esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis and Findings
### Quantitative Analysis
A notable 81% of respondents agree or strongly agree that regulations significantly influence their decision to adopt green logistics, demonstrated by a mean score of 4.18. Supply chain dynamics are also acknowledged as important by over 80% of participants, with a mean score of 4.14. Industry standards stand out as a major driver, with a high mean score of 4.21 and 83% agreement. Financial incentives are recognized by approximately 82% of respondents, with a mean score of 4.19, highlighting their role in promoting green logistics. Long-term cost savings, with the highest mean response of 4.26, are identified as a significant motivator. The overall economic benefits are considered crucial, as indicated by a mean score of 4.20 and about 83% agreement. The demand for eco-friendly practices is a key driving factor, with a mean score of 4.22 and about 83% agreement. Market preference significantly influences logistics strategies, as reflected by a mean score of 4.23 and about 84% agreement. Customersatisfaction plays an important role, with a mean score of 4.24 and 83% agreement. The commitment to CSR is underlined by approximately 82% agreement and a mean score of 4.22. Ethical considerations are emphasized by a mean score of 4.17 and about 81% agreement. The reputational benefits associated with green logistics are recognized, with a mean score of 4.17 and similar agreement levels. The strategic importance of sustainability is highlighted by about 82% agreement and a mean score of 4.18. Market differentiation is valued, with a mean score of 4.23 and 83% agreement. The strategic advantage of green logistics is underscored by a mean score of 4.19 and 82% agreement. The combined insights from the percentage of agreement and mean scores illustrate a strong recognition of the multifaceted benefits of adopting green logistics. This encompasses compliance with regulations, achieving economic and operational efficiencies, meeting customer demands, fulfilling CSR obligations, and gaining competitive advantages. The findings emphasize the perceived value of green logistics practices in fostering sustainable, efficient, and responsible business operations, highlighting the necessity for organizations to integrate these practices into their strategic planning and operations.
## 3 Proposed Solution: Modelling Components of a Communication Protocol as Resources  
The central component of MQTT architecture is the Broker, which is entitled to receive, process, and re-transmit all MQTT messages (managing publish and subscribe actions) following the protocol specification.  
We propose modelling the MQTT Broker as a UMA Resource Server (Resource Server Broker). This way, full compatibility is maintained with the current MQTT protocol specifications, while adding the authorization capabilities offered by UMA and not found in MQTT. MQTT is defined around topics. To provide authorization capabilities over topics, we propose modelling MQTT topics as UMA resources. According to UMA, every resource must have an owner. Therefore, topics will have an owner, which will be responsible of administering the access control policies associated with it.  
The main components of the proposed solution are shown in Figure 3. There are four communication channels between the different elements:  
* User interaction (A): Interaction between a user and a component through an interface.
* UMA flow (B): Set of requests and responses between the components defined in the UMA protocol.
* MQTT flow (C): Set of requests and responses between the MQTT client and broker defined in the protocol.
* Secure communication (D): Communication channel for the configuration of an IoT device.  
In the following sections, we are going to describe the functional entities and flows involved in the proposal.  
Figure 2: The main phases and entities of User-Managed Access (UMA). PAT: Protection API Token; RPT: Requesting Party Token.
# Location Based Indoor and Outdoor Lightweight Activity Recognition System
## 2 Related Work
A framework is presented based on the context model to accommodate semantics in both LBS and GIServices. In LBS, the context-aware framework facilitates information discovery, access, and composition. In terms of indoor localization systems based on wireless access points, these approaches measure the intensity of the received signal [40] and the fingerprinting method [41; 42] used to infer the user's location. Because of its low cost, small form factor, and ability to communicate without a battery, Radio Frequency Identification (RFID) has been considered an IoT enabler technology. This technology enables people and smart objects to connect digitally, be identified, and tracked [43]. Furthermore, it is primarily employed for supply chain traceability [44], including some critical supply chains such as [45; 46; 47]. However, this is not their only use, nowadays RFID is also used for localization in several contexts. For example, in the medical field, this IoT enabler can be used to collect information on patients who require home care, allowing them to be located within their homes and analyzing their behavior [48]. Furthermore, in recent years, Bluetooth has become one of the most popular indoor location technologies. Bluetooth positioning technologies among others [49; 50], depending on its configuration and enhancements, can provide accuracy down to meters or even centimeters. Consequently, it adapts better to tasks where pinpoint accuracy is not necessary. Moreover, aside from its low cost and ease of use, BLE-based positioning technology can produce reliable results. For instance, BLE has been used in museums and galleries to guide the visitors and provide information about the artworks [51; 52]. This method of indoor positioning is based on Bluetooth Low Energy beacons that are attached to objects, walls, and other surfaces and emit radio signals at predetermined intervals. These battery-powered devices communicate with other Bluetooth-enabled devices, such as smartphones. An application on the user's device (e.g., smartphone, tablet, smartwatch) detects Bluetooth signals and uses the Received Signal Strength Indicator (RSSI) to calculate the distance to the BLE beacon. Furthermore, newer Bluetooth versions have improved location services. The new Bluetooth 5.1 direction-finding feature, which uses the Angle of Arrival (AoA) and Angle of Departure (AoD), improves accuracy down to the centimeter level [53], allowing the development of a diverse range of reliable and accurate indoor positioning solutions and applications. In Table 1, an overview of methods and technologies used in the related studies is presented.
## 2 Literature Review
### Finding Changes in 2D and 3D Spatial Objects  
For 2D spatial entities, the primary techniques employed are segmentation and classification. These techniques can be described as follows:  
* Segmentation: This process involves dividing an image into regions or segments that correspond to objects or their parts [27]. In 2D, this is achieved based on x and y pixel coordinates, disregarding depth (z) information.
* Classification: After segmentation, color information in the form of red, green, and blue (RGB) values from each segment is used for classification [28]. Pixels or regions are grouped into categories or classes based on their RGB values. An alternative approach is using digital elevation model (DEM) data to assign z values to a 2D image, providing a measure of depth or elevation.  
The final step in evaluating changes is conducting a time-series analysis of the segmented or classified data. This analysis involves the comparison of the same spatial entity over multiple time points, looking for changes in its segmentation or classification.  
For 3D spatial entities, the process starts with segmentation but differs in the subsequent steps [29]. Rather than classification, the data are partitioned further according to pre-established rules that might be based on specific characteristics of the spatial entities, such as size, shape, or relation to other entities. After the rule-based partitioning, the altered (or current) and pre-existing 3D datasets are compared to identify changes. The use of \"rule-based\" methods, as described, signifies that the rules for partitioning or categorizing the data are not set and can be adapted based on the data or objectives of the analysis. They can be optimized over time based on the accuracy of their results.  
The primary objective of this study was to devise and implement scenarios for detecting changes in spatial objects to forecast potential disasters and the expansion of underground utility tunnels. For this, we used 3D LiDAR sensor data to analyze the spatial object extraction algorithm. Additionally, software was developed to assess the performance of the algorithm using simulated environments.
## 2 Materials and Methods
### Sample Collection--Ethical Approval  
Fish samplings were performed on a monthly basis throughout a full calendar year (October 2014-September 2015). In January 2015, due to flooding events in the study area, the access to Vistonis Lake was not feasible and therefore no samples are available from this month. Fish samplings were performed in the northern part of the lake, where water exhibits lower salinity levels, using Nordic-type benthic multi-mesh gill-nets following the requirements set by EU [37,38] in accordance with the EU (Directive 63/2010) legislation for the protection of animals used for scientific purposes. All necessary permissions were provided by the Management Body of the Delta Nestos and Lakes Vistonida--Ismarida. During each sampling, five individuals were collected and were all used for both histopathological and biochemical analysis (45 individuals in total). On the sampling days, fish were removed from the water and were immediately anesthetized by immersion in water with an overdose of concentrated solution of buffered ethyl 3-aminobenzoate methanesulfonate (MS-222). Five minutes after cessation of respiration, fish were removed from water and were euthanized, according to the protocols of the Canadian Council of Animal Care (Euthanasia of finfish) and the American Veterinary Medical Association, aiming to minimize fish psychophysical stress. Dead fish were placed on ice and from each sampled individual, one pair of gills was immediately fixed in 10% buffered formalin and stored for histopathological analysis. For the biochemical analysis, and immediately after dissection, the other pair of gills and liver tissue were dissected and retained in dry ice, until their transfer to the laboratory, where the samples were stored at \(-80\)\({}^{\circ}\)C until further analysis. Fish gills are, besides their respiratory function, one of the most important osmoregulatory organs, playing a critical role in ionic regulation [39]. The responses of fish gills under the variation of the physico-chemical parameters can be tested either histologically or on  
Figure 1: Map of the study area (Vistonis Lake, Northern Greece) (**A**) and sampling stations indicated with black dots (**B**) (Reprinted with permission from ref. [30]. Copyright 2108, Copyright Owners Assoc. Professors [PERSON] and [PERSON] and Dr. [PERSON]).  
biochemical level [40; 41; 42]. Moreover, the liver is also considered as an early stress indicator compared to other fish tissues, as shown by several researchers [43; 44; 21; 45].  
Parallel to fish sampling, water physico-chemical parameters such as temperature (\({}^{\circ}\)C), salinity (psu), dissolved oxygen (DO) (mg/L), pH and conductivity (\(\upmu\)S/cm) were also recorded at eight stations in the lake using a portable multi sensor (AquaRead, AP-2000) (Figure 1). Recording of the physico-chemical parameters was performed at each sampling station, both at the surface and above the lake's bottom (n = 16 records per sampling).
## 2 Methodology
### Link Budget Analysis
#### 2.6.2 Link Margin  
The link margin, expressed in dB, represents the ideal value to determine whether a communication link between a spacecraft and a ground station will be effective. Thus, the communication design should result in a positive link margin to guarantee a solid communication connection. The following equation is used to calculate the link margin, where the expected value of energy per symbol to noise ratio \((\frac{E_{s}}{N_{o}})\) is compared with the required value as follows:  
\[Link\ margin\ (dB)=\left(\frac{E_{s}}{N_{o}}\right)_{Expected}-\left(\frac{E_{s}} {N_{o}}\right)_{Req} \tag{3}\]  
Equations (4) and (5) define the required and expected \(\frac{E_{s}}{N_{o}}\):  
\[\left(\frac{E_{s}}{N_{o}}\right)_{Req}=\left(\frac{E_{b}}{N_{o}}\right)_{Req }+10\ log\ (SF) \tag{4}\]  
\[\left(\frac{E_{s}}{N_{o}}\right)_{Expected}=EIRP-L_{Tot}-G/T-Boltzman \tag{5}\]  
where \(\frac{E_{b}}{N_{o}}\) is the signal-to-noise ratio per bit, \(SF\) is the spreading factor, \(EIRP\) is the equivalent isotopically radiated power, \(L_{Tot}\) is the total losses, \(G/T\) is the ratio of receive antenna gain to noise temperature and Boltzmann is a constant.  
The \(EIRP\)[38] is expressed as follows:  
\[EIRP=P_{tx}+G_{tx} \tag{6}\]  
where \(P_{tx}\) is the transmitted power, and \(G_{tx}\) is the transmission antenna gain. Equation (7) shows the total losses in dB that affects the communication system.  
\[L_{Tot}=L_{FSPL}+L_{Polz}+L_{Rain}+L_{Poin}+L_{Imp} \tag{7}\]  
where \(L_{FSPL}\) is the free space path loss, \(L_{Polz}\) is the polarization loss, \(L_{Rain}\) is the rain loss, \(L_{Poin}\) is the pointing loss, \(L_{Imp}\) is the implementation loss. Moreover, \(L_{FSPL}\) is defined as follows:  
\[L_{FSPL}=20\ log\ (\frac{4\pi}{c})+20\ log\ (f)+20\ log\ (d) \tag{8}\]where \(f\) is the frequency in MHz, \(d\) is the distance in kilometer and \(c\) is the speed of light in kilometers MHz. The distance \(d\)[38] from the satellite to the GS is computed from Equation (9).  
\[d=\sqrt{\left(r+h\right)^{2}-\left(r\ cos\mathcal{O}\right)^{2}}-r\ sin\mathcal{O} \tag{9}\]  
where \(r\) is the radius of the earth, \(h\) is the altitude of the orbit, \(\mathcal{O}\) is the elevation angle between the satellite and GS. Finally, \(G/T\) is defined by Equation (10).  
\[G/T\ (dB/K)=Satellite\ antenna\ gain-10\ log\ (noise\ temperature) \tag{10}\]
# Research on Clustering-Based Fault Diagnosis during ROV Hovering Control
## 4 Performance Evaluation of FD
### Results of FD  
The FD algorithms applied in this study are shown in Figure 18. Before applying DBSCAN, a preprocessing step is performed after acquiring fault features such as RPM and current data. The data are scaled to expand the distribution of normal and fault data and then normalized to account for unit differences. Subsequently, samples of normal, breakage and entangled data are sorted, and DBSCAN is applied with \(\mathcal{E}=0.2\) and Minputs = 4, as mentioned in Section 2.3.  
Figure 17: Results of reaching the target depth from the tank bottom and performing hovering in different fault situations: (**a**) One-blade breakage; (**b**) two-blade breakage; (**c**) rope; (**d**) net.  
Figure 19 shows the clustering results obtained by applying DBSCAN to 200 samples of normal, two-blade damage, one-blade damage, rope, and net data, respectively. Breakage data are characterized by a lower current and higher RPM compared with normal data, whereas entanglement data are clustered separately owing to their relatively high current. Although the fault data and normal conditions did not have a linear relationship, as shown in Figure 6, the application of DBSCAN, which is effective for nonlinear clustering, resulted in fault classification.  
Figure 18: FD algorithms.
## 2 Numerical Modeling  
### Model Introduction  
This study is based on the Sichuan-Tibet Railway, a tunnel project under construction. The tunnel site conditions are classified as Type II, with a basic seismic intensity of VIII, a characteristic period of 0.45 s for the acceleration response spectrum, and a design peak ground acceleration of 0.20 g. The tunnel length is 12,323.1 m and the maximum burial depth is 1525.4 m; for the single-hole two-lane tunnels, the drill-and-blast method of construction is used with a span of 13.6 m. The tunnel lining consists of a primary support and secondary lining. The primary support is 0.25 m thick, with a concrete strength grade of C30; the secondary lining is 0.60 m thick, with a concrete strength grade of C35, and the tunnel support structure cross-section is shown in Figure 1. The exit section of the tunnel crosses an active fault, which, according to the latest GPS observations by the Institute of Geology of the China Earthquake Administration (CEA), is a right-handed strike-slip fault and is moving at a constant rate of 3 mm per year.  
Figure 1: Tunnel support structure.  
The fault intersects the tunnel at an angle of 70\({}^{\circ}\), with the fault fracture zone spanning 30 m in width. To ensure the computational accuracy of the numerical model, the longitudinal length of the model is set to 400 m based on multiple trial calculations. In the numerical calculation of the nonlinear dynamics of underground engineering, after the width of the numerical model is larger than 5-10 times of the width of the structure, the boundary will not affect the results of numerical analysis [23]. In this study, the width of the numerical model is chosen to be 14.8 times the tunnel width, resulting in a final model size of 400 m \(\times\) 200 m \(\times\) 100 m. In the numerical model, C3D8R hexahedral elements in ABAQUS (v.2020) are used to model the surrounding rock, fracture zones, and tunnels. In order to meet the requirements of dynamic calculation accuracy, the maximum mesh size of the model does not exceed 5 m. The numerical model is shown in Figure 2.
# Flexible Construction of a Partially Coherent Optical Array
## 1 Introduction
A typical feature of a vortex beam is the circular symmetry distribution of intensity distribution in the dark, with a continuous spiral phase distribution and an uncertain phase at the center point, which is called phase singularity [42]. The expression for vortex phase is \(\exp(il\varphi);l\) represents the direction of phase distortion, with a positive sign corresponding to counterclockwise distortion and a negative sign corresponding to clockwise distortion. Since its discovery, vortex beams have been extensively studied due to their wide range of applications. Recent studies have shown that vortex beams can be applied in free space optical communication [43], particle capture [44], microscopy imaging [45], astrometry [46], and other fields. To cope with application environments in different fields, high-quality vortex beam generation important. There are currently many methods for generating vortex beams, and the most commonly used methods are those using spiral phase plates (SPPs) [47; 48] and spatial light modulators (SLMs) [28]. Given some new applications of vortex beams, such as polarization detection [49] and edge enhancement images [50], the generation of vortex beams has become increasingly important. However, is it possible to construct a vortex beam without utilizing the vortex phase? Recently, research has shown that high-quality vortex beams can be generated by using a gradual-width Fermat spiral slit mask [51]. In this article, we were surprised to discover phase singularities in the CDOC phase distribution of the source plane of SCLs, which may be useful for constructing vortex beams in the future without utilizing the vortex phase. In this article, we propose a flexible and programmable method for constructing optical coherent lattices by manipulating the initial coherence structure. This method can modify the generated optical array from aspects of the spatial structure, such as the amplitude or the size of individual light spots, and the number and individual position of the light spots. A spiral structure array and its propagation properties are studied in detail as examples, by manipulating the initial beam parameters, e.g., the coherent length, and a spiral-like beam field can be achieved in the far field. We also discovered phase singularities in the phase distribution of CDOC in the source plane. Our discovery is useful for constructing vortex beams without the vortex phase. Therefore, through our proposed method, optical array can be flexibly modified to meet the requirements of diverse usage scenarios.
## 3 Results and Discussion  
### Cyclic Shear Fatigue Test  
Figure 1b shows the maximum recorded load with the increasing number of cycles for the NR and MRE samples. The NR sample had the same composition as that of the MRE sample, except for the presence of CIPs in the latter. The MRE sample was subjected to zero and external magnetic fields. The load of the samples increased with the increase in the number of cycles. In particular, the load of the MRE sample was higher than that of the NR sample. Owing to the higher stiffness of the MRE sample, a larger maximum load is required for its deformation. Moreover, the maximum load increases further under an external magnetic field, consistent with the known effect of an external magnetic field on the MRE sample [1; 27; 28; 29; 30]. Meanwhile, the material exhibited variable response to the fatigue cycles at a fixed strain. The load deviations increased as the number of fatigue cycles increased. This phenomenon is associated to the stress softening due to the sliding between the matrix chains and filler particles, hysteretic heating of the rubber materials by the energy loss in the strain cycle, and accumulation of crystallisation under non-relaxing conditions. Stress softening under cyclic loading, especially for filled rubber composites, has been described by the Mullins and Payne effects [8; 9; 10; 11; 12; 13; 31; 32; 33; 34; 35; 36; 37].  
Softening and hardening are complex events that occur in samples during the cyclic shear fatigue test, and these changes may affect the rheological properties of the MRE. Interestingly, the CIPs embedded in the MRE were attracted to the magnetic field and were released during the fatigue test under an external magnetic field. The detachment of the CIPs weakens physical bonds between the NR matrix and CIPs owing to the relative movement of magnetised CIPs and the large strain amplitude applied to the MRE [18; 38; 39; 40]. This can explain the slightly lower increase in the maximum recorded load under a magnetic field that in a zero field as the number of fatigue cycles increased.
## 3 PHM System Driven by Model Data Hybrid  
### System Architecture  
Referring to the design idea of the existing spacecraft health management system [21], the PHM system architecture proposed in this paper is shown in Figure 6, including the basic data layer and the health management layer. The basic data layer provides the data required to support health management, and the health management layer contains the health monitoring and fault diagnosis functions required to achieve on-orbit satellite constellation health management.
## Preliminaries of MPC and Framework Overview
### Framework Overview  
The framework of the proposed MPC scheme is shown in Figure 2. The MPC module consists of three blocks: the vehicle model, driving safety constraints and optimizer. As the vehicle states cannot always be measured, a state observer is necessary for state estimation. At each step, the  
Figure 1: Basic principle of MPC.  
estimated state \(\hat{\xi}_{k}\) and the measured control action \(u_{m}\) together with the \(u_{c}\) and \(\xi_{c}\) obtained from the feedback correction module are used to generate modified vehicle state \(\xi_{k}=\hat{\xi}_{k}+k_{1}*\xi_{c}\) and modified control input \(u_{k}=u_{m}+k_{2}*u_{c}\), where \(k_{1},k_{2}\) are integral coefficients. Accordingly, the vehicle model is developed while accounting for the road curvature \(\kappa\) and bank angle \(\phi_{t}\). Then, safe driving constraints are designed to prevent sideslips, rollovers and exceeding road boundaries. Finally, an MPC problem is formulated and solved to generate the optimal control sequence \(u^{*}\).