esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Multiobjective Formulation  
The multiobjective formulation is given in Equation (14). A penalty formulation was adopted to handle the problem constraints.  
\[\left\{\begin{array}{l}\min(\mathcal{C}_{1}(\mathbf{I})+\wp_{1}+\wp_{2}+ \wp_{3}),\\ \min(\mathcal{C}_{2}(\mathbf{I})+\wp_{1}+\wp_{2}+\wp_{3}),\\ \min(\mathcal{C}_{3}(\mathbf{I})+\wp_{1}+\wp_{2}+\wp_{3}),\end{array}\right. \tag{14}\]  
\[\wp_{1}=\left\{\begin{array}{ll}0&\text{if}\quad 0<T_{min}\leq T_{i}(j) \leq T_{max}\\ \psi&\text{otherwise}\end{array}\right., \tag{15}\]  
\[\wp_{2}=\left\{\begin{array}{ll}0&\text{if}\quad\alpha_{i}>\alpha_{lim}\\ \psi&\text{otherwise}\end{array}\right., \tag{16}\]  
\[\wp_{3}=\left\{\begin{array}{ll}0&\text{if}\quad\mathcal{P}_{i}\;\epsilon \;\text{fixed frame}\\ \psi&\text{otherwise}\end{array}\right., \tag{17}\]  
where \(\wp_{i,\;i=1,2,3}\) are the penalty functions and \(\psi\) is a large scalar.  
Multiple solutions coexist forming the Pareto front displayed in Figure 6. Without adding supplementary information about the desired result, all the nondominated points form a potential optimal solution.  
Figure 6: The Pareto front.  
In order to facilitate its interpretation, two dimensions representations of the Pareto front are given. Figure 7 shows the cable tension vs the elastic stiffness criteria, the cable tension vs dexterity criteria, the dexterity vs the elastic stiffness criteria and the histogram of each criterion. The three criteria are normalized, and their values vary from 0 to 1.  
The nondominated solutions for each two criteria present the preferable solutions if only the two corresponding objectives are considered. For the overall problem, the most desirable results are those which lay within the nondominated solutions of all the pairs illustrated in Figure 7. For the Pareto front presented above, the selection of the optimal solution was complicated since each solution minimized no more than two criteria and maximized those remaining. Additional subjective preference information was, therefore, needed to facilitate the decision-making.
# Boundary-Aware Hashing for Hamming Space Retrieval
## 3 Boundary-Aware Hashing
### Deep Neural Network Architecture  
BAH is an end-to-end deep supervised hashing method that learns the deep feature and the hash coding in the same network architecture using supervised information. Figure 2 depicts a high-level overview of our BAH architecture, which consists of two major components: deep feature learning and a loss function. Deep feature learning is used to learn the feature representation of hashing. The loss function is used to learn hash coding by supervised information.  
Deep feature learning includes a backbone network and a fully connected hash layer (_fch_). Following previous works [7; 9], we replace the softmax layer of backbone network with a _fch_ of \(K\)-neurons, which maps the high-dimensional feature representation into \(K\)-dimensional continuous code \(u_{i}\in\mathbb{R}^{K}\) for each image \(x_{i}\). We obtain compact binary hash codes with the sign function \(b_{i}=\text{sgn}(u_{i})\). A novel BAH architecture is designed for learning concentrated and compact binary hash codes. Note that we do not add the  
Figure 2: The architecture of the proposed Boundary-Aware Hashing (BAH) comprises two key components: (1) a backbone network for learning image representation of each image and an fch (hash layer) for learning the feature representation of hashing and (2) a boundary-aware penalization that explicitly characterizes the Hamming ball by the boundary information for preserving similarity relationship in the Hamming space, and an \(l_{2}\) loss for controlling the quantization errors and hash code quality. The boundary-aware penalization discriminatively penalizes the dissimilar data inside and outside the Hamming ball. The larger blue/red ball represents dissimilar/similar data of a large sample. The small blue/red ball represents dissimilar/similar data of a small sample. The fulcrum of the triangle is expressed as the boundary of the ball.  
tank activation function [33; 34] to the hash layer due to its saturation. We will elaborate on the details in the ablation study, which measures the impact of adding the tanh activation function.  
In terms of the loss function, to ensure that the compact and concentrated hash codes are used for hash table lookups, this paper proposes a novel loss function based on boundary-aware penalization. Furthermore, it adopts the widely used \(l_{2}\) loss function to control the quantization error of continuous value \(u_{i}\) to binary codes \(b_{i}\).
## 6 Conclusions  
In this paper, we propose a _C/N\({}_{0}\)_ estimation method that is based on an adaptive strong tracking Kalman filter, which considers the characteristics and application environment of the vector receivers. Based on the STKF and a measurement noise adaptation that is based on Allan variance, the ASTKF _C/N\({}_{0}\)_ estimation method estimates the _C/N\({}_{0}\)_ values of vector receivers in signal strength hopping and weak signal environments. The results of the first simulation test demonstrate that the estimated delay of the ASTKF method is significantly smaller than that of the AKF method when the _C/N\({}_{0}\)_ jumps. The results of the second simulation experiment demonstrate that the estimated standard deviation of the ASTKF method with an averaging time of 0.5 s is smaller than that of the NWPR and the VSM with a longer averaging time in both the strong signal and the weak signal simulation environments. Therefore, compared to the NWPR and the VSM, the ASTKF method can use a shorter averaging time to reduce the lag of the estimation results. The static field test data demonstrate the applicability of the ASTKF method to signals in practice, and the ASTKF method can track large variations in the true signal _C/N\({}_{0}\)_. In addition, the results of the field test support the conclusion of the simulation test that the estimated standard deviation of the ASTKF method is significantly smaller than that of the NWPR and the VSM.  
Figure 12: Satellite 10 _C/N\({}_{0}\)_ estimation box plot comparison.  
Figure 13: Satellite 24 _C/N\({}_{0}\)_ (0.5 s averaging time).  
**Author Contributions:** Conceptualization, S.L. ([PERSON]) and S.L. ([PERSON]); Methodology, S.L. ([PERSON]); Software, S.L. ([PERSON]) and J.Z.; Validation, S.L. ([PERSON]), J.Z., Q.F., and Y.Y.; Writing--original draft preparation, S.L. ([PERSON]); Writing--review and editing, S.L. ([PERSON]), S.L. ([PERSON]), J.Z., Q.F., and Y.Y.; Supervision, S.L. ([PERSON]); Project administration, S.L. ([PERSON]) and Q.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Quantitation of Odor-Active Compounds and OVA Calculation  
The odor-active compounds from the bulk of volatile compounds of SBT were identified by using a unique technique of odor-active compounds screening. To deeper evaluate the aroma contributions of these perceived odor-active compounds, their contents were quantitated in 11 SBT samples based on internal standard and their corresponding standards, and the quantitative results of these odor-active compounds supplied in Table 2 and Table S2. The major odor-active compounds of SBT were linalool (2124.78 \(\pm\) 696.93 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), linalool oxide II (1113.75 \(\pm\) 302.78 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), epoxylinalol (973.71 \(\pm\) 371.22 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), methylcal salicylate (786.55 \(\pm\) 409.55 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), geraniol (523.28 \(\pm\) 459.73 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), linalool oxide I (509.49 \(\pm\) 170.87 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), phenylethyl alcohol (362.70 \(\pm\) 167.55 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), \(\beta\)-ionone (292.96 \(\pm\) 43.00 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), benzeneacetaldehyde (259.48 \(\pm\) 204.12 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), (_E_)-nerolidol (207.37 \(\pm\) 79.16 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), dihydroactinidiolide (199.13 \(\pm\) 93.87 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), (_E_)-\(\beta\)-damascenone (123.08 \(\pm\) 46.69 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), geranyl acetone (107.28 \(\pm\) 20.18 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), and theaspirane (102.94 \(\pm\) 35.94 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg). In contrast, _D_-limonene (34.64 \(\pm\) 22.01 \(\mathrm{\SIUnitSymbolMicro kg}\)/kg), (_3_)-3-hexenyl
## 3 Results and Discussion
### Colloidal Stability of Various Late Dispersions
#### 3.3.2 Time Dependency of Colloidal Stability  
In addition to the observation that an increased particle size leads to increased colloidal stability we also observed a time dependency of the dispersions stability. To analyse this we measured the average particle size and the PDI of the latex particle dispersion with an average diameter of 195 nm and oligoglycidol\({}_{6}\) (**2a**) surface functionality at 0.05 mol\(\cdot\)L\({}^{-1}\) NaCl over time. Here we observed that for the first 10 min the average particle size stayed constant before it slowly started to increase (Figure 7a). Up to 25 min the average particle size indicates only dimers and agglomerates of more than 5 particles only start to appear after 55 min. Even though the average particle size suggest only a slow agglomeration, the development of the PDI over time demonstrates that the agglomeration starts immediately because a steady increase in PDI can be observed (Figure 7b). Due to the time dependency of colloidal stability of latex dispersions it is important to measure average particle sizes at the same time after sample preparation if different concentrations and particles should be compared. Therefore, we chose to record all average particle sizes in colloidal stability experiments 3 min after sample preparation.
# Lateral Oscillation Characteristics of Vehicle Trajectories on the Straight Sections of Freeways
## 5 Discussion
According to the theoretical results, the lane width of the existing freeway still has plenty of room for most vehicles, which confirms the previous studies [10,11]. Therefore, in order to increase traffic throughput, it is feasible to allocate narrower freeway lanes to specific vehicles such as passenger cars [36,37]. A new finding is that the lateral amplitude of the vehicle does not follow a linear trend as speed increases, and it has a tendency to stabilize or even decrease in the high-speed situation. Several existing studies have concluded that lane widening effectively reduces crash frequency because it provides more space [38]. However, some studies suggest that lane widening does not have a significant positive impact on improving traffic safety but also increases the lateral offset of vehicles [39]. Obviously, the findings of this study are more in favor of the latter. Wider lanes not only use up valuable road space but also increase driving risk [16,40]. Consequently, the lateral oscillation characteristics of vehicle trajectories play a critical role in the design of the lane width. Safety and efficiency must be balanced while designing lanes.
## 5 Comparative Analysis  
### Comparative Analysis of Interferometric Phase  
Utilizing DInSAR technology, this study generated two sets of interferometric phase maps (no filter) for SAR satellites in the study area, as depicted in Figure 7d,e, which show the interferometric phase results for LT-1 and Sentinel-1 data, respectively. The analysis reveals superior interferometric quality in LT-1 data, characterized by fewer residual phases in contrast to Sentinel-1 data, which exhibits lower quality with residual phases.  
Figure 7: (**d,e**) Interferometric phase maps of LT-1 and Sentinel-1 satellite data, respectively; (**a–c**) enlarged views of LT-1 satellite data, Sentinel-1 satellite data, and Google optical images in the first typical subsidence area; (**f–h**) the same for the second typical subsidence area. The dashed circles are areas of subsidence.  
Figure 7a-c exhibits the interferometric phases for LT-1 and Sentinel-1 data in the first identified subsidence area, with black circles marking the subsidence range. The LT-1 data display distinct boundaries and effective visual representation in its interferometric phase (as seen in Figure 7a), unlike Sentinel-1 data, which show more atmospheric phase errors and unclear phase boundaries (as in Figure 7b). Figure 7f-h focuses on the second identified subsidence area for both LT-1 and Sentinel-1 data.  
The interferometric fringes of LT-1 exhibit clarity and completeness (Figure 7f), in contrast to the noisy and indistinct fringes observed in Sentinel-1 imagery (Figure 7g). Furthermore, LT-1 data surpass Sentinel-1 data in detecting larger deformations.
## 6 Conclusions and Future Work  
To cope with the existence of selfish or non-cooperative nodes in a network, this article developed an EMS for VDTNs. This type of node has a significant impact on the entire network efficiency and may compromise cooperative node effectiveness because they consume resources (such as energy, memory, and buffer) from other network nodes to meet their demands. Such nodes are regarded as nodes having reputation values less than 10 as they decline packets immediately after reception. The EMS depends on the cooperative transfer of values used for nodes' reputation, as well as the four modules (categorization, neighbor assessment, punishment, and recommendation) to find and eliminate the selfish nodes from the entire network.  
The results of the proposed EMS are conducted in the NS2 simulation tool, which shows that EMS is effective at reducing the impact of selfish nodes on the performance of the entire network. The EMS is compared with two other existing schemes, namely IPS and SOS. The EMS surpasses the benchmark schemes in terms of packet delivery probability, package delivery delay, energy consumption, and the number of dropped packages. We plan to expand this monitoring system for energy-constrained selfish VDTNs in the future.  
This work was carried out in collaboration between all authors. [PERSON], [PERSON] and [PERSON] designed a detailed methodology. [PERSON] and [PERSON] managed the literature searches and detailed analysis. [PERSON] wrote the first draft of the manuscript in consultation with [PERSON]. The final draft of the paper was completed by [PERSON] along with [PERSON]. The funding acquisition was performed by S.F.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This manuscript has no associated data.  
The authors declare no conflict of interest.
## 3 Equilibrium Approximating of One-Shot Game
### Nash Equilibrium Approximation  
The Nash equilibrium is such that each player's strategy is an optimal response to the other players' strategies. For the two-player zero-sum Blotto Game \(G(\mathbf{R},\mathbf{M},u)\), its mixed approximate Nash equilibrium \((p^{*},q^{*})\in\Delta(\Delta:=\Delta\mathbf{R}\times\Delta\mathbf{M})\) should satisfy  
\[ \begin{array}{l}U_{r}(p^{*},q)>U_{r}(p^{*},q^{*})>U_{r}(p,q^{*})\\ U_{m}(p,q^{*})>U_{m}(p^{*},q^{*})>U_{m}(p^{*},q)\end{array}  \tag{7}\]  
According to [16], for \(\forall(p,q)\in\Delta\), we define the lower value of \(G(\mathbf{R},\mathbf{M},u)\) by  
\[\underline{v}(G):=\max_{p\in\Delta\mathbf{R}}\min_{q\in\Delta M}U(p,q) \tag{8}\]  
and define the upper value of \(G(\mathbf{R},\mathbf{M},u)\) by  
\[\overline{v}(G):=\min_{q\in\Delta\mathbf{M}}\max_{p\in\Delta\mathbf{R}}U(p,q) \tag{9}\]  
If the lower value is equal to the upper value, then the Nash equilibrium of the problem exists, and \(\underline{v}(G)\ =\ U(p^{*},q^{*})\ =\ \overline{v}(G)\). Sometimes, in practical applications, it is difficult to obtain an accurate Nash equilibrium solution. At this time, people solve the \(\varepsilon\)_-equilibrium_ that meets the requirements of use. For \(\varepsilon\geq 0\) and \(\forall(p,q)\in\Delta\), the \(\varepsilon\)_-equilibrium_\((p^{*},q^{*})\) is defined as,  
\[ \begin{array}{l}U_{r}(p^{*},q)+\varepsilon>U_{r}(p^{*},q^{*})>U_{r}(p,q^{*})- \varepsilon\\ U_{m}(p,q^{*})+\varepsilon>U_{m}(p^{*},q^{*})>U_{m}(p^{*},q)-\varepsilon\end{array}  \tag{10}\]  
where \(\varepsilon\)_-equilibrium_\((p^{*},q^{*})\) is sufficient for our performance needs and can reduce the computational burden. An important principle in designing the approximate Nash equilibrium in large-scale zero-sum games is optimal response dynamic iteration. Two representative algorithms are Double Oracle (DO) [17] and Policy Space Response Oracle (PSRO) [18]. The DO algorithm solves the approximate Nash equilibrium of large-scale zero-sum games by solving a series of subgames, and it has been proven to converge to the Nash equilibrium in finite zero-sum games [19]. Based on the works of [PERSON] [20], we take the SINR formula (as defined in Equation (1)) as the evaluation criterion for winning or losing the game, and design the DO-SINR algorithm to solve the problem of multi-channel power allocation under a one-shot confrontation scenario. The DO-SINR algorithm is as shown in Algorithm 1. First, setting the initial strategy sets \(\mathbf{R}_{0}\) and \(\mathbf{M}_{0}\) of \(r\) and \(m\), respectively, in each round of iteration, the approximate Nash equilibrium \((p_{t}\ast,q_{t}\ast)\) of the subgame \(G(\mathbf{R}_{t},\mathbf{M}_{t},u)\) is solved, then the best responses \(\tilde{R}_{t+1}\) and \(\tilde{M}_{t+1}\) are obtained for \({p_{t}}^{*}\) and \(q_{t}\ast\), respectively, and added to \(\mathbf{R}_{t}\) and \(\mathbf{M}_{t}\) respectively. The process loops until the termination condition \(\overline{v}_{t}-\underline{v}_{t}\leq\varepsilon\) is satisfied.
## 5 An SOA-Based Platform Example and Case Study Results
### Additional Details on the Results from the Sinteq AB Case  
Sinetiq AB has made its own cloud service/IoT platform, based on ideas and concepts from the Arrowhead Framework, but is not limited to using only this. The main problems addressed in customer cases are to help customers to get more SOA-ified, gain control of the present and future needs, and eventually, to meet the future with more flexibility and agility. Examples of applications and integrations built on the platform include: vehicle part manufacturing process, sensor information systems, sensor intelligence systems, integration of monolith systems with other systems, verification, and compliance tool for integrations and products. The main benefits can be seen in the ability to make advanced prototypes for customer integration problems really quickly (i.e., a magnitude faster than with traditional 1-to-1 integrations), and then to smoothly replace the prototypes with the real integrations with desired level of quality. The use of microservices enables de-coupling and thus, faster development and later changes as well as an efficient modeling for calculating the allocation of processing power to meet customer requirements concerning speed and availability. In addition, the increased flexibility enables the mixing of own components/services with off-the-shelf or customized ones from third parties. The possibility for rapid prototyping and agility in development are important factors in early business development, as a few examples can be realized, and then the customers learn the difference compared to the use of monoliths and the problems related to such. Sinetiq AB sees the initial documentation of interfaces and service definitions as the foundation for the subsequent efficient development-related phases, where development and tests get streamlined and only need to focus on what has been changed and the dependencies thereof. This also makes it very efficient to integrate to one or many systems at the same time. Sinetiq AB plans to augment the platform with components to monitor flow through services/nodes to measure if sufficient or when quality is too low, and also develop test tools with digital twins and simulation possibilities for services. Sinetiq AB considers that a system will never get finalized, and trying to make it all perfect from the start is not feasible and thus, it is better to build it piece by piece and continuously improve these pieces.
## 4 Conclusions  
* This research comprehensively evaluated the effect of using molasses as a modifier for neat bitumen in HMA using detailed experimental investigations. Based on the results obtained, the following conclusions have been made. The water content of molasses has a direct effect on the properties of the molasses' modified binder. The properties of the binder are negatively affected by the increase in the moisture content of molasses.
* Within a wavenumber range of 3550 cm\({}^{-1}\) and 3425 cm\({}^{-1}\), the Alcohol (O-H stretching) fully reacted with the functional group and, around wave number 750 cm\({}^{-1}\); Aromamins (C-H stretching) had partially reacted with the functional group.
* At low temperatures, the complex shear modulus of the molasses' modified binders increased with the increase in the content of molasses. However, at high temperatures, the complex shear modulus decreased. Therefore, molasses improves the rutting resistance of the bitumen binder at a low temperature.
* PG determination tests confirmed that neat bitumen does not undergo a major change in the PG before and after RTFO aging. However, when the percentage of the bio-binder was increased, the complex shear modulus increased, which resulted in improving the PG from PG58 to PG64. After the RTFO aging condition, PG58 became PG70.
* The non-recoverable creep compliance (J\({}_{\rm nr}\)) of molasses' modified binder increases as the temperature increases. A reduced J\({}_{\rm nr}\) value of the binders at 58 \({}^{\circ}\)C could result in improved rutting resistance potential.
* Overall, this study revealed that molasses treated for water content reduction could be used as a partial replacement for the bitumen binder to enhance the rheological properties of the binder and to produce a sustainable bio-asphalt binder.  
Conceptualization, W.H. and T.G., methodology W.H., data collection W.H., data analysis, W.H., validation, W.H. and T.G., paper formatting, T.G., original draft writing, W.H., revision and editing, T.G., research supervision, T.G. All authors have read and agree to the published version of the manuscript.  
This material is based upon work supported by the Ministry of Science and Higher Education of the Federal Democratic Republic of Ethiopia under Grant No. 23A742. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Ministry of Science and Higher Education. The authors are thankful to Texas Tech University and Jimma Institute of Technology for their support.  
The authors declare no conflict of interest.