esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Temperature on Channel Compensation in Optical Camera Communication
## 5 Methodology
### Experimental Setup  
The spectra of several LEDs (from a common cathode RGB LED) were measured at different driving currents (and hence different p-n junction temperatures) using a programmable current source and a spectrometer. The LED under test was mounted on an adapter for an integrating sphere's input, and it was directly connected to the current source. The temperature of the LED was estimated using a thermographic camera pointing to the LED's back, as it is the best position to estimate the p-n junction temperature. The LED device under test was driven at different DC currents in order to induce temperature variation due to the Joule effect. No heat sink was applied to the device to allow the thermography-based measurement and avoid heat loss in the p-n junction. The experimental setup is illustrated in Figure 3. All the devices were connected to the control PC through an Ethernet switch, except the spectrometer, which used USB. An automation script was developed in order to ease the characterization procedure.
# A Semantic Framework for Decision Making in Forest Fire Emergencies
## 7 Discussion
Furthermore, the ONTO-SAFE framework can be adaptable and expandable for any kind of natural disasters, such as floods, earthquakes, etc., as the ontology model promotes expressiveness and sustainability over the standard expert system. In addition, it is important to note that the ONTO-SAFE framework operates as a rule-based system, with rules based on user requirements and domain expertise. Consequently, both the rules and the outputs of the framework are inherently subjective, as they are customized to address specific needs and objectives. It is crucial to recognize that the effectiveness of our framework is significantly affected by the quality and accuracy of the input data derived from diverse sources. In the event of any concerns regarding data quality or potential biases within the input information, it is essential to acknowledge that they may impact the outcomes of the framework correspondingly. However, it is important to note that although response time optimization with the Douglas-Peucker algorithm is beneficial, it should not compromise the accuracy of the decision-making process. Achieving the right balance between response time and accuracy is a crucial aspect of system design, as it guarantees prompt response while upholding reliability and precision. Moreover, the proposed framework is designed to protect user data and maintain transparency in all interactions. Data privacy and security have been given paramount importance throughout the entire development of the project platform, as citizens' personal data are not accessible. The only data that are accessible is the information inserted by registered users during their registration process, which is subject to their consent and acceptance of our comprehensive privacy policy. Furthermore, when the ONTO-SAFE framework shares information with citizens and professionals, we ensure that no personal data are included. The only information that is shared is the location that users confirm to share with us, solely for the purpose of providing them with more accurate assistance. For future work, the ONTO-SAFE framework could be extended to consider the integration with real-time data streams and advanced analytics techniques, including machine learning algorithms. By incorporating real-time data, the proposed framework can enhance the accuracy and timeliness of decision support during forest fire emergencies. Real-time data sources such as weather conditions, satellite imagery, and sensor networks can provide valuable information for more precise fire behavior modeling and risk assessment. Additionally, the integration of machine learning algorithms can enable the framework to learn from historical fire data, identify patterns, and make predictions for improved whereas. Exploring these capabilities will lead to a further enhancement of the framework's adaptability and effectiveness in handling dynamic and evolving fire situations.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 3 Methodology  
### Point-Line Feature Extraction Network  
The point-line feature extraction network PL-Net is shown in Figure 2. Firstly, the ORB-FPN module was used to extract the features of each layer for the image, and the PG module completed the key-point extraction. Then, the center point map and displacement map were generated through the branch of the line-segment perception module. Finally, the point-line descriptor was generated through convolution and upsampling operations.
## 2 Case Presentation
PBMT was performed using a Lumix 2 laser device (Fisioline, Verduno, Italy), which is a bidiotic laser; this combines a high-power laser diode in the pulsed infrared wavelength and continuous laser diode in the red wavelength, which allows energy to be transmitted to the tissue at different subcutaneous levels. The LUMIX 2 laser was used at a wavelength of 910 nm with a total energy fluence of 12 J/cm\({}^{2}\), a super-pulsed frequency of 30 kHz, and a total energy delivery of 180 J to stimulate deep tissues and accelerate orthodontic tooth movement for a 15 min-long session on the exposed teeth. All the laser sessions were performed by the same operator. No pain was reported and no inflammation was evident following PBMT. The DICOM files from the initial CBCT were converted into an STL file through an open-source software called 3D Slicer (version 5.8.1) and this was used as a starting point (T0) to assess the movement of the canines following surgery. The STL file of the intraoral scans and of the CBCT were imported and superimposed to perform a comparison between the starting (T0) and final (T4) canines' position. The accuracy of 3D Slicer in measuring orthodontic tooth movement has been confirmed in multiple studies, demonstrating sub-millimetric precision when using CBCT image registration and digital surface models [23; 24]. Figure 4: Intraoral occlusal photo of the patient after operculectomy with cold blade. Figure 5: Intraoral occlusal photo of the patient 16 weeks after surgery. In light of its accuracy reported in the literature, it was chosen as the software to perform the measurements of this study. The 3D Slicer measuring tool was used for both STL files to quantify the distance between the occlusal plane and the canine cusp at T0 and T4 and so verified the actual eruption movement (Figure 6). This measurement was performed by tracing the occlusal plane using three reference points: the mesial-palatal cusps of the upper first molars and the incisal edge of the upper incisors in correspondence with the inter-incisive midline. A line was then drawn perpendicularly from the cusps of the impacted maxillary canines to the occlusal plane, obtaining the millimetric value of canine inclusion height. In this case, element 1.3 had an inclusion height of 7.21 mm, while element 2.3 had a height of 6.92 mm. At T4 the inclusion height of element 1.3 was 7.00 mm and of element 2.3 was 3.76 mm; this means that the canine eruption was of 0.21 mm for 1.3 and 3.16 mm for 2.3 (Figure 7). Since this is a case report, no statistical analysis was performed.
## 2 Experimental Investigation  
### Test Specim  
Six anchored blind bolted flush end-plate joints were designed to connect CFST columns to the steel beam. Figure 1 illustrates the details of the specimens, and Table 1 summarizes the details of the specimens. The end-plate thickness, tube wall thickness, blind bolt anchorage method, and beam section were the test parameters that were investigated. All the specimens used the blind bolts (Hollo-Bolts) to connect the steel beam to the square hollow section column. The blind bolts adopted in the tests were Grade 8.8 M16 Hollo-Bolts, which extended the bolt shanks and attached grade 8.8 anchor nuts at the end, as shown in Figure 2. The bearing surface of the anchor nut was 90 mm away from the inner surface of the steel tube. The columns in all the specimens were concrete-filled square hollow section columns with a cross-sectional dimension of \(200\times 200\times 5\) mm. The steel tube wall in the panel zone was externally welded with two 7 mm C-shaped channels to change its thickness. Plug welding was adopted to ensure that the C-shaped channel and steel tube wall could fit closely together during the test. The Hollo-Bolt used in specimen ST-5 extended only the bolt shank, without an anchor nut at the end. The beams used for the majority of the specimens in this study were commercially available H-shaped steel beams with a cross-sectional dimension of HN350 \(\times\) 175 \(\times\) 7 \(\times\) 11 mm; an H-shaped steel beam with a cross-sectional dimension of HN300 \(\times\) 150 \(\times\) 6 \(\times\) 9 mm was adopted for specimen ST-6.  
The blind bolts were snugly tightened with an impact wrench and subsequently tightened with a torque wrench according to the specified torque values. All the blind bolts were tightened to a torque of 190 N m, as recommended by the instruction given by Lindapter International, and the corresponding applied pre-load was 70 kN. Notably, prior to the tightening of the blind bolts, the anchor nuts were fastened to the screw by gluing or welding to prevent the nuts from falling off due to vibration when installing the blind bolts or casting concrete.  
Note: \(t_{\text{p}}\) is the end-plate thickness; for specimens ST-1 and ST-3, \(t_{\text{p}}\) = 12 mm; for specimens ST-2 and ST-4, \(t_{\text{p}}\) = 24 mm; specimens ST-4 and ST-5 have the same dimension, except ST-4 with nut and ST-5 without nut.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Composite Relative Measurement Technology of Spacecraft Formation
#### 3.6.1 GPS + RF Coupling Measurement Technology  
The composite relative measurement of GPS + RF [60] consists of the GPS receivers, the inter-satellite RF measurement sensor, and the relative navigation filter processor, as shown in Figure 23. The GPS receivers of the master satellites send ephemeris and observation data to the combined navigation filter and GPS orbiting processor. The GPS receivers of the slave satellites send ephemeris and observation data to the combined navigation filter through the RF sensor communication link. The GPS orbiting processor establishes the position and velocity of the primary satellites and sends them to the combined relative navigation processor for time-update solving. The RF sensor extracts high-precision inter-satellite baseline data that are further employed for combined filter updates. Combined with the relevant data of the GPS real-time orbit determination module, the GPS receiver unit, and the RF ranging module, the relative state information of the formation spacecraft is calculated via the EKF filter. The coupled GPS and RF relative measurement method can meet the real-time requirements of spacecraft precision formation measurement [61].  
Figure 22: Structure diagram of the GRACE satellite KBR ranging system.
## 3 Trajectory Optimization Techniques Overview
* The domain of convergence decreases due to the requirement of the initial guess being close to optimal solution. The feasibility of performing the on-board optimization routine depends on computation time and cost. Hence, the PSO technique is chosen as a primary optimization routine due to its capability for global convergence and its robustness to solve highly non-linear problems with greater computational efficiency. Moreover, this technique is used widely to solve diverse spacecraft trajectory optimization problems, and its on-board implementation was verified, through various case studies, for adequate convergence time and low computation cost [70].
# Multi-Augmentation-Based Contrastive Learning for Semi-Supervised Learning
## 2 Method
### Pseudo-Labels Generating  
The process of generating pseudo-labels is depicted in Figure 2. MAC-SSL leverages the model outputs \(p_{wb}^{u}=p(q|u_{wb}^{\prime})\) and \(p_{mb}^{u\prime}=p(q|u_{mb}^{\prime})\) obtained from its weakly augmented and moderately augmented versions \(u_{wb}^{\prime}\) and \(u_{mb}^{\prime}\), respectively, to compute an average prediction \(p_{b}^{u}\) for each unlabeled sample \(u_{b}\) in \(\mathcal{U}\). It is worth noting that \(p(q|u)\) represents the model's output for \(u\). Subsequently, a \"Sharpening\" [33] operation is applied to \(p_{b}^{u}\) to generate the pseudo-label \(q_{b}^{u}\) for \(u_{b}\). The corresponding computational steps are described as follows:  
\[p_{b}^{u}=\frac{1}{2}\big{(}p_{wb}^{u\prime}+p_{mb}^{u\prime}\big{)}\text{ and} \tag{1}\]  
\[q_{b}^{u}=\text{Sharpen}(p_{b}^{u},T), \tag{2}\]  
where the Sharpening operation is defined as follows:  
\[\text{Sharpen}(p,T)_{i}:=\frac{p_{i}^{\frac{1}{T}}}{\sum\limits_{j=1}^{L}p_{j }^{\frac{1}{T}}}, \tag{3}\]  
where \(p\) represents an input categorical distribution (specifically in MAC-SSL, \(p\) is the average class prediction generated from the model outputs \(p_{wb}^{u\prime}\) and \(p_{mb}^{u\prime}\), denoted as \(p_{b}^{u}\)) and \(T\) is a hyperparameter that signifies the \"temperature [41]\" of the class distribution. As \(T\to 0\), the output of \(\text{Sharpen}(p,T)\) will approach a \"one-hot\" distribution. Since \(q_{b}^{u}=\text{Sharpen}(p_{b}^{u},T)\) will be used as a target for the model's prediction for augmentations of \(u_{b}\), lowering the temperature encourages the model to produce lower-entropy predictions. \(T\) is set to a small value in this paper.
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Model Checking  
Model checking is an automated formal verification technique for determining whether a design meets a given specification. The behavior of the design is usually modeled using a _transition system_. The specification usually consists of a _property_ (or a set of properties) expressed in a temporal logic.  
Transition systems and temporal logics are discussed in detail in Sections 2.8 and 2.9, respectively. Section 2.10 formalizes the model checking problem as well as a particular subclass of that problem, called invariant verification. In Section 2.11, a way to symbolically represent transition systems is presented, whereas in Section 2.12 the invariant verification problem is reduced to a reachability problem on such a symbolic representation. These concepts form the foundation for the discussion in the remaining sections of this paper, which will each focus on specific model checking algorithms and techniques.
## 5 Conclusions  
This study evaluates the effects of sampling frequency of the acceleration signal, window length of the classification fragment, and number of features on classifier performance. The methods were chosen in order to reduce the requirements on computational power and available memory and are suitable for implementing physical activity classification in real-time systems.  
We acknowledge some limitations in our approach that could be improved on in the future studies. First, sampling frequency and window length values evaluated in this study were chosen as a representative of the values used in other studies (low value, mid-range value, high value), but the optimum value could be somewhere between or even out of the explored range. It would be possible to classify larger numbers of different activity types and the acceleration signals should be measured under normal daily living conditions, which would allow for better physical activity classification during everyday life. The results could be evaluated with other machine learning algorithms that are used for physical activity classification, such as support-vector machines, Bayesian networks, and k-nearest neighbor algorithms, in order to see if there are any differences in the effects of the explored parameters.  
Conceptualization, A.A., K.P., and I.F.; formal analysis, A.A. and K.P.; methodology, A.A., K.P., and I.F.; investigation, A.A., K.P., D.K., and M.L.; data curation, D.K. and M.L.; writing--original draft preparation, A.A.; writing--review and editing, K.P., I.F., and G.J.; visualization, A.A.; validation, M.L.; supervision, I.F. and G.J.  
The research was funded partly by the Estonian Ministry of Education and Research under institutional research financing IUTs 19-1 and 19-2, and by Estonian Centre of Excellence in IT (EXCITE) funded by European Regional Development Fund.  
The authors wish to thank [PERSON] and [PERSON] for assistance during the experiments, medical doctors [PERSON] and [PERSON] from the Department of Pulmonology, North Estonia Medical Centre Foundations, Tallinn, Estonia for providing the environment for the study, and also those subjects who so kindly participated in the experiments.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyzes, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Formulation for Joint Design Problem
### Problem Definition
#### 3.3.3 Rewards  
\(R_{i}^{t}(s_{i},a_{i},\mathbf{a_{-i}})\) is the instantaneous reward when SU \(n_{i}\) performs action \(a_{i}\) in \(s_{i}\) and other competing SUs execute actions \(\mathbf{a_{-i}}\). The considered reward function at time step \(t\) is calculated as follows:  
\[R_{i}^{t}(s_{i},a_{i},\mathbf{a_{-i}})=\left\{\begin{array}{cl}r_{i}^{t}(s_{ i},a_{i},\mathbf{a_{-i}}),&\text{if }u_{TD,i}\leq\kappa_{th}\\ 0,&\text{otherwise}\end{array}\right., \tag{16}\]where \(\mathbf{a}_{-i}=(a_{1},\ldots,a_{i-1},a_{i+1},\ldots,a_{N})\in\mathbf{A}_{-i}=\prod \limits_{j\in N\setminus\{i\}}A_{j}\) is other SUs' action vector, \(r_{i}^{t}(s_{i},a_{i},\mathbf{a}_{-i})\) is the utility function defined in Equation (4), and \(\kappa_{th}\) represents the maximal transmission delay threshold between SU nodes. If TL is smaller than the maximal delay threshold, the transmission is effective and the instantaneous reward is equal to the utility function. Otherwise, the agent will receive no reward.  
As described in Equations (5) and (6), SU \(n_{i}\) needs its own strategy \(\zeta_{i}(s_{i},\ a_{i})\) and the strategies of the previous nodes \(\zeta_{k}(s_{k},\ a_{k})\) to calculate the transmission delay and the average reward. Since every SU node only needs local observations and information exchange with the previous nodes instead of mutual information sharing between competing SU nodes, the problem is formulated as quasi-cooperative stochastic games, which is formally defined as:  
\[\begin{split}&\max\limits_{a_{i}\in\mathbf{A}_{i}}R_{i}^{t}(s_{i},a_{ i},\mathbf{a}_{-i})\\ & s.t.\qquad\quad u_{TD,i}\leq\kappa_{th}\end{split} \tag{17}\]  
Every agent chooses the action of route selection and spectrum access in terms of the strategy \(\zeta_{i}(s_{i},\ a_{i})\), which matches the definition of mixed-strategy game. Moreover, each SU cannot acquire the global information of competing SUs due to the uncertainty of multi-hop CRN. One of the significant characteristics in Mixed-Strategy Nash Equilibrium (MSNE) is that the players cannot obtain their opponents' strategies in advance. In other words, MSNE is a rational countermeasure when the strategies of other players are uncertain. Therefore, MSNE is adopted to solve the quasi-cooperative stochastic game [26].  
**Definition.**_A set of \(M\) strategies \((a_{i}^{*},\mathbf{a}_{-i}^{*})\) is an MSNE if, for every SU \(n_{i}\in M\):_  
\[R_{i}(a_{i}^{*},\mathbf{a}_{-i}^{*})\geq R_{i}(a_{i},\mathbf{a}_{-i}^{*}),\ for\ all\ a_{i}\in\mathbf{A_{i}}. \tag{18}\]  
In the following part, we study the method of speculating competing SUs' strategies only using information exchange with the previous nodes for SU \(n_{i}\), and solve quasi-cooperative stochastic game through multi-agent Q-learning.
## 5 Dynamic Response Characteristics and Safety Analysis  
### Dynamic Responses of Earlier Pilot Tunnel Induced by Blasting of Later Pilot Tunnel  
In order to study the effect of blasting excavation of the later pilot tunnel on the earlier pilot tunnel and to avoid the influences between adjacent pilot tunnels, the 1 st, 2 nd, and 4 th pilot tunnels were selected for numerical simulation. Figures 10 and 11 show the maximum vibration velocities of the 1 st pilot tunnel induced by the blasting of the 2 nd pilot tunnel and 4 th pilot tunnel, respectively.  
As shown in Figure 10a, the farther away from the blasting source of the 2 nd pilot tunnel the monitored point of the 1 st pilot tunnel was, the lower peak velocity at the monitored point will be. The peak velocity of the monitored point \(L=3\) m decreased with the increase in the distance between the monitored point and blasting source. The effects of the 4 th pilot tunnel on the 1 st pilot tunnel, shown in Figure 11a, were consistent with that of the 2 nd pilot tunnel. The maximum peak velocity in Figure 11a was 18.4 cm/s,  
Figure 11: Dynamic responses of the 1 st pilot tunnel induced by blasting the 4 th pilot tunnel: (**a**) Peak velocity of z-direction; (**b**) PPV of monitored point \(L=3\) m.  
Figure 10: Dynamic responses of the 1 st pilot tunnel induced by blasting the 2 nd pilot tunnel: (**a**) Peak velocity of x-direction; (**b**) PPV of monitored point \(L=3\) m.  
much larger than 1.34 cm/s in Figure 10a because the 4 th pilot tunnel is closer to the 1 st pilot tunnel than the 2 nd pilot tunnel. Meanwhile, the blasting area and charge of the 4 th pilot tunnel were larger. It indicates that the effect of the later pilot tunnel on the earlier pilot tunnel will be more pronounced if the blasting source is closer or the blasting area is more extensive.  
Figures 10b and 11b show the PPVs of the 1 st pilot tunnel at the minimum blasting distance \(L=3\) m. The vibration velocity increased to the peak within a relatively short time, gradually attenuated, then eventually stabilized. The velocity of the 1 st pilot tunnel peaked at approximately 0.03 s in the case of blasting the 2 nd pilot tunnel. However, the peak velocity was reached in less than 0.01 s when the 4 th pilot tunnel was blasted. The earlier pilot tunnel will take a shorter time to reach its peak value if the later pilot tunnel's blasting source and blasting area are closer and more extensive.