esa-sceva/satcom-chunk-collection huggingface dataset:
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 6 Conclusions  
In this paper, we propose the Ad-BBR algorithm to address the issues of data over-injection, unfairness between long and short RTT streams, and transmission instability caused by the periodic ProbeRTT phase observed in the BBR algorithm within multi-stream concurrent scenarios in high-speed wireless networks.  
The Ad-BBR algorithm determines the value of \(nRTT\) by tracking the size relationship between the current RTT and the previous minimum RTT in real time to determine the current link state, and based on the \(nRTT\) and the real-time link state, it distinguishes the transmission rate adjustment rules corresponding to different link states with finer granularity so as to reduce the phenomenon of over-injection of data and improve the fairness of the RTT in the multi-stream competition scenario. In addition, the new ProbeRTT phase-triggering mechanism determines whether to enter the phase based on the link state, which reduces the frequency of meaningless ProbeRTT phases and greatly improves the transmission stability.  
The experimental results indicate that compared with BBRv1, Ad-BBR demonstrates more stable and smoother data transmission in multi-stream competition scenarios. Specifically, its throughput increases by 17%, RTT fairness improves by approximately 30%, the retransmission rate decreases by 13%, and latency is reduced by about 20%. Overall, Ad-BBR outperforms several comparative algorithms across all key performance metrics.  
Future work may explore extracting state-space parameters from real network environments to train reinforcement learning models. By leveraging reinforcement learning, the agent can dynamically determine key parameters that affect BBR performance--such as the congestion window, sending rate, exploration interval, and ProbeRTT phase trigger conditions--thus enabling more adaptive and efficient congestion control. Furthermore, after completing simulation experiments, real network tests will be conducted to evaluate the reasonableness of the algorithm's complexity and to verify the consistency of the performance improvements.  
Conceptualization, M.W., X.Z. and M.G.; methodology, M.W., X.Z. and M.G.; software, X.Z. and M.G.; validation, X.Z. and M.G.; writing--original draft preparation, X.Z.; writing--review and editing, M.W., X.Z. and M.G.; visualization, M.W., X.Z. and M.G.; supervision, M.W., X.Z. and M.G.; project administration, M.W., X.Z., F.J. and M.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the Information and Communication Business Cost Project of JSCMC, Grant/Award Number SXJD-XXTXF-202303, and the Scientific Research Plan of JSLLKYJH, Grant/Award Number 23-JSLLKY011.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Causality Test Between Environmental Factors and Load Data  
### Smoothness Check for Time Series of Load and Meteorological Factors  
A prerequisite for the Granger causality test of various environmental factors affecting load changes is that the time series should be smooth, otherwise spurious regression problems may appear. Therefore, the smoothness of the time series of each indicator should be tested first. The Augmented Dickey-Fuller (ADF) test is commonly used to check the smoothness of each indicator series for unit root.  
The ADF test addresses \(p\)-order serial autocorrelation by applying a \(p\)-order autoregressive process to correct the time series [30]:  
\[y_{t}=\alpha+\varphi_{1}y_{t-1}+\varphi_{2}y_{t-2}+\ldots++\varphi_{p}y_{t-p}+ \varepsilon_{t} \tag{1}\]  
\[\Delta y_{t}=\alpha+\gamma y_{t-1}+\sum_{i=1}^{p-1}\beta_{i}\Delta y_{t-i}+ \varepsilon_{i} \tag{2}\]  
where \(\gamma=\sum_{i=1}^{p}\varphi_{i}-1\), \(\beta_{i}=-\sum_{j=i+1}^{p}\varphi_{j}\). Higher order serial correlation is controlled for by introducing the lagged difference term of the dependent variable into the right-hand side of the regression equation thereby. The hypothesis test is:  
\[\begin{cases}H_{0}:\gamma=0\\ H_{1}:\gamma<0\end{cases} \tag{3}\]  
If the test value of a time series data is lower than the specified criteria for the significant level of characteristics, it means that the series does not exist unit root so the null hypothesis cannot be accepted, which means that the series is smooth; on the contrary, the test value is higher than the specified criteria for the significant level of characteristics, it means that the series exists unit root so the null hypothesis is accepted, which means that the series is not smooth.
###### Abstract  
With the development of smart home technology, more and more electrical appliances can participate in demand response, providing support for active power balance of the power grid. However, the conventional centralized control method faces vast amounts of electrical appliances, resulting in problems such as communication congestion and dimension curse. This paper proposes a distributed control strategy for electrical appliances based on a multi-agent consensus algorithm. Considering the discrete response characteristics of the on/off loads, a priority ranking mechanism is established, and the customer cost function is established by a fitting method. Based on the incremental cost consensus (ICC) algorithm, the optimal power allocation of customers is realized through distributed control. Simulation and analysis of the examples verify the effectiveness of the proposed strategy.  
distributed control; multi-agent systems; on/off loads; priority ranking; demand response +
Footnote †: journal: Applied Sciences
## 2 Background
### Spectral Convergence  
Spectral convergence refers to an emerging class of co-operation, co-existence, and co-design techniques that allow modern radio systems to adapt to cluttered environments and exploit their neighbors for mutual benefit [11]. Previous studies [70; 71] demonstrated that co-design techniques offer better joint performance than traditional interference mitigation approaches to radar and communications coexistence. Spectral convergence promises significant performance enhancements for several RF systems, including radar systems for ground vehicles [48; 49] and UAVs [50; 51]; Li-Fi systems [52; 53]; and LiDAR systems [54; 55; 56]. These approaches offer high-performance solutions but must be carefully co-designed to mitigate interference to other systems on the same platform. Other related applications include ATM [57; 58] and asset tracking [59], which require cooperative positioning and communications services that greatly benefit from cooperative design strategies.
## 7 Our Approach--Hierarchical Weighted Attention Network
#### Comparison of HAN an HWAN
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Reuters** & **Ohsumed** & **20 Newsgroups** & **BBC** & **BBCSport** \\ \hline HAN & 81.79, \(\sigma=0.0015\) & 51.19, \(\sigma=0.0061\) & 87.17, \(\sigma=0.0060\) & **99.55**, \(\sigma=0.0014\) & **100.0**, \(\sigma=0.0\) \\ \hline TFIDF + add & **82.13**, \(\sigma=0.0016\) & 51.00, \(\sigma=0.0069\) & **87.43**, \(\sigma=0.0061\) & 99.01, \(\sigma=0.0046\) & 99.46, \(\sigma=0.0051\) \\ TFIDF + mul & 58.51, \(\sigma=0.0812\) & 25.15, \(\sigma=0.0404\) & 43.54, \(\sigma=0.3148\) & 79.60, \(\sigma=0.2893\) & 62.70, \(\sigma=0.3\) \\ TFIDF + concat & **82.06**, \(\sigma=0.0021\) & **51.66**, \(\sigma=0.0054\) & **88.14**, \(\sigma=0.0045\) & 98.79, \(\sigma=0.0046\) & 99.46, \(\sigma=0.0051\) \\ \hline TF + add & **82.03**, \(\sigma=0.0028\) & **51.20**, \(\sigma=0.0112\) & **87.47**, \(\sigma=0.0041\) & 99.46, \(\sigma=0.0044\) & 98.11, \(\sigma=0.0224\) \\ TF + mul & 61.94, \(\sigma=0.0138\) & 23.29, \(\sigma=0.0967\) & 69.57, \(\sigma=0.0439\) & 97.80, \(\sigma=0.0166\) & 96.07, \(\sigma=0.0349\) \\ TF + concat & 80.26, \(\sigma=0.0029\) & 46.57, \(\sigma=0.0108\) & 78.49, \(\sigma=0.0078\) & 99.01, \(\sigma=0.0068\) & 99.46, \(\sigma=0.0051\) \\ \hline BoW + add & **81.90**, \(\sigma=0.0041\) & 40.17, \(\sigma=0.0056\) & 77.62, \(\sigma=0.0176\) & 99.24, \(\sigma=0.0061\) & 96.88, \(\sigma=0.0220\) \\ BoW + mul & **82.59**, \(\sigma=0.0016\) & 50.39, \(\sigma=0.0270\) & 82.22, \(\sigma=0.0168\) & 98.97, \(\sigma=0.0030\) & 32.09, \(\sigma=0.2865\) \\ BoW + concat & 81.57, \(\sigma=0.0013\) & 39.27, \(\sigma=0.0147\) & 79.55, \(\sigma=0.0117\) & 98.79, \(\sigma=0.0056\) & 99.05, \(\sigma=0.0069\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Averaged overall classification accuracy (in percent) for HAN and variants of HWAN; cross-validation for Ohsumed, BBC and BBCSport, test-train split for Reuters and 20 Newsgroups. Restricting input length at 99-th percentiles of the number of sentences and words. Numbers in bold denotes results better than baseline HAN model. For the BBCSport dataset, the best performance was achieved by the HAN and HWAN models (100.00%). A result better than baseline was also achieved by Longformer (\(-\)0.68 percentage point.). For the BBC dataset, the best performance was achieved by the HWAN model (99.69%). A result better than baseline was also achieved by HAN (\(-\)0.45 percentage point.) and Longformer (\(-\)0.68 percentage point. ).
# Comparison of Abrasive Wear Resistance of Hardox Steel and Hadfield Cast Steel
## 3 Results  
### Microstructural Analysis  
Figure 4 shows the microstructures of the analyzed wear-resistant materials. Based on these images, it can be seen that samples taken from the mining equipment components show a coarse-grained austenitic microstructure, to which massive cast components were subjected due to slow cooling. In addition, precipitations of intermetallic phases were observed within the grains and at their boundaries. In an alloy containing elevated amounts of manganese and chromium (sample 1), unevenly distributed precipitations of ledeburitic carbides were also characteristic. It should be pointed out that the observed microstructural features are unfavourable from the point of view of impact strength and can cause its decrease even up to 25 J/cm\({}^{2}\)[42].  
Figure 3: View and layout of the T-07 tribotester [41].  
Figure 4: Microstructure of the analyzed wear-resistant materials. In the case of high-manganese cast steels **(a,b)**, a microstructure of coarse-grained austenite with precipitations of intermetallic phases within and at grain boundaries is observed. The discontinuity of the structure—interdendritic gaps—is also characteristic. In the case of Hardox **(c–f)** steels, a microstructure consisting of fine lath tempering martensite with areas of hardening (fresh) martensite is observed. PAG—prior austenite grain boundary, FM—fresh martensite. Light microscopy, etched with Mi1 Fe.  
The microstructure of all analyzed Hardox steels is composed of homogeneous, fine lath tempering martensite. The observed structure is also characterized by a hierarchical partition, based on the division of prior austenite grains successively into packets, blocks and laths. The block-forming laths of martensite are characterized by the same crystallographic orientation and thus represent the same variant of the formed martensite microstructure. Packets, on the other hand, form clusters of blocks with the same habitus plane, corresponding to the [111] plane of primary austenite. For steels with lower carbon content, areas composed of hardening martensite are also characteristic. This is determined by the steel's lower propensity for the spontaneous tempering processes.
# A Systematic Review of the Trends and Advances in IFC Schema Extensions for BIM Interoperability
## 6 Discussion
### Future Initiatives for the IFC  
The review of the extension cases in this study showed various solutions in which limitations of the IFC schema were improved to meet the interoperability needs of the AEC industry using extension approaches outlined in Section 4.2. Despite these efforts, recent studies have highlighted more innate and fundamental limitations of the schema that may not be resolved via extensions alone.  
For example, ref. [97] pinpointed key limitations in the current IFC standard schema, notably its reliance on the EXPRESS modeling language. This limited the IFC's adaptability to newer use cases and technologies, especially those requiring partial BIM data updates, significant data filtering, quick exchanges, and AI or machine learning applications. In response, the IFC 5 initiative emerged, aiming to modernize the IFC standard schema. Distinct from its predecessors, IFC 5 emphasizes modularity, normalization, and language independence (Figure 10). It aims for a broader compatibility and flexibility in representations. Another concern was that the existing IFC standards, rooted in EXPRESS and SPF, are not easily modeled with modern frameworks such as UML, XML, or JSON. IFC 5 aims to enhance compatibility with these frameworks, simplifying IFC integration with contemporary systems.  
Ref. [98] addressed interoperability issues that cannot be resolved using IFCs alone. The authors focused on issues particular to multi-domain collaborations, specifically the semantic disconnect existing between architecture-structure-MEP domains during BIM modeling processes. As a solution, they introduced cloud-based BIM (CBIM), which employs converting BIM models into RDF graphs and unifying them into a single, integrated ontology. Within CBIM, the BIM model functions as a cloud object, establishing inter-domain connections, and allowing seamless synchronization of changes between the models. The authors also developed inter-domain constraint classes to represent cross-disciplinary ties and a clash detection mechanism to ensure consistency. A prototype implementation of CBIM, illustrated using a hospital building case study, successfully detected and addressed clashes, ensuring consistency across multiple domains.  
These studies propose more fundamental changes to IFC schema development and provide insights into the future initiatives required going forward.
## 5 Experiment Conclusions
### Metrics  
#### 5.2.1 Robustness Metric  
Connectivity testing stands as one of the classical validation methods for assessing the importance of nodes within a network [10]. The importance sequence of nodes is established through a designated algorithm, after which these nodes are systematically removed one by one. The algorithm's accuracy is measured by the extent of network collapse after each node deletion. We use the robustness index \(R\) to describe the variation in network collapse upon node removal [45], denoted as:  
\[R=\frac{1}{N}\sum_{i=1}^{N}\sigma(i/N) \tag{13}\]  
\(N\) denotes the number of network nodes. Utilizing a normalization factor of \(1/N\) allows for the comparison of robustness across networks of different sizes. \(R\) values range from \(1/N\) to 0.5. Furthermore, \(\sigma(i/N)\) signifies the ratio of nodes within the largest remaining connected component post node elimination in comparison to the overall number of nodes in the graph. We measured the accuracy of DNC and the baseline methods by plotting the change in \(\sigma(i/N)\) after node removal. The region enclosed by the X and Y axes and the curve is used to determine the robustness index \(R\). If the area enclosed by the axes and the curve is minimized, and \(\sigma(i/N)\) decreases the fastest, this suggests that the network crumbles the fastest and the sequence of important nodes ranked by this method is the most accurate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & \(\mathbf{N}\) & \(\mathbf{E}\) & **\textless{}K\textgreater{}** & \(\mathbf{K_{max}}\) & \(\mathbf{C}\) & \(\mathbf{r}\) \\ \hline Dolphins & 62 & 159 & 5 & 12 & 0.2590 & \(-\)0.0436 \\ Polbooks & 105 & 441 & 8 & 25 & 0.4875 & \(-\)0.1279 \\ Adnoun & 112 & 425 & 7 & 49 & 0.1728 & \(-\)0.1293 \\ Jazz & 198 & 2742 & 27 & 100 & 0.5203 & 0.0202 \\ C\_elegans & 297 & 2148 & 15 & 134 & 0.3115 & \(-\)0.1520 \\ USAir97 & 332 & 2126 & 12 & 139 & 0.6252 & \(-\)0.2079 \\ Vote & 889 & 2914 & 7 & 102 & 0.1528 & \(-\)0.0288 \\ Email & 1133 & 5451 & 9 & 71 & 0.2200 & 0.0782 \\ Yeast & 2375 & 11,693 & 9 & 118 & 0.3100 & 0.4539 \\ Hamsterster & 2426 & 16,630 & 14 & 273 & 0.5375 & 0.0474 \\ Kohonen & 3772 & 12,718 & 5 & 740 & 0.2100 & \(-\)0.1204 \\ Dmela & 7393 & 25,569 & 6 & 190 & 0.0118 & \(-\)0.0465 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The typical metric values for these 12 networks. The structural parameters and topological properties of all of these networks, including the number of nodes \(N\) and edges \(E\), average degree \(<K>\), maximum degree \(K_{max}\), clustering coefficient \(C\), and assortativity coefficient \(r\).
# Global-Entropy Driven Exploration with Distributed Models under Sparsity Constraints
## 2 Signal Models for Static Process Estimation
### Distributed Signal Models
#### 2.2.1 Splitting-over-Examples
The latter is found by sequentially minimizing \(\mathcal{L}_{\text{SOE}}\) first over \(z_{k}\) and \(\mathbf{w}\), followed by an update of the dual variable \(\mathbf{u}_{k}\)[9]. By combining the linear and quadratic terms within the augmented Lagrangian in (6), a scaled form of the ADMM can be constructed. The corresponding algorithm is shown below. 1. At iteration \(i=0\), initialize \(\mathbf{z}_{k}^{[0]}=0\), \(\mathbf{w}^{[0]}=0\) and \(\mathbf{u}^{[0]}=0\). 2. Compute an estimate \(\mathbf{z}_{k}^{[i+1]}\) by solving: \[\mathbf{z}_{k}^{[i+1]}=\text{arg}\min_{z_{k}}\frac{\lambda}{2}\left\|\mathbf{\Psi}_{k} \mathbf{z}_{k}-\mathbf{y}_{k}\right\|^{2}+\frac{\rho}{2}\left\|\mathbf{z}_{k}-\mathbf{w}^{[i] }+\mathbf{u}_{k}^{[i]}\right\|_{2}^{2}.\] (7)
3. Use the averaged consensus algorithm of the network of agents for: \[\mathbf{\overline{z}}^{[i+1]}=\frac{1}{K}\sum_{k=1}^{K}\mathbf{z}_{k}^{[i+1]}.\] (8)
4. Compute an estimate \(\mathbf{w}^{[i+1]}\) of the weight vector by solving: \[\mathbf{w}^{[i+1]}=\text{arg}\min_{\mathbf{w}}\delta\left\|\mathbf{w}\right\|_{1}+\frac{K \rho}{2}\left\|\mathbf{w}-\mathbf{\overline{z}}^{[i+1]}-\mathbf{u}^{[i]}\right\|_{2}^{2}.\] (9)
5. Update the dual variable: \[\mathbf{u}_{k}^{[i+1]}=\mathbf{u}_{k}^{[i]}+\mathbf{\overline{z}}_{k}^{[i+1]}-\mathbf{w}^{[i+ 1]}.\] (10)
6. The algorithm is converged if \(\left\|\mathbf{u}^{[i+1]}-\mathbf{u}^{[i]}\right\|<t\). Otherwise, \(i\) is incremented, and the algorithm continues at Step 2. Let us point out that the communication step between the agents is only needed in (8) to compute the averaged vector \(\mathbf{\overline{z}}_{k}^{[i+1]}\). After the consensus update in (8), the other steps are calculated locally by each agent, yet an estimate of the weight vector \(\mathbf{w}^{[i]}\) will be the same for all agents by virtue of the ADMM algorithm.
## 4 Methodology
### 3D Analyst Viewsed GIS Calculation  
The ArcMap viewshed tool calculates visibility from an observer point (in this case the satellites) to a surface (the campus raster DSM). One restriction is that the objects must be in the same coordinate system; however, the novel methodology of calculating pseudo satellite positions proposed in this paper has enabled its application. Although the viewshed tool can incorporate earth curvature, the satellites visible to a surveyor will all be above the horizon and as no DSM for the areas outside the campus was used, all positions could be considered relative to the local plane. The first required field in the viewshed tool, SPOT (Figure 8), dictates the elevation of the observer point. In this case, SPOT was set to be the pseudo Z value of the satellite, Z'. Other optional fields were OFFSETA and OFFSETB. OFFSETA can be employed when modelling visibility of wind farms, radio masts or similar vertical structures as it allows a height value for of the structure to be defined. OFFSETA was set to an arbitrary low value of 1 m. OFFSETB allows a negative vertical height to be defined and was not required, therefore OFFSETB was left blank. Additional optional fields of AZIMUTH, VERT, RADIUS and YY were not included, as these are only required when the field of view or viewing range of the observer points are to be restricted. In this study, it was not, and by excluding these fields it defaults to the maximum value, _i.e._, a 0\({}^{\circ}\)-360\({}^{\circ}\) search in azimuth and vertical of unlimited range. If trying to increase processing times in future tests, this could be achieved by excluding all objects above the satellite. Specifying the time of day that satellite visibility over Maynooth Campus was done using an SQL statement. After this, the obstruction surface was applied and the viewshed calculated. Although five satellites are generally required for most satellite receivers in RTK mode, four is the mathematical minimum and this was applied as the lower cut-off for these tests. These two classes gave a definite yes/no answer for satellite visibility.  
Figure 6: Processing the 2D vector polygons (**a**) identifying the X,Y coordinates of each building polygon vertex (**b**) applying a 1 m offset to the polygons to aid in triangulated irregular network creation.  
Figure 8: Creating a viewshed observer point using the pseudo satellite position.  
Figure 7: Creating surfaces to model obstructions (**a**) TIN without 1 m offset exhibits poor definition of elevation changes (**b**) low quality raster DSM created using TIN without 1 m offset (**c**) 1 m offset results in an improved TIN (**d**) improved TIN results in an improved raster DSM.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 3 Theoretical Framework
### Multimodal Integration and Framework Design  
As depicted in Figure 1, the proposed framework integrates financial ontologies, stochastic models, and machine learning techniques into a cohesive methodology for market state prediction. This multimodal approach leverages the strengths of each component to enhance predictive accuracy and adaptability in financial markets.  
The predictive process is formalized as follows:  
\[\hat{S}_{t+1}=\Phi(\mathcal{O},\mathbf{h}_{t}[\mathbf{P}(t),\mathbf{z}_{d}, \mathbf{Z}(t)]), \tag{9}\]  
where \(\Phi\) denotes the multimodal function that synthesizes structured knowledge from the financial ontology (\(\mathcal{O}\)), deep learning embeddings from the LSTM hidden states (\(\mathbf{h}_{t}\)), and contextual sentiment data and exogenous variables (\(\mathbf{z}_{d}\) and \(\mathbf{Z}(t)\), respectively). The hidden state \(\mathbf{h}_{t}\) is further defined as a function of \(\mathbf{P}(t)\) (NMC outputs), \(\mathbf{z}_{d}\) (BERT outputs), and \(\mathbf{Z}(t)\) (exogenous inputs).  
The framework illustrated in Figure 1 demonstrates how these components interact. Financial ontologies (\(\mathcal{O}\)) provide a structured and hierarchical representation of financial indicators, enabling the classification and selection of relevant assets. The Non-Stationary Markov Chain (NMC) outputs (\(\mathbf{P}(t)\)) model the dynamic transitions between market states, while Bidirectional Encoder Representations from Transformers (BERT) generate embeddings (\(\mathbf{z}_{d}\)) from financial news, capturing the contextual sentiment that reflects market conditions. These components are complemented by exogenous variables (\(\mathbf{Z}(t)\)), such as volatility indices (GARCH, VIX), to form the inputs for the Long Short-Term Memory (LSTM) network.  
The integration of these modalities ensures that the framework can capture both the structured relationships in financial data and the temporal dependencies in market dynamics. By combining ontological knowledge, probabilistic state transitions, and machine learning insights, the framework provides a robust foundation for predicting future market states (\(\hat{S}_{t+1}\)).  
This multimodal design builds upon advancements in integrating diverse data modalities for predictive tasks, as highlighted by [PERSON] and [PERSON] (2020) [24]. Studies such as [PERSON] (2024) [25] and [PERSON] et al. (2024) [26] have demonstrated the advantages of such integrative approaches in financial applications, particularly in volatile and dynamic market conditions.