esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
This systematic review synthesizes current evidence on the use of MCA in evaluating functional outcomes following spinal fusion for AIS. While significant progress has been made in correcting spinal deformity and restoring biomechanical symmetry, our findingsilluminate critical areas where surgical outcomes could be optimized further. MCA provides an objective, high-resolution assessment of postoperative functional recovery in AIS patients, allowing for quantification of gait symmetry, trunk-pelvic kinematics, and joint range of motion. Compared to radiographic methods such as EOS imaging [7], MCA offers a functional, real-time evaluation without ionizing radiation. However, it does not provide direct structural measurements of spinal alignment and relies on external markers that may introduce variability due to soft tissue artifacts. Other functional assessment tools, such as three-dimensional gait analysis with force platforms, provide ground reaction force data but lack the detailed joint kinematics offered by MCA [26]. Similarly, sEMG allows for the assessment of muscle activation patterns, which can complement MCA findings but does not provide a full biomechanical profile of post-surgical adaptations [32]. The integration of MCA with these complementary modalities may enhance the precision of postoperative functional assessment in AIS patients.
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 4 Discussion  
### Comparison of Classification Accuracy with Existing Products  
Through field survey collection and statistics, the cropland area of the field data in the study area was determined. The total area of cropland was calculated by merging abandoned, intermittent, and active cropland, which was compared with the area of cropland in the existing classification products. The results of different products were compared with the field data as a reference, as shown in Table 4. The results showed that the cropland areas of our method had the lowest deviation from the field data. The ESA WorldCover 2020 and the ESRI land cover 2020 underestimated the areas of cropland in the study area. By superimposing with the GF-2 imagery, it can be found that many small farmlands were missed, and the ESRI land cover still had obvious misclassification (Figure 5c,d). In contrast, the GLCF_FCS30 and the CLCD in 2020 overestimated the areas of cropland in the study area. The results showed that some of the woodland areas were misclassified as cropland areas (Figure 5e,f). In our method, irrigated cropland F1 scores exceeded 0.836 and rainfed cropland F1 scores exceeded 0.759, which outperformed the existing products.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Area (km\({}^{2}\))** & **Field Data** & **Our Method** & **ESA** & **ESRI** & **GLC\_FCS30** & **CLCD** \\ \hline Cropland & 180.2 & 176.6 & 80.8 & 46.3 & 337.2 & 377.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cropland area statistics of different products.  
Figure 6: The mean of F1 score, overall accuracy, and kappa from 2019 to 2021. Error bars indicate the min and max intervals.
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 3 Proposed Method
### Extended Mesh Fingerprinting Scheme Using [PERSON]'s Code
#### 3.4.2 Extended Mesh Fingerprinting Scheme Using Tardos's Code  
[PERSON]'s fingerprint code directly applied to the embedding equation Equation (4) as the same way in the case of GD-PBIBD code. In addition, the detection of the embedded watermark described in Section 3.2.1 also can be used with the fingerprint code. Then, embedded bit information \(y^{*}\) is extracted using Equations (9) and (10).  
In contrast with the embedding and code extraction process, we should modified the accusation process described in Section 3.2.2. First, we examine the extracted fingerprint \(y^{*}\) as follows. Assume that the number of users is \(n\) and the length of the fingerprint code is \(m\), [PERSON] defined the \(n\times m\) matrix \(U\) and its entries are defined as follows:  
\[U_{ij}=\left\{\begin{array}{ll}\sqrt{\frac{1-p_{i}}{p_{i}}},&\text{if }X_{ji}=1,\\ \sqrt{-\frac{p_{i}}{1-p_{i}}},&\text{if }X_{ji}=0.\end{array}\right. \tag{27}\]  
where \(p_{i}\) values from the probability array used in the fingerprint code generation step, and \(Xji\) is an element in the fingerprint code matrix.2 Let the algorithm accuse user \(j\) on the extracted fingerprint \(y=\{0,1\}^{m}\) as input if:  
\[\sum_{i=1}^{m}y_{i}U_{ji}>Z, \tag{28}\]  
where \(Z=20c\log(1.0/\epsilon)\) as a threshold parameter. As described in [17], both false positive and false negative errors are controlled by the error bound \(\epsilon\). When the parameter \(\epsilon\) for the lower error bound decreases for the robust fingerprint system, the length of the fingerprint code dramatically increases. Therefore, because of the limited number of the spectral coefficients (\(<\)3000) from the cover mesh model, the usage of the [PERSON]'s code is relatively limited (e.g., the generated code with \(\epsilon=0.2,c=6\) has length 4024).
## 2 Methodology
### Prediction
#### 2.3.2 Error Minimized Extreme Learning Machine (EM-ELM)  
Although the structure of the ELM algorithm is simple and effective, choosing the optimal number of hidden layer nodes remains problematic. In contrast, the EM-ELM algorithm can add hidden nodes individually or in batches; the output weight value is updated each time a new hidden node is added, which reduces the computational complexity. Generally, the EM-ELM algorithm can be divided into two steps: initialization and learning. In the initialization phase, the initial network structure is constructed by giving an initial number of hidden layer nodes, and the initial hidden layer output matrix and initial residual error \(\varepsilon>0\) are calculated. In the learning phase, hidden nodes are added individually or in batches, the hidden output matrix is calculated, and the output weights and residual errors are updated. The specific flow of the EM-ELM algorithm is as follows:Suppose \(N\) arbitrary samples \((\mathbf{X_{i}},\mathbf{t_{i}})\in R^{n}\times R^{m}(i=1,2,\cdot\cdot\cdot,N)\), the maximum number of hidden layer nodes is \(L_{\text{max}}\), initial hidden layer node \(L_{0}\), and hidden layer activation function \(g(x)\).  
Initialization phase:  
(1) Initial hidden layer node \(L_{0}\) randomly generates initial weights and biases \((\mathbf{W_{i}},b_{i})_{i=1}^{L_{0}}\).  
(2) Calculate the initial hidden layer output matrix \(\mathbf{H_{0}}\) according to Equation (24).  
\[\mathbf{H_{0}}=\left[\begin{array}{ccc}g(w_{1},b_{1},x_{1})&\ldots&g(w_{L_{0 }},b_{L_{0}},x_{1})\\ \vdots&\vdots\\ g(w_{1},b_{1},x_{N})&\ldots&g(w_{L_{0}},b_{L_{0}},x_{N})\end{array}\right]_{N \times L_{0}} \tag{24}\]  
(3) Calculate the initial residual output error according to Equation (25).  
\[E_{0}=\left\|\mathbf{H_{0}}\mathbf{H_{0}^{\dagger}}\mathbf{T}-\mathbf{T}\right\| \tag{25}\]  
Learning phase:  
(1) \(k=0\), while \(L_{k}<L_{\text{max}}\) and \(E_{k}>\varepsilon\) (1) \(k=k+1\).  
(2) Randomly generate \(\Delta L_{k-1}\) nodes to add to the existing network. At this time \(L_{k}=L_{k-1}+\Delta L_{k-1}\), and the hidden layer output matrix \(\mathbf{H_{k}}=\left[\mathbf{H_{k-1}},\Delta\mathbf{H_{\Delta L_{k-1}}}\right]\), where:  
\[\Delta H_{\Delta L_{k-1}}=\left[\begin{array}{ccc}g(w_{L_{k-1}}+1,b_{L_{k-1} }+1,x_{1})&\ldots&g(w_{L_{k}},b_{L_{k}},x_{1})\\ \vdots&\vdots\\ g(w_{L_{k-1}}+1,b_{L_{k-1}}+1,x_{N})&\ldots&g(w_{L_{k}},b_{L_{k}},x_{N})\end{array} \right]_{N\times N_{k-1}} \tag{26}\]  
Equations (27)-(29) are used to calculate the output weights.  
\[\mathbf{D_{k}}=\Big{(}\Big{(}\mathbf{I}-\mathbf{H_{k-1}}\mathbf{H_{k-1}^{ \dagger}}\Big{)}\Delta\mathbf{H_{\Delta L_{k-1}}}\Big{)}^{\dagger} \tag{27}\]  
\[\mathbf{U_{k}}=\mathbf{H_{k-1}^{\dagger}}\Big{(}\mathbf{I}-\Delta\mathbf{H_{ \Delta L_{k-1}}^{T}}\mathbf{D_{k}}\Big{)} \tag{28}\]  
\[\mathbf{\beta_{k}}=\mathbf{H_{k}^{\dagger}}\mathbf{T}=\left[\begin{array}{ c}\mathbf{U_{k}}\\ \mathbf{D_{k}}\end{array}\right]\mathbf{T} \tag{29}\]  
End While.
# Unlocking Wellness: Probiotics as Key Drivers in Functional Food Innovation and Health Promotion
## 5 Technological Innovation of Probiotics in Functional Foods  
### Microencapsulation Technology  
Microencapsulation technology, a core method in probiotic delivery systems, effectively protects probiotics from gastric acid and bile through encapsulation within microcapsules, enabling targeted release under specific gastrointestinal conditions [131]. Different types of microencapsulation materials exhibit distinct physical and chemical properties, providing varying levels of protection for probiotics. Common microencapsulation wall materials and their respective advantages and disadvantages are listed in Table 3. Combining multiple types of wall materials can significantly enhance the protection of probiotics and improve their survival rates under adverse conditions [132]. [PERSON] [133] demonstrated that sodium alginate-chitosan bilayered microcapsules, featuring a dense network structure formed through electrostatic complexation, exhibited 82.4% protection efficiency in simulated gastric fluid after two hours, representing a 4.3-fold improvement over the unencapsulated control group and significantly enhancing probiotic survival rates in the gastrointestinal environment. Advances in materials science continue to yield innovative material combinations. [PERSON] et al. [134] reported that gelatin-graphene oxide composite hydrogels maintained a >90% survival rate of _L. plantarum_ LS14 at pH 3.0, with a compression modulus of 12.5 kPa, demonstrating superior mechanical stability. This formulation not only protects probiotics from gastric acid damage but also maintains structural stability in the gastrointestinal tract, ensuring slow release and sustained probiotic activity. These innovative microencapsulation approaches provide new strategic insights for probiotic delivery system development and help enhance the performance of probiotic products.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
## 3 Methods
### Trends of Growing Season Mean NDVI  
We first filtered out the 16-day NDVI retrievals in MOD13Q1 that were contaminated by clouds, cloud shadows, or aerosols. Atmospheric contaminations are indicated in the quality assessment (QA) band of MOD13Q1 (i.e., MODLAND QA) [51]. We included NDVI retrievals with the following QA criteria: (1) \"VI Quality\" (MODLAND QA Bits 0-1) must be equal to 0 (VI produced with good quality) or 1 (VI produced, but check other QA); (2) \"VI Usefulness\" (MODLAND QA Bits 2-5) must be less than 10; (3) \"Adjacent cloud detected\", \"Mixed clouds\", and \"Possible shadow\" flags (MODLAND QA Bits 8, 10, and 14) must be equal to 0; and (4) \"Aerosol Quantity\" flags (MODLAND QA Bits 6-7) must be equal to 1 (low) or 2 (intermediate). The missing NDVI after filtering were then substituted with the good-quality 16-day NDVI climatology for the period from 2000 to 2019 (Figure S1).  
The two 16-day NDVI in a month were composited to monthly NDVI using the maximum value compositing method. The monthly NDVI in the months from May to September were averaged to obtain the growing season mean NDVI. Growing season mean NDVI in the years from 2000 to 2019 were used to investigate the dynamics of vegetation activity in our study area.
## 4 Discussion
### Uncertainties in the BEPS Model  
The core idea of the BEPS model is that it adopts separate sunlit and shaded leaves modeling mechanisms. The two-leaf model has been proven to be more efficient in simulating the daily variation of canopy photosynthesis than the conventional \"big-leaf\" model [13]. The BEPS model employs the enzyme kinetics theory for C3 plants proposed by [PERSON] et al. [18] to generate the photosynthesis rate. In this study, we adopted a modified version that was applicable for C4 species [19] and integrated it into the BEPS model to couple with the Ball-Berry stomatal conductance model [81] to generate GPP as well as the water and heat fluxes. Although it is designed in an explicit and tightly linked manner that simulates the coupled hydrological, ecophysiological, and biogeochemical processes and the associated feedback mechanisms, it should be noted that numerous parameters should be assigned before modeling. One of the most important parameters in modeling photosynthesis, the maximum carboxylation rate (\(V_{\text{cmax}}\) in Equations (16), (18), and (19)), has been proven to vary temporally [102; 103; 48; 104], although it is usually assigned as a specific value for each PFT [105]. In this study, we adopted a constant value of 37 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\) by referring to [PERSON] et al. [48]. Although BEPS model simulated GPP better than the SIF-based and LUE-based model in our study, there are still some underestimations at high productivity and overestimations at low productivity. Therefore, additional studies are needed in generating the seasonal variation of \(V_{\text{cmax}}\). Former studies have demonstrated that \(V_{\text{cmax}}\) can be related with reflectance [106] or leaf chlorophyll content [107], which made it retrievable using remotely sensed data. Recently, a study conducted by [PERSON] et al. [48] presented an SIF-based approach for modeling seasonal \(V_{\text{cmax}}\), which provided a new method to assess plant photosynthesis using SIF.
## 3 Current Measurement Techniques
### Snow Property Determination Techniques  
As explained in the introduction, manual snow cover measurement methods are laborious, point-based, and destructive, hence limited in application for selected sample points. Furthermore, they can only be carried out at discrete time intervals and cannot be carried out in areas of high avalanche danger. Still, manual measurement remains the most reliable and accurate method (\(\pm\)5%) of measuring SWE, although there are several automated methods, each with its limitations, as explained below.
### Advantages of Using Cerberis3 in a Hub-and-Spoke Network Topology
#### 1.3.3 Monitoring and Visualization Features  
1. Central configuration and management: The Cerberis3 system is managed through a central equipment management system (EMS) that provides status updates on each QKD node.
2. SNMP integration: The system provides Syslog and SNMPv3 feeds that can be integrated into the customer's network management system, enabling secure monitoring and event handling.
3. Graphical visualization: The monitoring system can generate graphs and visualizations of the key performance metrics, allowing network administrators to track the health and performance of the QKD network.
4. Interoperability: The Cerberis3 system can interface with link encryptors from major vendors, ensuring seamless integration with existing encryption systems.  
By leveraging the advanced monitoring and visualization capabilities of the Cerberis3 QKD system, network administrators can ensure the reliable and secure operation of their point-to-point QKD infrastructure, enabling the widespread adoption of this quantum-safe technology.  
The Cerberis3 Quantum Key Distribution (QKD) system from ID Quantique can be integrated with existing optical transport networks (OTNs). The system supports interoperability with major OTN vendors, including ADVA and L2/L3 encryption vendors. This integration allows the Cerberis3 to interface with link encryptors from these vendors through standard and proprietary interfaces, ensuring seamless integration into existing network infrastructures. The Cerberis3 system can also interface with up to 80 encryptors through standard ETSI API or proprietary interfaces, further enhancing its compatibility with various network configurations.
## 4 Laboratory Experiment  
### Experiment Design  
According to the similarity criterion of transient electromagnetic method, the basic similarity criterion is obtained as shown in Formula (2), and the additional similarity criterion is expressed as shown in Formula (3).  
\[\sigma_{m}=\frac{L^{2}}{T}\sigma_{p} \tag{2}\]  
where \(p\) and \(m\) represent the prototype and model, respectively, \(\sigma_{p}\) and \(\sigma_{m}\) are the conductivity of the prototype and model, respectively, \(L\) is the geometric similarity ratio between the prototype and the model, and \(T\) is the time similarity ratio between the prototype and the model.  
\[\frac{V_{m}(t)/I_{m}}{V_{p}(t)/I_{p}}=\frac{N_{Tm}N_{Rm}}{N_{Tp}N_{Rp}}\cdot \frac{{I_{Tm}}^{2}{I_{Rm}}^{2}}{{I_{Tp}}^{2}{I_{Rp}}^{2}}\cdot\frac{T^{2}a}{b} \tag{3}\]  
where \(V(t)\) and \(I\) are the induced voltage and emission current in different models, respectively, \(N_{T}\) and \(N_{R}\) are the turn of transmitter coil and receiver coil, respectively, \(l_{T}\) and \(l_{R}\) are the length of transmitter coil and receiver coil, respectively, and \(a\) and \(b\) are the ratio factor of electric field and magnetic field between the prototype and the model, respectively.  
In the laboratory experiment, the same transient electromagnetic instrument is adopted, so the time of the two models is consistent. The time similarity ratio is equal to 1, and Formula (2) can be simplified as \(\sigma_{m}=L^{2}\sigma_{p}\), which indicates that the geometric similarity is the main control condition of basic similarity criterion.  
Formula (3) describes the proportional relationship between the observed data of the prototype and the model. Since two ratio factors of electromagnetic field between the prototype and the model are assumed to be a fixed constant, the proportionality coefficient depends on the specific size of the transmitter coil and receiver coil. Therefore, the transmitter coil and receiver coil cannot be designed according to the geometric similarity, which brings significant convenience to the design of two coils in the laboratory experiment.  
Based on the above two similarity criteria, the variation law of transient electromagnetic response curve in the prototype can be reflected by the transient electromagnetic response curve in the model, and only a certain coefficient ratio exists between the two conditions. In the door experiment, the geometric similarity ratio is set to 1, so the full-scale physical model experiment can be presented to meet the background field requirements of the site electromagnetic environment. Meanwhile, the shape of the two coils is square, the length of the transmitter coil and receiver coil is set to 500 mm and 250 mm, respectively, and the turn per coil is set to 5.
# Indoor Positioning Systems Can Revolutionise Digital Lean
## 2 Utilisation of Location-Information in Lean 4.0
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Lean Concept** & **KPIs** & **Measurement Tools** & **Potential of IPS** & **Relevant Application of IPS** \\ \hline Shortest lead time & Average lead and cycle time & Camera [73,74]; Bar-code scanner [73]; RFID [62,75]; Machine/Event logs [76]; IPS [37] & high & Real-time monitoring of the position of semi-finished products and resources, calculation of the lead and cycle times [42,43]. \\ \hline \multirow{7}{*}{
\begin{tabular}{} \end{tabular} } & Waste of Transportation & RFID [66]; Machine/Event logs [76]; IPS [72] & high & Real-time spaghetti diagram [72]. \\ \cline{2-5}  & Waste of Inventory & Camera [30]; RFID [62,66]; IPS [72] & high & Tracked items in inventory areas [77,78]. \\ \cline{2-5}  & Waste of Motion & Camera [74] & low & - \\ \cline{2-5}  & Waste of Waiting & Camera [73,74]; Bar-code scanner [73]; RFID [62,75]; Machine logs [76]; IPS [37] & high & Tracked semi-finished products, waiting times, internal stock levels [71]. \\ \cline{2-5}
7 wastes elimination & Waste of Over-processing & Manual audit [79] & low & - \\ \cline{2-5}  & Waste of Over-production & RFID [66]; IPS [37,72] & high & Discovered overproduction based on the tracked semi-finished products [71]. \\ \cline{2-5}  & Waste of Defect & Optical sensors [80]; RFID [81,72] & medium & Reduced defect based on IPS-based poke-yoke solutions and better monitored rework flows. \\ \hline Less inventory & inventory value & RFID [66]; IPS [72] & high & Improved control of the inventory level [78] and e-kanban solutions reduce internal inventories [82]. \\ \hline Standardized work & Deviation from standardized work & Camera [74]; RFID [62]; IPS [37] & high & IPS based dynamic work instructions improve operator work (Smart operator) [83] \\ \hline Continuous flow & Queueing time & RFID [62]; IPS [37,72] & high & Discovered queueing areas near the workstations [1]. \\ \hline Line balancing & Line balance factor & Camera [74]; RFID [62]; IPS [37] & high & Improved activity time analyses thanks to sensor fusion [43]. \\ \hline Quick changeover & Set-up and changeover time & Machine logs [84]; Manual audit [85] & high & Supported SMED projects [86] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The traditional concepts of LM with the potential of IPS. Locating sensors only may fail to comprehend the actual activities performed in the production. For example, locating sensors can signal that the product is in a workstation, but they cannot indicate whether the product is currently processed, especially in a manual processing step, while the operators were busy looking for tools or reading work instructions. Therefore, it is beneficial to use multiple sensory data for Lean 4.0, so IPS is mostly beneficial to enrich the existing information collected by other sensors and data stroded in an MES system.
## 4 Parameter Optimization  
### Point Selection  
The whole point cloud is too big for an intelligence algorithm to optimize. As a result, we must choose a few checking sites for iterative calculation. First, the point cloud is reconstructed using the uncalibrated parameters, and then the composite required points are selected from it. After target point selection, the selected points need to be denoised via radius filtering. Finally, the raw data are determined one-to-one with the point cloud using subscripts and selected for self-checking.  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Variable Name** & **Physical Meaning** & **Variable Type** \\ \hline \(x,y\) & The centroids of streak images & E \\ \(T\) & The delay matrix & M \\ \(\Theta\) & The angle matrix & M \\ \(t_{d}\) & The delay between sending and receiving lasers & E \\ \(r,s,t\) & The Euler angles between the image and the & C \\  & scanning coordinate & C \\ \(\varphi\) & The scanner’s raster reading & E \\ \(\Psi\) & The raster ruler’s zero error & C \\ \(\Phi\) & The scanner’s installation error & C \\ \(\Omega\) & The direction of motor rotation & C \\  & The Euler angles between the image and & C \\ \(u,v,w\) & POS coordinate & C \\ \(o,p,q\) & The installation position of the POS & M \\ \(\alpha,\beta,\gamma\) & The roll, pitch, and head angle of the POS & E \\ \(L_{0},B_{0},H_{0}\) & The longitude, latitude, and altitude of the POS & E \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables in the reconstruction process.  
Different areas play different roles in the self-checking process, and these points need to be selected using different methods. Target buildings have definite edges that can be boxed to an area with determinant latitudes and longitudes. Further, the flat ground's altitude slowly changes, so we can use it to adjust the vertical accuracy.  
Target buildings are real buildings in a city, which means they appear randomly in the point cloud. It is difficult to find a complete one-to-one mapping building in adjacent strips, but it is easy in two air lines. Therefore, we have three sets of control points:  
\(P_{b}\): target building points between different air routes.  
\(P_{f}\): flat area points among different air routes.  
\(P_{a}\): flat area points between adjacent scanning bands.