esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Color Attenuation Prior for Single-Image Haze Removal
## 4 Evaluations
### Qualitative Evaluation  
Figure 15 presents a hazy scene of landscape, to visually assess the dehazing performance of the proposed ICAP and other benchmarking methods. It is evident that, in this case, the methods with higher haze-removal ability are the DCP and ICAP. Considering the red cropped regions, it is observed that color distortion occurs in FFD, CAP, and HPM, while DCP and ICAP produce results that are consistent with the atmospheric scattering model. The green cropped regions, however, show that only the proposed ICAP has the ability to correctly remove haze in the sky region. DCP, due to its well-known drawback in handling the sky region, introduces an unrealistic color shift, turning the sky to yellow. The same problem also can be seen in FFD, CAP, and HPM, in which HPM also produces additional background noise.  
Figure 16 shows an image of a hazy outdoor scene as a second example. Since this image mainly contains non-sky regions, DCP and ICAP share the top dehazing performance. The zoomed-up color chart regions reveal that CAP and HPM exhibit lower dehazing power than the other three methods, since, not only do they suffer from color distortion, they also lose the details of the grass. Alternately, the FFD method produces a visually acceptable result, but it still has halo artifacts at the small edges of grass and color distortion, which can be seen in the first element of the first and third rows of the color chart. Regarding the green zoomed-up regions, even though FFD exhibits the best performance in restoring details, the unwanted halo artifacts in other regions lower its dehazing power in general. DCP also performs well in this region, but it tends to darken the image by removing too much haze, which may fade the details in dark regions. Thus, the proposed ICAP, which produces visually satisfactory results, can be considered as a high-performance method in haze removal. Figure 17 illustrates other examples of the real hazy scene, to demonstrate the dehazing capability of ICAP and other methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **SSIM** & **TMQI** & **FSIMc** & **FADE** \\ \hline DCP & 0.6580 & 0.7319 & 0.8208 & 0.8328 \\ FFD & 0.7200 & 0.7740 & 0.8055 & **0.8053** \\ CAP & 0.6864 & 0.7512 & 0.8252 & 1.0532 \\ HPM & 0.6424 & 0.7026 & 0.7879 & 1.7480 \\ ICAP & **0.7600** & **0.7892** & **0.8482** & 1.1277 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average SSIM, TMQI, FSIMc, and FADE results on the I-HAZE dataset.  
Figure 15: Qualitative assessment of different algorithms on a real hazy scene of landscape: (**a**) hazy image, dehazed images and corresponding zoomed-up regions of (**b**) DCP, (**c**) FFD, (**d**) CAP, (**e**) HPM, and (**f**), the proposed ICAP.
# Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms
## 5 Results and Discussion
### Phase I: Maze Exploration
#### 5.1.1 Benchmark Comparison  
The performance of our iAA model was poorer than the benchmarked technique for both maze sizes when tested with search group sizes of two agents and five agents, which is a very small size for a swarm, as can be seen in Figure 15. We emphasize that we chose such small groups to make a comparison with the closest relevant reference. However, our iAA algorithm was quicker to converge and needed fewer steps, when the group size was increased to just 10 agents. A likely reason for the better performance in our system is due to the poor exploration capability of small group sizes, such as those with 2-5 agents.  
For the maze sizes used, at least 7-10 agents would be necessary to promote quicker exploration that would lead to better results, as can be seen from the extrapolation of the trends in the results in Figure 15. These small group sizes were tested only for comparison with the results presented in the benchmark. Observing the interpolated line and looking at its trend as we change agent group size shows that our result is favorable compared to the benchmark, as can be seen from Figure 15. iAA also shows qualitatively similar behavior in a larger maze (right panel of Figure 15), with quantitatively better performance with 10 or more agents (quantitatively better than in the case of the smaller maze). Therefore, the more agents, and the bigger the maze, the more likely our proposed system is to outperform the benchmark study.  
Figure 14: The achieved maze coverage (**right**), measured as the percentage of explored cells in the maze, and the corresponding number of steps (**left**) for each of the three mazes shown in Figure 13. Exploration is a by-product of the search process.  
Figure 15: While the benchmark outperforms our approaches (iAA and iAA-B) for small group sizes, our technique iAA beats the benchmark [89] as the swarm size increases. Furthermore, an extended study shows that the trend of improvement in iAA is even more favorable as the swarm size continues to increase. This change in performance can be attributed to the swarming effect, which is better observed for a considerable search group size and does not show as much for a very small group (two agents or so).
## 4 Results and Discussion
### Effect of Fin Design Parameters  
#### 4.3.1 Fin Length  
As shown in the solid blue and red lines in Figure 7, pressure drop continues to go down, but the heat transfer area continues to increase when fin length on the hot side changes from 3 mm to 10 mm. As for the cold side, both pressure drop and heat transfer area increased persistently when fin length on the cold side changed within the same range. It seems that the optimum design of fin length on the hot side is near 6 mm, which matches the result of the NSGA-II.
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 4 Discussion  
Our goal was to gain a better understanding of the relationship between EVI breaks (abrupt changes), trends (gradual changes), and forest health. We shed light on this relationship by quantifying forest EVI breaks and trends in the AGPE and explaining variation in break magnitude with forest and climate-related predictors.  
Forest vegetation changes arise in part from stand-replacing (e.g., forest fires) and non-stand-replacing disturbances (e.g., hail tree damage). Negative EVI breaks likely reflect the impact of stand-replacing disturbances whereas negative trends (browning  
Figure 5: Predictors of the magnitude of EVI breaks (negative breaks in red and positive breaks in cyan tone boxes) from 2003 to 2022. The ranking reflects feature importance using the gain metric as estimated by XGBoost models. The same five predictors are in the top five but with slightly different rankings (connecting lines with slopes) except for the summer climate moisture index with a 3-year lag, which ranks fourth in both (connecting line with no slope). Forest protection status is low-ranking for both types of breaks. The XGBoost models were run with subsets of the detected breaks (negative breaks, \(n=116\) records; positive breaks, \(n=3263\) records).  
trends) are likely associated with non-stand-replacing disturbances. Conversely, positive breaks may reflect growth spurts associated with improved climate conditions, competitive release, or successful reforestation activities. Positive trends (greening trends) may reflect sustained growth or recovery after a disturbance. These important forest dynamics can be hidden when assuming that change occurs monotonically; hence, detecting breaks and differentiating between short- and long-term changes allow for a robust way of analyzing forest change [54; 56; 57; 63].
# Modeling Heat Consumption of an Office Building During COVID-19 Restrictions
## Nomenclature  
AHU: air-handling unit, DH: district heating, DHW: domestic hot water, HDD: heating degree day, HVAC: heating, ventilation, and air conditioning, O&M: operation and maintenance, PMV: predicted mean vote, SH: space heating, and TRV: thermostatic radiator valve.  
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# Communication Protocol Design for IoT-Enabled Energy Management in a Smart Microgrid
To be specific, we make the following contributions in this paper:  
* We develop a new communication protocol for direct communication between IoT-enabled HEMSs. For the proposed protocol, we have analytically derived the mathematical expressions for error probability at a certain HEMS when decoding a signal from another HEMS. Our analysis shows that the error probabilities accumulate as the messages are forwarded across multiple HEMSs. However, each HEMS can still decode nearby HEMSs with better reliability. * For a smart microgrid with multiple HEMSs, we have obtained mathematical expressions for the average BER in the overall network. It has been shown that the channel conditions between the HEMSs at the middle of the network have a greater impact on the worst case error performance, as well as the average BER for the proposed protocol. * We have performed a numerical simulation of the proposed protocol and have demonstrated the relative impact of channel conditions between different HEMSs in comparison with the benchmark direct DF mechanism. It can be observed that when one of the HEMSs suffers from poor channel conditions, the proposed protocol can still maintain the same level of error performance as compared with the case of good channel conditions at all HEMSs. However, direct DF exhibits a worse error performance even if one of the HEMSs suffers poor channel conditions. The rest of the paper is organised in the following manner. The system model of an IoT-enabled smart microgrid integrated with direct communication features between HEMSs is presented in Section 2. The information exchange protocols are explained in Section 3 for different possible combinations of HEMS indices across different time slots. The error performance analysis for the communication between two HEMSs, as well as for the overall microgrid comprised of multiple HEMSs, is performed in Section 4. The numerical simulation results are presented in Section 5 to illustrate the effectiveness of the proposed scheme. Finally, Section 6 concludes the paper.
# Identification and Characterization of Fibronectin-Binding Peptides in Gelatin
## 3 Results and Discussion
### DPI Analysis of the Effect of MW on FN Binding  
After FN was immobilized on the chip, the 1, 3, and 10 kDa peptides and bovine gelatin were, respectively, injected into the two channels of DPI and reacted with immobilized FN. The MW range of bovine gelatin was 1.4-33 kDa, as determined by gel filtration chromatography. The adsorption conditions of these samples were shown in Figures 9-11, including the real-time profile (A) and the resolved layer thickness-mass (B).  
Figure 8: MS/MS spectrum of \(m/z\) 851.4 corresponding to the sequence GFGSLDGAK.  
Figure 7: MS/MS spectrum of \(m/z\) 793.6 corresponding to the sequence GANGAP*GIAGAP*GFPPAAR. *\(m\)* indicated the hydroxylation modification of the proline.  
In Figure 9B, the thickness increased from 11.60 nm to 12.55 nm, and the mass increased from 3.68 to 3.86 ng/mm\({}^{2}\). The obvious increases in thickness and mass demonstrated that gelatin of MW range 1.4-33 kDa interacted with the immobilized FN. Certainly, not the entirety of the MW range (1.4-33 kDa) could bind to FN; thus, 1, 3, and 10 kDa peptides were further analyzed.  
Figure 9: Adsorption of gelatin to immobilized FN on the chip surface as shown by (**A**) the measured TM (black line) and TE (red line) phase changes, and (**B**) the resolved layer thickness (black line) and mass (red line).  
According to DPI data analysis, the 1 kDa sample failed to bind FN (Figure not shown). Although the thickness did not increase significantly, there was a significant increase in the mass in the 3 kDa sample (Figure 10). This proved that the 3 kDa sample bound to FN,  
Figure 10: Adsorption of 3 kDa sample to immobilized FN on the chip surface as shown by (**A**) the measured TM (black line) and TE (red line) phase changes and (**B**) the resolved layer thickness (black line) and mass (red line).  
Similarly, the mass and thickness of the 10 kDa sample indicated that it could interact with FN (Figure 11).  
It is known that the FN-binding activity of collagen is lower than that of gelatin [5]. Our study suggested that the peptides lower than 1 kDa did not readily bind to FN. Thus, the bigger MW of peptides, the better the FN-binding activity. In other words, there was stronger binding activity in the suitable MW range of peptides, because a modest MW might radically expose more FN-binding sites than collagen. In this paper, peptides in the range of 2-30 kDa exhibited better affinity to FN. Therefore, the MW plays an important role in the interaction between FN and peptides.  
Figure 11: Adsorption of 10 kDa sample to immobilized FN on the chip surface, as shown by (**A**) the measured TM (black line) and TE (red line) phase changes and (**B**) the resolved layer thickness (black line) and mass (red line).
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 3 The Bootstrapping Service: CoAP-EAP
### CoAP as EAP Lower-Layer  
One of the first questions that we should answer is the suitability of CoAP as EAP lower-layer. In particular, the requirements for a correct EAP lower-layer are specified in [12]. We can affirm that CoAP can be used as EAP lower-layer by contrasting its capabilities with these requirements:  
_Unreliable transport_. Although EAP does not assume that lower layers are reliable, CoAP provides reliability by means of Confirmable messages. This implies that retransmission timers at EAP level can be stopped for simplification, as recommend in [12]; _Lower layer error detection_. EAP assumes the lower layer has mechanism of error detection. CoAP is performed on top of UDP which already provides a checksum over the whole payload, where CoAP is transported; _Lower layer security_. EAP does not require lower layers to provide security services. CoAP exchanges can be performed without security; _Minimum MTU_. EAP requires a EAP lower-layer with a MTU size of 1020 octets or greater. CoAP assumes an upper bound value of 1024 octets in the payload, where EAP will be transported; _Possible duplication_. EAP does not require handling duplication of packets. Even so, CoAP provides a Message-ID for deduplication, which does not harm the EAP authentication process; _Ordering guarantees_. EAP requires the lower-layer to preserve the ordering. CoAP allows us to preserve this ordering by using the Message-ID values. Our bootstrapping service uses this field for that purpose, as described in Section 3.3.  
As observed, CoAP is able to cover each of the requirements and our bootstrapping service can safely rely on CoAP as a transport for EAP.
## 3 Model Development
### Notations  
In this study, a mathematical model-based optimization technique is applied to derive the optimal delivery routes for both UGVs and UAVs. Therefore, some notations denoted the decision variables, parameters, indices, and index sets are defined as follows to develop the mathematical model.  
Decision Variables  
\(y_{n,m}^{k,r}:\): \(k\) and it is delivered the \(m\)th sequence of the \(n\)th round, otherwise it has value of 0 \(t^{r}\) : : A decision variable representing the delivery arrival time of order \(r\) [second] \(s_{f}^{k,r}:\): \(k\) direction variable representing the customer satisfaction when an order \(r\) consisting of \(s_{f}^{k,r}:\): \(s_{f}^{k,r}:\): a type of goods \(f\) is assigned to a means of delivery \(k\) and delivered by it (\(0\leq s_{f}^{k,r}\leq 1\)) \(z^{r}:\): \(k\) decision variable of customer satisfaction when order \(r\) is delivered (\(0\leq z^{r}\leq 1\)) \(\mathrm{Parameters}\): \(a_{f,d}^{r}:\): \(a_{\mathrm{t}}^{r}\): \(k\) type of goods \(f\) should be arrived at a node \(d\), otherwise it has a value of 0 \(g^{r}\) : \(k\) constant representing the weight of the order \(r\) [kg] \(v^{r}:\): \(k\) constant representing the volume of the order \(r\) [cm\({}^{3}\)] \(T:\): \(k\) constant representing the time at which delivery of all orders should be finished [second] \(G^{k}:\): \(k\) constant representing the limitation of weight capacity of the means of delivery \(k\) [kg] \(V^{k}:\): \(k\) constant representing the limitation of volume capacity of the means of delivery \(k\) [cm\({}^{3}\)] \(t_{d,d^{r}}^{k}:\): \(k\) constant representing the elapsed time when means of delivery \(k\) is moved from node \(d\) to node \(d^{r}\) [second] \(t_{k}^{k}:\): \(k\) constant representing the service time to process an order \(r\) by means of delivery k other than elapsed time due to moving [second] \(\beta_{1,f}^{k,r}:\): \(k\) constant representing the fundamental customer satisfaction when an order \(r\) consisting of a type of goods \(f\) is assigned to a means of delivery \(k\) and delivered by it (\(0\leq\beta_{1,f}^{k,r}\leq 1\)) \(\mathrm{Parameters}\): \(k\) constant representing the reduced customer satisfaction according to the elapsed time when an order \(r\) consisting \(B:\): \(k\) constant representing a very large number  
Figure 2: Hypothetical beach area for providing a delivery service by both UGVs and UAVs.  
Decision Variables  
Indices and index sets  
\(K\) : A set representing the means of delivery \(k\)  
\(N\) : A set representing the delivery round (=\(n\)) of a means of delivery, while a round is defined  
as the number of departure from depot  
\(M\) : A set representing the delivery sequence (=\(m\)) of \(n^{\text{th}}\) delivery round  
\(R\) : A set representing the delivery order numbers (=\(r\))  
\(F\) : A set representing the type of goods to be delivered (\(f=1\):\(\text{food},f=2\):general items)  
\(D\) : A set of nodes (=\(d\)) representing the destination of the goods to be delivered
## 2 Methodology
### Flight Simulation  
Due to the rapid changes in aerodynamic forces and moments in turbulence, it is difficult to identify the individual modes of motion from these eigenvalues through digital 6-DOF flight simulations from one instant to another. Regarding the dynamic stability monitoring, one approach to solve this problem is to use the approximate modes of motion obtained from decoupled longitudinal and lateral-directional equations as guidance.  
The decoupled linearized longitudinal equations of motion, which are decoupled from the lateral-directional motions in Ref. [19], are given by  
\[i=-g\theta\cos\theta_{1}-X_{u}u+X_{\alpha}\alpha+X_{\delta e}\delta e \tag{25}\]  
\[U_{1}\dot{\alpha}-U_{1}\dot{\theta}=-g\theta\sin\theta_{1}+Z_{u}u+Z_{a}\alpha+ Z_{\dot{a}}\dot{\alpha}+Z_{q}\dot{\theta}+Z_{\delta e}\delta e \tag{26}\]  
\[\ddot{\theta}=M_{u}u+M_{a}\alpha+M_{\dot{a}}\dot{\alpha}+M_{q}\dot{\theta}+M_{ \delta e}\delta e \tag{27}\]where \(X_{u}\), \(X_{\alpha}\), and \(X_{\delta e}\) are the dimensional variations of force along the \(X\)-axis with the speed, angle of attack, and elevator angle, respectively; the other dimensional derivatives of \(Z\) and \(M\) are described and given in Ref. [19]. The decoupled lateral-directional equations of motion are:  
\[U_{1}\dot{\beta}+U_{1}\dot{\psi}=g\phi\cos\theta_{1}+Y_{\beta}\beta+Y_{p}\dot{ \psi}+Y_{\dot{\psi}}\dot{\psi}+Y_{\dot{\alpha}\dot{\delta}}\dot{a}+Y_{\dot{ \alpha}r}\dot{\delta}r \tag{28}\]  
\[\ddot{\phi}-\overline{A}_{1}\ddot{\psi}=L_{\beta}\beta+L_{p}\dot{\psi}+L_{r} \dot{\psi}+L_{\delta r}\delta a+L_{\delta r}\delta r \tag{29}\]  
\[\ddot{\psi}-\overline{B}_{1}\dot{\phi}=N_{\beta}\beta+N_{p}\dot{\phi}+N_{r} \dot{\psi}+N_{\delta a}\dot{a}+N_{\delta r}\dot{\delta}r \tag{30}\]  
where \(\overline{A}_{1}=I_{xz}/I_{xx}\) and \(\overline{B}_{1}=I_{xz}/I_{zz}\); the dimensional derivatives of \(Y\), \(L\), and \(N\) are described and given in Ref. [19]. The characteristic equations for Equations (25)-(27) and Equations (28)-(30) are polynomials of the 4 th degree and their roots are solved with a quadratic factoring method based on the Lin-Bairstow algorithm in Ref. [24].  
The 4 th degree polynomial of longitudinal characteristic equations has 4 roots; they are two complex conjugates [12]. The short-period mode is one of the two complex conjugates. The other is the phugoid mode (long-period mode). Each mode has the same real part, but with imaginary parts of equal magnitude and opposite signs. The 4 th degree polynomial of lateral-directional characteristic equations also has 4 roots; they are one pair of complex conjugates and two real values. The pair of complex conjugates represents the Dutch roll mode. One of the two real values represents the spiral mode and another one represents the roll mode.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 4 Location and Installation of the Measuring Devices  
### Considerations of the WMO  
The WMO establishes that, to provide representative measurements of the urban fabric, the AWSs must be situated in the urban canopy, within the UCZ, and in uniform placements with respect to the height of measurement, the distance to obstacles, and obstruction of the horizon. The following two sections set out the specific considerations concerning the horizontal and vertical scales; however, in urban areas, the WMO considers that it may be necessary to install the devices in places that are not recommended, such as close to buildings or sources of artificial heat. Similarly, it establishes that it is essential to ensure a complete, exact, and up-to-date collection of metadata, including information concerning the station, its geographical location, characteristics of the surroundings, and the date of installation, among other things.