esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Analysis
### Impact of Weathered Sand  
#### 3.2.1 Backbone Curve  
Figure 8 shows the improved backbone curve of weathered sand under the same number of freeze-thaw cycles. In the five freeze-thaw conditions, the backbone curves of soil samples with 10% sand admixture are the lowest, while the backbone curves gradually rise when the weathered sand content exceeds 10%. When the sand content is 30%, the  
Figure 7: Variation curve of dynamic elastic modulus of weathered sand modified expansive soil under different freeze–thaw cycles.  
backbone curve is above the initial position, analysis shows that the pore space in the soil body decreases gradually with the increase of sand doping, and the pore space of the soil body has been filled with weathered sand when the sand content is 30%, forming a complete supporting skeleton, which makes the particles embedded more tightly, and, therefore, can withstand greater loads. As can be from the figure, compared with the plain expansive soil, the mean amplitude changes of ultimate dynamic stress of soil samples with three kinds of weathered sand contents are \(-26.86\%\), \(-15.50\%\), and \(10.76\%\), respectively.
# Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine
## 2 Definition and Description of State of Health
Here, a novel SOH estimation approach based on a reasonable voltage region [\(V_{l}\), \(V_{h}\)] during the charging process may be helpful to perform the SOH estimation, where \(V_{l}\) and \(V_{h}\) denote the lower and upper limits of the voltage region, respectively. In order to select \(V_{l}\) and \(V_{h}\), three criterias should be considered. Firstly, the voltage curve starting from the low voltage \(V_{l}\) to the end voltage \(V_{h}\) at different cycle numbers can reflect the battery aging rate. As shown in Figure 3, the curves for two different voltage regions [3.8, 4.2] and [3.9, 4.0] at different cycle numbers are described. We can find that shape of voltage curves with different cycle numbers varies with the selected region. It means that the different charging voltage region curve can reflect the different variation trend of SOH. Secondly, the low voltage \(V_{l}\) should be as high as possible. If the charging voltage in the beginning is higher than the selected low voltage \(V_{l}\), it is difficult to guarantee the data's effectiveness and to achieve the SOH estimation. Thirdly, the range of the voltage region [\(V_{l}\), \(V_{h}\)] should be as narrow as possible to reduce data storage capacity and the computation labor.
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 5 Conclusions  
This paper proposes a novel 3D semantic segmentation method based on spatiotemporal feature aggregation specifically designed to address the challenges posed by blurred terrain boundaries and sparse point cloud data in outdoor mining environments. Both quantitative and qualitative experimental results on the TZMD_NUC outdoor mining dataset demonstrate that the proposed method significantly enhances recognition capabilities, particularly in achieving more accurate boundary feature segmentation. Additionally, experiments on the SemanticKITTI public dataset further validate the method's effectiveness for semantic segmentation in diverse outdoor environments, confirming its generalizability. Moreover, the spatiotemporal feature select state-space module enhances both computational efficiency and long-term feature modeling. Overall, the proposed method improves segmentation accuracy and reduces computational costs, making it a promising solution for real-time, resource-efficient 3D semantic segmentation in outdoor mining and other complex environments.  
[PERSON] and [PERSON] conceived the algorithms. [PERSON] provided the theoretical analysis. [PERSON], [PERSON] and [PERSON] were responsible for data collection and preprocessing. [PERSON] and [PERSON] conceived and supervised the project. All the authors contributed to the analysis of results and manuscript writing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 62272426), the Shanxi Province Science and Technology Major Special Project (Grant No. 202201150401021), and the Fundamental Research Program of Shanxi Province (Grant Nos. TZLH20230818005, 202303021212189, 202303021211153, 202203021212138, 202303021212206, 202303021212372, and 202403021212166).  
The data supporting the results of this work are available from the corresponding author upon reasonable request. Code for this article is available from the corresponding author upon reasonable request.  
Author [PERSON], [PERSON], [PERSON] were employed by the company Shanxi TZCO Intelligent Mining Equipment Technology Co., LTD. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Quantum Dot Solar Cells: Small Beginnings Have Large Impacts
## 2 Timescales of Excited State Processes
### Recombination  
Carriers can recombine radiatively (emitting a photon--Figure 3, pathway 3a) or nonradiatively (emitting phonon(s)). For nonradiative recombination there are two main mechanisms by which charges recombine: Auger recombination (Figure 3, pathway 2a) and Shockley-Read-Hall (SRH) recombination (Figure 3, pathway 3b). The PL quantum yield (QY) is determined by the competition between these processes and greatly depends on the surface chemistry because this effects SRH recombination via the passivation of surface traps [135].  
Nonradiative recombination via SHR, also known as trap-assisted recombination, is the main energy loss mechanism at low-excitation density for QD solids applied to optoelectronic devices namely solar cells and photodetectors as the PLQY is \(<\)50% in electronically coupled QD solids and Auger recombination is only efficient at high fluences [114; 136; 137; 138]. This type of recombination is typically only dominant when there is a high density of carriers (as in high power LEDs) and is responsible for the \"droop\" in LED efficiency at higher powers [139].  
[PERSON] et al. [140] employed a metathesis (cation-exchange) reaction to synthesize PbS QDs from CdS QDs. They found that cation-exchanged PbS have a lower trap density than \"conventionally\" (directly) synthesized PbS QDs. This is the first report to demonstrate bimolecular radiative recombination that is reasonably competitive with trap-assisted recombination in electronically coupled QD films. In conventionally synthesized QDs, electronic coupling leads to a complete quenching of the PL at room temperature as photoexcited charges diffuse to traps and recombine nonradiatively. This foreshadowed the surge of breakthroughs in solar cell PCE with halide ligand passivation strategies.  
Another special case of recombination is stimulated emission that forms the basis for lasing. Many QD materials have energy levels such that a population inversion can occur and lead to amplified spontaneous emission [141; 142; 143].
## 4 Data-Driven CIM Construction
### Example of Data-Driven CIM Construction
#### 4.3.2 Digital Twin City
For instance, in the event of a large-scale earthquake, CIM can help assess building damage, determine the evacuation routes, and ensure that the emergency services are dispatched efficiently to minimize disruption. CIM also enhances the ability of cities to respond swiftly to environmental and public safety challenges. By integrating real-time data into digital twin models, urban areas can anticipate problems and simulate scenarios to develop more effective responses. For example, in Helsinki, Finland, CIM plays a vital role in managing city-wide systems, including transportation, waste management, and energy conservation. The digital twin model created from CIM data allows decision makers to simulate the effects of various changes, such as a new public transportation system or the expansion of green spaces, on traffic patterns, energy usage, and public services. This helps the city plan for the future while ensuring the sustainable management of resources. In summary, CIM serves as the core data integration platform for digital twin cities, providing a precise and real-time representation of urban operations. Through its integration with digital twins, cities can more effectively simulate, monitor, and predict dynamic changes within urban environments [68; 69]. This data-driven, intelligent management approach improves the urban decision-making accuracy, enhances the operational efficiency, and elevates the quality of public services. The examples of Songdo, Dubai, Tokyo, and Helsinki illustrate the vast potential of CIM in creating resilient, efficient, and adaptable urban systems.
## 3 Results and Discussion
### DOM and HM Bonding Capability and Its Characteristics
#### 3.3.3 DOM-HM Binding Order
Notably,  
Figure 7: Synchronous and asynchronous 2D-FTIR-DOS plot of biochar /biochar + HA-DOM with Mn quenching titration (**A**) and biochar /biochar + HA-DOM with Cu quenching titration (**B**). BC-Mn: Biochar-derived DOM with Mn quenching, BC +H-Mn: Biochar + HA-derived DOM with Mn quenching, BC-Cu: Biochar-derived DOM with Cu quenching, BC +H-Cu: Biochar + HA-derived DOM with Cu quenching. polysaccharide-CO exhibited the strongest binding affinity for Mn and Cu, a finding that aligns with previous research by [PERSON] et al. [65]. Furthermore, additional studies have demonstrated that in the presence of polysaccharide-CO, carboxyl groups, and phenols also possess a high binding capacity for Cu. Specifically, phenols coordinate with Cu through oxygen atoms, forming a stable ring structure [66]. In BC + H-DOM, in addition to the above three functional groups, the C-H bond in aromatic compounds is also involved in the reaction, and its presence changes the response of other functional groups to Mn. Notably, aromatic compounds respond better to Cu than phenols, polysaccharides, and carboxylic acids. This may be due to the influence of the C-H bond in the aromatic ring on the electron density of other functional groups, or the spatial steric hindrance effect that alters the binding capacity of other functional groups to HM In BC + H-DOM, in addition to the above three functional groups of carboxyl, hydroxyl and polysaccharide-CO, the C-H bonds in the aromatic compounds were also involved in the binding to HM, simultaneously altering the response of other functional groups to HM. This may be attributed to the influence of C-H bonds in aromatic rings on the electron density of other functional groups, or to steric hindrance effects that change the binding ability of other functional groups with HM [64; 67].
## 4 Experiments  
This section focuses on evaluating the feasibility and robustness of the proposed rebar crossing point detection and path planning algorithm under controlled experimental conditions that closely replicate real operational scenarios. The experiments include camera calibration using a checkerboard pattern to obtain precise intrinsic and extrinsic parameters, coordinate transformation validation, crossing point localization, and trajectory planning tests. In addition, the system's recognition accuracy and positioning errors are thoroughly analyzed to demonstrate its effectiveness and reliability within the tested setup.  
Figure 11: Calibration results for the intrinsic and extrinsic camera matrices.
## 3 Proposed Method
### Multi-Head Attention Gating Unit
#### 3.2.1 Multi-Head Attention Mechanism  
The MHA mechanism can be utilized to capture long-term dependencies in the input sequence [34]. Its principle is to adopt multiple attention heads in parallel to capture the features of different subspaces in the input sequence. First, each attention head independently performs a linear transformation on the input. Then, the attention weights are calculated. Finally, the outputs of all heads are concatenated and linearly transformed to obtain the final output.  
According to Figure 5, the MHA is mainly composed of four linear layers, a scaled dot product attention layer, and a connection layer. The scaled dot product attention layer includes dot product operation, scaling calculation and softmax operation. The execution process of the MHA is to first pass the input vector through three different linear layers to generate the query matrix, key matrix, and value matrix, respectively. Afterward, the attention score matrix is obtained by calculating the dot product of the query matrix and the key matrix for each attention head. Then, the obtained attention score matrix is divided by a scaling factor to prevent the gradient from disappearing or exploding. Subsequently, a softmax operation is performed on the scaled attention score matrix to obtain the attention weight matrix. Then, the attention weight matrix is multiplied by the value matrix to obtain the weighted value matrix. Repeat the above steps for each attention head. Finally, the outputs of all attention heads are connected and passed through a linear layer to obtain the final output of the MHA mechanism.  
Figure 5: Structure diagram of the multi-head attention mechanism. Q. K, V represent the query, key, and value of the attention head, respectively.  
Assume that the input vector passes through a linear layer to obtain the query, key, and value of each attention head.  
\[Q_{i}=xW_{i}^{Q},K_{i}=xW_{i}^{K},V_{i}=xW_{i}^{V} \tag{13}\]  
where \(W\) expresses the weight matrix and the subscript \(i\) represents the \(i\)-th attention head.  
The output of each attention head can be expressed as  
\[H_{i}=\text{softmax}\left(\frac{Q_{i}K_{i}^{T}}{\sqrt{d_{k}}}\right)V_{i} \tag{14}\]  
where \(d_{k}\) represents the dimension of the key vector.  
Then, the outputs of all attention heads are concatenated and passed through another linear transformation to obtain the final output.  
\[M(Q,K,V)=\text{Concat}(h_{1},\dots,h_{n})W^{O} \tag{15}\]  
where \(W^{O}\) means the weight of the last linear transformation layer, and \(h\) denotes the total number of attention heads.  
The presented model can focus on information at different positions in the input sequence through the MHA, which improves the proposed model's ability to understand and express the input data. It is worth noting that traditional recurrent neural networks, which are commonly used for time series feature extraction, cannot be computed in parallel, while the MHA can be computed in parallel.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 7 3 GPP Release 20: Evolution to 6G
Self-interference cancellation techniques have enabled small-scale full-duplex radios, but large-scale full-duplex radios still face challenges. Realizing full duplex in large-scale networks requires breakthroughs in self-interference cancellation device integration, advanced interference measurement and management, and evaluation of full duplex gains across varying interference scenarios to ensure practical feasibility;
* **Reconfigurable intelligent surface (RIS)**: RISs are planar arrays of passive elements whose reflection phase can be dynamically controlled to steer or focus incident electromagnetic waves [118; 119]. Unlike network-controlled repeaters, RISs do not amplify signals but rather reshape the propagation environment by applying programmable phase shifts across their surface, offering a low-cost, energy-efficient means to enhance coverage. While existing and forthcoming 5G spectrum allocations can support initial 6G deployments by 2030, it is imperative to secure an additional centimetric spectrum, especially in the 7-15 GHz range. Complementary sub-terahertz (sub-THz) bands, which offer much largercontiguous bandwidths but with more limited coverage, are expected to gain relevance in later phases. The World Radiocommunication Conference (WRC)-27 IMT identification studies for the 7125-8400 MHz band, therefore, represent a crucial opportunity to achieve harmonized spectrum availability in time for 6G roll-out.
# An Optimal and Stable Algorithm for Clustering Numerical Data
## 2 Preliminaries
### k-AMH Clustering Framework
#### 2.2.2 k-AMH Algorithm for Numerical Clustering  
This section provides the procedure of the k-AMH numerical algorithm for numerical clustering. k-AMH numeric II [27] was chosen as the algorithm to be used for the new seeding mechanism. Please note that k-AMH numeric I could not be used in this case due to the maximization procedure. Similarly, k-AMH numeric II (simply called the k-AMH numeric hereafter) requires the testing and replacement of objects if and only if the objective function is minimized. Please note that this k-AMH numerical algorithm, the objective function is not based on a maximization process as imposed by the original k-AMH algorithm. Therefore, the k-AMH numeric algorithm follows exactly the original k-AMH steps above except for Step 3 which is the updating medoids, the k-AMH numeric replacement's object is based on the objective function as described in Equation (8). To partition \(X\) into \(C\), the k-AMH numeric framework generally requires the following steps:  
Step 1--First, initialize the number of clusters, \(k\).  
Step 2--Randomly select \(Z\) from \(X\) as the center of the clusters (better known as medoids).  
Step 3--Replace the medoids, \(Z\), by testing each one until \(X_{n}\). The updates are complete when the objective function is minimized as Equation (22).  
\[F(W,D)^{\tau}<F(W,D)^{t},r\
eq t;\forall t,1\leq t\leq(n-k) \tag{22}\]  
The \(w_{li}\) is updated as Equation (16) but using Euclidean distance as Equation (1).  
Step 4--Assign \(X\) to \(C\) when the final \(Z\) is obtained.  
The procedure above can be formalized in the form of pseudocode as Algorithm 3.  
Input: dataset \(X\), number of clusters \(k\), and weighting exponent \(\psi\)  
Output: Set of clusters \(C=\{C_{1},C_{2},\ldots,C_{k}\}\)
```
1: Select \(Z_{l}\) randomly from \(X\), where \(1\leq l\leq k\)
2:for each \(Z_{l}\)do
3:for each \(X_{i}\)do
4: Calculate \(F(W,D)\) as Equation (8)
5:if\(F(W,D)\) is minimized then
6: Replace \(Z_{l}\) with \(X_{i}\) as Equation (22)
7:endif
8:endfor
9:endfor
10: Assign \(X_{i}\) to \(C_{l}\) for all \(l\), \(1\leq l\leq k\) and \(i\), \(1\leq i\leq n\)
11:Output results
```
**Algorithm 3** k-AMH NUMERIC (\(X,k,\psi\)).
# Evaluation of Nitrogen and Phosphorus Removal
## 4 Discussion
### Nitrogen Removal Performance  
#### 4.2.1 Treatment Tanks with Moving-Bed Biofilm bioreactor (MBBR)  
This treatment system was designed to facilitate conditions for N removal via microbial transformations. The MBBR system in Tank 2 is the first step in this process, as it is designed for conditions for nitrification, where biofilm reactors and aeration provide oxygen and nitrifying microbial populations, for the conversion of NH\({}_{4}\)\({}^{+}\) to NO\({}_{3}\)\({}^{-}\). The treatment tanks were evaluated as an entire system since sampling occurred at the inflow to Tank 1 and outflow from Tank 3. A slight decrease in the average TN concentration and a significant increase in average NH\({}_{4}\)\({}^{+}\)-N concentrations from the System Inflow to Tank Outflow, suggest that ammonification, conversion of organic N to NH\({}_{4}\)\({}^{+}\), likely occurred in the tanks (Figure 4). The average NO\({}_{2}\)\({}^{-}\)+NO\({}_{3}\)\({}^{-}\)-N concentration entering the bioreactors from the tanks was 1.66 mg L\({}^{-1}\), however it is notable that for 11 of 15 monitored storm events, the NO\({}_{2}\)\({}^{-}\)+NO\({}_{3}\)\({}^{-}\)-N concentration at the Tank Outflow was below 0.05 mg L\({}^{-1}\) (the analyzing instrument's detection limit). In 2019, there were four consecutively monitored storms in mid-summer where Tank Outflow sample concentrations were between 3 and 7.7 mg L\({}^{-1}\); an increase from System Inflow, suggesting there might have been a time where nitrification successfully occurred in the treatment tanks. It is likely that nitrification performance was inhibited by an inadequate hydraulic retention time during storm events, which is further discussed below.  
It is important to note that monitoring of this system occurred in conjunction with storm events, when each component of the system overflows to the next component and autosampling is triggered by flow. In other words, the system is not monitored at times between storms. Generally, nutrient concentrations and mass loads were higher in Tank Outflow samples leaving the treatment tanks, compared to the System Inflow (Figures 4 and 6, Table 3), which may be attributed to conditions that occur in between storm events when the system was not monitored. This could potentially come from unmonitored silage leachate (i.e., not mixed with storm-driven runoff), which flows into Tank 1. Leachate concentration would be diluted within the treatment tanks with MBBR and also contribute to an increase in concentration measured in Tank Outflow during the following storm event.
# Hypoplastic Modeling of Soil-Structure Contact Surface Considering Initial Anisotropy and Roughness
## 3 Model Validation  
In this section, we utilize the hypoplastic constitutive model for contact surfaces, as it accounts for roughness and initial anisotropy, as detailed in Equation (36). The simulation outcomes are juxtaposed with a range of experimental results from the literature concerning soil-structure contact surfaces to ascertain the model's effectiveness. To align the model's stress-strain relationship curves with the experimental data, these curves are converted into relative displacements of the contact surface, employing an assumed contact surface thickness, as outlined in Equation (48). In the ensuing figures, the data points symbolize the experimental findings, while the continuous lines depict the model's predicted outcomes.