esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Image Inpainting Forensics by Using an Attention-Based Feature Pyramid Network
## 4 Experiments
### Quantitative Comparisons  
The advantages of AFPN can be effectively demonstrated through comparative experiments. This study employs three state-of-the-art inpainting forensics techniques, namely HP-FCN [20], MT-Net [21], U-FPN [23], and MVSS-Net [25], to detect the inpainted areas generated by GC inpainting methods. HP-FCN is a full convolution network with high precision that is utilized to identify the forged region generated by deep inpainting. The reason why we choose HP-FCN is that it is the first one to use the deep learning method to detect the deep inpainting image. MT-Net leverages the robust learning capability of neural networks to classify anomalous features in input images and exhibits a strong generalization performance across various conventional operation types, including inpainting operations. MVSS-Net uses multi-view feature learning to jointly exploit tampering boundary artifacts and the noise view of the input. Both MT-Net and MVSS-Net study the variety of possible attacks on the content, devising a generic method. The U-FPN model extends the feature pyramid network approach, leveraging the benefits of network feature extraction to effectively identify and inpainting traces. U-FPN is the first one to use FPN for image inpainting forensics. To ensure impartiality, this study evaluates the performance of three models, including those provided by the networks, and retrains them using the proposed training set. The results of this comparison are presented in Table 1, where a higher F1 score indicates superior performance.  
In order to provide a more precise explanation of the network's generalization ability, the gray value in the table was excluded from the average calculation. The data presented in the table indicate that the AFPN, as employed in this paper, outperforms the other three methods across all test sets. The performance of the U-FPN, which utilizes multi-scale feature information akin to AFPN, is deemed barely satisfactory at 76.45%. Conversely, HP-FCN's poor performance of 8.57% on the EC dataset and low average of 52.51% suggest limited efficacy and universality. Notably, the retrained MT-Net model exhibits a lower efficacy at 15.12% compared to the original model's 46.41%, yet it yields excellent results at 92.10% on the GC test set, which suggest that MT-Net's performance is acceptable. MVSS-Net is very similar to MT-Net. The performance of MVSS-Net is gratifying on SN (94.08%) and EC (83.52%), but is very bad on GC(1.86%). Their universality is notably lacking.
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 3 Proposed Multi-Color Channels Based GSR Model
### Implementation of the Proposed Method  
#### 3.2.1 Solution of the Coefficients  
In this paper, an alternating minimization algorithm [15] is used to solve the minimization problem of Equation (11). Solving the GSR model based on multi-color channels is to solve the \(\beta_{i}\) minimization problem in Equation (11). This subsection provides implementation details of solving the \(\beta_{i}\) minimization problem. Before solving the model proposed in this paper, the following theorem [13] is introduced.  
**Theorem 1**.: _Let \(x,~{}y\in R^{N}\), and \(X_{i},~{}Y_{i}\in R^{b\times m}\). \(e\in R^{N}\) is the error vector, i.e., \(e=x-y\). \(e_{j}\) is the jth element of the error vector. Suppose that \(e_{j}\) is independent and comes from a Gaussian distribution with zero mean and variance \(\sigma_{2}^{2}\). Then for any \(\varpi>0\), we have the following property to describe the relation between \(\frac{1}{N}\|x-y\|_{2}^{2}\) and \(\frac{1}{5}{\sum_{i=1}^{n}\|X_{i}-Y_{i}\|_{2}^{2}}\), that is_  
\[\begin{array}{l}\lim_{\begin{array}{l}N\rightarrow\infty\\ S\rightarrow\infty\end{array}}P\Bigg{(}\Bigg{|}\frac{1}{N}\|x-y\|_{2}^{2}- \frac{1}{5}{\sum_{i=1}^{n}\|X_{i}-Y_{i}\|_{2}^{2}}\Bigg{|}<\varpi \Bigg{)}=1\\ S\rightarrow\infty\end{array} \tag{13}\]  
_where \(P()\) represents the probability and \(S=b\times m\times n\). (The proof is referred to in [13])._  
Based on Theorem 1, Formula (11) is transformed into the following formula:  
\[\left\{\hat{\beta_{i}}^{C}\right\}_{i=1}^{n}=\arg\min_{\{\beta_{i},C\}_{i=1}^{ n}=i}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!to each \(Y_{i}^{C}\). In order to adapt to the local structure of the images, each \(Y_{i}^{C}\) corresponds to a learning adaptive dictionary \(D_{i}^{C}\). Formula (14) is transformed into  
\[\begin{array}{rl}\{\hat{\beta}_{i}^{C}\}&=\arg\min_{\{\hat{\beta}_{i}^{C}\}_{i= i}^{n}=1}\sum\limits_{i=1}^{n}\left(\frac{1}{2}{\|Y_{i}^{C}-D_{i}^{C}\beta_{i}^{C}\| _{2}^{2}}+\tau{\|\beta_{i}^{C}\|_{1}}\right)\\ &=\arg\min_{\{\hat{\beta}_{i}^{C}\}_{i=i}^{n}=1}\left(\frac{1}{2}{\|r_{i}^{C}-D _{i}^{C}\beta_{i}^{C}\|_{2}^{2}}+\tau{\|\beta_{i}^{C}\|_{1}}\right)\\ &=\arg\min_{\{\hat{\beta}_{i}^{C}\}_{i=i}^{n}=1}\sum\limits_{i=1}^{n}\left( \frac{1}{2}{\|\gamma_{i}^{C}-\beta_{i}^{C}\|_{2}^{2}}+\tau{\|\beta_{i}^{C}\|_ {1}}\right)\end{array} \tag{15}\]  
where \(Y_{i}^{C}=Hr_{i}^{C}\) and \(r_{i}^{C}=D_{i}^{C}\gamma_{i}^{C}\). The minimization problem of the Formula (14) is simplified to the solution to the problem of Formula (15). Different from the traditional GSR model, the proposed method obtains the coefficients via a soft threshold [35] as Formula (16).  
\[\hat{\beta}_{i}^{C}=soft\left(\gamma_{i}^{C},\sqrt{2\tau}\right),\ \ \ \forall i \tag{16}\]
## 3 Results and Discussion
### Structural and Optical Characterizations of the Cys-AuNCs and Cys-AgNCs
After the conjugations of the cysteine and metallic nanoclusters, the characteristic IR bands of the cysteine in the Cys-AuNCs and Cys-AgNCs presented a small shift compared to those of cysteine due to the change in the dipole moment of the cysteine conjugated onto metallic nanoclusters [44]. Most importantly, the stretching band of SH of the cysteine vanished because Au-S and Ag-S covalent bonds were generated in both the Cys-AuNCs and Cys-AgNCs [45; 46]. Overall, the results from the TEM images, UV-Vis absorption spectra, fluorescence spectra, and FTIR analyses indicated that the fluorescent Cys-AuNCs and Cys-AgNCs were successfully synthesized using the facile hydrothermal approaches. Figure 2: (**a**) UV–Vis absorption spectra of the Cys–AuNCs (black) and Cys–AgNCs (red). (**b**) Fluorescence spectra of the Cys–AuNCs (black) and Cys–AgNCs (red). The insets illustrate the orange-red fluorescence of the Cys–AuNCs (left) and blue fluorescence of the Cys–AgNCs (right) under irradiation from a hand-held UV lamp.
## 2 Materials and Methods
### Phosphate Solubilization, N Fixation Capacity, and Salt Tolerance Determination  
Twenty-eight morphotypes were obtained and tested for in vitro PGP (phosphate-solubilizing and N-fixing) activities and salt tolerance. Each colony was sampled with a sterile pin and respectively deposited on Pikovskaya (yeast extract 0.5 g L\({}^{-1}\), glucose 10 g L\({}^{-1}\), (NH4)\({}_{2}\)SO\({}_{4}\) 0.5 g L\({}^{-1}\), MgSO\({}_{4}\cdot\)7H\({}_{2}\)O 0.1 g L\({}^{-1}\), Ca\({}_{3}\)(PO\({}_{4}\))2 5 g L\({}^{-1}\), KCl 0.2 g L\({}^{-1}\), MnSO\({}_{4}\cdot\)2H\({}_{2}\)O 0.002 g L\({}^{-1}\), FeS-O\({}_{4}\cdot\)7H\({}_{2}\)O 0.02 g L\({}^{-1}\), agar 15 g L\({}^{-1}\)), Ashby (manitol 20 g L\({}^{-1}\), K\({}_{2}\)HPO\({}_{4}\) 0.2 g L\({}^{-1}\), NaCl 0.2 g L\({}^{-1}\), MgSO\({}_{4}\cdot\)7H\({}_{2}\)O 0.2 g L\({}^{-1}\), K\({}_{2}\)SO\({}_{4}\) 0.1 g L\({}^{-1}\), CaCO\({}_{3}\) 5 g L\({}^{-1}\), bromophenol blue 0.4% (\(w/v\)) stock solution in ethanol 10 mL L\({}^{-1}\), agar 15 g L\({}^{-1}\)), LBA, and LBA plates amended with 5%, 7.5%, and 10% NaCl (Figure 1). [PERSON]'s medium is a selective culture medium used to isolate and enumerate soil microorganisms capable of solubilizing inorganic phosphate [38]. The medium contains tricalcium phosphate (Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\)) as the insoluble phosphate source. Microbial phosphate solubilization is indicated by forming a clear halo zone around the colonies, signifying the breakdown of tricalcium phosphate. Ashby's Agar is a specialized growth medium devoid of nitrogen, facilitating the isolation and cultivation of nitrogen-fixing bacteria capable of utilizing atmospheric nitrogen as a source of this macro-element [39]. Luria Bertani (LB) agar is a nutrient-rich growth medium for cultivating bacteria [40]. When supplemented with NaCl, it creates an environment that favors the growth of salt-tolerant bacteria while inhibiting the growth of bacteria sensitive to higher salt concentrations.  
The plates were incubated at 30 \({}^{\circ}\)C for 48 h. The colony diameter of the morphotypes was measured on each growth medium. The reported colony diameter represents the average of three independent replicates. N-fixing capacity was evaluated by detecting the formation of colonies on Ashby plates, while colony and halo formation were considered distinct traits of phosphate-solubilizing bacteria. The solubilization index (SI) was calculated as SI = (colony diameter + halo zone diameter)/colony diameter [41].  
Five N-fixing bacteria were selected, genotypically characterized, and inoculated in lettuce plants.
## 5 Analysis and Discussion
### Analysis of Land Surface Settlement in the Engineering Construction Area
#### 5.1.1 Impact of Subway Construction  
Subway construction is an important symbol of urban economic development and mutually benefits city populations and logistics [29]. The construction and operation of urban subway tunnels causes land surface subsidence. The surface subsidence affects the surrounding buildings and underground pipelines to different degrees. Surface settlement occurs in urban subway tunnels due to excavation surface settlement, erosion settlement, consolidation settlement of the soil, etc. [30].  
Studies are conducted on the related settlement deformation between urban infrastructure health and metro tunnel construction to prevent surface subsidence and other disasters. Based on the current operation of the Kunming metro and metro under construction, Figure 14a shows the distribution of the Kunming subway superimposed on the regional deformation rate map. As can be seen in Figure 14b, there is significant surface settlement in the middle section of the extension of Metro Line 2, which is under construction (Yejia village-Liujia interval). The settlement area shows linear characteristics; the linear settlement area is highly consistent with the driving route of the extension line of Metro Line 2. The deformation rate in the settlement area is approximately \(-9.5\) mm/a. In this paper, three points along the settlement area of the subway are selected in the middle and at both ends for the analysis of the accumulated deformation. Figure 14c shows the accumulated vertical deformation at the positions of points P\({}_{1}\), P\({}_{2}\) and P\({}_{3}\). The maximum accumulated displacement reaches \(-40.98\) mm.  
It is concluded that the consolidation degree of underground soil is low. Surface settlement is caused by inward movement of soil during excavation. In the case of complex geological conditions (e.g., the cascade terrain) even ground subsidence may be induced [31]. Therefore, continuous monitoring is required during the construction cycle and after completion [6].
## 2 Model Methodology
### Problem Formulation for the \(\beta\)-Robust Situation with Multiple Scenarios  
Through solving the model (\(P_{s}\)), we can obtain the optimal solution for the \(p\)-median facility problem under any given scenario \(s\), and calculate the corresponding optimal function value, \(z_{s}^{*}\). Given a feasible solution for all scenarios, noted as \(X\), the \(\beta\)-robust solution can be defined below.  
**Definition 1**.: _Solution \(X\) is \(\beta\)-robust for all scenarios when the following constraints are satisfied:_  
\[\frac{z_{s}(X)-z_{s}^{*}}{z_{s}^{*}}\leq\beta,\ \forall s\in S, \tag{6}\]  
_where \(z_{s}(X)\) represents the objective value for solution \(X\) under scenario \(s\)._  
Then, the \(\beta\)-robust \(p\)-median facility location problem with uncertain demand can be formulated as follows.  
\[\beta\text{-}P:\ Minz=\sum_{s\in S}\sum_{i\in M}\sum_{j\in N}q_{s}\sigma_{is}c_ {ij}X_{ijs} \tag{7}\]  
Subject to  
\[\sum_{i\in M}\sum_{j\in N}\sigma_{is}c_{ij}X_{ijs}\leq(1+\beta)z_{s}^{*},\ \forall s\in S, \tag{8}\]  
\[\sum_{j\in N}Y_{j}=p, \tag{9}\]  
\[X_{ijs}\leq Y_{j},\ \forall i\in M,\ j\in N,\ s\in S, \tag{10}\]  
\[\sum_{j\in N}X_{ijs}=1,\ \forall i\in M,\ s\in S, \tag{11}\]  
\[X_{ijs},\ Y_{js}\in\{0,1\},\ \forall i\in M,\ j\in N,\ s\in S. \tag{12}\]  
Objective function (7) is to minimize the expected overall transportation cost. Submitting function (1) into (6), we can obtain Constraint (8), which ensures that the relative regret for any scenario \(s\in S\) is no more than the required level \(\beta\). The meaning of Constraints (9)-(12) is similar to the ones in \(P_{s}\), while all these constraints must be satisfied for all scenarios \(s\in S\).
# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
Our research orientation of designing a new foundation for wind turbines concentrate on expanding the resistance of a wider range of soil. Based on this, this paper proposes that the pile-bucket foundation (P-BF), which combines the suction bucket foundation (BF) and monopile foundation (PF) in a shallow position, enhances lateral bearing capacity. However, until now, no studies have focused on the lateral bearing capacity of the P-BF through the \(p\)-\(y\) curve or have modified the \(p\)-\(y\) curve by the API to be applicable to practical engineering. In this paper, the finite element software ABAQUS is used to establish the P-BF (Figure 1). Taking a large-diameter single pile with a diameter of 6 m and length of 80 m as the main body, the P-BF model is established by ABAQUS. We quantitatively analyze the bearing capacity, soil reaction, displacement distribution and \(p\)-\(y\) curve of three different diameter buckets. The \(p\)-\(y\) curve is studied to analyze the improvement in the lateral bearing capacity of the P-BF. The advantage of P-BF compared with the suction bucket is summarized. Figure 1: The geometry for the pile–bucket foundation; structural diagram.
# Long-Term Effects of External Sulfate Attack on Low-Carbon Cementitious Materials at Early Age
## 2 Materials and Methods
### Mixing, Casting, and Exposure Procedures  
The study was carried out on \(40\times 40\times 160\) mm\({}^{3}\) prismatic specimens. The preparation protocol of specimens closely adheres to the NF EN 196-1 standard [38] and unfolds as follows: initially, water and cementitious materials are mixed at a low speed of 100 rpm for a duration of 60 s. Subsequently, the speed is increased to 300 rpm for an additional 30 s. Following this, the mixture is allowed to sit undisturbed for 90 s, with the edges of the mixer bowl scraped during the initial 20 s of this interval. The subsequent step involves a second round of high-speed (300 rpm) mixing lasting for 60 s. Various adjustments in mixing speed were used to guarantee the homogenization of the cementitious materials. Employing different mixing speeds helps minimize losses and promotes an even dispersion of components, thereby preventing uneven distribution. Such adjustments are crucial for maintaining the properties of the resulting paste. The resulting paste is poured in one layer into the molds without vibration, but rather, is subjected to four external shocks using a hammer. After a curing period of 24 h, demolding occurs, and the specimens are immediately immersed in sodium sulfate solutions with a concentration of 15 g/L (corresponding to approximately 10.14 g/L of sulfate ions). The water-to-solid volume ratio is maintained at 8 to simulate exposure to an almost infinite sulfate source. To ensure uniform exposure, the specimens are positioned on plastic supports in the attacking solution. The entire setup is preserved at a constant temperature of 20 \({}^{\circ}\)C and carefully monitored (expansion and mass gain for the samples and Raman spectroscopy to check the variation of sulfate concentration in the attacking solutions, refer to Section 2.3) and characterized throughout the study. The sodium sulfate solution is renewed weekly for the initial three months, bi-weekly for the subsequent six months, and then monthly, thereafter, to ensure exposure conditions equivalent to an infinite sulfate source, especially since the used concentration of sulfate is relatively low.
###### Abstract  
The Fabry-Perot (FP) cavity is the essential component of an ultra-stable laser (USL) for gravitational wave detection, which couples multiple physics fields (optical/thermal/mechanical) and requires ultra-high precision. Aiming at the deficiency of the current single physical field optimization, a multi-physics and multi-objective optimization method for fixing the cubic FP cavity based on data learning is proposed in this paper. A multi-physics coupling model for the cubic FP cavity is established and the performance is obtained via finite element analysis. The key performance indices (\(V\), \(w_{F}\), \(w_{F}\)) and key design variables (\(d\), \(l\), \(F\)) are determined considering the features of the FP cavity. Different data learning models (NN, RSF, KRG) are established and compared based on 49 sets of data acquired by orthogonal experiments, with the results showing that the neural network has the best performance. NSGA-II is adopted as the optimization algorithm, the Pareto optimal front is obtained, and the optimal combination of design variables is finally determined as \(\{5,32,250\}\). The performance after optimization proves to be greatly improved, with the displacement under the fixing force and vibration test both decreased by more than 60%. The proposed optimization strategy can help in the design of the FP cavity, and could enlighten other optimization fields as well.  
FP cavity; multi-physics coupling; finite element method; data learning; surrogate model; evolutionary algorithm +
Footnote †: journal:  
_Applied Sciences_
## 6 Conclusions and Future Work  
In this paper, we have presented a novel geographic and partial network coding based routing protocol for UWSNs called GPNC. GPNC uses partial network coding for data delivery in order to decrease the number of sending packets and reduce collision between packets. GPNC can improve the network delivery rate and at the same time reduce the energy consumption and network delay. GPNC uses a new greedy approach to deliver encoded packets to the sink nodes and chooses candidate nodes according to a node's normalized packet advance (NADV) and residual energy. Simulation results have shown that GPNC improves network throughput and the data delivery ratio, while reducing energy consumption and network delay when compared with the baseline routing protocols.  
How to avoid \"void\" areas is very important for any greedy strategy, so we plan to investigate how the depth adjustment of some nodes can impact void areas and how opportunity forwarding of data packets to sink nodes can be incorporated into the GPNC routing protocol.
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 7 Discussions of Results and Research Findings
### Comparison of Contribution to Other Published Methods
#### 7.1.1 No Extra Hardware and No Required Knowledge of Light-Path  
Our method does not imply any extra power consumption, compared to other methods using power-consuming time domain FIR or trellis decoders. In [9; 11], there is no mention of how the \(TF_{measured}(f)\) (as in Equation (15)) is obtained. Besides, when using the number of ROADMs to estimate the order/bandwidth (applied to Equation (1)), the variability in both ROADMs and CMDs response can be big and hence higher margin for divergence from optimality. With the introduction of colorless CMD, new ROADM-based architectures described as Colorless-Directionless-Contentionless (CDC) Mux/Demux are heavily deployed in nowadays' mesh networks [41]. The latter may have more than hundreds of light-paths [42] and deploy multi-degrees ROADMs, up to 8 degrees using WSSs with 43 ports as presented in [43; 44], per site where any tunable Tx can be connected to any colorless CMD port. Having said that, pre-setting the correct pre-equalization TF or programming FIR filter taps properly takes an enormous effort and it's prone to mistakes. Since only the Rx can estimate the channel response, any automated method requires a feedback channel. In [45], the authors introduced FIR filters post-ADC in order to adaptively correct for bandwidth narrowing due to electronics and optical chains. Such filters will increase the design complexity and power consumption.
# Moving towards Personalized Medicine--The Broad Use of Aptamers for Targeted Theranostic
## 2 Cancer Therapeutics
### Applications in Immune Diseases  
The versatility of aptamers created an important opportunity for diverse application in the field of immunology both in diagnosis and therapy. Considering diagnostic techniques aptamers might be used for two main purposes: detection [49; 78; 79] and separation [80; 81; 82]. In therapy, aptamers have two other principal functions that include being a therapeutic drug itself [83] or being responsible for taking fewer drugs to treat the affected cell or organ and, in this way, reducing the need for higher doses of drugs that have relevant secondary effects [84; 85; 86].  
The gold standard techniques for immune diagnosis are flow cytometry and Enzyme-linked Immunosorbent Assay (ELISA) [87] and aptamers present features useful for both strategies, offering a broad range of solutions for the detection of biomolecules like proteins, including immunoglobulins or membrane receptors [49; 83; 85; 88], cells [80; 81; 89], or even Extracellular Vesicles (EVs) that have gained popularity in fighting diseases like cancer. For EVs, CD63 is being consistently used as one of the most promising targets for aptamers on the surfaces of EVs [79; 82]. Optamers might also be used for cell separation using, as an example, sorting equipment decoupled to flow cytometry, which is useful both as a diagnosis technique [80; 81] and also for therapy purposes in techniques like immunotherapy or HIV therapy [80; 90].  
[PERSON] et al. already reported a broad range of immune cell markers that include the most common markers, like CD4 or CD8, and also some unusual ones. This might be useful for diagnosis, cell sorting, and even therapy in a huge variety of diseases like cancer, HIV, or autoimmune conditions [49] (Table 2).  
Considering therapeutical strategies, aptamers might be a drug by themselves and although there is potential for exclusive use of aptamers as a therapy [83], the most common approach is using aptamers together as co-adjuvant therapy. For example, aptamer- T cells together with anti-PD1 immune-checkpoint monoclonal antibodies have a synergetic effect in reducing cancer, in vitro and in rats, and aptamer-linked small-interfering RNA chimeras to selectively knock down genes in mouse breast cancers [91; 92]. This approach allows a decrease in dose in immunotherapy applications and, in this way, reduces the secondary effects of therapies that might cause a lot of discomfort and distress in patients [83; 93; 94]. Supramers are used not only in order to destroy the disease agent, like a cancer cell [89], but are often used as immune modulators, reducing immune activity [86], targeting or influencing immune checkpoint inhibitors [89; 95; 96], and/or promoting immune cell activity and infiltration [91; 97].