esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art Energy Efficiency and IoT in SBE
### Advances in Energy Efficiency for Buildings  
Efforts to enhance energy efficiency in the BE have globally intensified, driven by regulatory frameworks such as the Energy Performance of Buildings Directive (EPBD) [10]. This EU directive establishes benchmarks for reducing energy consumption, integrating renewable energy sources, and improving overall building performance. The promotion of Nearly Zero Energy Buildings (NZEBs) under the EPBD has catalysed advancements in passive design strategies, high-performance insulation materials, and energy-efficient Heating, Ventilation, and Air Conditioning (HVAC) systems.  
Passive design strategies focus on using building orientation, natural ventilation, and daylighting to reduce reliance on mechanical systems. Recent studies demonstrate that integrating passive measures, such as double facades and advanced glazing systems, can enhance thermal performance and energy efficiency, especially in Mediterranean climates [19]. Similarly, the use of passive energy conservation strategies has shown significant potential for improving energy performance in residential buildings [20].  
Innovations in HVAC systems, supported by smart control and IoT, are transforming energy efficiency in the BE. Recent studies highlight the effectiveness of using IoT technologies to optimise HVAC operations, achieving substantial energy savings, while maintaining thermal comfort [21]. Advanced differential pressure control mechanisms in HVAC systems further contribute to reducing energy consumption [22]. Retrofitting existing buildings with smart and energy-efficient technologies is a key strategy for reducing carbon footprints. An integrative review of retrofit strategies underscores the importance of combining passive and active strategies to achieve optimal energy performance [23]. Incorporating digital twin technologies into retrofitting projects has also shown promise for enhancing thermal comfort and operational efficiency [24]. Adaptive thermal comfort models, which consider behavioural and psychological adaptations to thermal environments, are key for energy efficiency. The adaptive comfort approach, emphasising flexibility in temperature setpoints, has been shown to significantly reduce energy use while maintaining occupant satisfaction [25]. This approach is particularly relevant in educational settings, as demonstrated in university buildings where thermal comfort is critical for user performance and well-being [26].  
In summary, advances in passive design, adaptive models, efficient HVAC systems, and smart technologies contribute to enhancing building energy efficiency. But the successful implementation of these innovations relies on supportive policy frameworks, standardisation, and a comprehensive understanding of occupant behaviour and needs.
# Stronger Cumulative than Lagged Effects of Drought on Vegetation in Central Asia
## 5 Conclusions  
In this research, we researched the space and time changes in vegetation GPP and SPEI from 2000 to 2018 and analyzed and compared the lagged and cumulative effects of drought on vegetation GPP in Central Asia. The results showed the following:  
1. Vegetation GPP showed a slightly increasing trend, with a spatially decreasing distribution from north to south, and the SPEI showed a slightly decreasing trend, with a spatially drying distribution from north to south.
2. In Central Asia, 72.44% of the lagged months of drought impact on vegetation were concentrated in 1-3 months. The maximum correlation coefficient values of vegetation GPP and the 1-month SPEI were concentrated in the range of 0.15-0.35. A high correlation was distributed in southern and northwestern Kazakhstan, and the fastest vegetation response to drought was found when the average annual SPEI was around \(-0.5\)--\(-0.4\).
3. In Central Asia, 75.86% of the regional drought on vegetation accumulation months was concentrated at 9-12 months, and the maximum coefficients of the correlation between vegetation GPP and the n-month SPEI were concentrated in the range of 0.20-0.50. The coefficients of the correlation between vegetation GPP and the SPEI were largest when the average annual SPEI was close to \(-0.5\)--\(-0.4\), and the areas of high correlation were mainly located in southern Kazakhstan and eastern Uzbekistan.
4. The cumulative effect of drought on vegetation was stronger than the lagged effect in 86.75% of the regions in Central Asia, with the cumulative effect being much larger than the lagged effect in scrubland and the cumulative effect being the dominant effect in southern Kazakhstan and eastern Uzbekistan.  
Therefore, consideration of the cumulative effect of drought is essential in evaluating the influence of drought on vegetation growth. This research contributes to our more profound comprehension of the mechanism by which vegetation responds to drought in Central Asia and provides a helpful reference for coping with global climate change.  
[PERSON] analyzed the data and wrote the manuscript; [PERSON] conceived the ideas and revised the manuscript; [PERSON] contributed to the discussion and manuscript refinement; [PERSON] made important contributions to the figures; [PERSON] provided some of the research data. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Nos. 32260287 and 41961059), the Doctoral Research Start-up Fund Project of Xinjiang University (No. 620320025), the Sino-German interdisciplinary joint program for innovative talent training funded by the China Scholarship Council (CSC, No. 201807015008), and the Technology Innovation Team (Tianshan Innovation Team), Innovative Team for Efficient Utilization of Water Resources in Arid Regions (No. 2022 TSYCTD0001).  
**Data Availability Statement:** The data used in this study are available in the Section 2.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Application of Nanofluid in PTC
### Numerical Studies  
Numerical studies have been the researchers' most favorable tool to study the effect of nanofluids in PTC, since more than 60% of studies were done using numerical approaches. The main reason for that can be found on relatively cheaper cost comparing with an experimental study which might need higher cost for both system setup and material purchase. Numerical investigations include approximately 70% of studies done on the effect of nanofluid on PTC thermal, hydraulic, and thermodynamic behavior. It should be noted that ANSYS Fluent is the main program used for the numerical investigation followed by MATLAB and EES (Engineering Equation Solver) commercial software. Like experimental investigations, general enhancement in thermal efficiency is reported by researchers. Also, the effect of nanoparticle on pressure drop augmentation as well as enhancement in the convection heat transfer coefficient is studied. Finally, the exergy efficiency and entropy generation rate were investigated by some researchers [58; 59; 60; 61]. On the later issue, it was concluded that application of nanofluid enhances the exergy efficiency [62] and lead to a decrease in entropy generation rate [59].
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 3 Engineered Water Flooding (EWF)
### Limitations of EWF  
The results reported in the literature show that EWF can potentially be used as an EOR technique for carbonate rocks. The corefloods performed by [PERSON] et al. [108] showed \(\sim\)28% incremental oil recovery using chemically tuned water. Experiments performed by [PERSON] et al. [109] also showed encouraging results using diluted versions of seawater. However, there have also been unsuccessful cases where EWF did not lead to any incremental oil recovery in either sandstone or carbonate reservoirs [97; 98; 110; 111; 112; 113]. The injection of low-salinity water (440 ppm) in the North Sea Snorre field single-well pilot test resulted in negligible extra oil, despite a 2% incremental recovery of OOIP in laboratory corefloods. The main reason was the initial reservoir wettability, which was more water-wet.  
As with conventional water flooding, the problem involving unfavorable mobility ratios also exists with EWF due to the viscosity difference between the displaced and displacing fluids [97]. The use of LSWF in four different fields in Russia resulted in very little to negligible incremental oil recovery. Field tests in the Pervomaiskoye field showed a three-fold reduction in the relative permeability of the water and 3.5% extra oil recovery using LSWF. However, no incremental recovery was achieved using LSW (1.0 mol/L ionic strength) in the Bastrykskoye sandstone field pilot test, despite a five-fold reduction in the relative permeability of the water in corefloods. Furthermore, 2.7% additional oil was recovered in the Romashkinskoye field pilot tests using diluted seawater, whereas no LSW effect was observed in the Arkhangelskoye field [97]. One of the reasons for such discouraging results is the viscosity contrast between oil and injected LSW, which leads to an unfavorable mobility ratio and poor sweep efficiency. This problem may be even worse in pilot or field-scale applications if high permeability channels or layers exist in the reservoirs [114]. Hence, EWF has the potential to enhance the microscopic displacement efficiency, but on the other hand it can result in a poor volumetric sweep efficiency if a mobility control treatment is not considered.
## 7 Challenges and Future Directions
### Lightweight Cryptographic Algorithms  
Data leakage can be greatly increased by the billions of connections that exist between equipment, people, and information-sharing network. IoT systems are so prevalent and pervasive that there will inevitably be issues with network privacy and data security. Data safeguard, and information security must be ensured in a variety of activities, such as transportation services, personal affairs, operations, and information protection. Encryption is one of the approaches to do so. Failure to use encryption could result in data being intercepted, changed, or even permanently deleted. As a result, encryption methods--more specifically, dynamic encryption need to be used to protect the data and guarantee its confidentiality. Different attacks, such as insider attacks can not be resisted using encryption, and it can cause massive issues to the IoT network. Symmetric key based cryptographic methods might not be suitable for low memory devices. Similar level of security can be provided by ECC with lower size of the key. To avoid storage, PUF can be incorporated as a cryptographic method. PUF can produce unique keys. However, PUF can be impacted by external factors, such as voltage, pressure, humidity, noise, and many other factors. Reliability is a major issue for PUF for being deployed. Moreover, developing a strong PUF by maintaining all the required characteristics is difficult. Furthermore, different research showed that PUFs are susceptible to modeling attacks. XOR PUF shows better resistance compared to arbiter PUF, and a few others. ML is also used as a security primitive. ML is mainly used for intrusion detection rather than as a cryptographic algorithm. The cryptographic measures will raise transmission overhead that can be considered as a burden for IoT applications such as IoMT. Latency reduction and resource utilization are major concerns for IoT applications as real-time data communication is required for monitoring and decision-making in different IoT applications. Moreover, processing huge amount of data requires battery power. If the power consumed cryptographic algorithms are applied that could end the battery life and impact on life-cycle of the devices.
# Attention-Guided Wireless Channel Modeling and Generating
## 4 Experimental Simulation Analysis
### Data Loading and Generating Time Comparison  
The SE-GAN model's network structure has been meticulously optimized to achieve higher efficiency in both the data loading and generation processes for wireless channel data. This optimization ensures that SE-GAN not only improves the accuracy of generated data but also significantly enhances computational efficiency, making it more suitable for large-scale and real-time applications.  
To rigorously evaluate the time efficiency of SE-GAN, an experiment was conducted where both SE-GAN and ChannelGAN were required to generate 4000 data samples under identical conditions. The goal of this evaluation was to compare their performance in terms of processing speed during both the data loading and data generation stages. The experimental results, presented in Table 3, reveal a clear advantage for SE-GAN in terms of speed. Specifically, SE-GAN completes the data loading stage in a significantly shorter time than ChannelGAN, demonstrating its superior ability to handle and preprocess large volumes  
\begin{table}
\begin{tabular}{l l l} \hline
**Sim and Multi** & **ChannelGAN** & **SE-GAN** \\ \hline Sim & 0.039961 & 0.054668 \\ Multi & 11.345601 & 4.589909 \\ Multi/Sim & 283.919020 & 83.959355 \\ \hline \end{tabular}
\end{table}
Table 2: Similarity and diversity of different channel modeling and generating schemes.  
of data efficiently. Furthermore, SE-GAN also exhibits a marked improvement in the data generation stage, producing synthetic channel data at a faster rate than ChannelGAN.  
The reason behind this substantial improvement lies in the integration of the SE block within SE-GAN, which enhances the network's focus on important features by adaptively recalibrating channel-wise feature responses. This targeted attention mechanism allows SE-GAN to allocate computational resources more effectively, leading to increased learning efficiency and reduced data processing time. By dynamically prioritizing critical features, SE-GAN not only accelerates convergence during training but also ensures that the most relevant information is retained during data generation, further improving overall performance.  
These experimental findings strongly indicate that SE-GAN has a significant advantage over ChannelGAN in terms of both accuracy and efficiency. While ChannelGAN may still generate diverse data, it suffers from longer data loading and generation times, making it less suitable for scenarios requiring rapid processing and real-time performance. In contrast, SE-GAN's optimized structure and feature recalibration mechanism allow it to achieve superior computational efficiency, making it the preferred choice for high-speed, high-accuracy wireless channel data generation tasks.
## 4 Evaluation and Results
### Performance Testing  
Performance testing was conducted to determine the overall quality of the designed system, considering that CPU usage was one of the selected metrics for valuing the performance of an application. We conducted performance testing using the Profiler module provided by Unity3D, which records multiple areas of application performance and exhibits that information for analysis. As shown in Figure 7, the CPU usage chart displays the time spent on the application's main thread. It shows a good response time for displaying UI for 0.03 ms, rendering graphics for 0.59 ms, and running scripts for 0.12 ms, which concludes that the overall response of the application is less than a second, implying that the system is fast.  
Figure 8 shows system memory usage and information about how Unity allocates memory. A variety of resources were used, including texture memory, which describes the texture used in the system. The total space used by the system is 476.7 MB.
# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
## 3 Method
### Problem Statement  
In this work, we consider the recommendation task in which a recommender agent interacts with an environment (i.e., users). During the interaction process, the environment sequentially selects the items recommended by the recommender so as to maximize the cumulative reward of the recommender agent. Usually, the reward of the environment is set to the score that related to users' feedbacks, such as the rating for the corresponding item. Consequently, we model the recommendation process as an Markov decision process (MDP), whose key components are \(<S\), \(A,P,R,\gamma>\). The details of each component are defined as follows:  
* State \(S\): A state \(s\in S\) is defined as the historical interactions between the user and the recommender agent.
* Action \(A\): An action \(a\in A\) is the suggested items provided by the recommender agent.
* Transition probability function \(P\): \(P(s^{\prime}|s,a)\) is the transition function that determines the new state \(s^{\prime}\) after the recommender agent suggests item \(a\) under observation \(s\), which models the dynamics of user preference.  
* Reward function \(R\): \(R(s,a)\) is a function that calculates the immediate reward received by the recommender agent after the user provides feedback when given the recommendation items \(a\) under state \(s\).
* Discount factor \(\gamma\): \(\gamma\in[0,1]\) defines the measurement of present value of long-term rewards.  
In the recommendation process, the recommender agent interacts with a user by providing recommended items based on the user's state and then receives an immediate reward that indicates the user's feedback. Our goal is to find a recommendation policy \(\pi:S\to A\) that can maximize the cumulative reward for the recommender agent.
# Development of a System for Cyclic Shear Tests on Full-Scale Walls
## 3 Test Setup Description
### Loading Subsystem  
This subsystem comprises the following elements: a servo-actuator (Figure 9, serving as an active loading device), a pressure control unit, and the module with an acquisition system and servo-actuator control software.  
By applying forces towards the servo-actuator, this subsystem is responsible for imposing displacements in a controlled manner on the main test beam, which mobilizes other connection elements until, finally, the wall is requested at the top.  
The servo-actuator had a stroke of 400 mm and a load cell with a capacity of 200 kN both in tension and in compression. The pressure control unit provides (i) the hydraulic system of the servo-actuator with the necessary pressure to induce the displacements law (load protocol) and the (ii) electric current to the module and acquisition system. The pressure control unit also provides electric protection to all systems against current surges or power failure, ensuring that no data are lost.  
Figure 8: (**a**) Fixing the counterweight support rod to the reaction slab; (**b**) fixing the gantry to the reaction slab.  
Figure 9: Servo-actuator with 200 kN of capacity and 400 mm stroke.  
The control software allowed for programming the displacement law, imposing the displacements at constant speeds, at the end of the actuator and sufficiently slowly, allowing for disregarding the effects of inertial forces, in the following stages:  
* For in-plane shear tests: 0.1 mm/s (up to 5 mm) and 0.5 mm/s (from 5 mm on);
* For out-of-plane shear tests: 0.1 mm/s (up to 5 mm), 0.5 mm/s (from 5 mm to 7.5 mm), and 1.0 mm/s (from 7.5 mm on).
## 3 5G and TSN Integrated Prototype for Resilience and High Reliability
### 5G Non-Public Network on the Industrial Shopfloor
#### 3.1.1 5G Mid-Band System Operating in 3.7-3.8 GHz  
The 5G non-public network operates in the 3.7-3.8 GHz (5G n78 band) locally licensed industrial spectrum with 100 MHz of system bandwidth. Having multiple radio units at various locations on the ceiling provides excellent coverage in the whole factory hall of area ca. 3000 m\({}^{2}\). The mid-band spectrum has very good propagation characteristics, including for non-line of sight connectivity. We have used Qualcomm x55 modem chipset-based terminal devices in our tests on this system.
## 3 Results and Discussion
### 3.2. Phytochemical Analysis  
The phytochemical analyses were assessed for _Beta vulgaris_ L. aqueous extract and biosynthesized metallic/bimetallic nanoparticles. In particular, the results as shown in Figure 5 and Table S2 verified that the plant extract is rich in phenolic constituents, with 65.449 \(\pm\) 0.756 mg gallic acid/1 gm dry extract. The tannin contents (27.338 \(\pm\) 0.24 mg tannin acid/1 gm dry extract) are higher than flavonoids (6.309 \(\pm\) 0.314 mg catechine/gm dry extract) according to the analyses of the plant extract. It is worth mentioning that decreased values of phytochemicals can be seen in the analyses of metallic and bimetallic nanoparticles, verifying the contribution of these reactive species in the bio-reduction process for the biosynthesis of these nanoparticles. The comparison of the results of the metallic/bimetallic nanoparticles indicated that Ag-mNPs compromised the second order in the level of the phytochemical components. In addition, the Ag-TiO\({}_{2}\) bmNPs revealed higher phenolic contents than the Ag-SeO\({}_{2}\) bmNPs, while the Ag-SeO\({}_{2}\) bmNPs were marginally rich with tannin and flavonoid contents compared with the Ag-TiO\({}_{2}\) bmNPs. The color of _Beta vulgaris_ L. is due to betanin, signifying merely 75 to 90% of the total color [52]. The investigated components of the different issues of _Beta vulgaris_ L., as identified from the literature reports, were characterized as various compounds related to phenolic class, for instance, betanidin, prebetanin, isobetanin, neobetanin, amaranthin, and lampranthin [53,54, 55].  
Figure 4: Zeta potential analyses of the biosynthesized metallic/bimetallic nanoparticles.  
Figure 5: A comparison of the phenolic, flavonoid, and tannin contents of the tested samples.