esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
used transmission error and vibration signal as the indicators to evaluate gear wear conditions [14; 15] but lack a physical explanation from the perspective of dynamics/kinematics. Tooth wear has an important influence on mesh stiffness. [PERSON] et al. qualitatively analyzed the effects of gear wear on the phase and magnitude of gear mesh stiffness [16]. [PERSON] et al. carried out modal analysis to obtain the reduction of mesh stiffness in spur gears [17]. [PERSON] et al. proposed a wear prediction method which combined loaded tooth contact analysis (LTCA) method and gear dynamic model to evaluate the mesh stiffness of spur gears [18]. Using the same method, [PERSON] et al. studied the influences of tooth modification on thin-rimmed gears with tooth wear [19]. [PERSON] et al. used the variation of contact lines of helical gears to develop an accurate wear detection approach, in which the effects of tooth wear on mesh stiffness was analyzed [20]. A modified analytical mesh stiffness model was derived by [PERSON] et al. [21] who introduced the tooth wear into mesh stiffness. In our previous work, tooth wear was simplified as the change of tooth profile parameters and gear wear only reduces the amplitude of mesh stiffness [22]. The effect of tooth wear on the mesh stiffness was quantitatively analyzed, where tooth wear was equivalent to tooth profile deviation [23]. However, our existing studies mainly focus on the influence of tooth wear on geometric parameters through equivalence or finite element method, while ignore the new mesh relationships caused by tooth wear. Figure 1: Perfect and worn profile of internal spur gear. This paper focuses on the evaluation of tooth wear on the gear model, including the mesh relationship, mesh stiffness, and transmission error. Aiming at the shortcomings of existing gear modeling for tooth wear, the main contributions of this paper can be summarized as:  
1. An analytical model for a new mesh relationship of internal spur gear with tooth wear is proposed for the first time. 2. The multi-tooth mesh rule to evaluate the effect of tooth wear on the multi-tooth mesh relationship is proposed. 3. According to the new mesh relationship and the multi-tooth mesh rule, the effects of tooth wear on the mesh stiffness and USTE are studied. The proposed model will reveal the internal gear wear fault mechanism, which is helpful to build the condition indicators and realize the effective monitoring of the wear status of the helicopter transmission system. The rest of this paper is arranged as follows. In Section 2, the analytical model is introduced and a new mesh relationship of internal spur gears with tooth wear is derived. In Sections 3 and 4, the multi-tooth mesh rule is explored and the effect of tooth wear on the mesh stiffness and USTE is evaluated. Finally, conclusions are drawn in Section 5.
## 2 Theoretical Foundation
### Signal Detection Techniques  
In wireless communication systems, accurate signal detection is crucial because it directly affects how well symbol mistakes are fixed, how much data is sent, and how efficiently the spectrum is used. The transmission chain from the transmitter to the receiver works in different ways in SISO and MIMO systems. In MIMO systems, the extra complexity comes from the fact that they use multiple antennas. The existing signal detection techniques can be generally categorized into two main approaches:  
Figure 1: MIMO channel model.  
1. Conventional methods: These include statistical approximation and optimization techniques such as MLD, MMSE, and ZF. These methods, while effective, face limitations in complex MIMO systems due to increased computational demands and the need for precise channel information.
2. Machine learning-based approaches: These approaches focus on learning complex patterns and making data-driven decisions. Deep learning techniques, such as DNN, have shown promising results in various applications, including signal detection in wireless communication systems.  
This study builds on these basic ideas to suggest a DNN-based MIMO detector that is meant to fix the problems with current methods and work better in complicated MIMO situations.  
Traditional wireless communication systems heavily rely on conventional data detection techniques, including the following.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 4 Derivation of Offshore Dynamic Topography  
Dynamic topography, defined as the SSH and marine geoid separation, represents one of the most valuable parameters, in terms of marine dynamics. Accurate knowledge of DT can guarantee safe navigation at sea, help understand oceanographic processes, and be combined with a suitable geoid model to validate various SSH measurements. With the method developed by [15; 30], TG data and an HDM can be combined using Equation (3) to estimate the HDM-contained DB values at the locations of TG stations (at hourly temporal resolution \(t^{H}\)). Both studies used exact interpolators for DB gridding, which inherently assumes errorless TG data and TGs connections to the height system(s), as well as that the estimated DB at a TG station is an expected value for all nearby offshore locations; although, neither is necessarily true. Errors may always exist, due to instrument malfunctions, natural disasters, human errors, or poor maintenance and documentation [53; 54]. Such errors propagate to the derived offshore DT when the gridded DB is used to correct the initial HDM. Thus, a new approach utilizing DB qualities was developed for constraining TG data errors in offshore DB prediction.
## 2 Mixed variational formulations
### Primal Hellinger-Reissner formulation  
To derive the first block of equations one may use the compatibility (3) and constitutive equations (5)\({}_{1,2}\) to get rid of macro and micro strain fields, and subsequently testing the resulting two equations with a macro-stress field \(\underline{\underline{\tau}}\) and a micro-stress field \(\underline{\underline{\sigma}}\). The second block of equations may be derived testing the two equilibrium equations (1) with virtual macro and micro displacement fields, say \(\underline{v}\) and \(\underline{e}\). The achieved formulation reads: Find \((\underline{\underline{T}},\underline{\underline{S}},\underline{u},\underline {d  
Referring to the finite element discretization, one can use the standard \(P_{1}-P_{0}\) triangular approximation that has been actually implemented in the numerical examples to follow. When a macroscopic fracture is to be added to the model, functional spaces should be slightly modified to allow displacement discontinuities. Furthermore, stress flux continuity across the crack should be enforced by using suitable Lagrange multipliers.  
Formally, by testing the macro-equilibrium equation (1)\({}_{1}\) and using Gauss-Green formula one gets:  
\[\int_{\Omega}\underline{\mbox{div}}\,\underline{T}\cdot\underline{v}=\int_{ \Gamma}\big{(}\underline{T}\cdot\underline{n}\big{)}\cdot\underline{v}-\int_{ \Omega}\underline{T}:\underline{\
abla}^{s}\underline{v}. \tag{8}\]  
To deal with the inclusion of a macrorack one may specialize the line integral on \(\Gamma\) in (8) along the two adjacent edges of the fracture \(\Gamma_{c}\), with normal \(\underline{n}\). One has therefore to introduce the displacement jump that denotes the prescribed discontinuity along the crack. The continuous stress fluxes may be written as:  
\[\underline{T}\cdot\underline{n}=\underline{\mathcal{C}}^{-1}[\underline{u}], \tag{9}\]  
taking into account the constitutive parameter \(\underline{\mathcal{C}}\) that holds the cohesive law, i.e. the mechanical relationship that ties the vector of the displacement discontinuities to the vector of the stress-fluxes.  
According to the above issues, the achieved primal mixed variational formulation for microcracked bodies that embed discrete macrocracks reads: Find \((\underline{T},\underline{S},\underline{u},\underline{d})\in\,L^{2}(\Omega )\,\times\,L^{2}(\Omega)\,\times\,H^{1}(\Omega)\,\times\,H^{1}(\Omega)\) such that:  
\[\left\{\begin{array}{l
# Effect of Local Cyclic Loading on Direct Shear Strength Characteristics of Shear-Zone Soil
## 5 Discussion  
### Effect of Cyclic Loading on Shear Displacement  
Figure 14 compares the shear displacement obtained by local load-controlled cyclic loading tests with the same cyclic period (90 s) but different normal stresses. Under different vertical stresses, the differentiation of shear deformation after 40 cycles of loading was slight and virtually insignificant. Obviously, the shear displacement-cyclic number curve can be separated into two parts. The first part is from the beginning to the 40 th cycle, regarded as the initial stage of shear displacement. At this stage, the accumulative shear displacement increases evidently with an increase in the cyclic number, especially in the initial cycles. The second part is from the 40 th cycle to the end, regarded as the steady stage. Moreover, the shear displacement increases very slowly at this stage.  
However, each shear displacement responded differently to the number of cycles during the local cyclic loading of the second part. The slopes of the mean cyclic path (\(S_{100}\), \(S_{200}\), and \(S_{400}\)) increased slightly from 4.6686 \(\times\) 10\({}^{-4}\) to 9.88797 \(\times\) 10\({}^{-4}\), but the cyclic displacement amplitudes (\(d_{100}\), \(d_{200}\), and \(d_{400}\)) increased near two times, from 0.022 mm to 0.043 mm, with an increase in normal stresses. At a given cyclic period and number of cycles, the imposed normal stress plays a major role in local cyclic displacement, as previously noted by [PERSON] and [PERSON] [30].  
Figure 15 presents the cyclic shear stress behavior of the shear-zone soil with varying normal stress and cyclic periods. As shown in Figure 15a, the increasing vertical stress increases the amplitude of the cyclic shear stress, \(\tau_{\rm a}\). At vertical stress of 100 kPa, the shear stress of the mean position is \(\tau_{0}{}^{100}=54\) kPa and the amplitude of the cyclic shear stress is \(\tau_{\rm a}{}^{100}=7.88\) kPa, whereas the shear stress of the mean position and the stress amplitude increase to \(\tau_{0}{}^{400}=135\) kPa and \(\tau_{\rm a}{}^{100}=19.52\) kPa under normal stress of 400 kPa.  
Figure 15b shows the samples manifest diverse shear response patterns at varying cyclic periods under vertical stress of 100 kPa. The shear displacement increases with the increasing cyclic period and is composed of two different parts: elastic displacement \(d_{\rm e}\) and plastic displacement \(d_{\rm p}\). In the first cycle, the plastic displacement \(d_{\rm p1}\) increases from 0.023 mm to 0.066 mm as the cyclic period increases from 10 s to 90 s. However, in the last cycle, the plastic displacement drops to zero and the elastic displacement tends to become a stable constant \(d_{\rm e}=0.02\) mm. Even though the specimen is sheared under cyclic periods of 10 s, 60 s, and 90 s, the elastic displacements of the shear-zone soil finally approach the same value under a stable mean position and amplitude of the cyclic shear stress.  
Figure 14: Shear displacement versus the number of cycles with different normal stress (\(T=90\) s).
## 4 Biological Applications of EUV/SXR Microscopes Based on a Double Stream Gas-Puff Target Source
### Angular Projection SXR Tomography for Biological Nanoimaging
#### 4.2.2 Caffeine Fibers  
An interesting imaged sample was a caffeine crystal (caffeine anhydrous from the International Trade Center, ITC, purity 98.9-100%) dissolved in an aqueous solution. During the drying process, elongated caffeine crystals were created and subsequently deposited on top of the tip-holder, as shown in the SEM image of Figure 23a. Keeping our attention to a small fiber, enhanced with the rectangular detail in Figure 23a, it was possible to image the sample in the full angle of rotation, from 0\({}^{\circ}\) to 360\({}^{\circ}\), in steps of 10\({}^{\circ}\). Some of the projections are shown in Figure 23b. It can be noted that the SEM image shows much more caffeine fibers than the SXR image because some fibers were detached from the tip during the venting-phase of the SEM and the subsequent transportation of the sample to the SXR tomography system. The imaged sample was then reconstructed using the ImageJ software [93; 94]. A 3D reconstruction of the fiber surface (electron density) is shown in Figure 23c [59; 92]. Although the shape of the sample was overall reconstructed, some features were not properly reproduced, because of the not homogeneous thicknesses of the fibers. A more accurate reconstruction was performed applying the generalized total iterative constraint strategy [95] under Rytov approximation (more details are reported in [96]). The support constraint in the spatial domain was calculated before the reconstruction, minimizing the generation of artifacts due to the limited angular range of the acquired projections [97] (Figure 23d). The estimated spatial resolution of the 3D reconstruction was -1-2 \(\upmu\)m for the single voxel size of \(320\times 320\times 320\) nm\({}^{3}\).  
Figure 22: HEp-2 cells (human larynx epidermoid carcinoma cell line) in phosphate-buffered saline (PBS) medium on top of a 30 nm thick membrane. (**a**) Shows an optical microscope picture (visible light) acquired with a 40\(\times\) objective. (**b**) Shows the region of the membrane imaged in SXR, which was then acquired at different angles with the SXR tomographic system. (**c**) From the 12\({}^{\circ}\) projection, it is possible to observe the broken and out of plane Si\({}_{3}\)N\({}_{4}\) membrane. Figure modified, based on [59].
# Energy Analysis of Kalina and Kalina Flash Cycles Driven by Renewable Energy
## 3 Results and Discussion
### Performance of Kalina Cycle (KC) and KFC with Optimal Flash Pressures
As the ammonia fraction increases, the fractions of exergy consumed to generate power (\(\eta_{\alpha}\)), and destroyed in the HRV (\(D\)s) and condenser (\(Dc\)) increase; by contrast, those destroyed in power components (\(Dw\)), regenerator (\(Dr\)), and throttle valve (\(Dv\)) decrease. The exergy destruction ratio of flash vessel (\(Df\)) which exists only in KFC decreases from 9.8% to 0.3% in the range of \(x_{b}\) between 54% and 80%. The remaining ratios of the mixer (\(Dm\)) and coolant exhaust (\(Dco\)) are lower than 3%. The component at which largest exergy destruction occurs is different in both the KC and KFC, and depends on the range of ammonia fraction. For example, the HRV in both systems yields the largest exergy destruction for high enough values of \(x_{b}\). Figure 11: Dependence of (**a**) separate and (**b**) cumulative ratios of exergy consumption on the ammonia mass fraction for \(P_{H}=16\) bar in the KFC. Figure 10: Dependence of (**a**) separate and (**b**) cumulative ratios of exergy consumption on the ammonia mass fraction for \(P_{H}=16\) bar in the KC.
## 2 State of the Art
### Cargo Damage Classification Techniques  
Few studies have been published in the literature that specifically address the prediction and further avoidance of the collapse of pallets during transportation; however, many do focus on the classification of cargo damage. There exist different approaches to tackling this topic, with some of them based on questionnaires, while others endorse a data science-based methodology.  
In [14], the author proposes a classification of damage to palletized loads based on his experience in the field, which was further verified and validated through an analysis of questionnaire results sent to five selected groups potentially affected by load damage. The author concluded that damage to loads can be categorized into two distinct groups, based on whether the damage reduces the value of the transported products. The study in Tkaczyk [14] also emphasizes the importance of anticipating damage that diminishes cargo value due to the resultant externalities, such as the increased production of goods and packaging, and heightened transportation demands, which contribute to greater environmental pollution.  
In the study described in [PERSON] et al. [15], a machine learning methodology was employed to predict the severity of cargo loss during transport. To achieve this, interpretable models such as Decision Tree and Logistic Regression models were used. The analysis identified key factors influencing cargo loss, including transit types, product categories, and shipping destinations. Similar machine learning methods were employed in the study presented in [PERSON] et al. [16], albeit with a different objective. The research was focused on predicting and categorizing various types of accidents likely to occur on vessels in the Mississippi River. The study found that the constructed Neural Networks model yielded the most satisfactory results, outperforming those obtained from Multiple Discriminant Analysis- and Logistic Regression-based approaches.  
The significance of predicting insurance claims within maritime port operations is highlighted in [PERSON] et al. [17]. This study was pioneering in its application of machine learning techniques to assess the impact of various attributes on shipping container damage. Among several algorithms evaluated, including Neural Network and Decision Tree algorithms, the Random Forest algorithm demonstrated the most satisfactory performance. Notably, the duration a container spends in the storage yard emerged as the most predictive feature for assessing the likelihood of container damage claims.  
The study described in [PERSON] et al. [18] investigated the use of machine learning techniques, such as Random Forest, in conjunction with traditional methods like Decision Tree, to mitigate the operational challenges of processing insurance claims for customers with damaged shipping containers at a maritime port in Canada. The research findings revealed that the Random Forest algorithm yielded the most favourable results in assessing the severity of damage to shipping containers.
## 4 Results and Discussion
### Model Validation and Experimental Results
#### 4.1.1 Mesh Independence Analysis  
Figure 9 shows the mesh configurations with varying characteristic layer numbers. The MG-1, MG-2, and MG-3 grids progressively increased the radial mesh layers while maintaining a single element layer along the diaphragm thickness direction. The MG-4, MG-5, and MG-6 grids further increased the radial and thickness-direction layers. The MG-7 grid maintained five thickness-direction layers and increased both radial and central characteristic mesh layers. Figure 10 shows the FEM-calculated maximum radial stress results using these meshes. The results indicated that the MG-6 grid led to computational failure, which was attributed to its poor mesh quality. Additionally, FEM stress results from MG-1, MG-2, MG-3, MG-4, and MG-7 demonstrated that increasing radial mesh density progressively stabilized the computational results.
# On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method
## 3 Demonstration and Evaluation
### Demonstration Setup  
A flow chart of the demonstration and its realistic scenario are shown in Figure 4a,b. A server (Dell R740) acts as the 5G NR transmitter-generated 5G baseband signal. It modulates the user data with different modulation schemes which include QPSK, 16-QAM, 64-QAM and 256-QAM. As the modulated symbols were assigned to the 5G NR frame shown in Figure 4c, inverse Fast Fourier Transform (IFFT) processing and IQ modulation were applied to convert the OFDM frame to the time domain and guarantee the signal well matched with the IM/DD VLC system. Subsequently, a digital-analog (D/A) converter (USRP 2944R) and a DC bias transformed this digital signal to analog signal with an  
Figure 3: A block diagram of a typical 5G VLC multi-access scenario using the proposed security solution.  
appropriate DC component. This signal is sent to four LEDs (LUXEN 5050). Through an optical wireless channel, the signal from each LED is captured by a commercial receiver (HAM C5331-11) in sequence and digitized by an analog-digital (A/D) converter (USRP 2944R). Finally, the received signal is transferred back to the server to extract fingerprints.  
A 3D coordinate system was established to demonstrate the feasibility and accuracy of the proposed method for various distances and environments. According to Figure 4b, four LEDs were fixed on the ceiling in a known location. The receiver kept 1.042 m height and moved on a plane parallel to the ground. The distribution of the EVM (Error Vector Magnitude) of each LED in the whole area was tested to describe the optical wireless channel. Subsequently, signal samples from each LED were collected 10 times by the receiver at 24 different points. By using the proposed method, 960 LED fingerprints were extracted from all the samples and were randomly divided into training set and testing set in a ratio of 6:4. Several types of classifiers were employed to verify the accuracy of the extracted LED fingerprints.
# Sustainability as a Challenge and Driver for Novel Ecosystem 6G Business Scenarios
## 2 Materials and Methods
### Scenario Planning Process  
This research applies the anticipatory action learning process [33; 34; 35] in which professionals representing different stakeholders relevant to the topic of study come together to generate scenarios. Exploratory scenarios represent a foresight method that provides a means to depict, make sense of, and asses alternative future events, trends, and choices in a holistic manner. The scenarios were generated in a series of facilitated workshops during January-June 2020. Special attention was paid in the workshops to the coherence, variation, and validity of the generated scenarios [40; 41]. In practice, the scenario work process comprised teleconference sessions and individual homework carried out in steps [36; 37] as depicted in Figure 1.  
* Defining the focus and relevant time frame: 6G business in 2030-2035, and the global change drivers and focus domains of information technology and electrical engineering research towards the year 2030
* Identification of key factors and driving forces behind the scenarios
* Assessment of the forces in the previous step and choosing the key trends and uncertainties. The assessment was based on the anticipated impact (from small to great) and predictability of consequences (from known to unknown)
* Establishment of the scenario logic based on two orthogonal (unrelated) dimensions that represent significant uncertainties within the selected scenario focus and timeframe
* Creation of the scenario contents for each scenario by building on the identified trends and uncertainties
* Assessment and evaluation of the generated scenarios for their internal consistency, depth and richness of detail, plausibility, and stakeholder behavior [42]
* Utilization of Causal Layered Analysis [34], Futures Triangle [43], and the four perspectives of the Integral Theory [40] to ensure the quality of the scenarios.  
For providing high-quality scenarios, the participant composition in the workshops was aimed at ensuring wide variation in participants' backgrounds. Representatives of research, standardization and development, academia, the telecoms industry, government, and verticals were present in all workshops. The generated scenarios were also backcasted [44] to technology-themed 6G White Papers [45] in order to analyze their consistency with key strategic technology options and frames identified in earlier research. Building on desirable futures, backcasting entails working backwards to identify policies and programs that could connect them with the present. Foresight scenarios are by definition futures-focused. This means that the reliability and validity of the scenarios cannot be controlled. However, the quality of scenarios can be evaluated by how probable, plausible, and preferable they appear to be.