esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Components and Setup
### TEC Module Configuration  
A single-stage thermoelectric cooling module C2-55-2812 (company Traverse; MI, USA) was used to generate the cooling effect designed explicitly for high-volume cooling applications. Thermoelectric cooler works on the principle of the Peltier effect to generate a heat flux between the junction of two different materials. TEC module is made up of semiconductor material such as bismuth telluride, having a length of 55 mm, a width of 55 mm, and a height of 3.75 mm. The TEC module pressed between two heat spreading metal surfaces made up of aluminum- one on the hot side and another on the cold side. The cold side of the TEC module is attached with the plate type of heat sink to generate a cooling effect inside. The hot side is attached to the cold plate to remove generated heat. Table 2 gives a detailed description of the TEC characteristics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Values** \\ \hline Dimension & 304.8 mm \(\times\) 95.3 mm \\ \hline Thickness & 7.6 mm \\ \hline Configuration & 6-Pass \\ \hline Max. Pressure & 10 bar \\ \hline Max. Flow Rate & 4 lpm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermophysical properties of the cold plate used in the experiment.  
Figure 2: (**a**) Schematic presentation of the experimental setup, (**b**) details of heat exchanger module, (**c**) front view of heat exchanger with vertical fins, (**d**) cold plate, (**e**) copper tube Peltier heat exchanger, (**f**) insulated room with temperature sensors and heat exchanger, (**g**) Extended U-section enclosure.
# Bandwidth Density Analysis of Coded Free-Space Optical Interconnects
## 2 Related Work
By employing maximum likelihood decoding techniques, these metrics facilitate the separation and decoding of individual signals that are sent by various transmitters. The utilization of the modified codes, based on orthogonal designs, enables straightforward maximum likelihood decoding by employing the minimum Euclidean distance as a criterion. The authors' results showed that the considered FSOI model is capable of achieving a low bit error rate on the order of \(10^{-6}\) even when there is significant misalignment between the model components. The authors of [23] utilized extended Alamouti space-time coding in conjunction with card-to-card optical links. They provided experimental results that emphasized the ability of such coding scheme in decreasing the crosstalk in the optical system and enhancing the system sensitivity. In addition, their experimental results showed that space-time coding can expand the optical interconnection range by 48%. The authors of [24] used Manchester coding along with a differential phase shift keying modulation scheme to improve the performance of free-space optical communication systems. They found that using Manchester coding reduces the error rate and enhances the reliability of the system. These advantages were a direct result of the synchronization capabilities of the Manchester coding scheme. In [25], the authors introduced a novel two-fold turbo coding scheme to improve the performance of optical communication systems. They showed that the coding scheme significantly enhances the performance of the system in terms of the bit error rate and SNR as compared to the typical turbo coding scheme. The authors provided analytic and simulation results to support their study. The authors of [26] provided a reconfigurable beam-forming scheme to solve the problems of nonlinear-of-sight free-space optical communications. They used a synchronized array of optical transmitters to spatially reshape the wave fronts of the incident light beams over diffusing surfaces. This process of reshaping the wave fronts enhances the amount of diffused light intensity reaching the array of optical receivers. The authors provided experimental results that demonstrate the feasibility of wave-front shaping for overcoming the nonlinear-of-sight limitation in free-space optical communications.
## 3 Results
### The Measurement and Modeling of Zero Bead Geometry
As the wire feed speed and deposition rate increase, the average bead width (w) and height (h) also increase. Conversely, increasing the traverse speed leads to a decrease in the average width and height of the beads. This suggests that the wire feed speed and deposition rate primarily influence the heat input, while the traverse speed primarily affects the melt pool shape. In Figure 5, the correlation of the ratio and the wire feed speed with the width and height are shown. Looking at the evolution of the bead height and width, it follows that a higher ratio leads to a taller and wider bead, unlike the wire feed speed, which does not seem to have such a direct relationship, and is more likely to have a quadratic effect. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Test N\#** & **w1 (mm)** & **w2 (mm)** & **w3 (mm)** & **w (mm)** & **h1 (mm)** & **h2 (mm)** & **h3 (mm)** & **h (mm)** \\ \hline
1 & 5.20 & 5.40 & 5.30 & 5.30 & 1.80 & 1.90 & 1.80 & 1.83 \\
2 & 4.70 & 5.00 & 4.90 & 4.87 & 3.10 & 3.20 & 3.00 & 3.10 \\
3 & 6.30 & 6.30 & 6.30 & 6.30 & 3.20 & 3.40 & 3.20 & 3.27 \\
4 & 5.20 & 5.00 & 5.30 & 5.17 & 2.10 & 2.00 & 2.10 & 2.07 \\
5 & 6.90 & 6.50 & 6.60 & 6.67 & 2.60 & 2.40 & 2.20 & 2.40 \\
6 & 8.00 & 8.10 & 8.30 & 8.13 & 3.00 & 3.00 & 2.90 & 2.97 \\
7 & 4.30 & 2.30 & 3.60 & 3.40 & 2.60 & 2.52 & 2.50 & 2.54 \\
8 & 6.80 & 5.30 & 6.10 & 6.07 & 4.30 & 2.60 & 2.20 & 3.03 \\
9 & 8.30 & 8.30 & 7.60 & 8.07 & 4.00 & 4.00 & 3.40 & 3.80 \\ \hline \end{tabular}
\end{table} 
Table 3: Height and width of the zero beads for the different test conditions. Figure 4: Zero bead morphology 3D representation of the middle section of the first test. The implications of these findings for arc-DED Invar manufacturing are that the desired bead dimensions can be controlled via the adjustment of the welding parameters. For applications requiring wider and taller beads, higher wire feed speeds and deposition rates can be employed. As for the effect of currents and voltages in zero bead scenarios, although beyond the proposed test, it would have an impact on the melt behavior, solidification, and ultimately the geometry of the resulting bead. It is envisaged that the higher heat input will cause the melt pool to be wider, leading to a wider and lower bead. This analysis could be used to refine the manufacturing process, ensure consistency, and produce high quality weld beads in a variety of scenarios. For applications requiring narrower and shorter beads, higher traverse speeds can be used.
## 3 Results and Discussion
### Model Performance and Comparative Analysis
On the other hand, the logarithmic and power law models showed  
Figure 10: Surface response plots for different models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Equation** & **Coefficients** \\ \hline Logarithmic & F (60) = a + b \(\times\) COF \(\times exp\left[\frac{-40}{c+d\times\ln(1+MPD)}\right]\) & a = 0.0257, b = 1, c = 50, d = 50 \\ Power law & F (60) = a + b \(\times\) COF \(\times exp\left[\frac{-40}{c+d\times\text{MPD}}\right]\) & a = 0.0056, b = 1, c = 50, d = 50, e = 0.5017 \\ Polynomial & F (60) = a + b \(\times\) COF + c \(\times\) MPD + d \(\times\) COF2 & a = 0.4970, b = 0.50, c = \(-\)0.4145, d = 0.1856, e = 0.0883, \\  & + e \(\times\) MPD2 + f \(\times\) COF \(\times\) MPD & f = \(-\)0.0294 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The coefficients and corresponding equations for each model. comparable and comparatively lower sensitivity levels (-14-15%), suggesting increased stability and maybe more reliable performance in the presence of input variability [42; 43; 44; 45; 46].
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
## Fabry-Perot-Based PTS Gas Sensors  
Inspired by work done in the field of PT gas sensors, researchers investigated the possibility of designing unique configurations in which the induced RI modulation would be probed using a Fabry-Perot (FP) cavity. As described before, variations of the RI of a sample (not only gaseous) are primarily connected with changes in the density of the sample. A gas sample excited by the _pump_ laser experiences a localized heat gradient, and thus the refractive index decreases. The idea of using an FP cavity as a PT effect readout is directly connected with the physics underlying the principle of their operation. FP interferometers are constructed using two partially reflecting parallel mirrors separated by a certain distance. The intensity of light transmitted \(I_{t}\) through such a cavity is described by [58]  
\[I_{t}=I_{0}\frac{1}{1+\left(\frac{2\pi}{\pi}\right)^{2}\sin^{2}\left(\frac{ \Delta\varphi}{2}\right)} \tag{2}\]  
where \(I_{0}\) is the intensity of the incident light, \(F\) is the cavity finesse, and \(\Delta\varphi\) is the phase difference. Finesse of the cavity can be calculated as a function of the reflectivity of the mirrors \(R,F=\pi\sqrt{R}(1-R)^{-1}\). Whereas the phase difference is given by:  
\[\Delta\varphi=\Delta\varphi=\frac{2\pi}{\lambda_{0}}2 nd\cos\Theta \tag{3}\]  
where, \(n\) is the refractive index, \(\lambda_{0}\) is the wavelength of the incident light in vacuum, and \(\cos\theta\) is the angle of incidence. Based on the formulas described above, one can clearly see that a change in the refractive index inside the cavity will have a direct influence on the intensity of light transmitted through the FP interferometer, which can be conveniently detected. This approach was adopted in several PT gas sensors with great success.
## 4 Discussion
### Environmental Controls on GPP, ET, and WUE  
Sustained light availability of high intensity, especially during the growing season, results in high photosynthesis [22; 46; 60; 64] and ET [34; 36; 37; 63; 84], corresponding to a greater leaf area [28]. Air temperature is also one of the key drivers of GPP [22; 85] and ET [8; 37]. ET was at its peak during the growing season but declined during the non-growing season, suggesting that changes in seasonal climate conditions negatively affected ET. However, the effect of light and temperature on GPP and ET across clustered light/temperature levels was low. Soil type [86], water storage capacity [87], the timing of soil water recharge [88], and other factors such as LAI, stomatal conductance, and other environmental drivers are crucial in GPP and ET processes [89; 90; 91; 92].  
Net radiation and air temperature did not significantly explain the variability of WUE across ages. This finding indicated that in a future warmer climate, with increasing CO\({}_{2}\) concentration and higher solar energy [93], wetland forests may adjust control over carbon and water fluxes, and water use may become less efficient. However, the influence of net radiation and air temperature on WUE was weak. The control of soil water could only affect WUE when the groundwater table depth was very low (i.e., \(-50\) to \(-80\) cm). However, the amount of variance explained was only 35%. This result indicated that soil water availability had less impact on WUE even during extreme climate anomalies. One plausible explanation of the low response of WUE to climate is that biological controls may have a more significant influence over that of climate. These plant-centered processes may include leaf phenological changes, plant structural attributes, pests and diseases, among others [18; 59; 94; 95; 96]. Further investigation into these biological mechanisms is needed to examine their potential contribution to WUE.  
Little variations in WUE during drought implies that even if the processes involved in CO\({}_{2}\) and H\({}_{2}\)O fluxes varied individually, they converge towards relatively stable water use efficiency. This finding has significant implications for ecosystem modeling because it suggests that WUE can be modeled using a constant value (e.g., coefficient) despite the large variability in GPP, ET, and climate.
# Effect of Whey Protein Changes on Milk Flavor and Sensory Characteristics During Heating
## 3 Results and Discussion
### VOCs Characteristics of Different Heat-Treated Milk  
The VOCs in dairy products typically include alcohols, aldehydes, acids, esters, and ketones, which contribute significantly to the overall aroma. HS-SPME-arrow-GC-MS was used for the qualitative and quantitative analysis of the VOCs in the milk exposed to different heat treatments (Table S2). A total of 59 VOCs were identified, including nine alcohols, six aldehydes, fifteen acids, eleven esters, ten ketones, and eight other compounds. The total VOC content ranged between 112.39 \(\upmu\)g/kg and 595.24 \(\upmu\)g/kg. Alcohols originate via various metabolic pathways and typically produce floral, fruits, and grassy flavors in dairy products [43]. A total of nine alcohols were identified in the milk, with high levels of 1-butanol, 2-methyl-1-butanol, 1-hexanol, and 2-ethyl-1-hexanol. Aldehydes, such as hexanal and octanal, provided the milk samples with grassy and fatty flavors and increased at higher temperatures. (_E_)-2-nonenal presented a cucumber-like flavor and was higher after heat treatment at 75 \({}^{\circ}\)C and 100 \({}^{\circ}\)C. Acids, especially short- and medium-chain fatty acids, contribute significantly to the aroma of dairy products due to their low perceived thresholds. For example, acetic acid, propanoic acid, and butyric acid present a strong sour and putrid flavor [44]. However, long-chain fatty acids play a minor role in flavor due to their higher thresholds [44]. Although free fatty acids contribute to the aroma of dairy products, excessive amounts can cause a rancid flavor [44]. Not only are fatty acids aromatic compounds, but they are also precursors of other flavor compounds such as alcohols, aldehydes, ketones, and lactones [44]. Esters usually present pleasant flavors, such as sweet, peachy, and coconut aromas. Ketones are mainly present in the form of methyl ketones, which are free fatty acid derivatives. The free fatty acids are first oxidized to \(\beta\)-keto acids and then form the corresponding methyl ketones [44]. Furthermore, a strong correlation is evident between the methyl ketone concentration in milk samples and various heat indicators such as furosine, lactulose, and undenatured whey protein [45]. Ketones have low perception thresholds and contain 2-heptanone and 2-nonanone, which present soapy and hot milk flavors in dairy products [46]. Sulfur compounds are commonly associated with the cooking flavor of milk and significantly affect its flavor [43]. Two sulfur compounds, namely dimethyl sulfoxide and dimethyl sulfone, were found in the seven milk samples, and dimethyl sulfone presents unpleasant sulfur and burnt flavors. Dimethyl sulfide oxidation results in intermediate dimethyl sulfoxide formation [47]. Stacked histograms were created to visualize the level of each compound in the samples (Figure 2A,B). The raw samples contained the highest acid level at 514.52 \(\upmu\)g/kg,
# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
## 6 Conclusions  
In this paper, we start with a comparative study of different routing protocols using several comparison metrics. We deduce that the OLSR protocol provides the best performance.  
After this, we present the Ameliorated Greedy Forwarding method to enhance the efficiency of data transfer in the case of low mobility, which can cause the divergence of time to reach the destination. This divergence appears progressively when the difference between the speeds of the vehicles participating in the communication increases. The GFA mechanism takes into account the speed of each vehicle and prioritizes mobile vehicles compared to stationary ones. Using the new score, GFA compares the efficiency of candidate vehicles to reach the destination as quickly as possible. GFA is integrated on the G-OLSR protocol to prove the performance of our approach compared to the OLSR protocol. We use the SUMO traffic simulator to exact the map of Gueliz situated in the center of Marrakech city. The traffic trace file is integrated into the NS3 simulator. The results of the simulation prove that G-OLSR performs best compared to OLSR in terms of EED and PDR, especially for dense traffic.  
We note the limitation of G-OLSR when the traffic density is low or the nodes are abundant within the network. In this case, G-OLSR behaves nearly as OLSR.  
As a perspective, we will deal with this limitation and secure our new protocol to be safer and to secure the routing process more and more.  
Conceptualization, M.L.; Methodology, M.L. and S.R.; Data curation, S.R.; Validation, S.R.; Writing--Original Draft Preparation, M.L.; Writing--Review and Editing, M.L and S.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. The authors declare no conflict of interest.
## 2 Materials and Methods
### Material Characterization
#### 2.4.3 Optical Evaluation  
Optical properties of the films were obtained by determining the film reflection spectra (\(R\)) using the black (\(R_{0}\)) and white (\(R_{g}\)) backgrounds according the Kubelka-Munk theory of multiple scattering. The internal transmittance (\(T_{i}\)) of the films, ranging from 400 to 700 nm, was determined using Equation (1). The film lightness (\(L^{*}\)) and the color coordinates \(a^{*}\) (redness-greenness) and \(b^{*}\) (yellowness-blueness) were determined using Equations (2) and (3). The colorimetric and transparency properties of the films were also evaluated in terms of the hue angle (\(I_{ab}{}^{*}\)) and chroma (\(C_{ab}{}^{*}\)) following Equations (4) and (5).  
\[T_{i}=\sqrt{(a+R_{0})^{2}-b^{2}} \tag{1}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Sample** & **PHBV (wt \%)** & **MFC (wt \%)** & **MAH (phr)** & **ESAO (phr)** & **TGIC (phr)** & **DCP (phr)** \\ \hline PHBV & 100 & 0 & 0 & 0 & 0 \\ PHBV\_2.5\%MFC & 97.5 & 2.5 & 0 & 0 & 0 & 0 \\ PHBV\_5\%MFC & 95 & 5 & 0 & 0 & 0 & 0 \\ PHBV\_10\%MFC & 90 & 10 & 0 & 0 & 0 & 0 \\ PHBV\_ESAO & 100 & 0 & 0 & 1 & 0 & 0 \\ PHBV\_TGIC+DCP & 100 & 0 & 0 & 0 & 1 & 0.25 \\ PHBV\_5\%MFC\_ESAO & 95 & 5 & 0 & 1 & 0 & 0 \\ PHBV\_5\%MFC\_TGIC+DCP & 95 & 5 & 0 & 0 & 1 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of compositions according to the weight content (wt %) of poly(3-hydroxybutyrate-_co_-3-hydroxyvalerate) (PHBV) and microfibrillated cellulose (MFC) in which epoxy-based styrene-acrylic oligomer (ESAO), triglyceride isocyanurate (TGIC), and dicumyl peroxide (DCP) were added as parts per hundred resins (phr) of green composite.  
\[a=\frac{1}{2}\left[R+\left(\frac{R_{0}-R+R_{g}}{R_{0}\times R_{g}}\right)\right] \tag{2}\]  
\[b=\sqrt{a^{2}-1} \tag{3}\]  
\[{h_{ab}}^{*}=arctg\left(\frac{b^{*}}{a^{*}}\right) \tag{4}\]  
\[{C_{ab}}^{*}=\sqrt{a^{*2}+b^{*2}} \tag{5}\]  
Color difference (\(\Delta E^{*}\)) between the neat PHBV film and the films of PHBV containing MFC, or those processed with the compatibilizers, was evaluated using Equation (6).  
\[\Delta E^{*}=\sqrt{(\Delta L^{*})^{2}+(\Delta a^{*})^{2}+(\Delta b^{*})^{2}} \tag{6}\]  
where \(\Delta L^{*}=(L^{*}-L_{0}^{*})\); \(\Delta a^{*}=(a^{*}-a_{0}^{*})\); \(\Delta b^{*}=(b^{*}-b_{0}^{*})\); and \(L_{0}^{*}\), \(a_{0}^{*}\), and \(b_{0}^{*}\) are the color coordinates of the neat PHBV film. The color differences were evaluated according to the following criteria [36]: \(\Delta E^{*}<1\) indicates unnoticeable color change; \(1\leq\Delta E^{*}<2\) suggests that only an experienced observer can notice the difference; \(2\leq\Delta E^{*}<3.5\) means that an inexperienced observer notices the difference; \(3.5\leq\Delta E^{*}<5\) indicates a clear noticeable difference; and \(\Delta E^{*}\geq 5\) suggests that the observer notices different colors. All measurements were performed in triplicate.
## 4 Experiments and Analysis
### In-Car Deployment Test  
To further evaluate the real-time performance of the model, we deployed YOLO-AFR inside a vehicle and measured its frames per second (FPS). The deployment process is illustrated in Figure 11. A camera was installed within the vehicle to capture real-time footage of the driver. This footage was then transmitted to the model training server, which had the same configuration as our experimental environment, for real-time detection and output of results. The results indicated that our model achieved an average of 101.8 FPS during in-vehicle deployment, confirming its capability for real-time detection of dangerous driving behaviors.  
Figure 11: In-Car Model Deployment Architecture.  
Figure 10: Qualitative Comparison of Detection Results on Real-World Dangerous Driving Scenarios.
## 3 Results
### ROI Detection/Segmentation
In this approach, an over-segmentation of the femoral and tibial regions into distinct subregions was performed using superpixels and simple linear iterative clustering (SLIC). Subsequently, the k-means clustering method was employed in the spatial domain and intensity. From k equally spaced cluster centers, each pixel of the image was linked with the nearest cluster center, and the cluster was updated. This process was repeated until convergence, after which the superpixels were redeployed to the average color and position of the associated input pixels. The LBP (Local Binary Patterns) descriptor was employed to identify the most informative region, where the binary classification of knee osteoarthritis (OA and non-OA) based on logistic regression (LR) achieved optimal results. This study demonstrates that the external median side of the femur and tibia, where osteophytes are typically observed, represents the most informative regions. In this study, LPB was selected after a comparison of multiple descriptors using a 5-fold cross-validation approach and a total of 9012 radiographic knee images from the Osteoarthritis Initiative (OAI) and 3644 radiographic knee images from MOST. One of the popular algorithms for detecting and locating objects is You Only Look Once (YOLO), which has been in use since 2001 with progressively more accurate and faster iterations. [PERSON] et al. [16] have used YOLOv2 to detect the knee joint from radiographic images. In this study, the image size was adapted to the model input (256 \(\times\) 320). The model was trained on 5782 images, approved on 826 images, and tested on 1656 images collected from the OAI dataset. The number of anchor boxes was changed using k-means clustering to cluster knee-bounding boxes between 1 and 6. The use of weight decay of 0.0005 achieved the best performance, with a single anchor box with a recall of 0.922 against 0.91 without weight decay. Knee joints in the test dataset were detected without false positives and false negatives. Note that 1526 (92.2%) knee joints have IoU values greater than 0.75, and 1654 (99.9%) knee joints have IoU values greater than 0.50. [PERSON] et al. [17] employed YOLOV2 in a system designed to identify knee osteoarthritis. The proposed model is a combination of an open exchange neural network (ONNNX) and YOLOv2. The YOLOv2-ONNNX model comprises 31 layers, with YOLOv2 employed in conjunction with the pre-established architecture of ONNX. In this work, the ONNX model comprises 24 layers rather than the originally proposed 35. The model comprises two element-wise affine layers, seven convolutional layers, six batch normalization layers, three max-pooling layers, four activation layers, and two rectified linear unit layers. The YOLOv2-ONNNX model achieved an IOU of 0.96 and an mAP of 0.98. Table 5 provides an overview of the principal methodologies employed in the detection and localization of ROIs.