esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Stakeholder's Influence in Implementing CE Principles
#### 3.4.2 Designer  
The designer was ranked as the second most influential stakeholder in implementing CE principles, with a total of 27 points. This high ranking highlights the critical role designers play in embedding CE strategies into construction projects. Respondents view designers as key influencers because they are responsible for making decisions early in the project lifecycle, decisions that have a lasting impact on the building's functional and environmental performance throughout its lifecycle. A1, who ranked the designer as the most influential stakeholder, commented that \"_Designers can have a full life cycle perspective, unlike owners who may not have the experience to see the complete picture. If the designer can effectively communicate this perspective to the owner, it can significantly influence the owner's decisions regarding investment and the long-term benefits of design choices. Design choices have implications for all subsequent lifecycle stages. If the designer makes the right decisions from the beginning, it will positively impact the entire lifecycle\". Additionally, C3 commented that \"_It could be noticed and understood that designer plays a vital role in defining and documenting how products should be created, used and finally disposed. Implementation of CE principles influenced directly by designer decisions as it could be influencing selected materials, adoption of recyclable materials which can improve the Tribal Bottom Line achieving social needs, environmentally friendly materials and reducing cost through the use of modular design which supports reuse concept as well as extend lifecycle\"._ D1 also emphasized that designers who are well-knowledge in BIM can play a pivotal role in convincing owners of the benefits of LCC and LCA. By leveraging BIM, designers can effectively demonstrate the long-term financial and environmental advantages of adopting CE strategies, making it easier for owners to understand and commit to CE practices.
# Parametric Analysis of Control Techniques for 15 MW Semi-Submersible Floating Wind Turbine
## 3 Control Algorithms
### Individual Pitch Control  
The individual pitch control algorithm was designed to reduce the blade loads of the target FOWT in regions where the wind speed is higher than the rated value. The block diagram of the individual pitch control algorithm is shown in Figure 8. To reduce the blade loads, the individual blade pitch angles \(\theta_{B1 ipC\text{-}CMD}\), \(\theta_{B2 ipC\text{-}CMD}\), \(\theta_{B3 ipC\text{-}CMD}\) in the rotating frame are calculated through the feedback control that minimizes the tilt and yaw moments \(M_{H\_tilt}\), \(M_{H\_yaw}\) in the fixed frame to zero [26]. However, it is challenging to control the loads in the fixed frame using control inputs in the rotating frame, so a multi-blade coordination (MBC) technique known as the Coleman transformation is used [27]. The feedback control is performed on the loads \(M_{H\_tilt}\), \(M_{H\_yaw}\) in the fixed frame obtained from the Coleman transformation of the rotating frame loads \(M_{B1\_oop}\), \(M_{B2\_oop}\), \(M_{B3\_oop}\), and the IPC commands \(\theta_{B1 ipC\text{-}CMD}\), \(\theta_{B2 ipC\text{-}CMD}\), \(\theta_{B3 ipC\text{-}CMD}\) in the rotating frame are calculated through the inverse Coleman transformation. The control gain applied during the feedback control is adjusted as the control parameter.
# Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
## 2 Related Work
### AI in Accessibility and Multi-Modal Interfaces  
Emerging research on AI-driven accessibility suggests that LLMs can play a key role in translating visual or numeric data into descriptive text. For instance, recent studies have shown that LLMs can automatically annotate images or textualize data patterns with remarkable depth [23]. In the context of educational technology, [PERSON] et al. [24] demonstrated that AI systems can produce simplified, domain-appropriate explanations of complex learning materials, helping to make content more accessible to learners. Such capabilities are highly relevant to IoT dashboards: the complex sensor data might be analogous to complex educational content, where an LLM can generate explanations or summaries to aid understanding.  
Another relevant thread of work is on multi-modal interfaces, where information is presented through multiple synchronized channels such as visual, auditory, and textual modalities. Multi-modal design has been found beneficial not only for users with disabilities but also for users operating in constrained environments, such as technicians listening to a dashboard readout while repairing a machine or users who cannot look at a screen [25]. Research in human-computer interaction has explored voice-interactive charts and haptic feedback as complements to visual data, expanding the ways users can experience and interact with information [26]. In the web and data visualization contexts, providing both graphical and textual forms of data, as well as enabling natural language querying of visualizations, has been shown to cater to different user preferences and needs, enhancing accessibility and usability [27].  
Despite these advances, the synergy between real-time IoT data streams and AI-driven textual representation remains relatively unexplored. Most AI captioning and description systems are primarily designed to work with static images or pre-defined datasets, not with continuously updating sensor data that may change every few seconds [28]. This dynamic aspect introduces significant challenges for AI systems: descriptions must be updated or regenerated as new data arrives while maintaining coherence over time. Furthermore, traditional AI frameworks often lack native support for continuous data streams, as they are typically built for static, batch-processed datasets rather than live environments. There is a clear need for frameworks that can integrate live sensor data processing with intelligent, on-the-fly description generation.
# Cyber-Security Challenges in Aviation Industry: A Review of Current and Future Trends
## 2 A Systematic Literature Review
### Review Methodology
#### 2.1.2 Classification and Research Criteria  
A survey of peer-reviewed papers showed that a limited number of papers have been reported with regard to cyber-attack incidents in the civil aviation sector. As an example, only 1 publication was found in the Scopus database when searched using a combination of the following keywords 'cyber AND incident AND aviation AND industry'; and a total of 29 publications were found when searched using 'cyber AND attack AND aviation AND industry', of which 27 were journal articles and 2 were conference proceedings articles published in 2021. The trend in the number of relevant published papers shows a steady increase over the recent past; five journal and two conference proceedings articles were published in 2020; three journal and three conference proceedings articles in 2019; one journal/two conference proceedings articles in 2018; only three conference proceedings articles in 2017; one journal and one conference proceedings article were published in each of 2016, 2015 and 2013; and, finally, there was one publication as part of conference proceeding in 2012 and none thereafter. (Table 1). Worth noting is that no article focused on articulating cyber-attack incidents, rather propose different cyber-security approaches in securing the aviation infrastructure of both internal and external systems in the sector. Here, an extensive search was employed to surface relevant information from online repositories, web-based announcements, online articles and reports on websites of both primary and third-party organisations operating in aviation sectors. The review was supplemented by web-based aviation cyber-security reports, newspapers and news magazine, status reports, regulations and related information from regulatory agencies. The relevant incidents were tabulated based on the class of attack and according to the cyber-security triad of Confidentiality, Integrity and Availability. The source, year, location and type of attack, a more detailed description of the attack with the people affected and the possible cost implications for each incident were recorded.  
Furthermore, the review considered only cyber-attacks over the last 20 years, namely, from 2001 to 2021, as the only other documented incident within the industry between 1997-2014 was theft of an MSc thesis.  
The selected search term--the concatenation of keywords 'cyber AND incident AND aviation AND industry'--will exclude relevant literature that did not use/cite any of the keywords. Moreover, as the search is English-based, it excludes any potential non-English-language papers. The authors also acknowledge the possibility of excluding literature in databases with which their respective institutions have no established subscription.
## 3 Results
### Proactive Balancing
#### 3.1.1 Path Time

Table 3: The mean, standard deviation, and results of ANOVA of the proactive balance variables (path time, path length, direction error) and APA variables (APA time, APA distance, APA correction) in different target directions and target distances. The main effect is not shown if the interaction is significant (\(p\) < 0.05).
# Power Control Method for Energy Efficient Buffer-Aided Relay Systems
## 1 Introduction
Since analytical optimization methods, e.g., the resource allocation method in the work by the authors of [24], require the channel state information among the nodes at the transmitter, the practical implementation is challenging. Even any conventional one-dimensional numerical search algorithm or a learning-based method, e.g., a reinforcement learning, is not applicable as it requires the achievable rate at DN at each iteration. This causes a significant network overhead as the achievable rate information or channel state information is needed to be fed back from DN to RN. Therefore, we devise a simple power control method based on a model of the optimal transmit power of RN. To model the optimal transmit power of RN, we extract two essential parameters that determine the transmit power of RN: (i) constant power consumption \(P_{c}\) at RN and (ii) distance \(d_{rd}\) between RN and DN. The optimal transmit power of RN are then modeled with respect to \(P_{c}\) and \(d_{rd}\) by using a two-dimensional surface fitting method. For the RN power control, thus, only \(P_{c}\) and \(d_{rd}\) information is required at RN. Here, \(P_{c}\) is local information, which can be readily measured at RN. The distance \(d_{rd}\) can also be measured at RN based on the received signal strength from DN. Therefore, the proposed RN power control method is easily implementable at the RN. From the rigorous numerical results, it is verified that the proposed power control method can significantly improve EE compared to the fixed power control method regardless of \(P_{c}\) and \(d_{rd}\). Figure 1: Buffer-aided relay system model, where \(P_{s}\) and \(P_{r}\) are the transmit power of the source node (SN) and relay node (RN), respectively; \(d_{sr}\) and \(d_{rd}\) are the distance between SN and RN and between RN and DN, respectively; and \(b\) is the size of the buffer.
## 2 Material and Methods
### Characterizations  
To confirm the composition of PANI, the Fourier-transform infrared (FTIR) spectrum of PANI was recorded using a Nicolet iS50 FTIR apparatus equipped with an attenuated total reflectance accessory (Thermo Scientific, Waltham, MA, USA). Diffuse reflectance spectra (DRS) of pristine TiO\({}_{2}\) and the PANI/TiO\({}_{2}\) composite were measured using a diffuse reflectance UV-visible spectrophotometer (S-4100, Scinco Co., Ltd., Seoul, Korea) at a wavelength range of 200-800 nm and BaSO\({}_{4}\) powder as reference material. The morphology of the prepared PANI/TiO\({}_{2}\) sample was observed under a field-emission transmission electron microscope (FE-TEM; Tecnai G2 F30 S-Twin, FEI, Hillsboro, OR, USA). X-ray diffraction (XRD) pattern of 4% PANI/TiO2 sample was obtained using high power X-ray diffractometer (D/max-2500V/PC, Rigaku, Tokyo, Japan).
## 6 Performance Analysis
### Features and Comparison
In addition, it reduces the deployment and expansion overhead in WSN implementations. Some features of the proposed LDAP and other available technologies are summarized in Table 6. Unlike vehicle-mounted mobile SNs, the LDAP uses static SNs to reduce energy consumption and the complexity of WSN deployment. It also helps to implement mobile DSs over the wide WSN, which is not used in any type of existing solutions. The use of mobile DSs requires support for network topology changes, which is achieved easily by avoiding multi-hop data transmission as done in most of the conventional WSNs. Unlike the satellite link and other wireless technologies such as Zigbee, the LDAP is optimized for LoRa (higher TOA than non-CSS technologies), which makes it consumes less energy and capable of wider geographical coverage.
## 3 Methods
### EE Trend Analysis  
R/S analysis, also known as rescaled range analysis, is a statistical test applied to a time series [50]. The R/S analysis utilizes the least squares method, and the slope of the regression line equation represents the Hurst exponent (\(H\)). The Hurst exponent is linearly fitted using the least squares method, and relevant conclusions are drawn based on the results. The Hurst index can be used to analyze a set of time-series data, which change randomly with time. In this study, we utilize the Hurst index to monitor the variation of RSEI [51].  
The basic principle of R/S analysis: For a time series \(\{RSEI(\tau)\},t=1,2,3,\ldots,\tau\), where \(\tau\) is the total number of subseries in the time series \(RSEI_{t}\).  
Define the mean series:  
\[\overline{RSEI}(\tau)=\frac{1}{\tau}\sum\limits_{t=1}^{\tau}RSEI(\tau),(t=1,2,3,...,\tau) \tag{19}\]  
Cumulative deviation:  
\[X(t,\tau)=\sum\limits_{t=1}^{\tau}\big{[}RSEI(t)-\overline{RSEI}(\tau)\big{]},(1\leq t\leq\tau) \tag{20}\]  
Extreme deviation:  
\[R(\tau)=\max\limits_{1\leq t\leq\tau}(t,\tau)-\min\limits_{1\leq t\leq\tau}(t,\tau),(t=1,2,3,...,\tau) \tag{21}\]Standard deviation:  
\[S(\tau)=\left[\frac{1}{\tau}\sum_{t=1}^{\tau}\left(RSEI(t)-\overline{RSEI}(\tau) \right)^{2}\right]^{\frac{1}{2}},(t=1,2,3,...,\tau) \tag{22}\]  
The Hurst index \(H\) is defined based on the asymptotic behavior of the rescaled extreme difference range as a function of the time span of the time series.  
\[\log\left(R_{t}/S_{t}\right)_{\tau}=\log(C)+H\times\log(n), \tag{23}\]  
where \(R_{t}/S_{t}\) denotes the rescaled extreme deviation range, \(C\) is a constant, \(n\) is the series length, and \(H\) is the Hurst index with a value domain of \([0,1]\). In this study, we selected RSEI data from 2002-2022 for Hurst index analysis. When \(H=0.5\), the time series are completely independent; when \(0<H<0.5\), the future change is opposite to the past, and the closer \(H\) is to 0.0, the stronger the inverse persistence; when \(0.5<H<1.0\), the future change is consistent with the past change, and the closer \(H\) is to 1.0, the stronger the persistence.
## 2 Principle of the CMUDPD Technique
### Group B: CMUPDD of PAs Operating in Class A  
As shown in Figure 10, Group B has both varying supply voltages \(V_{\text{DD}}\) (28, 20, and 12 V) and bias voltages \(V_{\text{GG}}\) (\(-\)2.3, \(-\)2.5, and \(-\)2.7 V). The input powers of the corresponding modes are set at the 1 dB compression point (indicated by arrows). The similarities in the nonlinearities among the three modes are indicated by \(\varepsilon\) in Table 2. Compared with Group A, the values of \(\varepsilon\) in Group B are relatively higher. Nevertheless, this does not significantly affect the pre-distortion performance of CMUPD. In Group B, the PAs are in higher output power and greater bias voltage, so there is a higher probability for third-order inter-modulation compared to Group A. In group B, the memory polynomial (\(M=7\), \(K=3\)) can fit any of the three modes, and any of the three modes can be used as a feedback mode to extract the pre-distortion input for the entire mode group.  
The simulated linearization performances of Group B are depicted in Figure 9d-f, where CMUPDD1, CMUPDD2, and CMUPDD3 represent the pre-distortion results when the outputs of mode b1, b2, and b3 are used as the feedback paths, respectively. An analogous conclusion can also be drawn for Group B, where using b2 as the feedback mode produces superior pre-distortion performance. Group A's overall linearization performance is superior to that in Group B since the values of \(\varepsilon\) in Group A are lower than those in Group B. This indicates that the overall pre-distortion effects are determined by the accuracy of the memory polynomial model.
###### Abstract  
To address the inadequacies of traditional Advanced Orbiting Systems (AOS) multiplexing algorithms in accommodating the networked and diverse transmission demands of space data, this paper proposes an efficient network AOS integrated multiplexing algorithm based on elastic time slots. The AOS network traffic is categorized into three types based on its characteristics, and a strongly scalable AOS integrated multiplexing model is established, which consists of a packet multiplexing layer, a virtual channel multiplexing layer, and a decision-making layer. For synchronous services, an isochronous frame generation algorithm and a periodic polling virtual channel scheduling algorithm are employed to meet the periodic transmission requirements. For asynchronous non-real-time services, a high-efficiency frame generation algorithm and a uniform queue length virtual channel scheduling algorithm are utilized to satisfy the high-efficiency transmission requirements. For asynchronous real-time services, an adaptive frame generation algorithm based on traffic prediction and a virtual channel scheduling algorithm based on comprehensive channel state are proposed. These algorithms optimize frame generation efficiency and dynamically calculate optimal scheduling results based on virtual channel scheduling status, transmission frame scheduling status, virtual channel priority status, and traffic prediction status, thereby meeting the high dynamics, low latency, and high efficiency transmission requirements. Additionally, a slot preemption-based elastic time slot scheduling strategy is proposed at the decision layer, which dynamically adjusts and optimizes the time slot allocation for the three types of traffic based on the current service request status and time slot occupancy status. Simulation results show that the proposed algorithm not only achieves lower average delay, fewer frame residuals, and higher transmission efficiency, but also maintains high stability under different working conditions, effectively meeting the transmission requirements of various types of space network traffic.  
e 2025 12 15 1
# Ionization of Xenon Clusters by a Hard X-ray Laser Pulse
## 3 Results and Discussions
### XFEL-Flluence Dependence  
To discuss the fluence dependence of ionization dynamics for xenon clusters, we focus on the ion yields as a function of XFEL peak fluence. One can find two kinds of fluence dependence on singly (Xe\({}^{+}\), Xe\({}^{+}_{2}\), Xe\({}^{+}_{3}\)) and multiply charged (Xe\({}^{2+}\), Xe\({}^{3+}\)) ions, as shown in Figure 3a-e. In contrast, the yields of the multiply charged ions linearly increase with the XFEL fluence, while the yields of the singly charged ions become slightly saturated at a high peak fluence.  
One can find that creating multiply charged ions calls for a minimum required fluence; for example, Xe\({}^{2+}\) and Xe\({}^{3+}\) ions have been observed above \(\sim\) 0.9 and \(\sim\)2.1 ul\(/\)um\({}^{2}\), respectively. Red dashed lines are shown as guides for the eye in Figure 3b,c. By considering that the charges in a cluster are redistributed to lower the stored Coulomb energy, the minimum required charge to produce one doubly charge ion on the thin spherical shell of xenon clusters has been estimated using the following formula: \(Q_{\text{min,Xe${}^{2+}$}}=\frac{5}{2}(I_{12}-I_{01})\frac{R_{0}}{e^{2}}-4\)[66]. Here, \(I_{01}\) and \(I_{12}\) are the first and second ionization energies of xenon atoms, respectively.  
When \(R_{0}\) is 44 A, \(Q_{\rm min,Xe^{2+}}\) is estimated to be \(\sim\)66. At the XFEL fluence of 0.9 \(\rm\upmu J/\upmu m^{2}\), \(\sim\)80 fast and \(\sim\)580 slow electrons are emitted from the core-ionzied atoms in a xenon cluster with the size of 5000 atoms. With a simple assumption that the fast electrons of \(\sim\)80 can escape from the cluster system, the charges in the cluster at \(\sim\)0.9 \(\rm\upmu J/\upmu m^{2}\) are greater than the minimum required charge to produce a doubly charged ion on the thin-spherical shell. In other words, when the XFEL fluence is less than the minimum required, the charges are not inhomogeneously distributed.  
Let us now explore the fluence dependence of the yields of singly charged ions (Xe\({}^{+}\), Xe\({}_{2}^{+}\), Xe\({}_{3}^{+}\)). The increase in \(Q\) at a high XFEL fluence makes the charge distribution in a cluster even more inhomogeneous such that the thin-spherical shell has a larger number of charges. Therefore, even though \(Q\) becomes high at high XFEL fluences, the proportion of the charge in the quasi-neutral core to the total charge decreases. Since the singly charged ions are mainly produced from the quasi-neutral core, their yields become saturated at the high peak fluence. Another reason is that the average charge of atoms in the cluster increases with the XFEL fluence. The reduction in the number of neutral atoms in the cluster inhibits the formation of chemical bonds between an atomic ion and the surrounding neutral atoms. Therefore, the saturation degree of Xe\({}_{3}^{+}\) is greater than that of Xe\({}^{+}\) and Xe\({}_{2}^{+}\).
## 3 Results and Discussion
### Cost-Benefit Analysis of Combined Remediation  
As detailed in Table 8, a cost-benefit analysis was performed to assess the economic feasibility of the combined remediation method used in this study. The analysis highlighted that using locally accumulated steel slag, which otherwise occupied land resources [82] and leveraging _Bidens pilosa_--a native plant with very low seed costs--significantly reduced the remediation expenses.  
Further examination of Table 8 showed that the combined remediation approach not only performed better than most methods documented in existing literature regarding the stabilization of heavy metals but also benefited from the simplicity of material preparation and the low cost and availability of plant seeds.