esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 1 Introduction
Early fault detection facilitated by ML can significantly reduce equipment downtime, typically by 30% to 50%, and prevent energy waste resulting from malfunctioning systems [1]. * Occupancy estimation and detection: Accurate data regarding building occupancy is crucial for implementing demand-responsive adjustments to environmental conditions. This allows for a reduction in energy consumption in unoccupied spaces while ensuring optimal comfort levels when occupants are present [9]. Studies suggest that occupancy-based control strategies can achieve energy savings of 20% to 40% compared to traditional fixed schedules [10]. * Retrofit modeling: For existing buildings, ML models are utilized to predict the potential energy savings associated with various upgrade options. This capability helps building owners prioritize investments in initiatives that promise greater energy efficiency and a higher return on investment [1,11]. Contrary to previous reviews that focus on specific aspects, this review aims to provide an integrated analysis across the spectrum of ML techniques, applications, and implementation challenges within the context of smart building energy management. The objective is to synthesize current knowledge, critically evaluate the effectiveness of various ML techniques for specific energy management tasks, identify key challenges that limit widespread adoption, propose practical solutions, and highlight emerging trends and future research directions. By analyzing ML approaches and their inherent strengths and limitations, and offering a critical performance analysis alongside practical implementation guidelines, this review aims to serve both researchers striving to advance the field and practitioners actively working to implement ML solutions in real-world building environments. The remainder of the paper is organized as follows. Section 2 establishes the foundational knowledge necessary for understanding ML applications in smart buildings. Section 3 describes our systematic review methodology. Section 4 provides a detailed analysis of ML techniques used in building energy management. Section 5 examines critical data management processes. Section 6 explores specific ML applications in energy prediction. Section 7 presents real-world implementations and comparative analyses. Section 8 identifies the current limitations and future opportunities. Section 9 discusses emerging trends. Section 10 provides practical implementation guidance. Section 11 outlines future research directions. Finally, Section 12 synthesizes key findings and implications.
## 5 Discussion  
This study highlights the potential and challenges of using text-to-image models to generate specialized output, such as chemical diagrams, code snippets, mathematical content, flowcharts, and paragraph-level text. These tasks pose significant challenges due to their need for precise structural alignment, semantic accuracy, and complex compositional reasoning. For chemical diagrams, text-to-image models such as Stable Diffusion and DALL-E struggle with the inherent visual complexity, including bond angles and atom labeling, which exceed the general visual capabilities of current systems. Generating syntactically correct code in images is similarly difficult, as such tasks require strict adherence to hierarchical structures like indentation and brackets. Although GPT-4 excels in code generation as a text-based LLM, it cannot reliably render accurate visual representations. Mathematical content, including equations and symbolic computation, presents another hurdle. Models often produce visually plausible but structurally incorrect outputs as they fail to maintain the proper alignment of symbols and operators. Flowcharts, which require hierarchical relationships, logical structuring, and precise spatial organization, remain largely unexplored but face similar challenges of alignment and coherence. Finally, generating long blocks of coherent text within images remains an unresolved issue, as models tend to produce garbled or fragmented text due to resolution limitations and difficulty maintaining semantic consistency across lines. These tasks require precise structural alignment, semantic accuracy, and complex compositional reasoning, which remain significant hurdles for current models. Chemical diagrams, with their intricate bond angles and atom labeling, and mathematical equations, requiring symbol alignment and formatting, push the boundaries of these models' visual capabilities. Similarly, generating syntactically correct code, logical flowcharts, and coherent multi-line text exposes the models' limitations in main  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Language** & **Ideogram** & **Dale3** & **Stable Diffusion** \\ \hline English & \(\text{NCL}\text{COK}\) & \(\text{NEW}\) & \(\text{VORK}\) \\ Chinese & \(\text{NEM}\text{YORK}\  
taining alignment and semantic consistency. Although advances in multimodal systems, fine-tuning strategies, and task-specific benchmarks show promise, substantial progress is still needed to achieve reliable and accurate outputs for these challenging domains. In the future, targeted innovations and interdisciplinary approaches will be critical to enhancing the performance of text-to-image models in these specialized applications.
## 3 Theoretical Analysis
### Electromagnetic Propagation  
The propagation of an EM wave in a lossy medium is ruled by the complex propagation constant of the medium \(\gamma\), which reveals how the signal is attenuated during its spreading in space. Its general expression is reported in Equation (1) and is the combination of a real component \(\alpha\), also called attenuation constant and accounting for the wave amplitude decay in 1/m, and an imaginary component \(\beta\), also called phase constant since it is related to the variations of the wave phase in rad/m [32; 33].  
\[\gamma=j\omega\sqrt{\mu\widetilde{\epsilon}}=\alpha+j\beta. \tag{1}\]  
The unknown terms in Equation (1) are the angular frequency \(\omega=2\pi f\), with \(f\) the frequency of the propagating signal, the permittivity \(\widetilde{\epsilon}\) and the magnetic permeability \(\mu=\mu_{0}\mu_{r}=4\pi\times 10^{-7}\,\mathrm{H/m}\times\mu_{r}=1.2566 \times 10^{-6}\,\mathrm{H/m}\times\mu_{r}\), with \(\mu_{0}\) the permeability of vacuum and \(\mu_{r}\) the permeability of the medium (\(\mu_{r}=\)1 in case of salt water since it is a non-magnetic material). Concerning the permittivity, it is a complex number depending on the conductivity, the dielectric constant of the medium and the frequency of the propagating signal, and influenced by some other parameters such as the temperature [34]. It can be defined as  
\[\widetilde{\epsilon}=\varepsilon_{0}\widetilde{\epsilon_{r}}=\varepsilon_{0}( \epsilon_{r}^{\prime}-je_{r}^{\prime\prime})=\varepsilon-j\frac{\sigma}{\omega} \tag{2}\]  
with \(\varepsilon_{0}=8.85\times 10^{-12}\,\mathrm{F/m}\) the permittivity of vacuum and \(\sigma\) the electrical conductivity of the medium in S/m, \(\widetilde{\epsilon_{r}}=\epsilon_{r}^{\prime}-je_{r}^{\prime\prime}\) is the complex relative permittivity of the medium. Consequently Equation (1) becomes  
\[\gamma=j\omega\sqrt{\mu\widetilde{\epsilon}}=j\omega\sqrt{\mu\epsilon(1-j \frac{\sigma}{\omega\varepsilon})}=\alpha+j\beta. \tag{3}\]  
The constants \(\alpha\) and \(\beta\) can be derived according to the following formulas [32; 33; 35]:  
\[\alpha=\omega\sqrt{\frac{\mu\varepsilon}{2}\left(\sqrt{1+\left(\frac{\sigma} {\omega\varepsilon}\right)^{2}}-1\right)} \tag{4}\]  
\[\beta=\omega\sqrt{\frac{\mu\varepsilon}{2}\left(\sqrt{1+\left(\frac{\sigma} {\omega\varepsilon}\right)^{2}}+1\right)}. \tag{5}\]  
When the EM wave propagates across two different media (\(m_{1}\) and \(m_{2}\)), the phenomena of reflection and refraction at their interface must be accounted for. In case of normal incidence, the expressions of reflection and transmission coefficients simplify becoming [36]  
\[\Gamma=\frac{\eta_{m_{2}}-\eta_{m_{1}}}{\eta_{m_{2}}+\eta_{m_{1}}} \tag{6}\]  
\[\tau=\frac{2\eta_{m_{2}}}{\eta_{m_{2}}+\eta_{m_{1}}} \tag{7}\]  
where \(\eta_{m_{2}}\) and \(\eta_{m_{1}}\) are the complex intrinsic impedances of the media in \(\Omega\)[36] computed as  
\[\eta=\sqrt{\frac{j\omega\mu}{(j\omega\varepsilon+\sigma)}}. \tag{8}\]
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 3 Results
### Spatial and Temporal Evolution Characteristics of Vegetation in YRB  
#### 3.2.1 Time Variation Characteristics of NDVI and SIF  
Based on the climate and vegetation growth characteristics of YRB, this paper defined March to May as spring, June to August as summer, and September to November as autumn. The temporal variation trend of NDVI and SIF in YRB from 2001 to 2020 is shown in Figure 6. NDVI increased significantly (\(p<0.001\)) in spring and autumn at the rate of 0.396 \(\times\) 10\({}^{-2}\) and 0.373 \(\times\) 10\({}^{-2}\), respectively. The average NDVI value of annual and summer is significantly higher than that of spring and autumn. NDVI in annual and summer showed a significant increasing trend at the rate of 0.496 \(\times\) 10\({}^{-2}\) and 0.469 \(\times\) 10\({}^{-2}\) respectively (\(p<0.001\)), and the fluctuation trend was similar. SIF in spring, summer, and autumn showed a significant increasing (\(p<0.001\)) trend at the rates of 0.074 \(\times\) 10\({}^{-2}\)  
Figure 4: Interannual variation of SPEI in YRB 2001 to 2020 (**a–d**). (**a**) Annual, (**b**) spring, (**c**) summer, and (**d**) autumn.  
\(0.122\times 10^{-2}\), and \(0.069\times 10^{-2}\) respectively. From 2010 to 2012, annual NDVI and annual SIF have a trend of decreasing to obvious increasing. From 2001 to 2020, annual NDVI and annual SIF increased at the rates of \(0.5\times 10^{-2}\) and \(0.34\times 10^{-2}\), respectively.
## 3 Results and Discussion  
### Minimum Inhibitory Concentration (MIC)  
The minimum inhibitory concentration (MIC) of GSE and GE against \(L\). _monocytogenes_ LMG23775 was assessed. For GSE and GE, the MIC was expressed as mg gallic acid equivalents (GAE)/L and \(\mu\)g allicin/mL, respectively, since gallic acid and allicin have been identified as the main antibacterial compounds of the respective extracts [10; 30]. In further experiments, GSE and GE at their MIC were referred to as GSE-MIC and GE-MIC, respectively.  
The MIC of GSE was equal to 500 mg GAE/L, corresponding to 25% of the concentration of the pure GSE. [PERSON] et al. [8] investigated the effect of GSE, prepared according to the same protocol as in the current study, against _Campylobacter jejuni_, reporting a MIC of 20 mg GAE/l. Since the MIC of GSE depends on different variables including extract composition, concentration of bioactive compounds, the solvent used, and also on the used definition of MIC [8], the comparison of resultswith other studies involving different GSE preparation protocols is not straightforward. Taking these limitations into account, [PERSON] et al. [30] reported that the MIC of their (Voidomato) GSE against _L. monocytogenes_ was approximately 210 mg GAE/L. Effective GSE concentrations in other studies ranged from 160 mg GAE/L to higher values for microorganisms such as _Aeromonas hydrophilia_, _Bacillus cereus_, _Enterobacter Aerogenes_, _Enterococcus faecalis_, _Escherichia coli_, _Pseudomonas aeruginosa_, _Salmonella Typhimurium_, and _Staphylococcus aureus_[8, 31, 32]. Consequently, the GSE used in the current study exhibits similar antimicrobial activity to those used in similar studies.  
The MIC of GE was equal to 7.5 \(\upmu\)g allicin/mL, corresponding to merely 0.05% of the concentration of pure GE. This MIC is lower than the 30.8 \(\upmu\)g allicin/mL reported by [PERSON] et al. [22] for their GE against _L. monocytogenes_. The MIC in the current study is, however, similar to the MIC against different Salmonella servovars ranging from 8.1 to 10.1 \(\upmu\)g allicin/mL, as reported by [PERSON] et al. [7] for their aqueous GE. MICs reported in other studies ranged from 4.1 to 27.5 \(\upmu\)g allicin/mL for oral bacteria [33] and from 0.15 to 1.9 \(\upmu\)g allicin/mL for various fungal pathogens [34]. Hence, the GE used in the current study also exhibits similar antimicrobial activity to those used in similar studies.
# Construction Management Risk System (CMRS) for Construction Management (CM) Firms
## 3 Analysis of Risk Factors and System Functions According to Field Operation Management of CM Firms
### System Functional Block Diagram  
As shown in Figure 5, the CRMS functional block diagram comprises seven modules (project overview, payment delay management, customer dissatisfaction management, contract revocation management, project delay management, legal dispute management, and consortium management) and 20 sub-modules.  
Figure 4: Diagram of system structure.  
The functions of each module are as follows:  
* The project overview module comprises two sub-modules and seven functions. This module provides detailed information about the status and organization of each project;
* The payment delay management module comprises three sub-modules and seven functions. This module provides graphical information about the status of contracts whose payment has been delayed and information regarding the payment progress of all projects. Additionally, it allows for the establishment of different management plans for projects with payment delays of 30, 60, and 90 days. When the payment delay exceeds 90 days, a field evacuation plan can be made;
* The customer dissatisfaction management module comprises three sub-modules and 10 functions. This module provides information regarding reception status of customer dissatisfactions, information on interviews with the contractor, service improvement status, etc. Additionally, it provides information regarding customer satisfaction surveying plans;
* The contract revocation management module comprises two sub-modules and three functions. This module provides information about contract revocations caused by financial conditions of the contractor and payment status of service charges generated by contract revocations;
* The project delay management module comprises three sub-modules and four functions. This module provides monitoring information of all projects, allowing the establishment of countermeasures regarding project delays. Furthermore, it gives information related to interview schedules with contractors and site evacuation plans;
* The legal dispute management module comprises four sub-modules and seven functions. This module provides information regarding response procedures and interview schedules of the contractor; this information can be used as preparation for legal disputes. Additionally, when consultations with contractors are done, this module can monitor the current status and results of such consultations. Furthermore, when disputes with contractors occur, this module provides information on lawyers specializing in specific dispute types and legal reviews of each lawyer;
* The consortium management module comprises three sub-modules and seven functions. This module provides information regarding the current status of partner companies, errors committed by the construction site staff, and the processing status of such errors. Additionally, it allows for record management of minutes and agreements made during consortium meetings.  
Figure 5: Structure of the construction management risk system CRMS.
## 8 Implementation and Discussion
### Discussion
#### 8.1.1 Expected Data Volumes in the BFS System  
Hypothesized data volumes stored in a BFS could vary depending on the size and frequency of transactions within the diverse needs of business entities. Based on references to existing systems, here are some anticipated data volumes:  
* Each business transaction, including metadata, could range from a few kilobytes to several megabytes, depending on the complexity and the amount of data involved (e.g., digital signatures, transaction details, or timestamps). For a mid-sized enterprise, this could translate to hundreds of transactions per day, each adding a few kilobytes to the BFS system's business transaction register.
* Accounting data, including journal entries and ledgers, are likely to be smaller but still significant, especially when aggregated over time.
* Financial statements generated instantaneously or at the end of accounting periods will be stored on the BFS system's filing history blockchain. Each statement, including balance sheets, income statements, and so on, could range from a few kilobytes to several megabytes depending on the complexity and detail. For a large corporation with frequent transactions, the data volume could reach several terabytes over time.
* Overall, the total data volume will depend on the frequency and complexity of transactions and the number of entities involved. Assuming an average of 500 transactions per day, with each transaction consuming around 5 KB, the annual data volume would be approximately 912 MB. Financial statements and other reports would add to this volume, making it feasible for businesses to manage data efficiently on the blockchain.
## 2 Preliminaries
Moreover, since \(-f\) is a convex function, we also have that \(f\) is subdifferentiable at \(\mathbf{u}\). Let \(F:\mathbb{R}^{p}\times\mathbb{R}^{r}\to\mathbb{R}\) be a convex-concave function. Following the above definition of a subgradient for a convex function, we extend this concept to define the subgradients of a convex-concave function as follows. And, for a fixed \(\mathbf{v}\in\mathbb{R}^{r}\) and \(\overline{\mathbf{u}}\in\mathbb{R}^{p}\), we call a vector \(\widetilde{\
abla}_{\mathbf{u}}F(\overline{\mathbf{u}},\mathbf{v})\in\mathbb{ R}^{p}\) a subgradient of the convex function \(F(\cdot,\mathbf{v})\) with respect to \(\mathbf{u}\) at the point \(\overline{\mathbf{u}}\) if  
And for a fixed \(\mathbf{u}\in\mathbb{R}^{p}\) and let \(\overline{\mathbf{v}}\in\mathbb{R}^{r}\), we call a vector \(\widetilde{\
abla}_{\mathbf{v}}F(\mathbf{u},\overline{\mathbf{v}})\in\mathbb{ R}^{r}\) a subgradient of the concave function \(F(\mathbf{u},\cdot)\) with respect to \(\mathbf{v}\) at the point \(\overline{\mathbf{v}}\) if  
\[\left\langle\widetilde{\
abla}_{\mathbf{v}}F(\mathbf{u},\overline{\mathbf{v}} ),\mathbf{v}^{\prime}-\overline{\mathbf{v}}\right\rangle+F(\mathbf{u}, \overline{\mathbf{v}})\geq F(\mathbf{u},\mathbf{v}^{\prime}),\qquad\text{for all }\mathbf{v}^{ \prime}\in\mathbb{R}^{r}.\]  
Let \(\mathcal{X}\subset\mathbb{R}^{p}\) be a nonempty subset and \(\mathbf{u}\in\mathbb{R}^{p}\). If there is a point \(\mathbf{v}\in\mathcal{X}\) such that  
\[\|\mathbf{u}-\mathbf{v}\|\leq\|\mathbf{u}-\mathbf{z}\|,\]  
for all \(\mathbf{z}\in\mathcal{X}\), then \(\mathbf{v}\) is said to be a _metric projection_ of \(\mathbf{u}\) onto \(\mathcal{X}\) and we usually symbolize it by writing \(P_{\mathcal{X}}(\mathbf{u})\). Note that if \(\mathcal{X}\subset\mathbb{R}^{p}\) is a nonempty closed convex set, then for each \(\mathbf{u}\in\mathbb{R}^{p}\), there exists a unique metric projection \(P_{\mathcal{X}}(\mathbf{u})\).
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 4 Overview of Energy Consumption in WSNs
### SNs Energy Dissipation Issues
The development of a routing protocol for WSNs that balances the energy consumption by the SNs in the network can be tricky because of the trade-offs in selecting these decision variables. However, the advantages of an energy-balanced routing protocol for WSNs cannot be overemphasised. This is more paramount in order to extend the network lifetime for a reasonable duration while also maintaining the full network functionality. The developers of energy-balanced routing protocols also consider other design objectives or advantages such as scalability, reliability, accuracy, and many more. Therefore, energy-balanced routing protocols for WSNs are sometimes developed to give a balance in some of these multiple desirable objectives (multi-objective) [54]. Yet, our attention is focused on the routing protocols whose main objective is to proffer a solution to energy consumption issues relating to WSNs. We analyse these energy-efficient and energy-balanced routing protocols based on the simple concept of how the SNs in the network dissipate energy during the data transmission phase. The technique(s) defined by the routing algorithm to conserve the energy dissipated by SNs when they convey their sensed information to the BS forms the basis of our classifications. Accordingly, we categorised the studied energy-efficient and energy-balanced routing protocol based on their mode of communication towards the BS. With this in mind, we also grouped these studied routing protocols based on the solution types or algorithms, and the input decision variables used to select the route to the BS from the source node.
## 3 Damage Characteristics
### Microscale Damage
#### 3.2.1 Microscale Physical Damage  
SEM technology stands as one of the most widely used and practical approaches for investigating the microscale characteristics of geomaterials. The fundamental principle of SEM is acquiring surface morphology images at the microscale by employing electronic signal imaging techniques. SEM experiments were performed on both untreated samples (\(N=0\)) and samples subjected to wet-dry cycling (\(N=5\)) for the acquisition of SEM images. Figure 4 presents the SEM images, where Figure 4a corresponds to the image at \(N=0\) and Figure 4b represents the image at \(N=5\). The qualitative analysis of representative SEM images reveals that prior to wet-dry cycling, the microstructure of the samples predominantly existed as stacked aggregates, accompanied by a minor quantity of slightly elevated mineral particles adhering to the surface. The particles are tightly bound, primarily through face-to-face and edge-to-face contacts, with a limited number of pores, mostly small- to medium-sized, and fewer large pores. The pore connectivity is poor, exhibiting minimal development of microscale crack. The edges and corners of the particles exhibit relative sharpness, with low roundness. The structure within the field of view is relatively compact and sturdy.  
However, following five cycles of wet-dry cycling, the microstructure of the soil changed significantly. These changes include, among others, the following: the original dense structure of the soil is disrupted, and the particles undergo severe damage, including fragmentation and detachment. The morphology of the aggregates undergoes alteration. Stacked aggregate breakage is accompanied by an increase in edge-to-face contacts and a decrease in face-to-face contacts between particles. The pore size within the samples increases, with enhanced pore connectivity and the formation of microscale crack. The edges and corners of the particles transition to a relatively rounded state, improving roundness. The structure within the visual field transitions to a looser configuration.