esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this work, a robust wavelength-multiplexed TDLAS spectrometer was employed for simultaneous time-resolved detection of atomic potassium, H\({}_{2}\)O and gas temperature close to softwood, Miscanthus, wheat straw and Salix particles combusted in a laboratory reactor. The experimental approach enabled precise and repeatable K(g) measurements in both the devolatilization and the char conversion phase. During devolatilization, K(g) concentrations were several orders of magnitude higher than during char combustion, with wheat straw releasing the most K(g) overall. The fact that the measured K(g) concentrations were largely independent of particle mass points to chemical composition and conversion conditions being the main factors that govern K release, not primarily the absolute K content in the fuel. For wheat straw and Salix, the experimental K(g) data were, for the first time, compared to simulations of the K(g) release behaviour using a numerical particle model solely based on the fuel composition and reactor operating conditions. A good agreement was observed, providing confidence in the model, which further predicts that for combustion KCl and K(g) dominate during devolatilization and KOH during char conversion. The work constitutes a first step towards validating the model predictions for the distribution of volatile K between gaseous K(g), KOH and KCl.  
Conceptualization, F.M.S.; experimental realisation and data collection, Z.Q. and F.M.S.; numerical simulations, H.F.; writing--original draft preparation, F.M.S., Z.Q. and H.F.; writing--review and editing, F.M.S.; visualization, F.M.S. All authors have read and agreed to the published version of the manuscript.  
Funding: [PERSON] gratefully acknowledges funding from the Swedish Energy Agency (project no. 36160-1), the Kempe foundations (project no. JCK-1316), and the Swedish strategic research program Bio4 Energy. [PERSON] acknowledges the support from the Swedish Energy Agency through KC-CECOST, project no. 22538-4. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC (Beskow).  
Not applicable.  
Data Availability Statement: Data supporting the results of this work can be obtained from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
## 2 Preliminary Information
### Domain-Adversarial Adaptation Network  
DAT maps two different domains into a common subspace to eliminate the differences between the domains [54]. As Figure 2 shows, a domain-adversarial adaptation network consists of three parts--feature extractor \(\theta_{f}\), classifier \(\theta_{c}\) and domain discriminator \(\theta_{D}\)[55].  
Given a labeled source dataset \(D_{s}\) and an unlabeled target dataset \(D_{T}\), both are input into \(\theta_{f}\), and then the features of \(D_{s}\) are input into \(\theta_{c}\) and obtain the predicted class. According to the predicted label and the true label, class loss \(L_{C}\) is calculated. Moreover, the features of \(D_{s}\) and \(D_{T}\) are input into \(\theta_{D}\) to obtain the predicted domain, followed by the domain loss \(L_{D}\).  
In the training process of domain-adversarial adaptation networks, all the parameters are updated by Equation (1):  
\[\begin{array}{c}\theta_{f}\leftarrow\theta_{f}-\alpha\Big{(}\frac{\partial L _{C}}{\partial\theta_{f}}-\beta\frac{\partial L_{D}}{\partial\theta_{f}}\Big{)} \\ \theta_{C}\leftarrow\theta_{C}-\alpha\frac{\partial L_{C}}{\partial\theta_{D}} \end{array} \tag{1}\]  
where \(\alpha\) and \(\beta\) are positive constants. When this paper updates the parameters, this paper multiplies the gradient of \(L_{D}\) to \(\theta_{f}\) by a negative constant \(-\beta\), which prevents \(\theta_{f}\) from extracting features for domain discrimination.  
In general, domain-adversarial adaptation networks can be thought of as a two-player game. In this game, the players are the domain discriminator \(\theta_{D}\) and the feature extractor \(\theta_{f}\)[56]. The target of \(\theta_{D}\) is to identify the domain of each sample, but \(\theta_{f}\) strives to deceive \(\theta_{D}\). After long-term adversarial training, \(\theta_{f}\) specifically extracts domain-invariant features.  
Figure 2: The structure of a domain-adversarial adaptation network. The lighter the output color, the closer the value is to 0; the darker the output color, the closer the value is to 1.
# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 6 Results
### RSS-Based Localization Accuracy  
The proximity algorithm results in a mean location estimation error of 340 m and a median of 294 m. In many applications, these rough location estimates can be sufficient, especially when the cost and complexity of the localization needs to be reduced to a minimum.  
To optimize the ranging algorithm for our test environment, several urban path loss models have been evaluated. As shown in Table 1, all path loss models yield similar location estimation errors. The 3 GPP AH-macro model yields the smallest mean and median localization error of 320 m and 259 m, respectively. Although the ranging algorithms perform only slightly better than the proximity algorithm, one needs to keep in mind that only a single eNB antenna sector is used to estimate the position of the mobile node.  
Finally, a fingerprint-based \(k\)NN algorithm is evaluated and optimized. As suggested in [17], different RSS representations and distance metrics are evaluated, along with a parameter sweep of \(k\), which is the amount of neighbors considered during the matching phase. In general, the mean estimation errors vary from 184 m to 207 m. In contrast to previous research, the lineal (i.e., positive and normalized) RSS representation yields the smallest location estimation error. In combination with  
Figure 5: Procedure of the fingerprint-based \(k\)NN algorithm.  
the Pearson \(\chi^{2}\) distance metric, the smallest location estimation error of 184 m is achieved for the test set. After iterating over different values of \(k\), it appears that using the ground truth reference locations of the \(k=2\) nearest neighbors results in the highest localization accuracy.  
The location estimation errors from the test data set are validated by running the algorithm with the optimal parameter set again with unseen data from the validation data set. This yields a mean and median location estimation error of 204 m and 132 m, respectively. Moreover, 95% of all measurements result in a localization error under 679 m. The location estimation errors for the outdoor proximity, ranging and fingerprinting algorithms are summarized in Figure 6.
## 4 Discussion
### Is a Higher Dose of NMES More Beneficial?  
In general, higher doses and longer duration of standard care intervention may produce better functional outcomes. Typically, the efficacy of constrained-induced (CI) therapy may support this view. In fact, few studies seem to have paid attention to this consideration. Intuitively, it was expected that a higher dose of NMES is more effective for motor recovery. However, the present findings suggest that higher dose and/or longer duration may not always produce better outcomes. The dose-response relationship between NMES and UE function for stroke patients remains uncertain [19; 22]. A trial by [PERSON] et al. compared the NMES benefits between low doses (30 min/session) and high doses (60 min/session) of NMES and reported no significant difference [19]. In a study by [PERSON] et al., three different doses (30 min, 60 min, 120 min) of NMES were applied for the chronic phase; the authors suggested that a dose of 120 min is most effective for UE function [22]. They also suggested that future studies should investigate various combinations of treatment dose administration for designing intervention programs suitable for clinical practice. In addition, a recent study demonstrated that 20 min of NMES could facilitate UE motor recovery in the acute phase of stroke [32]. It implied that, by comparison, lower doses and shorter durations of intervention ameliorate UE disability during the early acute phase of stroke. In support of this, the effective threshold of minimal dose and duration needs to be identified.
## 2 Study Area and Data
### Field Data  
We sampled 68 samples during the growing season of 2011 (10 June to 15 June and 20 July to 25 July), and sample locations are indicated with the orange dots in Figure 1. Distances between samples were at least 1.5 km to avoid spatial auto-correlation. The size of each sample was 300 m \(\times\) 300 m to provide overlapping coverage for the corresponding MODIS pixel (250 m \(\times\) 250 m). Given the high cost associated with sampling biomass in such large sample areas, and the fact that endemic grass species are not very heterogeneous within the 300 m \(\times\) 300 m plots, we subsampled the large sample areas by using five 1 m \(\times\) 1 m quadrats (one in the center and four about 100-150 m away from the center); the data from the subsamples were then used to represent the grassland growth conditions of the corresponding sample area. Specifically, plants in each 1 m \(\times\) 1 m quadrant were clipped to the ground surface and then dried at 70 \({}^{\circ}\)C to obtain the dry weight. The average dry weight of the five 1 m \(\times\) 1 m quadrats was calculated and used to represent the mean biomass of the 300 m \(\times\) 300 m sample [48].
# Lignin to Materials: A Focused Review on Recent Novel Lignin Applications
## 1 Introduction  
The overdependence on fossil fuels has raised increasing concerns about climate change and an energy crisis, which has warranted research to search for renewable and clean energy alternatives. Lignocellulosic biomass is the most abundant and renewable source of organic carbon on Earth, presenting the best option to achieve a sustainable biorefinery in the future [1]. In recent decades, there has been significant research conducted to convert biomass components into biofuel and value-added products. Although some sectors in biorefineries, including lignocellulosic bioethanol, have been widely researched, the economic feasibility is often discussed when it comes to commercialization. From the technoeconomic perspective, the success of future biorefineries is highly dependent on lignin valorization.  
Lignin can be obtained from a variety of natural sources, including woody biomass, agricultural residues, and energy crops. Regardless of the type of lignin, there are typically two pathways for lignin valorization. One pathway uses the lignin as a macro-polymer to produce valuable materials; the other pathway involves the depolymerization of lignin into low-molecular weight monomers [2]. Lignin monomers can undergo derivatization through various chemical processes to be converted into desired products [3].  
The world annually produces around 100 million metric tons of lignin, worth approximately 732.7 million USD [3]. Since lignin is a cheap and abundant natural polymer, much research has been spent on valorizing lignin. In recent decades, perspectives on lignin have changed from a wasteproduct used as a low-grade fuel and animal feed to valuable products such as polymers, adhesives, and others [3; 4; 5; 6]. More recently, lignin valorization has received interest in other sectors, like the medical and electrochemical energy materials sectors. The scope of this review will focus on the valorization of technical lignin in advanced materials.
###### Abstract  
A new strategy that repairs severely damaged reinforced concrete (RC) columns after an earthquake is proposed as a simpler and quicker solution with respect to the strategies currently available in the literature. The external concrete parts are removed from the column surface along the whole plastic hinge region to uncover the steel reinforcement. The transverse steel is cut away, and each longitudinal rebar is locally substituted by steel rebar segments connected by welding connections to the original undamaged rebar pieces outside the intervention zone. The new rebar segments have a reduced diameter achieved by turning to ensure plastic deformation only in the plastic hinge, protecting the original rebar and the welding connections. The connection is specifically designed to be effective and simple, and is directly realized on column reinforcement. Finally, the removed concrete is restored by a jacket built with high-performance concrete with steel or polymer fibers. The use of concrete with high volume fraction of polymer fibers to repair the column is investigated for the first time in this paper. This concrete was characterized by compression and flexural tests in the laboratory and its mechanical characteristics were compared with those of the concrete with steel fibers, which are being increasingly used in construction. The repair strategy was applied to two RC columns (1:6 scaled bridge piers), tested by asymmetric cyclic tests. The results show that the column strength, stiffness, and ductility were restored, and the energy dissipation capacity improved. The experimental evidence was investigated by fiber models in OpenSees.  
seismic behavior; RC structure; repair; rebar substitution; retrofitting; high performance fiber reinforced concrete; concrete jacket; plastic hinge; energy dissipation; hysteretic damping
## 2 Problem Description  
At the moment k, the observation information (distance, azimuth, elevation) of UAV \(A\) and \(B\) to cooperative platform C and the sea target \(T\) are \(\mathbf{Z}_{AC}=\left[r_{AC}\quad b_{AC}\quad e_{AC}\right]^{T}\), \(\mathbf{Z}_{AT}=\left[r_{AT}\quad b_{AT}\quad e_{AT}\right]^{T}\), \(\mathbf{Z}_{BC}=\left[r_{BC}\quad b_{BC}\quad e_{BC}\right]^{T}\), \(\mathbf{Z}_{BT}=\left[r_{BT}\quad b_{BT}\quad e_{BT}\right]^{T}\). Attitude information (yaw, pitch, roll) of the platform's own inertial guidance output is \(\mathbf{\mu}_{A}=\left[\mathbf{\Psi}_{A}\quad\theta_{A}\quad\phi_{A}\right]^{T}\), \(\mathbf{\mu}_{B}=\left[\mathbf{\Psi}_{B}\quad\theta_{B}\quad\phi_{B}\right]^{T}\). The cooperative platform provides its own accurate geodetic coordinate information \(\mathbf{X}_{C}^{d}=\left[L_{C}\quad B_{C}\quad H_{C}\right]^{T}\), and the posture is shown in Figure 1.  
The observation information of UAV sensors usually consists of three parts, true value \(\left[r_{t}\quad b_{t}\quad e_{t}\right]^{T}\), systematic error of sensor measurement \(\left[r_{s}\quad b_{s}\quad e_{s}\right]^{T}\), and random error of sensor measurement \(\left[r_{w}\quad b_{w}\quad e_{w}\right]^{T}\), so the observation information of UAVs \(A\) and \(B\) on the target and the cooperative platform are related as follows:  
\[\left\{\begin{aligned} & r_{AT}=r_{ATt}+r_{As}+r_{Au}\\ & b_{AT}=b_{ATt}+b_{As}+b_{Aw}\\ & e_{AT}=e_{ATt}+e_{As}+e_{ATw}\end{aligned}\right. \tag{1}\]  
Figure 1: Schematic diagram of the situation.  
\[\left\{\begin{array}{l}r_{BT}=r_{BTt}+r_{Bs}+r_{Bw}\\ b_{BT}=b_{BTt}+b_{Bs}+b_{Bw}\\ e_{BT}=e_{BTt}+e_{Bs}+e_{Bw}\end{array}\right. \tag{2}\]  
\[\left\{\begin{array}{l}r_{AC}=r_{ACt}+r_{As}+r_{Aw}\\ b_{AC}=b_{ACt}+b_{As}+b_{Aw}\\ e_{AC}=e_{ACt}+e_{As}+e_{ATw}\end{array}\right. \tag{3}\]  
\[\left\{\begin{array}{l}r_{BT}=r_{BTt}+r_{Bs}+r_{Bw}\\ b_{BT}=b_{BTt}+b_{Bs}+b_{Bw}\\ e_{BT}=e_{BTt}+e_{Bs}+e_{Bw}\end{array}\right. \tag{4}\]  
The attitude information of the UAV is also composed of true value \(\left[\psi_{t}\quad\theta_{t}\quad\phi_{t}\right]^{T}\), sensor measurement systematic error \(\left[\psi_{s}\quad\theta_{s}\quad\phi_{s}\right]^{T}\), and sensor measurement random error \(\left[\psi_{w}\quad\theta_{w}\quad\phi_{w}\right]^{T}\), so the attitude information of UAVs \(A\) and \(B\) is related as follows:  
\[\left\{\begin{array}{l}\psi_{A}=\psi_{At}+\psi_{As}+\psi_{Aw}\\ \theta_{A}=\theta_{At}+\theta_{As}+\theta_{Aw}\\ \phi_{A}=\phi_{At}+\phi_{As}+\phi_{Aw}\end{array}\right. \tag{5}\]  
\[\left\{\begin{array}{l}\psi_{B}=\psi_{Bt}+\psi_{Bs}+\psi_{Bw}\\ \theta_{B}=\theta_{Bt}+\theta_{Bs}+\theta_{Bw}\\ \phi_{B}=\phi_{Bt}+\phi_{Bs}+\phi_{Bw}\end{array}\right. \tag{6}\]  
The tracking of the target directly from raw data will produce large errors, especially as the influence caused by systematic errors among them is difficult to eliminate, so a method is needed to estimate and reduce the systematic errors of observation data and attitude information without the geodetic coordinates of the observation platforms, so as to complete the accurate tracking of targets at sea.
## 3 Results
### Research Questions' Answers
#### 3.2.5 RQ4  
How Are the Flood Impacts Seen in the Traffic Networks?  
The integration of flood and traffic models, which can be either static or dynamic, is necessary to capture the relationship between traffic and flood. Dynamic integration takes into account the temporal propagation of floods, whereas static integration assumes a stable flood condition throughout the modeling period. The static integration of the flood and traffic models produces just a binary representation of flooding, which can be a reasonable solution for assessing the effects of the flood on the transport system when the flood propagation speed is as slow as deniable [18; 21; 22; 25; 29; 30; 33; 38; 40; 45; 46; 47; 48; 49; 50; 51]. The dynamic integration applies the likelihood of road closures or car accidents as a result of flooding [25; 29; 38; 46; 49; 50]. The articles analyzed mainly used flood depth as a criterion to evaluate whether a road is closed due to traffic or requires speed reduction [40; 47; 48]. Previous research has suggested that a water depth of 0.3 m is the most common criterion for street closures due to flooded vehicle stability thresholds, with a speed reduction to 20 km/h for water depths lower than 0.3 m [30]. However, considering other factors, such as the impact of flood velocity on traffic flow in addition to flood depth, may improve the accuracy of the results [21; 50]. Furthermore, floods are complex natural hazards that involve dynamic interactions between floodwater and traffic [20; 21; 25; 32]. As a result, dynamic integration of flood and traffic models may provide more accurate results regarding the indirect impacts of floods on transportation networks.  
Of the articles analyzed in this SLR, 30 articles applied a static situation, and 11 articles applied a dynamic situation for flooding. Nonetheless, 3 articles considered and analyzed both static and dynamic integration of flood and traffic in their analysis [24; 30; 52].
## 1 Introduction  
The World Health Organization indicates that 17.9 million people died from cardiovascular diseases in 2019, which represent 32% of all deaths worldwide [1]. Atherosclerosis is a progressive disease characterized by the accumulation of lipids and fibrous elements in the large arteries, and it is the primary cause of heart disease and stroke [2]. Atherosclerosis is closely related to the calcified plaque detected in coronary arteries [3, 4], which is known as coronary artery calcium (CAC), and its detection is considered to be one of the strongest indicators to predict the presence of atherosclerosis in patients who do not have symptoms yet [5, 6, 7]. The most common way to detect CAC is through a cardiac computed tomography (CT) for calcium scoring (CTCS), which consists of a noncontrast enhanced CT of the heart [8], which is interpreted by quantifying the coronary calcium score (CS) in terms of the Agatston score [9, 10, 11]. CS is determined on CTCS by identifying calcified lesions that are represented on CT as pixel islands with an intensity greater than 130 Hounsfield units (HU) belonging to one of the coronary arteries: left main (LM), left anterior descending (LAD), circumflex (CX), and right coronary artery (RCA) [9].  
Digital image processing provides techniques frequently used in the identification of CAC, for example, the design of automatic algorithms to obtain CS, the segmentation of coronary arteries, and the relationship between other clinical indicators and CAC. For the development of these studies, some researchers use open-source medical image processing software such as 3 DSlicer [12] to manually segment lesions or regions of interest (ROIs); the manually obtained information is supplied to computed or statistical systems for analysis and/or interpretation [13; 14; 15]. Another open-source software option is ImageJ [16], which, in addition to allowing for manual operations with images, allows for developers to use predefined semiautomatic functions and the possibility of creating custom macros or plugins to automate some image process analyses [17; 18; 19; 20; 21]. An alternative to the previously defined software is the use of the OpenCV open-source library [21] which allows for implementing more complete image processing techniques through predefined classes and functions [22; 23; 24; 25].  
The development of applications with web technology has increased significantly because they present advantages such as (1) no installation required, (2) automatic updates, and (3) universal access form any device connected to the internet [26]. Web applications have been developed in medicine [27; 28; 29] especially during COVID-19 [30; 31; 32], academia [33; 34], ecology [35; 36], and biology [37; 38], among others [39; 40; 41]. This article presents a web application to determine the CAC on the basis of the Agatston score evaluated with digital processing techniques in the corresponding images included in a CTCS.
## 4 Results and Discussion
### Analytical Results  
In the design stage, the von Misses stress was used as the criterion to determine the design points. The stress values for different cases are presented in Table 3, and the maximum thickness was selected for further analysis in this study. The results of buckling calculations for different compartments are  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{4}{c}{**Geometric Parameters**} & \multirow{2}{*}{**Stress Components**} & \multirow{2}{*}{**Von Misses Stress**} & \multirow{2}{*}{**W(x, y)**} \\  & & \multicolumn{2}{c}{**of the Sheet**} & & & & \\ \cline{2-7}  & \(a\) & \(b\) & \(t\) & \(\alpha_{x}\) & \(\sigma_{y}\) & \(\tau_{xy}\) & \\ \hline \multirow{3}{*}{Steel} & 798 & 1522 & 36 & 170.52 & 76.16 & 0 & 148.15 & 2.3 \\  & 868 & 1522 & 37.8 & 171.47 & 83.98 & 0 & 148.37 & 3 \\  & 798 & 868 & 27.7 & 156.39 & 139.45 & 0 & 148.57 & 1.1 \\ \hline \multirow{3}{*}{Aluminum (depth of 20 m)} & 798 & 1522 & 30 & 82.89 & 42.78 & 0 & 71.78 & 3.5 \\  & 868 & 1522 & 31.7 & 82.99 & 46.07 & 0 & 72.05 & 4.6 \\  & 798 & 868 & 23.7 & 74.95 & 68.36 & 0 & 71.93 & 1.7 \\ \hline \multirow{3}{*}{Aluminum (depth of 60 m)} & 798 & 1522 & 51.9 & 83.16 & 42.69 & 0 & 72.02 & 2 \\  & 868 & 1522 & 54.9 & 82.95 & 46.12 & 0 & 72.00 & 2.7 \\ \cline{1-1}  & 798 & 868 & 41 & 74.96 & 68.41 & 0 & 71.98 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of compartments made of different materials.  
reported in Table 4. The geometric parameters including length (\(a\)), width (\(b\)), and thickness (\(t\)) and the force are obtained in mm and Newton, respectively.  
The results show that the maximum critical buckling value (MCB) for steel compartments occurred in sheet No. 2. Moreover, the MCB was obtained in sheets No. 6 and 2 for aluminum housing at a depth of 20 and 60 m, respectively. We conclude that the state with the maximum critical buckling value has a higher strength.