esa-sceva/satcom-chunk-collection huggingface dataset:
# Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty
## 3 Autonomous Controller Design
### Adaptive Filter Control Design
\(\delta\) can be then obtained by the batch processing, and there exists:  
\[\delta=\left(\mathbf{\Phi}^{\rm T}\mathbf{\Phi}\right)^{-1}\mathbf{\Phi}^{\rm T}\mathbf{Y} \tag{21}\]  
where \(\mathbf{\Phi}=\left[\mathbf{\varphi}^{\rm T}(1)\ \mathbf{\varphi}^{\rm T}(2)\,...,\ \mathbf{\varphi}^{\rm T}(n)\right]^{\rm T}\) and \(Y=\left[y(1),\ y(2)\,...,\ y(n)\right]^{\rm T}\). Hence, we can solve Equations (15) and (16) for \(a_{\rm i}\), \(b_{\rm i}\). The actual bending frequency can be calculated by:  
\[\omega^{*}=\frac{1}{T_{s}}\sqrt{\ln z_{r}\ln z_{r}^{*}} \tag{22}\]  
where \(T_{s}\) is the sampling time, \(z_{r}\) and \(z_{r}^{*}\) are conjugate poles with maximum imaginary part of discrete system as shown in Equation (14). Considering the first order modal parameter uncertainty mentioned above, we can obtain \(z_{r1}=-0.5041+0.816\)i, \(z_{r1}{}^{*}=-0.5041-0.816\)i, \(z_{r2}=-0.7860+0.6081\)i, \(z_{r2}{}^{*}=-0.786-0.6081\)i and \(\omega_{1}{}^{*}=0.11\ \rm rad/s\), \(\omega_{2}{}^{*}=0.66\ \rm rad/s\). Then the controller as shown in Equation (13) can be redesigned. The technical process can be readily accomplished by following the line in the above section. Then, the adaptive-filter-based pointing controller is finally given by:  
\[T_{y}(s)=-(520s+10)\cdot\frac{82.645s^{2}+0.1s+1}{82.645s^{2}+18.2s+1}\cdot \frac{2.3s^{2}+0.01s+1}{2.3s^{2}+2s+1}\cdot\psi(s) \tag{23}\]  
Only the uncertainties of the first order modal parameters are discussed above. If the uncertainties on the modal parameters of other modes are also considered, such as the second-order modal parameters, the actual bending frequencies could change and the magnitude will present discontinuity as well. Then, the adaptive filter control is designed as similar to the above process, and is thus omitted here.
# Health State Assessment of Road Tunnel Based on Improved Extension Cloud Model
## 2 Construction of Indicator System
### Indicator Selection  
A static evaluation system for health status assessment of road tunnels is generally constructed by selecting basic evaluation indicators from tunnel detection projects, as road tunnels are influenced by multiple factors [15; 29; 30]. In fact, the basic evaluation indicators change over time, and the reflection of the health status of the road tunnel at the time of detection by the static evaluation system is not conducive to the formulation of later maintenance measures. The road tunnel health status evaluation system is set based on engineering reality and selection principles.  
The evaluation system includes basic static evaluation indicators such as crack length, crack width, water leakage degree, damage degree of maintenance road, abnormality degree of a drainage system, pavement condition index (PCI), riding quality index (RQI),and skid resistance index (SRI). Additionally, it includes dynamic evaluation indicators that reflect the trend of tunnel health changes, such as the crack number change rate, water leakage volume change rate, pavement condition index (PCI) change rate, riding quality index (RQI) change rate, and skid resistance index (SRI) change rate, as shown in Figure 2.
# Optimal Robust LQI Controller Design for Z-Source Inverters
## 1 Introduction
Given the uncertain and time-varying nature of some regulator parameters such as load or storage elements, it is of vital importance to seek for control methods which can deal efficiently with performance requirements, disturbances, and uncertainties. Conventional controllers have a simple and efficient structure but are still not robust against perturbations. The robust control methods, such as \(H_{\infty}\)[26], \(\mu\)-synthesis [27], quantitative feedback theory [28], linear matrix inequality (LMI)-based approaches [29], and sliding mode control [30] have been applied to overcome both structured and unstructured uncertainties of different converters. Among these robust control techniques, the linear quadratic integral (LQI)-based control offers optimal control for uncertain systems with given weighting matrices \(\mathcal{D}\) and \(\mathcal{R}\)[31]. To the best of the authors' knowledge, it can be concluded from the literature that there is no published work addressing the LQI-based control of the ZSIs. Hence, considering the uncertain and non-minimum phase nature of the system, an LQI-based robust controller is designed to achieve an optimal robust performance against uncertainties and load disturbances in which controller parameters are tuned using the bat optimization algorithm. Most importantly, the stability robustness of the proposed method is also investigated on the basis of the maximum sensitivity specification. By considering the inverter in different operating states and using state-space averaging (SSA), a non-minimum phase model with a right-hand-plane real zero is derived for the inverter. To fairly assess the performance obtained by the proposed controller, the results are compared with those obtained through PI and state feedback (SF) controllers within dual-loop schemes. The simulation results reveal that despite the perturbed condition and non-minimum phase issue, the synthesized controller preserves no overshoot and less undershoot transient response with an acceptable disturbance rejection. The rest of this paper is organized as follows: circuit analysis and the uncertain model of the inverter are represented in Section 2. Section 3 explicates the synthesis procedure of the proposed controller. Simulation results are presented in Section 4. Eventually, conclusions are summarized in Section 5.
## 2 Results  
### Transcriptome Sequencing, Assembly, and Annotation  
Since no genomic or transcriptomic resources for _S. barbata_ were available, we performed RNA-seq based deep transcriptome profiling to derive a de novo transcriptome assembly. In order to achieve complete representation of the _S. barbata_ transcriptome, total RNA was extracted from flowers, aerial parts, and root (Figure S1; Table S1). Individual libraries for each sample were sequenced using the Illumina HiSeq(tm)2000 platform in paired-end mode, resulting in a total of 237,517,352 strand-specific paired-end reads with lengths around 150 nt (35,627,602,800 bases) (Figure S2; Table S2). In total, 88,980 transcripts were obtained with an average length of 1370 nt and an N50 length of 2144 nt, indicating high assembly quality; these isotigs were further grouped into 52,211 unigenes, and there were 22,421 (42.94%) unigenes longer than 1000 bp (Figure 2; Table S3). Those assemblies were routinely annotated based on protein similarity using BLAST searches against several public databases, such as Swiss-Prot, TrEMBL, Pfam (HMM), Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes (Figures S3 and S4). Taken together, 36,659 unigenes (70.23%) were assigned at least one putative function from one of these databases.
## 5 Results and Analysis
### Uncertainty Analysis (UA) of Models  
Both training and testing observed samples were considered for logical comparison of predicted discharge values for these models. The quantification of uncertainty and its analysis using appropriate variable is useful for knowing the technical contribution in a decision-making environment. In this study, the knowledge base for analysis was observed and predicted discharge values. Initially the error \((e_{i})\) between the observed (\(Y_{O_{i}}\)) and predicted (\(Y_{E_{i}}\)) value is formulated using Equation (27).  
\[e_{i}=\left|Y_{O_{i}}-Y_{E_{i}}\right| \tag{27}\]  
Then mean (\(e_{\mu}\)), standard deviation (SD) \(\left(\sigma_{e}^{2}\right)\), standard error (SE), upper bound (UB), lower bound (LB) and width of confidence (WCB) bound of error are calculated. The margin of error (ME) is estimated with 95% confidence interval with 0.05 level of significance \((1-\alpha)\). The smaller value of mean, UB, LB, SE, ME and WCB indicates better accuracy of a prediction model.  
Figure 19: Statistical comparison for all models for catchment Teifi at Glanteif: (**a**) train, and (**b**) test.  
Figure 18: Prediction capability of the eight models in test case (**a**) WELM, (**b**) WKELM, (**c**) WPSO-ELM, (**d**) WEO-ELM, (**e**) WDNN, (**f**) WSVR, (**g**) WANN, and (**h**) WGBM for the catchment Teifi at Glanteifi.
## 7 Conclusions  
In this manuscript, we proposed an extended Kalman filter design for tracking time-of-flight (ToF) and clock offsets in a distributed two-way ranging (TWR) network. We proposed this solution to specifically address the sensitivity of the existing one-shot approaches to intermittent channel impairments. Tracking solutions often increase the computational complexity of the estimation processes, which we explicitly mitigate by integrating the estimation process into the EKF solution rather than filtering the outputs of another estimator. We implemented the proposed solutions in a simple MATLAB simulation environment and benchmarked their performance against the corresponding optimal one-shot estimators in the Monte Carlo simulations. We demonstrated that this solution achieves comparable performance to existing, optimal estimators and, in the case of the second-order formulation, actually reduces the computation run time by an order of magnitude.  
In the future, we will extend the initialization of the noise covariance matrices to include a real-time adaptive estimation process instead of initializing them using fixed values based on either knowledge of the estimation problem or initial calibration. This is commonly referred to as adaptive extended Kalman filtering (AEKF) and makes the system more robust to wide changes in received signal strength or other channel impairments. In parallel, we are also currently investigating computationally efficient methods for extending these results to higher-order Markov models on the underlying processes.  
Conceptualization, S.S. and A.H.; Data curation, S.S.; Formal analysis, S.S. and A.H.; Funding acquisition, D.W.B.; Investigation, S.S. and A.H.; Methodology, S.S. and A.H.; Project administration, D.W.B.; Resources, D.W.B.; Software, S.S.; Supervision, D.W.B.; Validation, S.S. and A.H.; Visualization, S.S., A.H. and D.W.B.; Writing--original draft, S.S. and A.H.; Writing--review and editing, S.S., A.H. and D.W.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the policies of Arizona State University regarding pending patents and publications.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AoA & angle of arrival \\ EKF & extended Kalman filter \\ FANET & flying ad hoc network \\ GPS & Global Positioning System \\ IoT & Internet of Things \\ ITS & intelligent transport system \\ LoS & line of sight \\ NTP & Network Timing Protocol \\ RMSE & root mean square error \\ RSS & received signal strength \\ TDoA & time difference of arrival \\ ToA & time of arrival \\ ToF & time of flight \\ TWR & two-way ranging \\ UAV & unmanned aerial vehicle \\ WSN & wireless sensor network \\ \end{tabular}
## 3 Results
### Global SIF Product: Spatial Patterns and Seasonal Cycles of SIF
Our new 0.05\({}^{\circ}\) SIF also generally tracked the seasonality of tower GPP at these sites (Figure 7b,d,f,h), demonstrating its great potential in monitoring vegetation photosynthesis. During the senescence stage when the new 0.05\({}^{\circ}\) SIF and tower GPP were close to zero (Figure 7e,g), the coarse-resolution SIF still had relatively high values, possibly due to the heterogeneity within the coarse-resolution grid cell. Figure 6: Spatial patterns of 8-day global OCO-2 SIF (GOSIF) from January through December in 2016. The units are W m\({}^{-2}\) μm\({}^{-1}\) sr\({}^{-1}\). For each month, the second 8-day SIF map is shown here. To further evaluate the GOSIF product, we also examined the relationship between 8-day SIF and tower GPP for 91 flux tower sites. The new 0.05\({}^{\circ}\) GOSIF showed high correlation with tower GPP from 91 EC flux tower sites (Figure 8, R\({}^{2}\) = 0.73, \(p\) < 0.001). We also examined the relationship between SIF and GPP for each biome. SIF was strongly correlated with GPP for evergreen needleleaf forests, evergreen broadleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, croplands, and wetlands, and a moderately strong relationship was observed for evergreen broadleaf forests (Figure S2 from the online Supplementary Material). Figure 7: The seasonal cycles of our new 0.05\({}^{\circ}\) SIF, 1\({}^{\circ}\) OCO-2 SIF directly aggregated from discrete SIF soundings, and tower-based gross primary productivity (GPP) at four eddy covariance (EC) flux tower sites: (**a,b**) AU-Das, savannas (SAV), (**c,d**) AU-Stp, grasslands (GRA), (**e,f**) DE-Geb, croplands (CRO), and (**g,h**) US-PFa, mixed forests (MF). The tower-based GPP was temporally aggregated to 8-day intervals to be consistent with the temporal resolution of the new 0.05\({}^{\circ}\) SIF.
# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
## 2 Materials and Methods
### Building a Fusion Model  
In order to fully utilize the advantages of different submodels, a multistrategy model fusion method was designed. Specifically, by introducing learnable weight layers for the outputs of each submodel, the contribution of each submodel in the final prediction can be dynamically adjusted. The fused output is combined through addition operation and finally generates a comprehensive prediction result through a fully connected layer.
# Sustainable Cold Chain Management: An Evaluation of Predictive Waste Management Models
## 2 Literature Review
### Theoretical Frameworks: Resource-Based View (RBV) and Supply Chain Resilience Theory  
The integration of IoT and AI in cold chain logistics is strongly supported by the Resource-Based View (RBV) and Supply Chain Resilience Theory. These frameworks underscore the strategic importance of advanced technologies in maintaining competitive advantage and adapting to demand fluctuations.  
* Resource-Based View (RBV)  
The RBV posits that valuable, rare, inimitable, and non-substitutable (VRIN) resources provide sustainable competitive advantage [11]. IoT and AI align with RBV by acting as unique resources that enhance operational efficiency, reduce waste, and improve sustainability. Deploying these tools strategically enables firms to respond more effectively to demand changes, thereby minimizing waste. [PERSON] et al. [1] argue that companies that invest in AI and IoT within their cold chain operations position themselves competitively by reducing operational costs and environmental impact, supporting the RBV framework.
* Supply Chain Resilience Theory  
Supply Chain Resilience Theory highlights the importance of flexibility and responsiveness in the face of disruptions. Cold chains, which are highly susceptible to fluctuations in demand and environmental conditions, benefit from resilient systems that can swiftly adapt to prevent spoilage and waste [12]. By enabling predictive adjustments, AI strengthens resilience in cold chains, while IoT sensors provide real-time insights that inform proactive decision-making [3]. [PERSON] et al. [6] observed that IoT-based monitoring allowed one logistics firm to prevent waste in 20% of shipments by rapidly addressing disruptions, demonstrating resilience. The behavioral aspects of supply chain disruptions during the COVID-19 pandemic reinforce the critical role of adaptability, particularly within resilience frameworks [13].
# A Systematic Analysis of Narrowband IoT Quality of Service
## 2 Narrowband IoT Fundamentals
### Deployment Modes and Resource Grid  
NB-IoT occupies a total system bandwidth of 180 kHz and can be deployed in-band within a single LTE Physical Resource Block (PRB) or operate in the LTE guard band on each side of the LTE carrier. Alternatively, it can occupy a single GSM carrier of 200 kHz in stand-alone mode, as shown in Figure 1.  
The integration of NB-IoT into existing 4G networks is facilitated by reusing the LTE resource grid as shown in Figure 2. The NB-IoT downlink is based on OFDM with 15 kHz carrier spacing and always uses all 12 subcarriers. Downlink resources are allocated as subframes, which span 1 ms and contain two slots with seven OFDM symbols each. As such, each allocated subframe contains two PRBs. The downlink radio channel is estimated using Narrowband Reference Symbols (NRS) that are present in the last two OFDM symbols of each slot. In the uplink direction, SC-FDMA is used, and the resource grid can be configured to use either 15 kHz or 3.75 kHz carrier spacing.  
Figure 1: NB-IoT allows flexible deployment along existing mobile networks: (**a**) LTE in-band deployment, (**b**) LTE guard-band deployment, (**c**) GSM stand-alone deployment (adapted from [14]).  
Additionally, NB-IoT introduces the concept of Resource Units (RU) in the uplink, which allows allocating one or more subcarriers of a single PRB to different User Equipment (UE). These subcarriers are referred to as tones, and the resulting configurations are called single and multi-tone operation. The possible allocation schemes [15] are illustrated in Figure 3.
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
###### Abstract  
The rapid development of wireless network technology and the continuous evolution of network service demands have raised higher requirements for congestion control algorithms. In 2016, Google proposed the Bottleneck Bandwidth and Round-trip propagation time (BBR) congestion control algorithm based on the Transmission Control Protocol (TCP) protocol. While BBR offers lower latency and higher throughput compared to traditional congestion control algorithms, it still faces challenges. These include the periodic triggering of the ProbeRTT phase, which impairs data transmission efficiency, data over-injection caused by the congestion window (CWND) value-setting policy, and the difficulty of coordinating resource allocation across multiple concurrent flows. These limitations make BBR less effective in multi-stream competition scenarios in high-speed wireless networks. This paper analyzes the design limitations of the BBR algorithm from a theoretical perspective and proposes the Adaptive-BBR (Ad-BBR) algorithm. The Ad-BBR algorithm incorporates real-time RTT and link queue-state information, introduces a new RTprop determination mechanism, and implements a finer-grained, RTT-based adaptive transmission rate adjustment mechanism to reduce data over-injection and improve RTT fairness. Additionally, the ProbeRTT phase-triggering mechanism is updated to ensure more stable and smoother data transmission. In the NS3, 5G, and Wi-Fi simulation experiments, Ad-BBR outperformed all comparison algorithms by effectively mitigating data over-injection and minimizing unnecessary entries into the ProbeRTT phase. Compared to the BBRv1 algorithm, Ad-BBR achieved a 17% increase in throughput and a 30% improvement in RTT fairness, along with a 13% reduction in the retransmission rate and an approximate 20% decrease in latency.  
TCP congestion control; BBR; multi-flow competition; RTT fairness +
Footnote †: journal:  
[PERSON]  
[PERSON], [PERSON], [PERSON], [PERSON]: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR.  
Future Internet
## 2 Passive FSR Geometry
is valuable information describing the target in the situation when the range measurement is ambiguous, which is the case in passive FSR. Thus, any additional characterization of the target movement is significant in this case. In the literature, different approaches are proposed in order to estimate motion parameters, such as spectrogram analysis [22], chirp rate estimation in the time-frequency (TF) domain [23], or the Radon transform [20]. This paper refers to the latter example, and the authors of this work proposed the method known from the literature to improve the resolution of the TF distribution using the TF reassignment technique in order to distinguish the kinematic parameter of the cooperative target using the Hough transform, which can be interpreted as a discrete realization of the Radon transform [24; 25]. The obtained outcomes are compared to existing methodology [20; 26; 27; 28; 29], and the improvement in the estimation precision is shown.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 4 Results and Discussion
### Testing Phase of Weld Seam Detection Using YOLO Algorithms
The performance was evaluated in terms of total time taken for the whole process and validation accuracy, which are listed in Table 8.  
The total time was calculated based on pre-processing, GAN dehazing, the testing phase with 212 images of deep learning models, and contour detection for weld seam extraction. The training time was already listed in Table 7, so it was not considered. The validation accuracy was tested for nonhomogeneous weld fume images and dehazed images. It was found that accuracy was improved after dehazing the images. Additionally, PSNR was high for dehazed images with the value of 12.23, whereas it was low for fume images with the value of 11.44. This shows that the GAN module played a major role in accurate weld shape detection. It can be inferred that the total processing time taken for YOLOv5 with denoising and contour detection was 6.426 s, and for EfficientDet the total time was 7.185 s. The overall performance of weld detection was accurate for YOLOv5 and EfficientDet. The Faster RCNN processing time was high with the value of 44.626.
## 2 Background and Related Work
### BERT Model  
BERT (**B**idirectional **E**ncoder **R**epresentations from **T**ransformers) is a bidirectional language model based on the encoder part of the Transformer model (i.e., the left part of Figure 1). BERT alleviates the unidirectionality constraint by using a \"masked language model\" (MLM) pre-training objective [10]. Specifically, the BERT model is pre-trained with two unsupervised tasks: masked language model (MLM) and next sentence prediction (NSP). As for MLM, approximately 15% of the tokens are selected, with 80% of them replaced with [MASK] tokens, 10% left unchanged, and 10% replaced with other random tokens. The model utilizes contextual semantic information to predict the masked words at these positions. As for NSP, given two sentences (A and B), the model needs to determine whether B logically follows A.  
Figure 2 shows the main structure of the BERT model, with the stages of _pre-training_ and _fine-tuning_. In the pre-training stage, the model is trained on a large corpus of unlabeled text to learn the relationships between words. The learned model is then fine-tuned on downstream tasks.  
The internal structure of the BERT model utilizes the encoder part of the Transformer model, with multiple layers stacked together. The output of the BERT model is a vector for each input token. In the fine-tuning stage, the BERT model can be applied to various downstream tasks by utilizing its one-to-one input-output features. For example, in a sentence pair classification task, the output corresponding to the input's first token can be used for classification. In a named entity recognition (NER) task, the output for each word can represent its position (beginning, middle, or end). In the SQuAD task, the model can output the probabilities for the start and end positions of an answer. Thus, the BERT model can be applied to a wide range of downstream tasks [10].