esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Retail Environments and Product Categories  
The included studies covered a rather diverse range of retail stores, showing that scents can affect consumer behavior towards very different product categories. Clothing was the product category most frequently considered in the included literature, which fits expectations as the use of scents is popularly associated with some clothing brands and their stores (e.g., Abercrombie & Fitch). Other product categories included accessories (e.g., jewelry items, glasses), books and magazines, household appliances, slot machines, and travel packages (Table 1). Accordingly, very different retail environments, ranging from general-purpose stores to specialized ones such as fashion, optics, and book stores. All included studies focused on customer behavior towards tangible goods except for two which focused on, respectively, money gambled on slot machines in a Las Vegas Casino [39], and tour sales in a travel agency [21]. One study [22] was conducted on a florist shop, although it involved a product category (flowers) that naturally emits scents; in that study, the target scent (lavender) was diffused by way of an electric fragrance diffuser, so it met our criterion that scents should originate from the environment and not the product itself.  
The duration of the studies, i.e., the time during which scents were diffused in the store, varied significantly among studies, ranging from a few days (e.g., [39]) to several weeks (e.g., [21; 26]). Very short time spans pose some problems as customer behavior is known to be affected by a variety of factors, such as weather and time of the day [48; 49], which may act as a confounder. Accordingly, most studies included an experimental period of at least one week and/or limited the data collection to a set time of the day.  
Finally, all studies but two had an exclusive focus on the effect of scents in a retail environment, and accordingly sought to minimizing variation in other store atmospherics. The two exceptions were two studies which also considered the effect of in-store music and its interactions with scents [40; 42].
# Impact of Chirp in High-Capacity Optical Metro Networks Employing Directly-Modulated VCSELs
## 2 Chirp Measurements
The optical signal then propagates in a standard single-mode fiber (SSMF) coil, and is received by an EDFA preamplifier followed by a p-intrinsic-n (PIN) photodiode with 25-GHz electrical bandwidth; a variable optical attenuator (VOA) performs the measurements at constant received power. The SSMF length ranges from 10 km to 34 km in order to change the cumulated CD at the receiver end. At first, the optical back-to-back contribution featuring the spectral distortions due to the electro/optic (E/O) and opto/electronic (O/E) processes is measured and excluded in order to measure only the fiber power transfer function (i.e., removing the VCSEL and receiver transfer functions). Equation (2) is used to fit the obtained curves; the measured (continuous curves) and theoretical (dashed curves) frequency responses for the tunable and short-cavity VCSEL sources are shown in Figure 2a,b on 10-GHz and 25-GHz ranges, respectively. The different frequency ranges have been chosen owing to the different E/O bandwidths. As expected, when the accumulated dispersion increases, the frequency dips move towards lower frequencies, reducing the electrical spectrum undistorted region. However, a difference in the position and the depth of the first frequency dip for the two lasers clearly appears, confirming the different chirp regime of the two devices. The tunable VCSEL, in fact, is mainly limited by the transient chirp, while the short-cavity device shows even a significant adiabatic chirp, as visible by the limited extent of the first frequency dip, which almost disappears for 24-km and 34-km SSMF channel transfer functions. The theoretical curves after fitting (dashed lines in Figure 2) show a good agreement with the experimental measurements, allowing a reliable estimation of VCSEL chirp parameters in both cases, reported in Table 1. The short-cavity VCSEL shows higher \(\alpha\) and \(\kappa\) factors, as expected from the device design based on a short-cavity technology. This laser design is capable of achieving wide modulation bandwidth, but a strong frequency chirp is induced during direct modulation, limiting its effectiveness in the presence of network nodes with narrow channel bandwidth.
## 3 Results and Discussion
### Adsorption Experiments of Pb(II)/Cd(II) with O-MWCNT\({}_{S}\)
#### 3.2.2 Temperature  
The activation energy (Ea) not only reflects the dependence of the reaction rate on temperature, but also predicts whether the reaction rate is temperature-dependent [36]. The rate constants \(\mathrm{k_{obs}}\) and the Arrhenius formula at different temperatures were utilized to determine the Ea for the adsorption of Pb(II)/Cd(II) by O-MWCNT\({}_{\mathrm{S}}\). Fitting the logarithm of \(\mathrm{k_{obs}}\) versus 1/T gives a linear plot (Figure 4). The correlation coefficients of \(\mathrm{Ink_{obs}}\) and 1/T for Pb(II)/Cd(II) were negatively correlated at 0.89 and 0.80, respectively. The activation energy Ea was calculated, using the slope, to be 83.68 kJ\(\cdot\)mol\({}^{-1}\) for Pb(II) and 172.88 kJ\(\cdot\)mol\({}^{-1}\) for Cd(II). From this, it follows that the adsorption of Pb(II) on the surface of O-MWCNT\({}_{\mathrm{S}}\) is more convenient than that of Cd(II).  
Figure 3: Influence of pH on adsorption removal.  
Figure 2: XPS spectrum of oxidized multiwalled carbon nanotubes. (**a**) XPS full spectrum of oxidized multiwalled carbon nanotubes; (**b**) fine spectrum of C 1s; (**c**) fine spectrum of O 1s.
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 2 Related Work  
In this section, we will present a brief summary of the main characteristics of existing rate adaptation algorithms (RAA). RAAs can be classified into various categories according to the metrics that they use to evaluate the channel or link quality such as frame loss and SNR in [9], or consecutive transmission count, frame loss ratio, transmission time, throughput, SNR, bit error rate, and combined metrics in [10]. We chose to classify RAAs into three categories: (i) Explicit Feedback, in which RAAs base their bit rate adaptation on the receiver feedback, (ii) Implicit Feedback, in which RAAs base their adaptation on the information available on the sender side, and (iii) Hybrid, which is a category that combines information from feedback of the receiver and information available to the sender. In what follows we will describe each of these categories by selecting representative RAAs of each category. Table 1 summarizes the information about the different RAA algorithms presented in this section.
## Appendix B Area of the Elliptical Trajectory of the AUV  
Let us start from the basic equation of an ellipse.  
\[\frac{x^{2}}{a^{2}}+\frac{y^{2}}{b^{2}}=1 \tag{7}\]  
Simplify Equation (7) in terms of y:  
\[y^{2}=\frac{a^{2}b^{2}-x^{2}b^{2}}{a^{2}} \tag{8}\]  
\[y=\pm\sqrt{\frac{a^{2}b^{2}-x^{2}b^{2}}{a^{2}}} \tag{9}\]  
\[y=\pm\ \frac{b}{a}\sqrt{a^{2}-x^{2}} \tag{10}\]  
We ignore the (-) sign, because we are in the first quadrant, so the equation becomes:  
\[y=\ \frac{b}{a}\sqrt{a^{2}-x^{2}} \tag{11}\]  
In order to find the area of the ellipse, we integrate it from zero to a.  
\[\int_{0}^{a}\left(\frac{b}{a}\sqrt{a^{2}-x^{2}}\ \right)\text{{dx}} \tag{12}\]  
\[\text{{Let}}\ x=a\sin\theta\ \ \text{{then}} \tag{13}\]  
\[\text{{dx}}=a\cos\theta\ d\theta \tag{14}\]\[\frac{b}{a}\,\int_{0}^{\frac{\pi}{2}}\sqrt{a^{2}-\left(\mathrm{asin}\theta\ \right)^{2}}\times a \mathrm{cos}\,\theta\;d\theta \tag{100}\]  
\[\mathrm{a}b\,\int_{0}^{\frac{\pi}{2}}\sqrt{1-\left(\mathrm{sin}\theta\ \right)^{2}}\times \mathrm{cos}\,\theta\;d\theta \tag{101}\]  
\[\mathrm{a}b\,\int_{0}^{\frac{\pi}{2}}\sqrt{\left(\mathrm{cos}\theta\ \right)^{2}} \times \mathrm{cos}\,\theta\;d\theta \tag{102}\]  
\[\mathrm{a}b\,\int_{0}^{\frac{\pi}{2}}\sqrt{\left(\mathrm{cos}\theta\ \right)^{2}} \times \mathrm{cos}\,\theta\;d\theta \tag{103}\]  
\[\mathrm{a}b\,\int_{0}^{\frac{\pi}{2}}\frac{\left(1+\mathrm{cos}2\theta\ \right)}{2}\;d\theta \tag{104}\]  
After further simplification, we get:  
\[A_{FQ-ellipse}=\frac{ab\pi}{4} \tag{105}\]  
The area of the first quadrant of the ellipse = \(\frac{ab\pi}{4}\). Let \(b=r\); then, we can write:  
\[b=\frac{\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+\alpha}{\pi} \tag{106}\]  
\[a=\frac{\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+\alpha}{\pi}+\frac{ \sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+\alpha}{\pi}\beta \tag{107}\]  
\[A_{ellipse}\,=(\frac{\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+\alpha}{ \pi}+\frac{\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+\alpha}{\pi}\, \beta]\,\frac{\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+\alpha}{\pi})\pi \tag{108}\]  
\[A_{ellipse}\,=\frac{1}{4}[(\frac{\sum_{\forall\ e(i,j)\in EMST}d\left(i,j \right)+\alpha}{\pi})^{2}+(\frac{\sum_{\forall\ e(i,j)\in EMST}d\left(i,j \right)+\alpha}{\pi})^{2}\beta]\pi \tag{109}\]  
\[A_{ellipse}\,=\frac{\left(\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+ \alpha\right)^{2}+\left(\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+ \alpha\right)^{2}\beta}{4} \tag{110}\]  
\[A_{ellipse}\,=\frac{\left(\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+ \alpha\right)^{2}}{4} \tag{111}\]  
The above equation is the area of the right top quadrant of the ellipse, so in order to find the area of the whole ellipse, multiply this equation by four. Finally, the area of the ellipse is computed as:  
\[A_{ellipse}=(\sum_{\forall\ e(i,j)\in EMST}d\left(i,j\right)+\alpha)^{2}[1+\beta] \tag{112}\]
## 4 Classification of Auto-Configuration Protocols
### Stateful Protocols
#### 4.1.2 Daap  
Dynamic Address Allocation Protocol (DAAP) [12] is based on the concept of address assignment by a leader. The leader functionality is shared among all network nodes. When a new node joins the network, it becomes the leader until the next node joins. The leader maintains the highest IP address within the _ad hoc_ network and a unique identifier is associated with the network.  
Each node stores the highest IP address, which is that of the leader, and periodically sends HELLO messages to its neighbours. These HELLO messages include the network identifier so that any merging and partitioning can be detected. When a node receives a HELLO message with a different network ID, merging is detected, if a node does not receive the message that contains the current network ID, then after a timeout, a partition is detected.
## 5 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Fitness Function** & \(\mathbf{K_{p}}\) & \(\mathbf{K_{D}}\) & \(\mathbf{K_{I}}\) \\ \hline \multirow{3}{*}{**DE**} & ISE & \(diag\{9381,6743,9950\}\) & \(diag\{10,000,1405,2720\}\) & \(diag\{4263,194,268\}\) \\  & IAE & \(diag\{100,100,000,8905\}\) & \(diag\{100,1062,1110\}\) & \(diag\{6231,761,363\}\) \\  & MSMAE & \(diag\{100,8679,9976\}\) & \(diag\{4088,1616,2500\}\) & \(diag\{1104,138,100\}\) \\ \hline \multirow{3}{*}{**GA**} & ISE & \(diag\{8697,5218,9903\}\) & \(diag\{5647,3336,7018\}\) & \(diag\{6390,1908,1018\}\) \\  & IAE & \(diag\{517,7841,4345\}\) & \(diag\{2701,4737,1814\}\) & \(diag\{4421,4754,144\}\) \\  & MSMAE & \(diag\{4221,9594,7538\}\) & \(diag\{7750,9450,9421\}\) & \(diag\{2079,5081,296\}\) \\ \hline \multirow{3}{*}{**PSO**} & ISE & \(diag\{4190,5728,6905\}\) & \(diag\{6708,7823,4957\}\) & \(diag\{5826,8660,317\}\) \\  & IAE & \(diag\{5171,5771,7265\}\) & \(diag\{3234,8737,6333\}\) & \(diag\{732,8454,397\}\) \\ \cline{1-1}  & MSMAE & \(diag\{9300,8031,6380\}\) & \(diag\{1065,9266,4150\}\) & \(diag\{9634,8893,226\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Resulting gains for nonlinear contour. Figure 3: Fitness function best values for nonlinear contour. Figure 2: Fitness function best values for linear contour. For the linear contour, it can be deduced from Figure 4 that the DE-ISE case was the one to produce the lowest contour error from all the cases, with DE-MSMAE coming a close second. In fact, DE produced the best optimization results for all fitness function cases. GA and PSO, on the other hand, produced contour errors an order of magnitude greater than DE, with the exception of PSO-IAE that was numerically closer to DE-IAE. GA produced the worst results, something that was expected due to the convergence rates as shown in Figure 2.  
For the nonlinear contour case, the contour errors were numerically close for all three optimization methods. For nonlinear contour tracking, DE-IAE produced the lowest contour error. GA was once again the worst algorithm with the exception of GA-IAE that actually produced the second lowest contour error. The PSO algorithm produced contour errors relatively close to DE for ISE and MSMAE but featured  
Figure 5: Contour performance for linear contour. Figure 6: Contour performance for nonlinear contour. Figure 7: Mean required torques for both contours. Taking a close look at the required torque produced by each gain solution set in Figure 7, one can see that all optimization algorithms produced similar mean torques values for the first actuator while the torque from the other two actuators varied greatly. In the linear contour, DE, which produced the lowest contour errors, produced the lowest mean required torques. Similarly, the best performing algorithm for the nonlinear contour, DE, produced the lowest required mean torques, with the exception of the third actuator where all DE cases produced the highest mean torque.
## 2 Numerical Methods and Model Setup
### Wall Film Model
#### 2.4.1 Spray Impingement Model  
The physical mechanisms during spray /wall impingement are complex because of the large number of parameters involved. The outcome of any impingement is governed by the droplet properties (density, viscosity, or surface tension), the impingement conditions (droplet diameter or droplet velocity), and wall characteristics (wall temperature, surface roughness, or wall film thickness). Therefore, the Stochastic Kuhnke Model was used to calculate the spray/wall interaction in this study. The model classifies all relevant impingement behaviors into four regimes based on critical temperature and the Weber number: rebound, spread, splash, and evaporative splash. The spread and splash regimes are the main causes of wall film formation. The transition regime is introduced to improve the prediction accuracy of the wall film production quantity. The critical value of regime transition is as described below. The upper and lower limits of the critical deposition temperature are defined as follows:  
\[T_{d,u}=T_{sat}+\Delta T_{o} \tag{9}\]  
\[T_{d,l}=T_{d,u}-\Delta T_{r} \tag{10}\]  
with upper limit of deposition temperature \(T_{d,u}\); droplet saturation temperature \(T_{sat}\); upper limit offset of deposition temperature \(\Delta T_{o}\); lower limit of deposition temperature \(T_{d,l}\); temperature difference of deposition range \(\Delta T_{r}\).  
The critical Weber number for the transition between the spread and splash regimes is defined according to the initial conditions of the wall:  
\[We_{crit,w}=A_{c}\cdot La^{-0.18} \tag{11}\]  
\[We_{crit,d}=A\cdot La^{-0.18} \tag{12}\]  
where \(We_{crit,w}\) and \(We_{crit,d}\) comprise the critical Weber number for impinging wet and dry walls, respectively; \(A_{c}\) refers to the Laplace number constant; \(La\) corresponds to the Laplacenumber; \(A\) is a function of wall roughness. The regime map of the Stochastic Kuhnke Model is sketched in Figure 1.
# Production Optimization in a Grain Facility through Mixed-Integer Linear Programming +
## 5 A Real-World Case Study: Optimization in a Grain Processing Facility in Uruguay
### Conception of the Model  
This subsection describes the process followed to conceive the model introduced in this article.  
The primary need of the company was to improve the efficiency of the batch production scheduling process, in terms of minimizing the related costs while meeting all constraints. The company had been using a non-formalized production planning procedure, based on common sense and intuitive criteria, referred to hereafter as ad hoc.  
With the purpose of better understanding the problem and providing the company with a primary quick tool to improve the planning, a simple model of the problem was created using a Microsoft Excel spreadsheet. Several cells played a role equivalent to that of decision variables \(x\) and \(t\) of the mathematical formulation in Section 3. The spreadsheet was organized with rows representing the weeks and columns representing (tons of) products to produce on each line. The spreadsheet allowed for manually changing values on each cell, while controlling the corresponding use of lines and storage capacities, and showing eventual constraint violations using conditional formats. A heuristic procedure (described in Section 6.3) was conceived on top of the spreadsheet and used to create the real 2020 production plan followed by the company. The heuristic method conveyed a reduction of 20% on the number of cleaning cycles needed when compared to the ad hoc planning that the company had already devised. The spreadsheet was of paramount importance to help the company managers and the consultant team to agree on all details of the problem and its further modeling as a MILP optimization problem. Heuristic production planning is used as a reference baseline to compare the results computed by the proposed MILP model, as described in the next section.
## 4 Results  
### Qualitative Evaluation of the SSDA Smoke Detections  
#### 4.1.1 Qualitative Evaluation Using True-Color Images  
We evaluated our SSDA performance qualitatively by comparing the smoke detection patterns with the corresponding true-color imagery over two big fire events in Australia and California during 2019 and 2020. The early stage of these two fire events burned in small areas with thin plumes (Figure 6a,c). Figure 6a1,c1 illustrate that the SSDA detected the majority pixels within these fire plumes successfully, even for one extremely small smoke plume in the south of Figure 6a. Additionally, the spatial extents of these smoke plumes could be separated well from other surface types, except for the extremely thin smoke pixels at plume edges that were too thin to be detected.  
Figure 6b,d shows the late stage of the Australia and California fire events, respectively. Bushfires dominated in Australia. In Figure 6b1, the SSDA smoke detections match quite well with the spatial pattern of smoke plumes on the true-color images over land, even for the relatively thin plumes in the north of Figure 6b. Some smoke aerosols over oceans were not detected successfully by SSDA due to interferences from oceanic glints, which were grouped into dark surfaces and removed prior to evaluation for the presence of smoke (Figure 5 step1). Californian smoke plumes have primarily been from large forest fires that spread for several thousand kilometers, with a high density in the center and a low density toward the edges. Despite the heterogeneous spatial pattern, Figure 6d1 shows that the SSDA still detected the overall pattern of Californian fire smoke over both land and ocean very well with a clear separation from clouds and bright surfaces, although a very few thin smoke pixels at smoke boundaries were missed (Figure 6d).  
Overall, evaluation of SSDA performance showed that spatial patterns of smoke plumes matched quite well with true-color images for both low-density fire plumes in the  
Figure 6: Comparison of the SSDA smoke detection results (**a1–d1**) with true-color images (**a–d**) for big fire events in Australia and California. Small red dots on the true-color images are S-NPP VIIRS daytime fire hotspots. The locations of these four true-color images are shown as red squares on the top row insets. (**a,b**) are the early and late stages of the Australian fire event in 2019, respectively. (**c,d**) are the early and late stages of California 2020 fire event, respectively.  
early stage and high-density fire plumes in later stages of fire events, even under some unfavorable heterogeneous conditions.
## 2 Materials and Methods
### Methods
#### 2.3.2 Land-Use Classification  
Because the aim was to study trends in the land-use changes and not to develop new methodologies for image classification, standard image classification procedures were used. In order to get comparable results for change detection, an unsupervised classification method was selected. ISODATA (Iterative Self-Organizing Data Analysis Technique) [39] is a commonly used unsupervised classifier. Basically, it is an iterative clustering method which assigns pixels to clusters (classes) based on class means and a minimum distance rule. The clustering is continued until the maximum number of iterations has been performed or a maximum percentage of changed pixels has been reached between two iterations. The ISODATA algorithm is widely used in land-use classification and several scientific studies have been carried out using ISODATA clustering for EO data e.g., [18, 19, 40].  
After the preliminary processing steps, the image stacks were classified using the unsupervised ISODATA classifier. The image stacks were classified to 40 clusters using 10 iterations and convergence threshold of 0.95. The total number of clusters was varied systemically and 40 clusters were considered enough to recognize all the relevant land-use classes in the study area that could be distinguished with the used SAR data. Similarly, for the optical data 36 clusters were applied. After the ISODATA classification steps, the clusters were manually merged into eight (SAR) or seven (optical) final land-use classes. Urban areas and non-cultivated agriculture (_i.e._, rice field after harvesting) are easily mixed in optical data. Therefore, urban class was not used for optical data. The official land-use map 2005 of NIAPP [18] was used as a reference map to visually recognize what land-use class each of the clusters represented. In addition, the SPOT optical data was used for visual interpretation. Due to the lack of reference data, the derived land-use map of 2005 was used as a reference to merge the classes of the classified 2009 image stack and the derived land-use map of 2009 was again used as a reference for merging the 2011 classes. Settled areas, coconut, sugarcane and orchard were classified into the same class, because of the low data resolution. The final classes were:  
1. Water
2. 2 \(\times\) Irrigated rice crop
3. Aquaculture
4. 2 \(\times\) Rainfed rice crop
5. Rainy season rice and shrimp culture (Rice in the rainy season and shrimp in the dry season)
6. Settled areas, coconut, sugar cane and orchard 7. 3 \(\times\) Irrigated rice crop
8. Urban (only SAR)  
The summary of the land-use maps produced is presented in Tables 4 and 5.