esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Controller Based on Fuzzy Logic
### Set up the Control Laws  
Based on the input variables that are joint position errors e(t) and velocity errors \(\dot{\text{e}}\)(t), we develop a fuzzy rule table to calculate the adjustment amount of joint force/torque u(t). At the machining time, the input variables are signals of position errors e(t), velocity errors \(\dot{\text{e}}\)(t) of joints within the physical domain {X\({}_{1}\),..., X\({}_{5}\)} of a fuzzy set, represented by a linguistic value in the domain {NB, NS, Z, PS, PB} for each variable. The controller will generate a signal to control the amount of force/torque adjustment u(t) within the physical domain {X\({}_{1}\),..., X\({}_{5}\)}, represented by a linguistic value in the domain {NB, NS, Z, PS, PB} and this u(t) signal corrects the input values of coordinate errors \(\dot{\text{e}}_{i}\), velocity errors \(\dot{\text{e}}_{i}\) of joints approaching the physical value domain described by the linguistic value Z.  
So, from the two input variables e(t), \(\dot{\text{e}}\)(t), along with 5 linguistic values of each variable, a rule system will be set up inclusive of 5\({}^{2}\) = 25 control rules for the combination of variables e(t), \(\dot{\text{e}}\)(t), as Table 3.  
The formula to set the fuzzy compositional rule system for the fuzzy controller (41).  
\[\begin{array}{l}\text{R}:\overset{\text{n}}{\underset{\text{i=1}}{\text{U}}} \text{R}_{\text{i}}\\ \text{R}_{\text{i}}:\left[\left(\text{e(t)}\cup\dot{\text{e}}(\text{t})\right) \rightarrow\text{u(t)}\right],i=1,\ldots,25\end{array} \tag{41}\]  
From Table 3 and Formula (41), there is the fuzzy compositional rule system (42) including fuzzy conditional statements:  
\[\left\{\begin{array}{l}\text{If e(t) is NB and }\dot{\text{e}}(\text{t})\text{ is NB then u(t) is PB or}\\ \text{If e(t) is NB and }\dot{\text{e}}(\text{t})\text{ is NS then u(t) is PB or}\\ \vdots\\ \text{If e(t) is PB and }\dot{\text{e}}(\text{t})\text{ is PB then u(t) is NB}\end{array}\right. \tag{42}\]
## 3 Experimental Results and Analysis
### Performance Metrics
#### 3.2.2 Structural Similarity Index (SSIM)  
The SSIM index is a popular metric to quantify the analogy between the original image and the recovered image using the concepts of the human visual system (HSV) [42]. It measures the quality of a recovered image by quantifying the difference in contrast, luminance and structure. The mathematical equation of this metric is:  
\[\text{SSIM}\left(x,y\right)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2 \sigma_{xy}+c_{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left( \sigma_{x}^{2}+\sigma_{y}^{2}+c_{2}\right)} \tag{14}\]  
where \(x\) and \(y\) are windows of images \(X\) and \(Y\), and the explanation of the statistical parameters of this model can be found in several references.  
\[MISSIM\left(X,Y\right)=\frac{1}{n}\sum_{i=1}^{n}SSIM\left(x_{i},y_{i}\right) \tag{15}\]  
The mean SSIM is between two images, \(X\) and \(Y\), over (\(n\)) windows.  
\[NC=\frac{1}{h_{w}\times w_{w}}\sum_{m=1}^{h_{w}}\sum_{n=1}^{w_{w}}\overline{ \left(W(m,n)\oplus W^{*}(m,n)\right)} \tag{16}\]  
Figure 6: Examples of forgery detection results from the CASIA V 1.0 dataset: the original image, the forged image and the forgery detection result.  
\[TAF=\frac{1}{h_{w}\times w_{w}}\ \sum_{m=1}^{h_{w}}\sum_{n=1}^{w_{w}}W(m,n)\cdot W^{*} (m,n) \tag{17}\]  
\[BER=\frac{X}{h_{w}\times w_{w}} \tag{18}\]  
_NC_ is the normalized correlation coefficient, and (\(h_{w}\times w_{w}\)) is the size of the watermark. _TAF_ is the tampering assessment function and \(W(m,n)\) and \(W^{*}(m,n)\) are the original and extracted watermarks. BER is the bit error rate, \(X\) represents the number of bits that changed because of noise, \(\oplus\) is the exulsive OR operation, and (\(\cdot\)) is the AND operation.
# Fatigue Performance of Tunnel Invert in Newly Designed Heavy Hauilway Tunnel
## 4 Fatigue Test of Tunnel Base Structures
### Fatigue Test System  
The top center of the IASL is the most vulnerable position in the tunnel bottom. According to the high static and low dynamic bilateral confinement stress state, a fatigue test system was designed to study the bending fatigue performance of the heavy haul railway tunnel, as shown in Figure 6.  
The test box size was \(400\times 300\times 250\) mm. The bedrock was not completely rigid, the elastic resistance coefficient of V-grade surrounding rock was 193.5 MPa/m [15], and the interaction between the bedrock and the inverted arch specimen was simulated using 27 uniformly distributed springs. The equivalent spring stiffness was 215 kN/m. The bilateral horizontal forces of the specimen were applied using digital hydraulic jacks (No. 3 in Figure 6) and the vertical load of the specimen was applied using MTS815 (No. 4 in Figure 6) (MTS system Co., Eden Prairie, MN, USA).  
The concrete (C35) composition of the test specimens is provided in Table 3.  
The mechanical parameters of the specimens are shown in Table 4. Cube specimens were used to reflect the static elastic modulus and compressive strength of the C35 concrete, and rectangular specimens were used to reflect its dynamic elastic modulus and bending strength. The ultimate bending strength of the specimens was 2.5 MPa, and the corresponding peak load was 40 kN (mean value) when the specimens cracked.  
Figure 6: Fatigue test system. 1. Side support; 2. bottom spring; 3. digital hydraulic jack; 4. MTS (Mechanical Testing and Sensing) system; 5. contact steel plate; 6. model box.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **Cement** & **Fine Aggregate** & **Coarse Aggregate** & **Fly Ash** & **Plasticizer** & **Water** \\ \hline kg/m\({}^{3}\) & 277 & 747 & 1075 & 108 & 3.85 & 153 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Concrete composition.
# Corpsia--A Feasible Second Best Solution?
## 2 Institutional and Technical Background
### Challenges for CORSIA's Climate and Regulatory Integrity  
Most challenges for regulatory and climate integrity differ by project type, region, and size, and some are unilateral. By implementing safeguard clauses and verification and monitoring procedures, CORSIA-eligible verification standards try to ensure the climate and regulatory integrity of carbon offsetting.  
For a better understanding of the eligible carbon offset market under CORSIA, it is important to know that the CDM which laid the foundation of certified offsetting started as a cost-efficient scheme for developing countries to comply with emission reduction targets. Compared with today's certification procedures, offsets were less regulated [14; 15]. Moreover, no particular institutional or financial incentives have been implemented that benefit local communities. As a response, private programs such as the Gold Standard have developed project methodologies take sustainable development benefits and safeguard clauses into account [16]. Offset portfolios and certification procedure have been adjusted in response to shortcomings in regulatory and climate integrity. For example, Gold Standard has decided to exclude controversial project types such as large dams from its portfolio [17]. The CDM has regularly adjusted its monitoring and verification methodology. As a result, project-specific characteristics are much more important for integrity than differences between verification standards [10]. However, challenges remain.
## 12 Limitations of Derived Equations  
The derived equations in this study aim to enhance the practical applicability of RQD-based assessments in diverse geological and engineering contexts. However, it is critical to recognize their limitations and outline scenarios where they are most applicable, ensuring their effective application in real-world drilling and production. The limitations of the derived equations are the follows:  
**Directional Sensitivity**: Many RQD-based equations are sensitive to the orientation of boreholes relative to fracture orientation. For instance, equations such as Equation (14) and Equation (17), which relate RQD to fracture frequency (\(\lambda\)), assume uniform fracture distributions and may not accurately reflect anisotropic conditions. This limits their reliability in regions with strong directional jointing or highly anisotropic rock masses.  
**Threshold Dependency**: Several equations, including the corrected RQD (RQD\({}_{\text{c}}\)) and modified RQD (MRQD), rely on threshold values for fracture length or spacing. In scenarios with extreme fracture densities or fine-scale discontinuities, these thresholds may lead to an over- or underestimation of rock quality.  
**Rock Strength and Weathering**: Equations such as those linking RQD to the deformation modulus (Equation (39)) or uniaxial compressive strength (Equation (46)) assume intact rock behavior. They may not perform well in weathered or heavily fractured rock masses where the properties of weak zones dominate.  
**Scaling and Resolution**: Techniques such as discrete fracture network (DFN) modeling and seismic RQD calculations depend on the scale of observation. As such, the derived equations calibrated for laboratory cores (e.g., Equations (9) and (30)) may not accurately predict field-scale behavior without further adjustments.
# A Multichannel-Based Deep Learning Framework for Ocean SAR Scene Classification
## 2 Dataset and Data Processing  
In this study, we mainly use ocean SAR images from Sentinel-1 (S-1) in different wave modes because of its routine wave mode measurements at the global scale. To train a greater deep learning classification architecture, we construct training datasets from the TenGeoP-SARwv database. All the related datasets are illustrated in the following.
## 4 Common Latitude Power Series Expansions with Geocentric Latitude as Variable
### Power Series Expansion of Authulic Latitude with Geocentric Latitude as Variable  
According to the map projection theory, on an ellipsoid, the area of an arc-type trapezoid bounded by the equator, the geocentric longitude of \(\phi\), and the two lines of longitude separated by one degree, which is the authulic latitude function, is generally expressed as  
\[F(\phi)=\int_{0}^{\phi}M(\phi)r\,d\phi=a^{2}(1-e^{2})\int_{0}^{\phi}\frac{\cos \phi\sqrt{\left(1-(2-e^{2})e^{2}\cos^{2}\phi\right)}}{\left(1-e^{2}\cos^{2} \phi\right)^{2}}d\phi. \tag{17}\]  
Given the radius squared \(R^{2}\), the area bounded by the equator, the geocentric longitude \(\phi\), and the two lines of longitude separated by one degree are \(F(\phi)\). Therefore, according to the spherical integral formula, we conclude that  
\[\sin\vartheta=\frac{F(\phi)}{R^{2}}=\frac{F(\phi)}{a^{2}(1-e^{2})A}. \tag{18}\]  
In Equation (18)  
\[A=1+\frac{2}{3}e^{2}+\frac{3}{5}e^{4}+\frac{4}{7}e^{6}+\frac{5}{9}e^{8}+\frac{ 6}{11}e^{10}, \tag{19}\]  
and \(\vartheta\) is the authulic latitude. The manual differentiation is bypassed, and the power series expansion formula of the authulic latitude with geocentric latitude as the variable is  
\[\vartheta(\phi)=\phi+b_{2}\sin 2\phi+b_{4}\sin 4\phi+b_{6}\sin 6\phi+b_{8}\sin 8 \phi+b_{10}\sin 10\phi. \tag{20}\]  
The coefficients in Equation (20) are expanded in terms of \(e\) up to \(e^{10}\) and in terms of \(n\) up to \(n^{5}\), which gives in Table 6.
# Optimization of Valorization of Chicken MDCM to Produce Soluble Protein and Collagen Peptides
-fresh meat [7]. Due to the inexpensive cost and high nutritional value, MDCM is a potential sustainable source of meat protein for food applications. Despite its good nutritive properties being suitable for people on gluten-free and keto diets, MDCM is still underutilized [8]. The same authors described high-pressure homogenization as a novel trend for meat and fish byproduct valorization for functional ingredients. However, further processing is needed to meet the requirements of regulation and sensory properties regarding food use. Collagen is a major structural protein in connective tissues of bone, tendon, and skin. In the form of small peptides, it is commonly used in cosmetic applications and nutraceuticalials [9]. Enzymatic hydrolysis is a promising technology for converting the meat industry byproducts into food-grade ingredients through producing hydrolysate rich in nutrients and suitable functional properties. [PERSON] et al. [10] described MDCM as a potential material for producing gelatin through enzymatic treatment as a conditioning step, and by implementing experimental design, these authors could describe the optimum conditions of processing to achieve high conversion rates (30-32%) and functionality, such as gel strength. The properties of the hydrolysate can be adjusted by choosing appropriate enzymes and varying processing parameters such as the enzyme-substrate ratio, pH, temperature, and time of hydrolysis [11]. Commercial microbial (Alcalase\({}^{\circled}\), Protamex\({}^{\circled}\), Flavourzyme\({}^{\circled}\), and Neutrase\({}^{\circled}\)) or plant (papain, bromelin, actinidain) proteases have been used to hydrolyze beef, chicken, and pig bones, pigskin, and marine fish to produce hydrolysate of increased value [12; 13; 14; 15; 16]. For example, hydrolysate of chicken bone extract, made using Protamex\({}^{\circled}\) or Flavourzyme\({}^{\circled}\)[14; 17], and veal bone extract, made using Neutrase\({}^{\circled}\)[18], were found to be nutritional and flavorful. This made them potential ingredients as natural meat flavor enhancers for food products including soups and sauces. To this end, the aim of this study was (i) to produce functional protein hydrolysate from mechanically deboned chicken meat (MDCM) using noncommercial protease enzyme blends (Meatco, the Netherlands), (ii) to gather multidisciplinary information about the properties of the hydrolysate, (iii) to further process the residual bone material to extract collagen with pressurized hot water and optimize the process, (iv) to determine the molecular weight distribution and techno-functional properties of the collagen peptide fractions, and (v) to preliminarily assess the techno-economic feasibility of the developed processes.
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 5 Conclusions  
This paper proposes a novel Dynamic Graph Convolutional Network (Res-DGCN) based on residual structures for predicting the inflow and outflow of populations in irregular urban areas. The model combines spatial attention mechanism and spatio-temporal convolution including graph convolution in the spatial dimension and conditional convolution in the temporal dimension to effectively capture the spatio-temporal correlations in population flow data. Extensive experiments were run on two real-world datasets, and the outcomes show that the proposed model performs better than the baseline models. Our model is a general model from the perspective of traffic prediction. In this paper, we propose the spatial attention module, which can extract multi-order neighbor node features and can be applied to the fields of visual detection and image segmentation. The improved conditional convolution module can capture the connection of proximity time slices with downward perception capability, which can be extended to apply to image super-resolution and natural language processing fields. Res-DGCN also has the limitation that it requires crowd flow data to have both temporal and latitudinal information of origin and destination, two features that cannot be easily transferred to other tasks dealing with spatio-temporal data. Currently, the work in this paper focuses on single-step crowd flow prediction. In future work, we will consider implementing multi-step prediction and studying the heterogeneity of crowd flow data to make the prediction model more interpretable, and we plan to combine the Res-DGCN model with other deep learning methods to better learn the spatio-temporal features hidden in the crowd flow data.  
Author Contributions: Conceptualization, C.H. and X.L.; methodology, C.H.; software, C.H.; validation, C.H., Y.S. and S.W.; formal analysis, C.H. and J.Z.; investigation, F.Y. and Y.S.; writing--original draft preparation, C.H.; writing--review and editing, C.H. and S.W.; visualization, C.H. and F.Y.; supervision, X.L.; project administration, C.H.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Hunan Province (2021 JJ30456,2021 JJ30734), the Open Research Project of the State Key Laboratory of Industrial Control Technology (No. ICT2022B60), and the National Defense Science and Technology Key Laboratory Fund Project (2021-KJWPDL-17), the National Natural Science Foundation of China (61972055), the Research Foundation of Education Bureau of Hunan Province, China (20C0030).  
Not applicable.  
This data and models used during the study appear in this article.  
The authors declare no conflict of interest.
## 5 Conclusions  
The use of intra-operative blood salvage, dialysis, and artificial organs and devices is associated with the application of moderate mechanical stress on RBCs. It has been previously demonstrated that prolonged application of supraphysiological mechanical stress may promote the spread of abnormal erythrocyte morphology [21], phosphatidylserine externalization [40], the elevation of cell fragility [41], and increased vesiculation [40; 41]. Ultimately, these changes lead to cell hemolysis [27; 31; 32; 33; 34; 35; 36].  
We analyzed the alteration of rigid RBC subpopulation weight following mechanical stress. The main starting point of our study is the assumption that the RBC population is not homogeneous and, therefore, the response to the mechanical impact depends on the initial state of the cells. We conclude that the overall result depends on the initial proportion of undeformable cells: a stress-induced change in the proportion of rigid cells increased (\(\Delta\%\)UDFC \(>\) 0) when its initial value was low, and decreased (\(\Delta\%\)UDFC \(<\) 0) when its initial value was high.  
The findings and considerations above suggest that moderate mechanical stress induces impairment in RBC deformability. However, since the erythrocyte population is a mixture of cells with different levels of accumulated damage [78; 79], the impact of mechanical stress causes a variable response. Thus, cells with a low initial degree of damage partially lose their ability to deform. In contrast, cells with a large number of membrane defects are destroyed (hemolyzed) under the action of the applied mechanical impact. Thus, on the one hand, there is a deterioration in the deformability of most cells, and on the other hand, there is selective destruction of \"aged\"/inferior cells. Moreover, we have shown that the above conclusions are valid for various cases of mechanical action on RBCs (rotation with steel balls, blood salvage, and preparation of stored RBCs units [58]). This allows us to hope that the results of this study will present the need to consider the patient-to-patient variability of RBC properties when artificial organs are developed.  
Conceptualization, S.Y. and G.B.; methodology, G.B.; investigation, T.T. and G.B.; resources, S.B.; writing--original draft preparation, T.T. and G.B.; writing--review and editing, S.Y., S.B. and D.A. and A.G.; supervision, S.Y.; project administration, S.B. All authors have read and agreed to the published version of the manuscript.  
This study was supported by a grant from the Israel Science Foundation (to [PERSON]; 341/18) and from the Haemonetics (Boston, MA, USA) to S.
# A Taxonomy for Security Flaws in Event-Based Systems
## 2 Background
### Event-Based Systems  
The EBSs' popular attributes (e.g., scalability, evolvability, and low coupling [16; 17; 18; 19; 20]) are fundamentally enabled by their communication model. In EBSs, the components (i.e., the units of computation and data) communicate asynchronously with each other by using messages [48]. A message typically describes one or more observed events. An event is any occurrence that can be observed by a component (e.g., a change of the component's state or a change in the environment of the system) [49]. An event and its corresponding message are often conflated in literature for convenience. In this paper, the term \"event\" will be used to refer to these concepts broadly. A connector is an architectural element tasked with effecting and regulating interactions among the components [1]. Although there exist several connector types, in this paper, a connector will always refer to an event-based distribution connector [1] that distributes events to associated components. We will use the term \"event broker\" to refer to this concept broadly.  
In EBSs, the components do not have explicit references to each other and are only able to invoke an event broker directly [49]. Consequently, the addition, removal, and updating of components can be achieved relatively easily during runtime [50]. A component can be an event producer or a consumer, or both. Communication between the components is processed via \"source\" and \"sink\" [51]--a source is an event interface invoked to publish events by a producer component; a sink is an event interface that an event broker invokes to transfer an event to a consumer component. When a producer publishes an event, the event broker routes the event to the appropriate consumers based on the system configuration, along with the routing and filtering policies [49]. When the event broker transfers an event to a component's sink, the component consumes the event. Each sink declares an event type and only allows the processing of events that match its declared type. In this paper, we will target the following three event types commonly used in today's EBSs [48]: (1) nominal, (2) subject-based and (3) attribute-based. Nominal event types are explicitly declared in a system's programming language and subsequently enforced at compile-time. In subject-based event typing, each event type is defined through a string value that captures an event's name. Similar subject-based event types can be organized into naming hierarchies (e.g., Weather/Country/City). In attribute-based event typing, an event type is defined through a set of attributes, where each attribute is a pair of name and value. Event types can be further defined into more specific event subtypes.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Frequency Modulation (FM)  
The frequency modulation (FM) is widely used with global coverage for FM radio broadcasting. It encodes the information in a carrier wave by varying the frequency of the wave. It utilizes the very high frequency (VHF) spectrum, which is less affected by weather conditions and obstacles. In fact, FM is very appropriate for outdoor localization, since FM signals are widely available, can travel hundreds of kilometers, can interact with landscapes and environmental dynamics, and can still be effective.  
For indoors applications, a year-long experiment, performed in [92], evaluated the ambient FM radio signals by analyzing the robustness of the FM signal features to human presence and weather conditions. It was concluded that ambient FM radio signals were barely affected; therefore, they are suitable for both standalone and hybrid positioning solutions. In particular, FM was compared with Wi-Fi for localization using the fingerprinting technique, and it was concluded that the FM RSS values at a given frequency did not change dramatically over time, whereas the Wi-Fi RSSI signatures were affected the most by temporal variations, causing the localization accuracy to decrease. In addition, the combination of the two provided higher localization accuracy, compared to Wi-Fi alone. Therefore, the combination of the FM and Wi-Fi-based approaches may lead to a hybrid positioning solution in the future.