esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
###### Abstract  
A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications.  
wireless sensor networks; low power networks; long range networks; algorithm engineering; self-stabilization; implementation; protocols; software design; cross-layer; cross-platform 1
## 2 Methods
### Study Areas  
Three case study regions were selected (Figure 1), one in China and two in Canada. The first forested study region (12 km \(\times\) 12 km, 54 \(\mathrm{\SIUnitSymbolMicro N}\), 104 \(\mathrm{\SIUnitSymbolMicro W}\), Figure 1a) is located near Saskatoon, Canada. This study site is characterized by rapid changes in phenology and a short growing season [5]. The dominant vegetation is coniferous forest. The second study region (30 km \(\times\) 30 km, Figure 1b) is located in Jiangxi province, China, centered on the Qianyanzhou (QYZ) flux tower (26.74159 \(\upN\), 115.05777 \(\upE\)), which belongs to the Chinese Ecosystem Research Network (CERN). The dominant vegetation type is coniferous forest, containing species of _Pinus massoniana_, _Pinus ellitotti_, _Cunningham lanceolata_ and _Schima superba_[23]. The climate in this region is warm and humid, with an annual temperature of 17.9 \(\upC\) and annual rainfall of 1,485 mm [24]. The third study region (15 km \(\times\) 15 km, Figure 1c) is located in Quebec, Canada, centered on the Eastern Old Black Spruce (EOBS) flux tower (49.69247 N, 74.34204 W), which belongs to the Fluxnet Canada Research Network/Canadian Carbon Program (CCP). The vegetation in the area is dominated by a coniferous boreal forest, containing species of _Picea mariana_ and _Pinus banksiana_[25]. The climate in this region is warm and humid, with an annual temperature of 0 \(\upC\) and annual rainfall of 1,461 mm [26]. The time intervals for these three areas gradually increase, and the performance of the approach was tested at monthly, annual and multi-yearly time intervals.
## 2 Results and Discussion
### Results
#### 2.1.3 High Levels of H3K79 me2 Favor Inclusion of Male-Specific Exon of \(\mathit{Imp}\)
(**A**) Schematic representation of _Imp_ and _Bmdsx_ genes showing the distribution of quantitative (q) PCR amplicons used in the analysis; (**B**) Mapping of H3K79 me2 across _Imp_ (left panel) and _Bmdsx_ (middle panel) and of H3K36 me3 across _Imp_ (right panel) in female (black) and male (white) larval tissues by chromatin immunoprecipitation (ChIP)-qPCR. Values represent the means \(\pm\) SE of six qPCR values from one representative of five independent experiments. * \(p<0.05\), **\(p<0.01\)**, Student's \(t\)-test; (**C**) ChIP assays with antibodies to H3K79 me2 and H3 and chromatin prepared from 60-pooled negative control embryos of each sex (left panel) or 60-pooled _DOTIL_ siRNA-injected embryos of each sex (right panel). The relative enrichment of H3K79 me2 on _EF-2_ exon2 or along _Imp_ was quantified by qPCR using primer sets indicated in A and expressed as a fraction of histone H3. Values represent the means \(\pm\) SE of two independent qPCR assays from one representative of two independent experiments. * \(p<0.05\), Student's \(t\)-test. The percentage of input was normalized to unmodified H3.
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 5 Case Study: An Inclined Single-Tower Cable-Stayed Bridge
### Bridge Overview  
The Sanya Landscape Bridge is a special-shaped inclined single-tower cable-stayed bridge that uses UHS in the support of the bridge tower for the first time. The design idea of this bridge is a mast with a sightseeing platform (Cloud Ring) as the bridge tower. The Cloud Ring not presents a trendy and fashionable city image of Sanya in China for citizens and tourists, but also has the meaning of eternity and being everlasting, which gives a strong romantic feel to the bridge. The landscape design rendering of the bridge is shown in Figure 15. The total length of the bridge is 233.4 m (length of steel deck), and the bridge span is arranged as 99.8 m + 51.0 m + 25.0 m + 27.4 m + 30.2 m.  
The main beam is a double-sided I-shaped steel beam, which presents a \"Y\" shaped asymmetric curve widening shape on the plane, and the beam width is 7.2-11.7 m, with a main beam height of 0.88 m. The bridge tower is a steel inclined single tower with a trapezoidal section, and has a shuttle shape. Its length is 85 m with an inclination of 60\({}^{\circ}\). The viewing platform is set away from the bottom of the tower, 50 m in a vertical direction.  
The cable-stayed bridge is equipped with the south rear ground anchor cable (S1\(\sim\)84), the north rear ground anchor cable (N1\(\sim\)N4), the front ground anchor cable (Q1\(\sim\)Q4), the south main beam cable (B1-S\(\sim\)B9-S), the north main beam cable (B1-N\(\sim\)B9-N), and the rhombic cable (Y1-\(\gamma\)6). The three-dimensional model and details of the components of the bridge are shown in Figure 16.  
Q420 steel is used for the main beam and bridge tower, and Q345 steel is used for the pier below the main beam. The rear ground anchor cables adopt strands with a tensile strength not less than 1860 MPa, and the other stayed cables adopt a parallel steel wire with 1670 MPa tensile strength. The construction process of the bridge is divided into 16 stages, and the specifics are shown in Table 2.  
Figure 15: The Sanya Landscape Bridge.
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 3 Results
### Soybean_2  
Figure 6 presents the results derived from the comprehensive evaluation of the entire test dataset using the key metrics MAAPE and NRMSE. In this context, method C (conventional) excels by displaying a greater prediction performance over method A (augmented), exceeding it by 53.95% in terms of MAAPE and an equally remarkable 52.10% in terms of NRMSE. Method C significantly outperforms method A in both evaluated metrics when using the entire dataset. Figures MAAPE_20 and NRMS_20 house the metric analysis specifically for the top 20% of the test dataset. In this context, method A shows greater performance than method C, with a reduction of 46.14% in MAAPE_20 vs. MAAPE and a decrease of 48.80% in NRMSE_20 vs. NrMSE. These results clearly emphasize the substantial improvement in the performance of method A in this specific scenario. For a more detailed analysis, refer to Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **Metric** & **Average** & **SE** \\ \hline Soybean\_1 & A & MAAPE & 0.1209 & 0.0019 \\ Soybean\_1 & A & MAAPE\_20 & 0.0382 & 0.0006 \\ Soybean\_1 & A & NRMSE & 1.8669 & 0.0024 \\ Soybean\_1 & A & NRMSE\_20 & 1.6865 & 0.0058 \\ Soybean\_1 & C & MAAPE & 0.0550 & 0.0009 \\ Soybean\_1 & C & MAAPE\_20 & 0.0659 & 0.0011 \\ Soybean\_1 & C & NRMSE & 0.8807 & 0.0009 \\ Soybean\_1 & C & NRMSE\_20 & 2.7979 & 0.0168 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction accuracy results for dataset Soybean_1 using conventional (C) and augmented (A) methods, with metrics MAAPE and NRMSE for the total testing and MAAPE_20 and NRMSE_20 for the best 20% testing.  
Figure 6: Prediction accuracy performance results for the Soybean_2 dataset using conventional (C) and augmented (A) methods in terms of mean arctangent absolute percentage error (MAAPE), mean arctangent absolute percentage error for the best 20% cultivars (MAAPE_20), normalizing the mean square error (NRMSE), and normalizing the mean square error for the best 20% cultivars (NRMSE_20).
## 2 Materials and Methods
### Mesh Division and Boundary Conditions  
As shown in Figure 3, using the seven-teeth gear as an example, the internal flow field of a circular arc spiral gear pump can be extracted as follows, and Simerics MP+ version 6.0.0 software can be used for mesh division and numerical simulation. Pumplinx is a professional computational fluid dynamics software designed to simulate fluid flow in pumps and other fluid machinery and is widely used in industry for its robustness and accuracy in simulating complex fluid dynamics problems. The whole mesh area is divided into three main parts: the inlet cavity, gear cavity, and outlet cavity. In order to ensure the mesh quality while reducing the simulation error, a quadrilateral-structured mesh is used for the gear domain.  
Set a standard atmospheric pressure for the suction inlet boundary condition of the circular arc spiral gear pump; the outlet pressure of the pressurized oil is 5 MPa, the speed is 7000-10,000 RPM, the gas mass fraction is 5 \(\times\) 10\({}^{-5}\), the density of the hydraulic oil of the conveying medium is 800 kg/m\({}^{3}\), and the dynamic viscosity is 0.007 Pa \(\cdot\) s. The computational model selects the turbulence model and cavitation model, in which the  
Figure 3: A mesh model of the domain of the flow field domain of circular arc spiral gear pump. The mesh is divided into three parts, namely the inlet, gear, and outlet cavity.  
Figure 2: Three-dimensional drawing of gears. (**a**) \(Z=6\); (**b**) \(Z=7\); (**c**) \(Z=8\); (**d**) \(Z=9\).  
turbulent flow model is set to a standard model. The default converge criterion is 0.1 for transient simulations; the rest of the settings are kept as default.  
Pumplinx provides three formats for numerical solving, namely an upwind solving format, a central solving format, and a second-order upwind solving format. In this paper, the simulations are set up to select the second-order upwind for the velocity-based solution format and the upwind for the pressure-based solution format. Both the 'turbulent kinetic energy' and the 'turbulent energy dissipation rate' are set as upwind.  
The gear pump rotates five cycles in total, in which the time step of rotation of each tooth is 30, and each step corresponds to a rotation of 1.714\({}^{\circ}\); then, 210 steps are rotated per circle. The mesh motion is handled using a dynamic mesh approach to accurately capture the transient cavitation effects during gear meshing. Compare the outlet flow at the corresponding time step in two adjacent cycles to ensure the periodic stability of the simulation results. When the relative difference is less than 5%, the simulation is considered to achieve the stability requirement, and the resulting stable state is used for analysis.
# Patellofemoral Joint Stress During Front and Back Squats at Two Depths
###### Abstract  
The purpose of this study was to identify differences between patellofemoral joint stress (PFJS), patellofemoral joint reaction force (PFJRF), quadriceps force, trunk and knee flexion angles, and horizontal position of applied load relative to the knee and heel between the front squat (FS) and back squat (BS) exercises at two depths (60 and 80% of leg length, where 60% represents a lower squat depth). Twenty-two healthy college-aged females (age: 22.23 \(\pm\) 1.86 years, mass: 67.65 \(\pm\) 9.60 kg, height: 171.34 \(\pm\) 6.38 cm) participated in this study. Mechanical variables were measured or estimated using a 15-camera 3D motion analysis (180 Hz) system and force platforms (1800 Hz). Five repetitions of each squatting technique at each depth were performed. Multivariate testing showed a difference in patellofemoral loading variables, trunk and knee kinematics, and bar position relative to the heel and knee (\(p\) = 0.00) between squat depths. There was no difference between techniques, no interaction between depth and techniques (\(p\) > 0.05). Follow-up univariate analyses showed differences in PFJS, PFJRF, quadriceps force, horizontal bar position relative to the heel and knee, and knee and trunk flexion between squat depths. The similar joint stress observed between FS and BS may be explained by compensatory trunk mechanics or the use of a light external load.  
knee joint loading: quadriceps muscle force; trunk flexion Article
###### Abstract  
The removal of high-concentration m-cresol from industrial wastewater remains a significant challenge due to its toxicity and persistence. In this study, a commercially available functionalized resin with a high BET surface area (1439 m\({}^{2}\) g\({}^{-1}\)) and hierarchical pore structure was employed for the adsorption of pure m-cresol at an initial concentration of 20 g L\({}^{-1}\), representative of coal-based industrial effluents. Comprehensive characterization confirmed the presence of oxygen-rich functional groups, amorphous polymeric structure, and uniform surface morphology conducive to adsorption. Batch experiments were conducted to evaluate the effects of resin dosage, contact time, temperature, and equilibrium concentration. Under optimized conditions (0.15 g resin, 60 \({}^{\circ}\)C), a maximum adsorption capacity of 556.3 mg g\({}^{-1}\) and removal efficiency of 71% were achieved. Kinetic analysis revealed that the pseudo-second-order model best described the adsorption process (R\({}^{2}\) > 0.99). Isotherm data fit the Langmuir model most closely (R\({}^{2}\) = 0.9953), yielding a monolayer capacity of 833.3 mg g\({}^{-1}\). Thermodynamic analysis showed that adsorption was spontaneous (\(\Delta\)G\({}^{\circ}\) < 0), endothermic (\(\Delta\)H\({}^{\circ}\) = 7.553 kJ mol\({}^{-1}\)), and accompanied by increased entropy (\(\Delta\)S\({}^{\circ}\) = 29.90 J mol\({}^{-1}\) K\({}^{-1}\)). The good agreement with the PSO model is indicative of chemisorption, as supported by other lines of evidence, including thermodynamic parameters (e.g., positive \(\Delta\)H\({}^{\circ}\) and \(\Delta\)S\({}^{\circ}\)), surface functional group characteristics, and molecular interactions. The adsorption mechanism was elucidated through comprehensive modeling of adsorption kinetics, isotherms, and thermodynamics, combined with detailed physicochemical characterization of the resin prior to adsorption, reinforcing the mechanistic understanding of m-cresol-resin interactions.  
p 2025 18 3628 Article
## 5 Conclusions  
Based on the findings in this study, the selection of a certain pin structure must be made based on the expected loads in the use case of the hybrid parts. If an isotropic behavior of the joint is required, cylindrical pin structures seem favorable; if a main direction of load is expected, a polygonal pin with specifically chosen orientation could be beneficial. Thus, the findings of this work enable the design of customized joints adapted to the application with the aid of adapted pin geometries and orientations, which increases the versatility of the joining process.  
Furthermore, a more detailed investigation of the pin extrusion of non-rotationally symmetrical geometries based on the different orientation of the elliptical pin structures has shown that the orientation in relation to the rolling direction of the blank has an influence on the required forming force, which can consequently affect the work hardening of the pin structure.  
In future studies, it is necessary to investigate multi-pin arrays in order to increase the maximum transmittable forces. Therefore, it is required to determine minimum pin spacing in dependency of the pin's dimensions, fiber orientations and restrictions from a pin manufacturing standpoint. In addition, the effect of the orientation of non-rotationally symmetric pin geometries on the strain hardening of the pin structures should be investigated in future work, since strain hardening of the pin structure, especially in the pin base, plays a crucial role in the subsequent shear strength of the joint. In addition, the production of undercutting pin geometries via cold extrusion requires further studies, as it is expected that undercuts can increase the joint strength under shear loads, especially with a fiber orientation of 90\({}^{\circ}\), where pin extraction is the dominant failure mode, which could be counteracted with an undercutting pin. Furthermore, future studies are planned to investigate different laminate structures, such as bi-axial laminates.  
Conceptualization, J.P.; methodology, J.P. and D.R.; formal analysis, J.P. and D.R.; investigation, J.P. and D.R.; resources, D.D. and M.M.; writing--original draft preparation, J.P. and D.R.; writing--review and editing, J.P., D.R., D.D. and M.M.; visualization, J.P. and D.R.; supervision, D.D. and M.M.; project administration, D.D. and M.M.; funding acquisition, D.D. and M.M. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-TRR 285 C01-Project-ID 418701707. We acknowledge financial support by Deutsche Forschungsgemeinschaft and Friedrich-Alexander-Universitat Erlangen-Nurnberg within the funding programme \"Open Access Publication Funding\".  
Not applicable.  
All relevant data is represented in this publication. Data sharing is not applicable to this article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Prototypical Graph Contrastive Learning for Recommendation
## 1 Introduction
By guiding all users and items to semantically construct potential neighborhood relationships and learning preference information, ProtoRec effectively addresses the challenge of distinguishing noise from meaningful features, thereby enhancing their latent representations [13; 14; 15]. Compared to existing prototypical contrastive learning approaches that utilize only a small amount of prototype information, ProtoRec refines prototype features for each user and item during training, ensuring the diversity of augmented information and preventing excessive similarity among embeddings within the same preference group. Furthermore, ProtoRec adopts a single-view propagation strategy, avoiding the over-smoothing problem caused by extensive and deep usage of graph convolution, while also reducing the overall complexity of the model. Our contributions can be concisely summarized as follows:  
* A novel preference prototype-based feature augmentation contrastive learning recommendation framework is proposed. * A preference learning module is proposed to enrich prototype information, along with a prototype filtering network to regulate feature augmentation, improving the model's stability. * Compared with existing prototype-based methods, ProtoRec achieves maximum gains of up to 16.8% and 20.0% in recall@k and NDCG@k on three real-world datasets. Additional experiments were performed to further analyze the rationale behind ProtoRec. The remainder of this paper is organized as follows: Section 2 reviews and summarizes the fundamental methods of GNNs. In Section 3, we present and analyze the proposed novel prototype-based graph contrastive learning recommendation method in detail. Section 4 conducts extensive experiments to evaluate the effectiveness of our approach in terms of recommendation performance, uniformity, ablation studies, and parameter analysis. Section 5 discusses related work from the perspectives of GNNs and CL. Finally, Section 6 concludes the paper and discusses future works.
# Fluid Net Models: From Behavioral Properties to Structural Objects
## 4 Discontinuities and Non-Monotonicities
### The Firing Rate Variation Case
: _Let \(\langle N,\boldsymbol{\lambda},\boldsymbol{m}_{0}\rangle\) be an MTS TCPN system with \(\boldsymbol{m}_{0}\) fixed and \(\boldsymbol{\lambda}\) arbitrary. If the equilibrium throughput is monotonic with respect to \(\boldsymbol{\lambda}\), then it is continuous with respect to \(\boldsymbol{\lambda}\)._ \begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{2}{c}{**Equilibrium Throughput w.r.t. \(\lambda_{1}\)**} \\ \hline \(\boldsymbol{\lambda_{1}^{\prime}}\)**Interval** & \begin{tabular}{c} **Equilibrium** \\ **Throughput \(\boldsymbol{f^{\prime}}_{e}\)** \\ \end{tabular} & \
\begin{tabular}{c} \(\boldsymbol{m_{e}}\)**Activates** \\ **Configuration:** \\ \end{tabular} &
\begin{tabular}{c} \(\boldsymbol{m_{e}}\)**Belongs to** \\ **Region:** \\ \end{tabular} \\ \hline \(0<\lambda_{1}^{\prime}<2\) & \(\alpha^{\prime}=\frac{10\lambda_{1}^{\prime}}{2+\lambda_{1}^{\prime}}\) & \(\mathcal{C}_{13}=(p_{1},t_{1}),(p_{3},t_{2})\) & \(\mathcal{R}_{13}\) \\ \hline \(\lambda_{1}^{\prime}=2\) & \(\alpha^{\prime}=5\) & \(\mathcal{C}_{11}=(p_{1},t_{1}),(p_{1},t_{2})\) and \(\mathcal{C}_{13}=(p_{1},t_{1}),(p_{3},t_{2})\) & \(\mathcal{R}_{11}\cap\mathcal{R}_{13}\) \\ \hline \(\lambda_{1}^{\prime}>2\) & \(\alpha^{\prime}=\frac{\lambda_{1}^{\prime}}{\lambda_{1}^{\prime}-1}\) & \(\mathcal{C}_{21}=(p_{2},t_{1}),(p_{1},t_{2})\) & \(\mathcal{R}_{21}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Equilibrium throughput w.r.t. \(\lambda_{1}\) and its characteristics of the MTS net depicted in Figure 8a. The upper part of the diagram of Figure 10 shows the relations among continuity, monotonicity, and deadlock-free monotonicity. The left side, implication (a), is Theorem 2. Focusing on the right side and taking into account Definition 6, a monotonic system never decreases its system throughput when the firing rates are increased, thus it cannot be zero. Hence, the system should be deadlock-free monotonic (arrow (b)), which is formalized in the next statement. **Proposition 4**. : _Let \(\langle N,\mathbf{\lambda},\mathbf{m}_{0}\rangle\) be an MTS TCPN system with \(\mathbf{m}_{0}\)fixed and \(\mathbf{\lambda}\) arbitrary. If the equilibrium throughput is monotonic with respect to \(\mathbf{\lambda}\), then it is deadlock-free monotonic with respect to \(\mathbf{\lambda}\)._  
Proof. : It derives directly from Definition 6. The reverse of implications (a) and (b) in Figure 10 are not true, an example of this can be found in Figure 2 of [15]. Moreover, another example is approached in Section 5.