esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Deep Learning Methods for HDR Reconstruction
### HDR Video Reconstruction  
Some existing methods for direct HDR video capture rely on dedicated hardware solutions for that task, such as line-by-line exposure/ISO or internal/external beam splitters [174]. A more practical alternative for reconstructing HDR video is, just as with HDR image reconstruction, to use consecutive frames from an LDR video with alternating exposures to generate the frames for the HDR video. Classical approaches to accomplish this were initiated by [PERSON] et al. [175], who introduced an algorithm to align neighboring frames with alternating exposures to a reference frame with an intermediate exposure. However, the motion among frames present in videos often leads to ghosting artifacts in this method. Therefore, [PERSON] and [PERSON] [176] improved this approach by introducing block-based motion estimation and refining the result using color similarity and filtering. [PERSON] et al. [177] introduced an optimization approach based on patches that synthesizes images at multiple exposures to reconstruct an HDR frame. These approaches usually require considerable processing time for the optimization and still show ghost artifacts from alignment inaccuracies.  
With the progress in deep learning, various CNN-based HDR video reconstruction models have been introduced, representing a significant leap in the quality of resulting videos and mainly employing optical flow or attention blocks. Table 8 shows a quantitative  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Metric** & **LF-Diff** & **DiffHDR** & **LS-Sagiri** & **Zero-Shot** & **Exp. Diff.** & **Cond. Diff.** & **DITMO** \\ \hline PSNR-1 & 44.76 & 44.11 & — & — & — & 16.97 & — \\ \hline SSIM-1 & 0.9919 & 0.9911 & — & — & — & — & — \\ \hline PSNR-\(\mu\) & 42.59 & 41.73 & — & — & — & 81 & — \\ \hline SSIM-\(\mu\) & 0.9906 & 0.9885 & — & — & — & — & — \\ \hline HDR VDP-2 & 66.54 & 65.52 & — & — & — & — & — \\ \hline HDR VDP-2.2 & — & — & — & — & — & 52.29 & — \\ \hline BRISQUE & — & — & 19.72 & — & — & — & — \\ \hline NIQE & — & — & 20.31 & 2.34 & — & — & — \\ \hline TMQI & — & — & — & 0.8915 & — & — & — \\ \hline MANIQA & — & — & 0.57 & — & — & — & — \\ \hline CLIP-IQA & — & — & 0.67 & — & — & — & — \\ \hline NoR-VDP++ & — & — & — & — & — & — & 54.337 \\ \hline PU21-Pique & — & — & — & — & — & — & 8.789 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of performance for Diffusion Models.  
comparison of state-of-the-art HDR video reconstruction methods, including three optical flow-based models [30; 31; 32] and two attention-based models [33; 34].
## 1 Introduction  
Ultra-precision diamond cutting has been widely used because of its advantages of high precision and fast speed. In the process of ultra-precision cutting, diamond tool wear (DTW) is an unavoidable physical phenomenon and influences high-precision surface quality [1; 2]. Even tiny DTW can have a direct influence on the machining accuracy and surface quality of the machined surface. Especially in the processing of hard brittle materials or special large parts, DTW will become the bottleneck by limiting the qualified rate and performance of parts [3]. To avoid product failure and cost loss caused by DTW, it is important to establish a set of tool condition monitoring (TCM) systems to monitor the tool condition. TCM can maximize the life of diamond tools and minimize the accuracy influence caused by DTW.  
There are two types of approaches for predicting tool wear, namely, model-based approaches and data-driven approaches. In contrast to model-based approaches, data-driven approaches model the data utilizing a learning process, avoiding any assumptions of their underlying distribution [4], as well as the influence of the hypothesis on the accuracy of the model [5]. Many data-driven approaches for tool wear prediction are developed based on machine learning such as artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT) [6]. Conventional machine learning is limited in its ability to process raw data, which has a negative influence on their generalization to normal machining conditions [7]. In tool condition monitoring under complex and changeable conditions, the deep learning model can overcome the shortcomings of traditional machine-learning-based monitoring approaches and obtain higher monitoring accuracy.  
Based on the background described above, this paper presented a digital twin system [8] based on a hybrid deep learning (HDL) model to predict the wear state of diamond tools in the ultra-precision machining process. The digital twin system can monitor the wear condition of the diamond tool in real time and ensure the machining accuracy of the products in the processing. The high-dimensional feature extraction of the HDL model has a stronger generalization ability for the complex machining environment. Therefore, the digital twin system will have higher monitoring accuracy. The effectiveness of the proposed HDL model was verified by comparing it with a variety of machine learning models.  
The rest of this paper is organized as follows. Section 2 reviews related work. Sections 3 and 4 present the proposed approach and its key enablers, respectively. Section 5 includes the experiments and discussions on the disadvantages of the proposed approach. Section 6 draws the conclusion.
## 6 Numerical Simulation  
The REMUS UAV model is used to verify the performance of the controller, and the simulation parameters are selected according to [35] as follows: \(m=30.5\), \(X_{u|u|}=-1.62\), \(X_{\bar{u}}=-0.93\), \(c=0.5+0.02\sin(0.01\pi t)\), \(C_{u}=0.2\). \(\Delta f(x,u)\) is set as \(2+\sin(u)\cos(x)\). The reference trajectory is set as \(x_{d}=0.5+0.5\cos(\frac{\pi}{6})\). The initial state is \(x(0)=2\) m and \(u(0)=0.2\) m/s.  
The output feedback barrier adaptive control scheme (48)-(50) is adopted for simulation, with the parameters and gains in the proposed adaptive control scheme (48)-(50) set as follows: \(t_{d}=2,b_{z}=0.3,k_{1}=0.05,k_{2}=0.05,k_{3}=0.05,\mu_{1}=10,\mu_{2}=10,\mu_{3 }=10\), \(\xi_{g1}=2,\xi_{g2}=10,\xi_{g3}=1,\xi_{g4}=-1\), \(\varepsilon=0.004,a_{0}=5,a_{1}=0.5,b_{0}=2,b_{1}=0.5\). The simulation results are shown in Figures 2-5. Figure 2 shows the tracking curve of surge displacement, with the corresponding tracking error plotted in Figure 3. From Figures 2 and 3, we can see that a favorable tracking performance was obtained. From Figure 4, we can see that \(|z_{1}|\) is constrained within \([0,b_{z}]\). Figure 5 shows the profile of the control input.  
For comparison, a traditional adaptive control algorithm is adopted for simulation as follows:  
\[\tau= -z_{1}^{c}-\gamma_{2}z_{2}^{c}-\hat{\mathbf{\omega}}^{T}\mathbf{\psi}( \mathbf{X})-\hat{\mathbf{\theta}}^{T}\mathbf{\varphi}\] (52) \[\hat{\mathbf{\omega}}= \gamma_{3}z_{2}^{c}\mathbf{\varphi}(\mathbf{X})-k_{1}\gamma_{3}\hat{\mathbf{ \omega}},\] (53) \[\hat{\mathbf{\theta}}= \gamma_{4}z_{2}^{c}\mathbf{\varphi}-k_{2}\gamma_{4}\hat{\mathbf{\theta}},\] (54) \[\hat{c}_{p}= \gamma_{5}|z_{1}^{c}|can see that the tracking precision in the output feedback barrier adaptive control scheme is better than that in the traditional adaptive control scheme. Comparing Figure 4 with Figure 8, we can see that the deferred output error constraint is achieved in the output feedback barrier adaptive control scheme, whereas no system constraint is achieved in the traditional adaptive control scheme. Comparing Figure 5 with Figure 9, we can see that the control input of the output feedback barrier adaptive control is smoother and smaller than that of the traditional adaptive control scheme in the early stage of operation.  
Figure 3: Surge tracking error (output feedback barrier adaptive control).  
Figure 2: Surge displacement(output feedback barrier adaptive control).  
Figure 4: Deferred output error constraint (output feedback barrier adaptive control).  
Figure 5: Control input (output feedback barrier adaptive control).  
Figure 6: Surge displacement (traditional adaptive control).  
Figure 8: Absolute value of surge position tracking error (traditional adaptive control).  
Figure 7: Surge tracking error (traditional adaptive control).  
Figure 9: Control input (traditional adaptive control).
###### Abstract  
Due to climate change, the risk of forest fires has increased in Europe, resulting in challenges in the allocation of salvaged wood. We studied the raw material potential for wood products of Norway spruce and Scots pine sawn log trees that remained standing after a large forest fire in Kalajoki, Finland, in July 2021. Eight burned trees, with four reference trees per species, were sampled as standard specimens, and measurements were analyzed with linear mixed models. The effects of fire on the modulus of elasticity and rupture, Brinell hardness, moisture gradient, and color were measured on clear wood specimens of sapwood and heartwood. The wood density, level of fire damage, and height location of a tree were used as additional predictors. The results show some changes in the sapwood material. Spruce wood underwent stronger changes after the fire than pine wood, probably due to spruce wood having a thinner bark and a longer crown. The moisture content decreased in spruce, and the color darkened in both spruce and pine. Changes in the mechanical properties were mostly negligible, but a small increase in the Brinell hardness in spruce and a small decrease in the modulus of rupture in pine were observed. Fresh salvaged wood can be a suitable material for middle-quality and lower-quality wood products. The spread of char and soot into wood and wood processing machinery still limits its usage, especially for spruce.  
Pisa abies; Pinus sylvestris; wildfire; forest fire; salvaged wood; sawn wood; wood products; clear wood properties; moisture gradient; density; mechanical properties; wood color +
Footnote †: journal: Applied Sciences
## 2 Geolocation and Target Tracking System
### Urban Environment Characteristics  
In the urban area, the sensors (receivers) may be affected by receiving multi-signals at the same time. This type is called the Non-line of Sight (NLOS) signal. NLOS phenomenon occurs due to the reflection of the original signal when it collides with buildings, towers, and trees [31; 32; 33]. Figure 3 presents an NLOS that occurs in the urban environment. During calculation of the geolocation and emitter tracking in the urban area, an NLOS signal should be taken into consideration. For example, if we determine the emitter location as NLOS by TDOA or FDOA measurements, it will be directed to highly bias filtering [34; 35; 36].  
Geolocation and target tracking algorithms may be affected by NLOS error, which may produce very high errors in geolocation and target estimation. In addition, it may decrease the dependence of the geolocation and target tracking system. To increase the reliability of the geolocation and target tracking system, it should take account of these types of errors [37].  
Figure 3: NLOS signal reflected from high buildings, towers, or trees.  
Figure 2: Flow of geolocation and target tracking system.
## 4 Reputation Model
### Smart Objects' Activities  
These interactions are mainly determined by their group membership, aiming to enhance the likelihood of satisfactory exchanges by being part of a well-performing group. It is assumed that every Smart Object is assisted by a proper software agent.  
An (i) **activation** task is carried out the first time that an SO joins the framework. To this end, let SO be an IoT device (and an associated trust agent **t**) and let **E** and **e** be an edge/fog domain active in the framework and its associated software agent, respectively. To start the activation process, an SO affiliation request is sent to **e**, after which the SO's agent **t** will become active and the domain agent **e** will provide it with the following:  
* A **reputation score**, set to **0.5 by default**, is utilised to initially associate the SO with a group that is active within that specific domain;
* A **unique set** of cryptographic keys, with one key being public and the other private;
* The public key of **e**;
* A **digital certificate**, endorsed by entity e, confirming the SO's identity and group affiliation, and including a timestamp indicating the certificate's expiry date.  
An SO interested in a resource \(r\) can perform a simple (ii) **resource search** task using a simple message exchange. In more detail, the trust agent **t**, hosted from its associated SO, that is interested in **r** (i.e., the consumer) sends to agents belonging to the same edge domain a message consisting of  
* The **membership** of the consumer group;
* A description of the resource being searched.  
All SOs capable of fulfilling this request can respond by sending a signed message containing the provider group membership and a description of the offered resource, including its price.  
(iii) **Resource provisioning** is performed whenever a potential consumer has received one or more messages from providers in response to their request. Let us denote the potential consumer as \(SO_{i}\), and the potential provider of the service as \(SO_{j}\). Let us denote as \(i\) and \(t_{j}\) their respective trust agents. When \(SO_{i}\) selects the resource offer from \(SO_{j}\), then \(t_{j}\) and \(t_{i}\) will proceed as follows:  
* Each of them signs their certificate and mutually exchanges these certificates;* A negotiation stage is possibly carried out;
* Finally, they perform the resource delivery and payment steps.  
The (iv) **trust management** task is the last step, after the resource provisioning. The SOs involved in the resource provisioning send a message to the agent **e**. The payload of the message is represented by the feedback indicating the \"personal\" satisfaction regarding the counterparts, and the signed certificate of the counterpart. The sender agent's private key is used to sign this message. The main operation performed by the agent **e** after the message is received is represented by an update the reputation scores of the involved SOs (the provider and the consumer).
## 2 Theory and Formulation
#### Hamilton's Principle  
For obtaining governing equations and their related boundary conditions using [PERSON]'s principle, the following equation must be used [37; 38; 39; 75; 76; 83]:  
\[\int\limits_{\Gamma_{1}}^{t_{2}}{(\delta K-\delta U-\delta W)dt}=0 \tag{8}\]  
The parameters of \(K\) and U are the kinetic and strain energy, respectively [84; 85; 86; 87; 88; 89; 90; 91].  
\[K=\int\limits_{V}{0.5\times\rho\bigg{[}{\bigg{(}\frac{\partial u}{\partial t} \bigg{)}^{2}+\bigg{(}\frac{\partial v}{\partial t}\bigg{)}^{2}+\bigg{(}\frac{ \partial w}{\partial t}\bigg{)}^{2}}\bigg{]}dV} \tag{9}\]  
The first variation of Equation (9) is presented in the Appendix A.  
\[\begin{array}{l}\delta U=\frac{1}{2}\iint\limits_{V}{\sigma_{ij}\delta e_{ ij}dV}=\\ \\ \hskip 14.226378 pt\left[\begin{array}{l}N_{xx}(\frac{\partial u_{0}}{ \partial x}-\frac{\delta w_{0}}{R_{1}})+M_{xx}\frac{\partial\delta\phi_{x}}{ \partial x}-P_{xx}c_{1}\bigg{(}\frac{\partial\phi_{x}}{\partial x}+\frac{ \partial^{2}\delta w_{0}}{\partial x^{2}}\bigg{)}\\ \\ +N_{yy}(\frac{\partial\phi v_{0}}{\partial y}-\frac{\delta w_{0}}{R_{2}})+M_{ yy}\frac{\partial\phi_{x}}{\partial y}-P_{yy}c_{1}\bigg{(}\frac{\partial\phi_{y}}{ \partial y}+\frac{\partial^{2}\delta w_{0}}{\partial y^{2}}\bigg{)}\\ \\ +N_{xy}\frac{\partial u_{0}}{\partial y}+N_{xy}\frac{\partial\phi v_{0}}{ \partial x}+M_{xy}\bigg{(}\frac{\partial\phi_{x}}{\partial y}+\frac{\partial \phi_{y}}{\partial x}\bigg{)}\\ \\ -P_{xy}c_{1}\bigg{(}\frac{\partial\phi_{x}}{\partial y}+\frac{\partial\phi_{y}}{ \partial x}+2\frac{\partial^{2}\delta w_{0}}{\partial x\partial y}\bigg{)}\\ \\ +(Q_{xz}-3S_{xz}c_{1})\Big{(}\delta\phi_{x}+\frac{\partial\delta w_{0}}{ \partial x}\Big{)}+(Q_{yz}-3S_{yz}c_{1})\bigg{(}\delta\phi_{y}+\frac{\partial \delta w_{0}}{\partial y}\bigg{)}\\ \end{array}\right]dA}\end{array} \tag{10}\]  
The parameters that are introduced in Equation (10) are presented in the Appendix A.
## 2 Related Work
### Energy Efficient Routing
#### 2.1.4 Energy Criteria Taken into Account for Route Selection  
These criteria are: (1) the minimum energy path like [18], (2) the path avoiding nodes with low residual energy like REAR [19], and (3) the path of minimum energy while avoiding nodes with low residual energy, like EOLSR (see Section 3).  
Furthermore, a routing protocol can maintain a single or multiple path to reach a destination.
## 5 Results and Discussion
### B1C Signal Components Code Bias Comparison  
The new signals B1C and B2a are the two main public service signals of BDS-3 with a wider bandwidth, higher ranging accuracy, and better interoperability. New navigation messages B-CNAV1 and B-CNAV2 are modulated by B1C and B2a, in which the orbit description accuracy and ionosphere correction accuracy are significantly improved compared with BDS-2 with new orbit parameters and global ionosphere model BDGIM based on a spherical harmonic function [21]. The B1 signal with the smallest ionospheric error is the preferred signal for single-frequency users as well as multi-frequency users, so research on the B1C code bias is of great significance for precise positioning. The B1C signal has two signal components, B1C_pilot and B1C_data, which are respectively transmitted by the QMBOC (6,1) and BOC (1,1) modulation mode with a center frequency of 1575.42 MHz. The split-spectrum design of BOC and QMBOC is helpful to improve the ranging accuracy.  
The code bias of the two components may vary due to the different modulation modes, so the RMS values of the two signal components of all satellites are shown in Table 3, in which the last data \"MEAN\" refers to the mean values of all satellites. Meanwhile, the trends in the MP combination time series are compared in Figure 8. The code bias of the B1C_data is slightly larger than that of the B1C_pilot data, and the MP combination time series have similar variations. The difference is small and can be ignored. Other satellites have demonstrated similar results, which will not be shown herein.
## 3 Control Algorithm
### Summary of the Proposed Method  
With reference to the analysis above, a trial-and-error method can be carried out to obtain the suitable proportional and integral coefficients for acceptable performance, and the following steps are required to implement the proposed method:  
1. Measure the stator currents, dc-link voltage, and the NPV;
2. Calculate the stator currents and voltages in the \(d\)-\(q\)-axes via coordinate transformation;
3. Estimate the \(d\)-\(q\)-axes current \(\hat{i}_{d}\), \(\hat{i}_{q}\) via the designed current observer, as shown in Figure 5;
4. Obtain the estimated value of the electric angular velocity using (16), and then the rotor position angle \(\theta\) can be calculated by (17);
5. Acquire \(i_{q}\)_ref_, the reference values of the \(q\)-axis current, via the PI controller of the speed error, and then set \(i_{q}\)_ref_(\(k+1)=i_{d}\)_ref_, \(i_{q}\)_ref_(\(k+1)=i_{q}\)_ref_;
6. Substitute the 27 switching states of the 3L-NPC inverter and the rotor position angle into (4) and (5) to calculate the \(d\)-\(q\)-axes voltages at the (\(k\))th under a different voltage vector, and, at the same time, predict the stator current at the (\(k+1\))th;
7. Calculate and compare the cost function values under the different voltage by substituting the reference current values obtained in Step 5 and the predicted current values obtained in Step 6 to receive the optimal vector \(V_{min}\)\(\in\)\(\{V_{1}\), \(V_{2}\),..., \(V_{27}\}\) satisfying \(\min\{g\}\);
8. Judge whether the obtained vector is one of the small vectors. If not, apply the optimal vector to the inverter. Otherwise, perform the next step;
9. If \(i_{mp}\times U_{0}\geq 0\), directly exert the corresponding small vector on the inverter; otherwise, adopt its redundant counterpart to drive the machine.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 3 Implementation  
This section focuses on the implementation of the proposed solar-powered satellite access points. The prototype is shown in Figure 2.
# A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach
## 4 Suggested Swarm-Inspired Metaheuristics
### Glowworm Swarm Optimization (GSO)  
The Glowworm Swarm Optimization (GSO) is a swarm-inspired metaphor approach proposed by [PERSON] and [PERSON] [29] and reflects the grouped movement of glowworm particles controlled by a luminescent quantity named luciferin. The latter controls the interactive behaviors between glowworms in tracking food and organizing their swarm. This behavior is modeled by the GSO along four distributed steps:* Initialization phase: constitutes the construction of initial population of candidate solutions.
* Neighbors search: models the interaction between adjacent glowworm entities to discover better positions.
* Luciferin update: translates the previous operation by moving to better partial solutions.
* Location update: regroups the local solutions to approach the best global solution.  
GSO is a variant of the Ant Colony Optimization (ACO) [30] that offers solutions to continuous functions. GSO proved better positive convergence results than ACO in the multimodal functions proving its ability to detect multiple local optima.  
The luciferin intensity value (\(L_{j}\)) of glowworm agent is updated from two instant points (\(t\)) to (\(t+1\)) following Equation (7):  
\[L_{j}(t+1)=(1-\rho)L_{j}(t)+(\gamma\times F(p_{j}(t))) \tag{7}\]  
Considering:  
* \(L_{j}\): the luciferin value.
* \(\rho\): the update factor with \(0<\rho<1\).
* \(F\): the fitness function.
* \(\gamma\): the luciferin enhancement constant.  
Equation (8) calculates for each glowworm agent the probability of selection (\(Prob_{ij}\)) according to its updated luciferin value as following:  
\[Prob_{ij}(t)=\frac{L_{j}(t)-L_{i}(t)}{\sum_{k=1}^{N_{j}}(L_{k}(t)-L_{i}(t))} \tag{8}\]  
Considering:  
* \(N_{j}\): the number of candidate glowworms of glowworm \(\mathbf{i}\).
* \((L_{j}-L_{i})\): the luciferin difference between glowworms \(\mathbf{j}\) and \(\mathbf{i}\).  
The illustration of the GSO is given in Figure 1.  
Figure 1: Flowchart of Glowworm Swarm Optimizatin (GSO) procedure.