 400 g x 30 \(\upmu\)m/m\({}^{2}\) day.  
As described in [PERSON] et al. (2022), main plots hosted the different mulching films that covered raised beds along the entire length of a tunnel (38.5 m); each main plot was divided in two sub-plots to test the two cvs.. Each raised bed, with an isosceles trapezoidal cross-section, was 0.8 m wide at the bottom, 0.6 m at the top, 0.35 m high at both sides, and 0.4 m apart from the other. The whole area dedicated to the trial was 1200 m\({}^{2}\), and within each experimental plot, an area was delineated containing 10 plants in which ripe berries were harvested and sampled to assess the VOCs profiles. Fruits analysed in this study were sampled at the last harvest on 1 June 2021.
## 5 Conclusions and Future Trends  
This review highlighted significant differences in dough rheology and the characteristics of baked goods for wheat flour and gluten-free products. Notwithstanding the efforts of bakers, scientists, and the food industry, manufacturing still presents significant technological problems, illustrated by the poor quality of gluten-free breads currently available on the market [3; 13; 14; 17]. The absence of gluten has a huge, negative impact on dough rheology and bread characteristics, generating doughs that are much less cohesive and elastic, and bread with lower volume and poorer texture, compared to wheat-based products [13; 14; 15; 17]. The unique structure and function of gluten makes the search for alternative ingredients that can mimic the gluten network extremely challenging.  
This review has highlighted the importance of selecting the optimal improvement strategy (or combination of strategies), as a function of the desired product. The most promising results seem to rely on the correct management of total water content and kneading, the use of hydrocolloids (in particular HPMC), the use of enzymes (in particular glucose oxidase and TGase), and the use of alternative sources of protein. Moreover, this review highlighted other, encouraging innovative strategies. These include: increasing starch gelatinization with HP treatments; dry heat treatment of gluten-free flows; and sourdough fermentation, particularly with lactic acid bacteria that produce functionalized exopolsaccharides able to (naturally) replace commercial hydrocolloids or gums.  
In conclusion, several strategies could be adopted to improve gluten-free dough rheology and baked products. Although the literature is rich, further investigations are needed, particularly with respect to the most innovative, recent strategies. Moreover, the gluten-free industry should embrace current trends in the wheat-bakery industry, which seek to reduce environmental pressure. This could be done using local, alternative sources of protein (such as native legumes, or protein sources that are the by-product of other production chains), provincial gluten-free flours, and zero km ingredients. The COVID-19 pandemic has led us to reconsider the relevance of short supply chains, and local production [57], and this latter recommendation could be considered as an additional improvement strategy, as consumer interest in the environmental impacts of food production will only increase in future.  
A.C.: conceptualization, methodology, validation, data curation, writing (original draft), writing (review & editing), visualization, project administration; N.O.: analysis, investigation, data curation, visualization, writing (original draft); E.C.: conceptualization, validation, writing (original draft), writing (review & editing), visualization, supervision, project administration. All authors have read and agreed to the published version of the manuscript.  
This research was not funded by any agencies in the public, commercial, or not-for-profit sectors.  
The authors declare no conflict of interest.
## 1 Introduction
### Research Contributions and Paper Structure  
To solve the problem discussed above, i.e., resource allocation in MEC networks considering user cooperation, this paper will provide a fast and accurate solution for wireless communication resource allocation in MEC networks. The promotion of resource allocation ability by user cooperation will also be discussed. As mentioned above, the innovation of this paper includes putting forward a network frame that can achieve user cooperation, introducing a user cooperation mechanism in MEC resource allocation and explaining why this mechanism can help. We will present our conclusions after the problem introduction, network modeling, optimization problem description, function design, algorithm introduction, simulation and analysis. In this way, a solution for rapid resource allocation in the scenario of 'one MEC server serving users in multi-cells' and a mechanism called user cooperation for the further promotion of network resource allocation performance are explained. According to our final results, with the user cooperation considered, network quality can be improved by about 10%, at most.  
The structure of this paper is organized as follows. Section 2 lays out preliminary knowledge, including an introduction of the problem and modeling. Section 3 includes a description of the optimization and algorithm. Section 4 includes the simulation and analysis.
# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## Appendix A  
Based on experimental validation, the dehazing algorithm proposed in this paper not only benefits object detection but also enhances the dehazing effect on images. The training of the dehazing model utilized a training dataset, VOC_fog_train_10, which includes data augmented with 10 different levels of haze. To verify the dehazing performance of the improved model, we conducted extensive experimental evaluations on AODs. We tested the model's performance on four different datasets, each with varying levels of haze (light, moderate, heavy, and random haze), and compared its performance with mainstream dehazing algorithms. Table 1 presents our experimental results.  
As shown in the experimental results, our proposed AODs demonstrates significant advantages across various metrics. On the randomly hazed dataset, our model achieved a PSNR of 22.729 and an SSIM of 0.84123, outperforming several other comparative algorithms. Additionally, our model excels in dehazing under moderate haze conditions. Beyond quantitative metrics, we also conducted subjective visual comparisons. The dehazing effects of different algorithms on the VOC_fog test dataset are illustrated in Figure 1.  
From the results shown in Figure A1, the improved AODs dehazing algorithm outperforms the other algorithms in terms of dehazing effectiveness and realism. It better eliminates the color distortion and blurriness caused by haze. On one hand, it avoids issues such as the color shift and excessive darkness seen in images processed by the DCP (Dark Channel Prior) algorithm. On the other hand, it addresses the low saturation problem of the original AODNet algorithm. Furthermore, compared to the Retinex dehazing algorithm, the improved AODs excels in preserving detail and maintaining scene structure in the dehazed results, providing clearer and more realistic images. In summary, the proposed AODs performs well in terms of brightness and texture restoration.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Exploratory Attack Methods
#### 4.1.7 DeepFool Attack  
DeepFool Attack (DFA) is an untargeted adversarial example generation technique proposed by [PERSON] et al. in [54] to calculate the minimal Euclidean distance (i.e., \(L_{2}\) distance metric) between the original input (i.e., image) and the adversarial example's decision boundary.  
In neural networks, these decision boundaries invariably exhibit nonlinearity. However, to calculate a linear decision boundary that distinguishes samples from different classes, the authors assume that the neural networks operate as entirely linear systems, with class regions being defined by hyperplanes. From this linearization assumption, the DF algorithm calculates the smallest perturbation needed to reach the decision boundary. Then, from the new point, the same operation is iteratively performed multiple times until an adversarial example is found. Formally the minimal perturbation needed to produce an adversarial sample is expressed by (9).  
\[\delta(X|f)=\underset{r}{min}\|r\|_{2}\quad s.t.\quad f(X+r)\
eq f(X) \tag{9}\]  
here \(r\) is the minimal perturbation, \(\delta\) is the robustness of the affine classifier \(f\) to the original input \(X\) for \(f(x)=W^{T}.x+b\) where \(W\) is the weight of the affine classifier and \(b\) is the bias of the affine classifier.  
As white-box attack, the DFA method offers an efficient and precise approach to assess the resilience of ML models. It achieves this by generating adversarial samples with smaller perturbation sizes compared to those generated by FGSM and JSMA methods while having higher deception ratios. However, it is more computationally expensive than both.
## 2 Centralized Treatment Plants
### Industrial Wastewater Remediation
#### 2.3.9 Glass Industry  
A full-scale horizontal subsurface flow constructed wetland (HSFCW) system has been proposed for the first time to treat wastewater from the glass manufacturing industry in Iran. In order to de-risk the treatment approach, a pilot system consisting of a settling tank and an HSFCW was tested for 4 months, and the results were used to build the full-scale system able to treat 10 m\({}^{3}\)/day for almost one year. The tested CW was planted with _Cortaderia Sellana_, a plant known for its high phytolith production and its capacity to absorb silica-soluble particles in plant tissues. Actually, high removal rates of BOD\({}_{5}\), COD, TSS (90, 90 and 99%, respectively) and TN and TP (>90%) were obtained, allowing for the recycle and reuse of the treated effluent in the glass manufacturing processes. TSS, which appeared to be the major pollutant in this industrial wastewater, was efficiently removed [111].
## 4 Results  
### RQ1. What Is the Perception of Food Sustainability of University Students?  
Seventy-six percent of respondents think about the sustainability of the food they consume, twenty-eight percent are very concerned about the sustainability of their food, and six percent never think about it. When obtaining information about food sustainability, we found that 35% of respondents follow food profiles or information on social media.  
The conceptualization of sustainable food has an interpretation that implies, almost equally, the consumption of local food (62%), buying locally (59%), and consuming less processed products (59%). A lower consumption of animal protein is only considered by 28% of respondents.  
Taking into account these conceptualizations of what sustainable food implies, 68% self-perceived their practices as somewhat sustainable, while 22% perceived their practices as not very sustainable. Nine percent consider their practices to be very sustainable.  
Self-perception of sustainable consumption habits is related to conceptualizations of sustainability consumption like shopping locally and consuming less meat (F = 3.155; Sig. = 0.014). Those who perceive their consumption habits as less sustainable do not give as much importance to meat consumption in the conceptualizations of sustainability but focus more on local consumption (F = 2.529; Sig. = 0.039).
# Numerical Simulation of a Submerged Floating Tunnel: Validation and Analysis
## 3 Numerical Study of Shaking Table Model for Scaled-Down SFT
### Results Analysis  
The numerical simulation was conducted in two steps, the first being a static equilibrium calculation, and the second being a dynamic seismic response calculation. In the  
Figure 6: Numerical model diagram of SFTs. (**a**) Numerical model diagram of a submerged floating tunnel (SFT) simulated using the coupled Eulerian–Lagrangian method. (**b**) SFT model (numbers are anchor cable numbers).  
latter, a sinusoidal seismic wave was applied horizontally to the bottom of the model, and a seismic time course analysis was conducted. Figure 7 shows comparisons of the time curve of the horizontal acceleration at the center of the SFT body, the horizontal hydrodynamic load-time curve on the outer surface of the tube body, and the time curve of the axial force of the anchor cable, with the experimental results reported by [PERSON] and [PERSON] [17; 18]. The hydrodynamic load on the outer surface of the tube body mainly comprised frictional resistance and differential pressure resistance. The former represented the frictional force generated by the water body tangentially on the surface of the tube body and was a direct manifestation of the viscosity of the water body. The latter was an indirect manifestation of the viscosity of the water body, and it was largely caused by the reciprocal vibration of the tube body in the horizontal direction, which led to the water body alternately horizontally separating to the left and right of the surface of the tube body, thus producing pressure differences in the horizontal direction. The calculation results showed that the hydrodynamic load in the vertical direction at this time was much smaller than that in the horizontal direction, and was therefore not chosen as the hydrodynamic load in the vertical direction.
## 4 Experiments
### Comparative Results
#### 4.4.1 Results on the Guangzhou City Dataset  
The visualized and quantitative comparison results over the Guangzhou City dataset are shown in Figure 4 and Table 1, respectively. From the quantitative comparison, our MSGATN can achieve the best \(F1\) and \(OA\) (90.54% and 97.19%). However, DBN and GaborPCANet achieved the best precision and recall, respectively. Although the proposed MSGATN does not achieve the best precision and recall, our method still provides relatively reliable performance in terms of precision and recall. For example, compared with the DBN, despite DBN reaching the best precision (98.05%), it obtained the second-worst performance in recall (78.51%). Therefore, our MSGATN can acquire more balanced performance for the four metrics. Different from other approaches, the proposed MSGATN adopts a multi-scale graph attention network to effectively capture the representative features of unstructured data, thereby improving the detection accuracy. Regarding the visual results, the proposed MSGATN exhibits the fewest false detections compared to the other five methods. Specifically, the GaborPCANet presents many false alarms compared to the proposed MSGATN. Moreover, although the visual results of the DBN show fewer false alarms, a large number of missed pixels are unavoidable. Compared with these methods, our proposed MSGATN can obtain a more balanced performance in terms of false detections and missed detections. Moreover, the proposed MSGATN can provide more complete change information compared with other methods, except GaborPCANet. Overall, the visual results also yielded similar conclusions to the quantitative comparisons.  
Figure 4: The results of different methods on the Guangzhou City dataset: (**a**) PCA_K-means, (**b**) ASEA, (**c**) GaborPCANet, (**d**) DBN, (**e**) CWNN, and (**f**) proposed MSGATN.
# Interference Monitoring from Low Earth Orbit: The OPS-SAT Experiment +
## 2 Experiment Setup  
The LEO-based RFI monitoring demonstration is the result of the convergence and coordination of two streams of activities carried out by two distinct working groups.
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 4 Direction for Future Work
### Using Significant Features  
The selection of appropriate features plays a vital role in training a model for producing effective results. Features extracted statically and dynamically both hold their contributions to the detection of malicious behavior. Most of the surveyed papers have used a subset of features or have used either statically extracted or in some cases dynamically extracted features only which paves way for the concern that some features which might be quite decisive in detecting malicious nature, may have been ignored. Many papers indicated that non-optimal features were focused on and should be taken care of in future work. Using the combination of static and dynamic features can train the model with better learned capabilities. To deploy the anti-malware system in real time environment, extracting dynamic features can pose a problem as per the limitations available in the market. In such a case, the application of neural networks can be helpful. Neural networks can deal with the images of samples of both malicious and benign classes. This way rather than focusing on any feature, all the static semantic features of the samples can be focused on. The usage of neural networks automates the process of feature engineering. Rather than selecting the features on the hit and trial method, embedding layers of the neural network can be used to automatically select the most contributing features.
# Comprehensive Evaluations of NLOS and Linearization Errors on UWB Positioning
## 2 Methods
### Least Square  
While using LS, the tag coordinates \(X=\left(\delta_{x},\delta_{y},\delta_{z}\right)\) can be computed as shown in (2) [28]  
\[\mathbf{X}=\left(\mathbf{A}^{\mathrm{T}}\mathbf{A}\right)^{-1}\mathbf{PA}^{\mathrm{T}}\mathbf{b} \tag{2}\]  
\[\text{where }\left\{\begin{array}{l}x=\hat{x}+\delta_{x}\\ y=\hat{y}+\delta_{y}\\ z=\hat{z}+\delta_{z}\end{array}\right.,\mathbf{b}=\left[\begin{array}{ccc}d_{1}- \hat{d}_{1}\\ d_{2}-\hat{d}_{2}\\ \vdots\\ d_{N}-\hat{d}_{N}\end{array}\right],\mathbf{A}=\left[\begin{array}{ccc}\frac{ \hat{x}-x_{1}}{d_{1}}&\frac{\hat{y}-y_{1}}{d_{1}}&\frac{\hat{z}-z_{1}}{d_{1}} \\ \frac{\hat{x}-x_{2}}{d_{2}}&\frac{\hat{y}-y_{2}}{d_{2}}&\frac{\hat{z}-z_{2}}{d_{2 }}\\ \vdots&\vdots&\vdots\\ \frac{\hat{x}-x_{N}}{d_{N}}&\frac{\hat{y}-y_{N}}{d_{N}}&\frac{\hat{z}-z_{N}}{d_ {N}}\end{array}\right],\text{ and }\mathbf{P}\text{ is the priori}\]  
weight matrix.