esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
This technology has been studied in recent years for application in fields such as food safety inspection, quality identification of agricultural products, and biological contaminant detection [17], alongside, analysis techniques such as spectral unmixing and multivariate analysis-based band selection and classification have also been investigated for obtaining meaningful information from vast hyperspectral data. In the food industry, non-destructive measurements are applied to evaluate food quality and safety by collecting and analyzing the characteristics of agricultural products, such as the physical attributes and chemical content [18; 19; 20; 21; 22; 23; 24]. Other hyperspectral imaging applications have addressed environmental, defense, and biomedical areas, based on deep learning technology [25]. Hyperspectral fluorescence imaging is a sensitive optical technique that uses selected light excitation of a sample to induce light emissions from the sample at wavelengths different from the excitation light of 365 nm [6; 22; 26]. In studies targeting detection of bacterial biofilm, feces, organic residues, and insects in agricultural produce for food safety, non-destructive and non-contact fluorescence imaging using fluorescence regions from 480 to 560 nm and from 670 to 690 nm has been demonstrated to be an effective technique for rapid detection of contaminants [6; 16; 26; 27; 28; 29; 30; 31]. The aim of this study is to investigate a hyperspectral fluorescence imaging technique for rapid detection of fruit residues on the surfaces of stainless steel food processing equipment, which can potentially harbor bacteria if inadequately cleaned. In particular, this study focuses on fresh fruit residues on 2B-finished and #4-finished stainless steel, which are commonly used in food processing equipment [32]. Fluorescence imaging characteristics are investigated for apple, orange, honeydew melon, and watermelon residues on 2B- and #4-finished stainless steel, and a residue-detection algorithm is developed using optimal waveband selected for each type of fruit residue.
## Thermal Poling of New Double-Hole Optical Fibers  
[PERSON], [PERSON], [PERSON] *, [PERSON], [PERSON] and [PERSON] *  
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China; [EMAIL_ADDRESS] (S.W.);  
[EMAIL_ADDRESS] (Z.C.); [EMAIL_ADDRESS] (W.X.); [EMAIL_ADDRESS] (Q.H.)  
* Correspondence: [EMAIL_ADDRESS] (N.C.); [EMAIL_ADDRESS] (S.L.)  
Received: 7 April 2019; Accepted: 22 May 2019; Published: 28 May 2019  
**Featured Application: This work has potential applications for all-fiber electro-optic modulators, all-fiber optical-to-frequency converters and optical switching.**  
**Abstract:** Fused silica are common fiber materials which have macroscopic central symmetry without second-order nonlinearity. Studies have shown that thermal poling of fused silica fibers can destroy this macroscopic central symmetry, resulting in second-order nonlinearity or linear electro-optical effects. In this paper, a new type of double-hole optical fiber is designed. A two-dimensional (2D) numerical model is used to simulate the movement of ions and the formation of space charge region by finite element analysis. It is found that the single round square hole structure of the new double-hole fiber promotes the thermal poling process. The effective second-order nonlinear coefficient \(\chi_{\text{eff}}^{(2)}\) of the new double-hole poled fiber is 0.28 pm/V at the core center, which is 0.05 pm/V higher than that of the circular double-hole poled fiber. In the fiber core, the radial distribution of the internal electric field and of \(\chi_{\text{eff}}^{(2)}\) is calculated and analyzed. The results of this paper are of great significance for the application of thermally poled fibers on nonlinear all-fiber devices.  
**Keywords:** new double-hole optical fiber; thermal poling; second-order nonlinearity; finite element analysis
# Non-Linear Creep-Relaxation Constitutive Damage Model for Aging Concrete
## 2 Theoretical Formulation
### Components of Strain and Thermodynamic Processes
#### 2.1.2 Thermodynamic Processes  
To formulate the constitutive equations that describe the behavior of plain concrete under cyclic creep, it is necessary to understand the different thermodynamic processes that take place in concrete upon loading. Energy due to these processes can be stored or dissipated, depending on whether the process is reversible or irreversible. In the case of concrete subjected to cyclic creep, and assuming that the material is also subjected to a uniform temperature and an isothermal process, these processes are the state of solidification, the state of strain and its magnitude, and hardening (which depends on both strain and solidification degree).  
The state of solidification of the concrete is known to be associated with the heat of hydration, which is a result of the chemical reaction between water and cement, which leads to the solidification of the cement gel [42; 43]. Although the concrete reaches the desired strength at 28 days, the process of solidification continues due to the new hydration of cement particles trapped inside the matrix or the solidification of new layers of cement gel inside the capillary pores. Therefore, this process aids the concrete to harden, by increasing the amount of volume of solidified matter in time [42; 43].  
Concrete is considered a viscoelastic aging material. Under the application of stress (strain), the material stores strain energy while the material is subjected to stress less than half of its strength. However, if the concrete is subjected to a stress higher than half its strength, the material develops microcracks that create an irreversible state of viscoplastic strain [43]. In CDM, these microcracks are known as damage. For the damage to develop, the material must store and dissipate energy [55]. Therefore, a process to account for damage growth must be taken into consideration. In addition, as the damage develops inside the material, strain hardening or softening occurs. Hence, a process known as damage strengthening must be considered.  
Likewise, during casting, concrete behaves as a non-Newtonian fluid; and as previously mentioned, the solidification of concrete is a process that continues for some time. Therefore, it is reasonable to assume that particles of this non-Newtonian fluid remain trapped inside the matrix, such that, after sustained loading is applied, these particles start to flow. This is consistent with experimental data [46], where it has been shown that CSH particles, inside the matrix, migrate when the application of sustained loading occurs. These CSH particles do not return to their starting point once unloading takes place. This migration creates a permanent strain that cannot be recovered. Therefore, in [43], a viscous flow strain was considered to account for such microscopic behavior. Figure 2 summarizes the thermodynamic processes taking place in the concrete.
## 2 Preliminaries
### Secure Comparison Protocol
#### 2.2.1 \(\mu\)-bit Integer Comparison ([PERSON], 1966)  
Assume that [PERSON] and [PERSON] respectively have two \(\mu\)-bit integers, \(a\) and \(b\), and consider how to compare two integers without revealing the values of \(a\) and \(b\) between [PERSON] and [PERSON]. Let us define the binary vectors for \(a\) and \(b\) as follows: \(a^{\mathsf{b}}=[a_{0},\ldots,a_{\mu-1}]\) and \(b^{\mathsf{b}}=[b_{0},\ldots,b_{\mu-1}]\). In addition, let us define the following two binary vectors \(a^{\mathsf{b}}_{i}\) and \(b^{\mathsf{b}}_{i}\) (\(1\leq i\leq\mu-1\)) where the first \(i\)-bits are the same as those in \(a^{\mathsf{b}}\) and \(b^{\mathsf{b}}\) while the remaining upper bits are set to zeros.  
\[\begin{split} a^{\mathsf{b}}_{i}&=[a_{0},\ldots,a_ {i-1},0,\ldots,0],\\ b^{\mathsf{b}}_{i}&=[b_{0},\ldots,b_{i-1},0,\ldots,0 ].\end{split} \tag{3}\]  
To compare the two integers \(a\) and \(b\), let us define the following \(d_{i}\):  
\[d_{i}=w_{i}+v_{i}, \tag{4}\]  
where  
\[\begin{split} w_{i}&=\left\langle a^{\mathsf{b}}_{i }-b^{\mathsf{b}}_{i},a^{\mathsf{b}}_{i}-b^{\mathsf{b}}_{i}\right\rangle\geq 0, \\ v_{i}&=a_{i}-b_{i}+1.\end{split} \tag{5}\]  
Here, \(w_{i}\) is an inner product for calculating how many bits are different between \(a^{\mathsf{b}}_{i}\) and \(b^{\mathsf{b}}_{i}\). Therefore, \(w_{i}=0\) implies that the first \(i\) bits of \(a\) and \(b\) are the same (i.e., \([a_{0},\ldots,a_{i-1}]=[b_{0},\ldots,b_{i-1}]\)). Next, we look at the \((i+1)\)-th bit of \(a\) and \(b\) when \(w_{i}=0\) is satisfied. Here, \(v_{i}=a_{i}-b_{i}+1\) could have the three values: \(0\), \(1\), or \(2\). If \((a_{i},b_{i})=(0,1)\) (i.e., \(a<b\)), \(v_{i}=0\); otherwise, \(v_{i}=1\) or \(2\). That is, if \(v_{i}=0\), \(a<b\) is satisfied under \(w_{i}=0\); otherwise, \(a\geq b\) is satisfied. Therefore, to identify whether \(a<b\) is satisfied, we merely need to check if \(d_{i}\) in Equation (4) is \(0\) for any position of \(i\) (\(i\in\{0,1,\ldots,\mu-1\}\)).
# Smart Collaborative Caching for Information-Centric IoT in Fog Computing
## 4 Validation and Discussion
### Scenario One: Simple Status Inquiry (One Request and One Response)
Therefore, the \(L_{iB}\) and \(L_{iW}\), in terms of minimum and maximum delay, for each round are \(2\times RTT\) and \(N\times RTT\), respectively. The valuerange of latency can be obtained according to Equation (5). Both the packet number and transmission latency calculation process of the other candidates can follow the same methodology. \[\left[\sum_{i=1}^{100}L_{iB}/100,\sum_{i=1}^{100}L_{iW}/100\right]=\left[\frac{ 201 RTT}{100},\frac{(1+100N)RTT}{100}\right] \tag{5}\]  
In Figure 3(a), the packet number statistics for different schemes are illustrated. The horizontal axis is set to the number of clusters in the network. To better highlight the difference, we adopt logarithm for vertical axis display. The curve with black (at the top of the figure) demonstrate the packet number in ICN Flooding scheme. Although content caching is enabled in all clusters, plenty of unnecessary packets are still generated and forwarded inside whole network. It is significant to design an appropriate policy even ICN is adopted in IoT scenarios. The curves with red and blue (at the middle area of the figure) represent the IP Network and ICN SCC, respectively. As we expected, with the increasing of network scale (in terms of cluster number), the traffic volume of traditional IP network is getting bigger. The growth rate in this scheme is not as high as that of ICN Flooding. However, it still brings some additional burdens for the clusters. For ICN SCC scheme, the caching cost and searching efficiency are properly balanced. The growth rate of packet number is obviously controlled. The curve with green (at the bottom of the figure) shows that the fluctuation of ICN SCC-E is always stable. It proves that one hop searching is effective in SCC and the clusters can find the content with limited cost. In Figure 3(b), the transmission latency statistics for different schemes are illustrated. From top to down, the gaps between ICN Flooding and IP network are quite small. Due to the similarity of macro procedures in these two schemes, the advantages of ICN is not fully demonstrated. However, using SCC and SCC-E to replace flooding scheme can respectively reduce the latency 80.7% and 85.4% if 20 clusters exist in current network. With the increasing of clusters, such performance enhancement is more obvious. For a particular case, when the number of clusters \(N\) is changed from 80 to 140, we record the fluctuations in the vertical axis. The growth rate of ICN Flooding and IP Network curves is 74%. The values in ICN SCC curve grow 10.2%, which is quite slow comparing with previous two schemes. The values in ICN SCC-E curve are invariant.
## 2 Materials and Methods
### Primitive X-Y Filtering  
We refer to primitive filtering as a selection method of relevant atoms in the horizontal and vertical (X, Y) scanning directions. For any arbitrary orientation of the biomolecular structure within the fixed screen canvas, first the tip scanning grid is obtained by determining the farthest possible tip contacts with the atomistic structure in the east and west directions (scanning range X), and those in the south and north directions (scanning range Y). We provide an illustration in Figure 1. The size of grid cells corresponds to the given scan step. As a next step, it would be reasonable to consider each individual grid cell with the tip placed at its center and calculate the tip contacts with all the atoms to select the largest height value of the structure relative to the AFM substrate.  
We replace this generally inefficient procedure by looping just once over all the atoms instead. For each atom, a contact grid which covers the (X, Y) area from which the tip can touch this atom is determined as a sub-grid of the scanning grid (Figure 1). Its size apparently depends on the tip shape geometry and the distance of the atom to the AFM substrate. Then, for each cell of the contact grid, the collision height of the tip with this atom is computed (see Ref. [22] for details) and compared with previous height values computed from a tip collision with other atoms, always storing the larger height value. Thus, after looping over all the atoms, each cell of the scanning grid is assigned a single value which represents the sample height relative to the AFM substrate, as resulting from a convolution of the tip shape with the biomolecular surface. Simulated AFM using only primitive X-Y filtering (we refer to as the XY-F method) is obviously still highly inefficient.  
Nonetheless, we will later apply this method simply to demonstrate the supremacy of the more sophisticated filtering method and computation of simulated AFM described next.  
Figure 1: **Primitive X-Y filtering.** (**A**) The atomistic Van der Waals representation of a biomolecular structure is displayed in the scanning view perspective within the visible screen canvas (X, Y) outlined in black. The relevant scanning grid is shown in red, with the cell size corresponding to the given scan step. The four tip shapes illustrate the farthest possible tip contacts with the structure in the north, east, south, and west directions required to determine the scanning grid size. For a selected atom (blue circle), the computed tip contact grid is shown in blue. (**B**) The biomolecular structure is placed on the AFM substrate in a front view perspective. For a selected atom (blue circle), the contact range of a cone-shaped scanning tip is illustrated for the X direction.
## 3 Design of Fault Detection and Estimation Observer
### Robust Fault Detection  
The following fault observers are designed:  
\[\begin{array}{ll}\dot{\hat{x}}_{d}(t)&=A\hat{x}_{d}(t)+A_{m}\hat{x}_{d}(t-d(t) )+B(I+F(t))u_{c}\\ &+\Gamma_{1}(x(t)-\hat{x}_{d}(t))+\Gamma_{2}(x(t-d(t))-\hat{x}_{d}(t-d(t)))\end{array} \tag{10}\]  
where \(\hat{x}_{d}(t)\) is the estimated value of the system state in the body coordinate system, \(\Gamma_{1}\), \(\Gamma_{2}\) is the gain matrix, and is taken as the multiple of the identity matrix. Let the system state estimation error be \(\ddot{\tilde{x}}_{d}(t)=\dot{x}(t)-\dot{x}_{d}(t)\), combining with Equations (7) and (10) as:  
\[\ddot{\tilde{x}}_{d}(t)=A\tilde{x}_{d}(t)+A_{m}\tilde{x}_{d}(t-d(t))-\Gamma_{ 1}\tilde{x}_{d}(t)-\Gamma_{2}\tilde{x}_{d}(t-d(t))+f+Dv(t) \tag{11}\]  
According to the matrix norm compatibility principle:  
\[\left\{\begin{array}{ll}\|A\tilde{x}_{d}(t)\|\leq k_{1}\|\tilde{x}_{d}(t)\| \\ \|A_{m}\tilde{x}_{d}(t-d(t)\|\leq k_{2}\|\tilde{x}_{d}(t-d(t)\|\leq k_{2}\| \tilde{x}_{d}(t)\|\end{array}\right. \tag{12}\]  
where \(k_{1}\) is \(max\{SVD(A)\}\), \(k_{2}\) is \(max\{SVD(A_{m})\}\), and \(SVD(\bullet)\) is the singular value of the corresponding matrix.  
**Theorem 1**.: _If the fault detection threshold is set to \(x_{fd}=\frac{\underline{2}}{(\lambda_{min}(\Gamma_{1})+\lambda_{min}(\Gamma_{ 2}))-(k_{1}+k_{2})}\), at this time, when the system state is met \(\|\tilde{x}_{d}\|>x_{fd}\), the system fault is detected for the failed air-ground platform system in Equation (7), on the premise of meeting the Assumption 3._  
**Proof of Theorem 1.** By Considering the Lyapunov candidate as:  
\[V=\frac{1}{2}\tilde{x}_{d}^{T}\tilde{x}_{d} \tag{13}\]  
Its derivative is  
\[\begin{array}{ll}\dot{V}&=\tilde{x}_{d}^{T}\ddot{\tilde{x}}_{d}=\tilde{x}_ {d}^{T}(A\tilde{x}_{d}+A_{m}\tilde{x}_{d}(t-d(t))-\Gamma_{1}\tilde{x}_{d}- \Gamma_{2}\tilde{x}_{d}(t-d(t)+f+Dv(t))\\ &\leq-[(\lambda_{min}(\Gamma_{1})+\lambda_{min}(\Gamma_{2}))-(k_{1}+k_{2})] \|\tilde{x}_{d}\|^{2}+\tilde{x}_{d}^{T}f+\tilde{x}_{d}^{T}Dv(t)\end{array} \tag{14}\]  
where \(\lambda_{min}(\cdot)\) represents the smallest eigenvalue of the corresponding matrix.  
When \(f=0\), that is the system does not fail, we can get  
\[\dot{V}\leq-[(\lambda_{min}(\Gamma_{1})+\lambda_{min}(\Gamma_{2}))-(k_{1}+k_{ 2})]\|\tilde{x}_{d}\|^{2}+\overline{d}\|\tilde{x}_{d}\|^{2} \tag{15}\]  
If \(\|\tilde{x}_{d}\|\leq\frac{\overline{d}}{(\lambda_{min}(\Gamma_{1})+\lambda_{ min}(\Gamma_{2}))-(k_{1}+k_{2})}\), then \(\dot{V}\leq 0\). It obtains that when the system does not fail, the system state is stable. When \(f\
eq 0\), if \(\dot{V}\leq 0\), it obtains that:  
\[\|\tilde{x}_{d}\|\leq\frac{\|f\|+\overline{d}}{(\lambda_{min}(\Gamma_{1})+ \lambda_{min}(\Gamma_{2}))-(k_{1}+k_{2})} \tag{16}\]  
Thus, it obtains that when the system state \(\|\tilde{x}_{d}\|>x_{fd}\) is satisfied, the fault is detected.  
**Remark 2**.: _The above proof cannot obtain the necessary and sufficient condition of fault detection threshold. When the system state is \(\|\widetilde{x}_{d}\|\leq x_{fd}\), the system also has a certain probability of failure._
## 3 Results and Discussion
### Response Analysis  
The dynamic behavior of the proposed hybrid control system was analyzed across three distinct time periods, each characterizing different aspects of system performance. The analysis reveals significant improvements in system response characteristics compared to conventional methods.  
In the initial response period (0-5 s), the hybrid system demonstrated superior performance in multiple aspects. The rise time showed a 27.1% improvement, indicating faster system response to input changes. The most notable enhancement was observed in the overshoot characteristics, with a 43.1% reduction compared to conventional methods. Additionally, the settling time improved by 24.5%, accompanied by enhanced stability characteristics. These improvements can be attributed to the LSTM network's ability to accurately predict system dynamics and the Genetic Algorithm's optimal parameter selection.  
Figure 3: Comparison of controller performance.  
The system's response to disturbances during the intermediate period (5-12 s) exhibited significant improvements in disturbance rejection capabilities. The hybrid method achieved 40% faster disturbance rejection compared to conventional approaches, while the peak deviation was reduced by 35%. The response pattern showed more robust recovery characteristics, with improved steady-state maintenance. This enhanced performance can be expressed mathematically as follows:  
\[\tau_{r}=0.6\tau_{c} \tag{20}\]  
where \(\tau_{r}\) represents the disturbance rejection time of the hybrid system and \(\tau_{c}\) represents that of the conventional system.  
During the parameter variation period (12-20 s), the system demonstrated exceptional adaptability to changing conditions. The hybrid approach showed 45% better adaptation to parameter changes, which can be represented as follows:  
\[\Delta P_{h}=0.55\Delta P_{c} \tag{21}\]  
where \(\Delta P_{h}\) and \(\Delta P_{c}\) represent the parameter variation responses of the hybrid and conventional systems, respectively. The system maintained minimal output fluctuations during parameter changes and achieved faster convergence to new steady-state conditions. The enhanced robustness to system variations can be attributed to the real-time adaptation capabilities of the LSTM network.  
The comprehensive response analysis demonstrates that the hybrid system consistently outperforms conventional methods across all operational phases. The improved performance metrics are particularly significant in industrial applications where rapid response, disturbance rejection, and adaptation to parameter variations are critical requirements.  
These results validate the effectiveness of integrating LSTM-based model identification with Genetic Algorithm optimization in enhancing overall system response characteristics. The improvements in stability, disturbance rejection, and parameter adaptation make the proposed hybrid system particularly suitable for complex control applications requiring robust performance across varying operating conditions.
# Sustainable Plant-Based Diets and Food Allergies: A Scoping Review Inspired by EAT-Lancet
## 2 Materials and Methods
### Data Extraction and Thematic Organization  
Data were extracted using a standardized form capturing the following: study design, population characteristics, type of dietary intervention/exposure, outcome variables (e.g., sensitization rates and symptom severity), and proposed mechanisms of action. Special attention was given to mediators such as Treg cells, IL-4/IL-13, GPR43, occludin, zonulin, and mast cell activity.  
Studies were grouped into two main thematic domains:  
1. Prevention of allergic sensitization and development of food allergies (\(n=31\)), focusing on microbiota shaping, early-life exposures, and immunological priming;
2. Modulation of allergy-related symptoms in sensitized individuals (\(n=22\)), focusing on immune balance, inflammatory regulation, and gut barrier function.  
Each domain was further stratified based on dietary models (e.g., EAT-Lancet, vegan, and Mediterranean) and functional dietary components, including fermentable fibers, polyphenols, unsaturated fats, food processing methods, and dietary diversity.  
Given the diversity of study designs, populations, and outcome measures, the data were synthesized using a narrative approach, with thematic clustering according to shared mechanistic and nutritional domains.  
As this is a scoping review, we did not perform a formal risk of bias assessment. This methodological choice aligns with the primary aim of mapping the existing literature rather than evaluating study quality or producing cumulative effect estimates.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 4 Representative Areas of Application of AI in Ophthalmology
### Possible Evolution  
#### 4.5.1 Wearable Equipment and Cloud Computing  
Due to the aging of the population, the demand for ophthalmic services will increase because of the high incidence of glaucoma and other age-related eye diseases.  
Today, glaucoma monitoring is an expensive issue for individuals and the healthcare system. Wearable devices and cloud platforms could be appropriate solutions, as shown by several zonal and governmental diabetic retinopathy screening projects.  
An approach that can identify glaucomatous features by means of wearable fundus cameras and a cloud-based AI architecture has been developed [90], as illustrated in Figure 7. The examinee's fundus photos are directed to a cloud platform, where they are analyzed. The platform generates a review report. Subjects with a high-probability glaucoma result may be directed to ophthalmologists for additional examinations. For the rest of the subjects, a new test in 12 months is recommended.