esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling and Data Sources
### Model Evaluation and Validation
#### 3.2.4 RMSE Test  
The root mean square error (RMSE), as per [PERSON], et al. [38], was utilized to determine the percentage error between observed and actual values, as shown in Equation (5). The RMSE formula, an essential tool in model evaluation, compares observed values with predicted ones to ascertain the model's accuracy. This metric elucidates the model's performance in terms of prediction accuracy.  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(x_{obs,i}-x_{model,i})}^{2}} \tag{5}\]  
where \(x_{obs,i}\) is the actual observed value, and \(x_{model,i}\) is the predicted value by the model.  
In this study, the utilized stepwise regression and ARIMA models demonstrated efficiency and accuracy in forest fire prediction. The stepwise regression model effectively identified key meteorological factors, streamlining the model construction process, while the ARIMA model enhanced time series analysis, improving the accuracy of future event predictions. The integration of these two models in this research proved effective in understanding and predicting forest fire dynamics, offering new perspectives and tools for future research and practical applications.
## 4 Experimental Results and Analysis  
In this section, the performance of the proposed CMADDPG-based spectrum resource allocation algorithm in Platooning Vehicular networks is evaluated and results are closely analyzed. On a highway, a wide crossroads scenario with vehicles traveling on a twin track is presumed based on [9] that delivers the vehicle velocity, the vehicle's travel direction together with V2I and V2V channel modeling, path-loss modeling, and shadowing. Small-scale fading is modified while large-scale fading is kept constant for each episode. Moreover, the global reward \(r^{i}_{glo}\) in (26) is normalized for accuracy to the same range of local reward. Similarly, the transmit power of each vehicle in the platoon and the Base station determines the speed of offloading between two vehicles, and between the edge nodes and the platoon vehicles, respectively. The simulation and neural network parameters are tabulated in Table 2. Consequently, the effectiveness of the proposed algorithm is determined by employing three benchmark algorithms based on the literature.  
1. Benchmark 1--MADDPG: In the MADDPG algorithm, the set of agents concurrently learns together to maximize the global and local reward in a cooperative environment. Every platoon in the architecture is equipped locally with a platoon dataset-trained actor and critic network. The RSU comprises global critics that are responsible for regularly criticizing the competence of action preferred by the platoons and also ensures the platoon's coordination.
2. Benchmark 2--Fully Decentralized MADDPG: In the Fully Decentralized MADDPG algorithm, the platoons determine the action depending on the information obtained as a completely decentralized decision rather than counting the global critics.
3. Benchmark 3--Fully Centralized DDPG: In the fully centralized DDPG algorithm, it is the responsibility of the RSU to gather all the platoon's information in order to make decisions about the action to be performed in a centralized manner.
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 4 Experiments and Results
### Comparing the Performance of CNNs within TensorFlow and PyTorch  
The accuracy, recollection, F1-score, and precision values for PyTorch were slightly higher than those for TensorFlow (Tables 5 and 6). This indicates that PyTorch provides better enactment for huge convolutional and completely allied systems using GPU and Google Colab GPU. The maximum accuracies for PyTorch were 0.49, 0.53, and 0.51 (Table 5), and for TensorFlow were 0.97 and 0.96 (Table 7 and Figures 10 and 11). There was no significant difference in performance between the PyTorch and TensorFlow frameworks for YOLOv5, YOLOv4-Tiny, and YOLOv3-Tiny's inference speeds (Table 8).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Network Model** & **Precision** & **Recall** & **[EMAIL_ADDRESS]** & **Detection Time** & **GFLOPs** & **GPU** \\ \hline YOLOv4-Tiny & 0.89 & 0.96 & 0.97 & 9.43 ms & 6.787 & Tesla T4 \\ YOLOv3-Tiny & 0.91 & 0.90 & 0.96 & 12.38 ms & 5.448 & Tesla K80 \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of YOLOv3-Tiny and YOLOv4-Tiny models for accuracy rate.  
Figure 7: F1-scores for detection.  
Figure 8: _Cont._  
Figure 8: Examples of _Crisium arrense_ for YOLOv3-Tiny predictions.  
Figure 10: Loss and Accuracy Graph of YOLOv4-Tiny.  
Figure 9: Examples of _Cirsium arrense_ for YOLOv4-Tiny Predictions.
## 3 Results
### Implementation Barriers of Sleeping Assets
#### 3.4.6 Technical and Technological Barriers  
As mentioned earlier, the utilization of sleeping assets in logistics often requires technical adaption and technological development, which present various logistical and financial barriers. The use of alternative routes, for example, is acknowledged as highly complex and time-consuming. In terms of technical aspects, on-site equipment can significantly vary among service providers and usage forms. Due to these technical barriers, logistics companies find the interim utilization of idle real estate unattractive as they prefer assets that offer long-term planning security.  
Technological barriers are related to information and communication technologies (ICT). [PERSON] [56] highlights that the size of the company and the available technology are crucial factors influencing the adoption of ICT in logistics. When considering collaborative logistics concepts, it becomes necessary to harmonize ICT systems to ensure smooth collaboration among different companies. However, according to one of our interview partners, the challenge does not lie in making ICT systems compatible between different providers but rather in fostering a willingness to cooperate and establish interfaces. Companies face multiple barriers in adapting their ICT, such as high initial capital, administrative procedures, and information security [57].  
Furthermore, an important barrier to cooperation is the issue of tracking and tracing. CEP service providers strive to offer a fully transparent supply chain. However, this objective may be hindered when using, for example, white label hubs. Digitalization and further technological advancements have the potential to address this challenge. Another technological barrier when it comes to the use of logistics concepts, e.g., smart loading zones, shared mobility services, or hubs, is the lack of a management tool. Interview partners experienced a lack of technological support, such as booking systems, in relation to these concepts.
## 7 Discussion
### Single Pump PSA with DOF  
In the single pump PSA case, the effect of longitudinal dispersion variation leads to the emergence of high-order PSA gain sidebands (\(>\)20 dBm signal gain and a bandwidth of 22 GHz for \(q=1\)) at large pump-signal detunings, of the order of a few Terahertz. The ability to achieve PSA functionality at these large detunings in a single pump configuration using just longitudinal dispersion variation is already an interesting aspect of the investigated system. Furthermore, the obtained numerical results show us the presence of an anti-gain dip within the high-order gain band. The spectral position of this dip can be controlled either by varying the input pump phase \(\theta\), or by changing the phase of the dispersion modulation \(\varphi_{\text{mod}}\), allowing the system to behave as a tunable notch filter within the gain band. Furthermore, to flatten and broaden the gain band, the dispersion profile was generated with multiple optimized dispersion oscillation frequencies. This led to the flattening of the gain band and broadened the spectrum almost two folds, however at a cost of a reduced signal gain. Although such a multi-frequency profile is highly interesting in terms of its bandwidth and tunability, fabrication of such a fiber will require precise control over the drawn fiber's core radius, and might pose a technological hurdle in practice. Nevertheless, these results are highly encouraging for the development of modern all optical signal processing techniques. For example, in all optical network as in [54], add/drop operation of modulated optical signals can be achieved through a real time control over the pump phase that can precisely position an anti-gain dip at the frequency of a channel to be dropped in a WDM signal. We should also remind, that the longitudinal sinusoidal dispersion variation of the DOF should provide an additional advantage in launching larger pump powers into the system as already discussed in Sections 5 and 6.  
Figure 18: Plot of conversion efficiency as a function of **(a)** the pump-signal wavelength separation and **(b)** input pump power, both from the experiment (filled circles) and the NLSE simulation (dotted and dashed lines). The data corresponding to the DOF is shown in blue while that of the standard HNLF is in red. The green dashed curve correspond to a hypothetical DOF (hDOF) with the same length, \(\gamma\) and \(\overline{\beta}_{2}\) as the HNLF. Other parameters are provided in the text.
## 3 Results and Discussion
### Monitoring of the Fermentation Process  
The fermentation process carried out by LAB strains, added as starters, was closely monitored. It began with the preparation of a liquid inoculum in SSE. As the LAB grew in SSE medium, the initial pH decreased from 5.6 to specific values for each strain--3.80 (_F. sanfrancisensis_ SD22), 3.88 (_W. cibaria_ SD123), 4.38 (_Ln. citreum_ SD168) and 3.95 (_Lt. sakei_)--with an average of 4.00 \(\pm\) 0.26. These findings align with the typical pH changes observed in LAB cultivated in flour and semolina extracts, as documented by [PERSON] et al. [26].  
After three days of propagation, a mixture containing all four LAB strains was obtained. This mixture was then used to develop sourdough by adding semolina and sterile tap water. The resulting mature sourdough had a pH of 3.82 and a TTA of 14.88 mL NaOH  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **L*** & **C*** & **H*** & **RGB** \\ \hline Semolina & 91.57 \(\pm\) 0.06 \({}^{\text{a}}\) & 23 \(\pm\) 0.14 \({}^{\text{a}}\) & \(-\)1.43 \(\pm\) 0.0 \({}^{\text{a}}\) & \\ MPP & 59.13 \(\pm\) 1.80 \({}^{\text{b}}\) & 35.02 \(\pm\) 1.51 \({}^{\text{b}}\) & 0.80 \(\pm\) 1.35 \({}^{\text{b}}\) & \\ \(p\) value & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & n.a. \\ \hline \hline \end{tabular} Results indicate mean value \(\pm\) 5.D. (standard deviation) of lightness (L*), chroma (C*), and hue angle (h”) parameters of colour between semolina and mango peel powder (MPP). Data within a column followed by different letters are significantly different (_p_ \(<\) 0.05). n.a. = not analysed.  
\end{table}
Table 1: Comparative analysis of colour parameters.  
0.1 N per 10 g. The pH and TTA values of the sourdough were similar to those recorded in _Ln.holzz/felii_ SD148, _Lp. plantarum_ SD96, and _Lp. pentosus_ SD130 [37]. Additionally, comparable results were found for _Lv. brevis_, _Ln. citream_, and _W. citaria_[38] in multiple-species sourdough starter inocula. After seven consecutive days of refreshment, the sourdough was deemed ready for use as a leavening agent in bread production, with a pH of approximate 4, as reported by [PERSON] et al. [39].  
The pH and TTA data for doughs at time points T\({}_{0}\) and T\({}_{2}\) are presented in Table 2.  
Notably, there was a significant negative correlation between pH and TTA (R\({}^{2}=-0.8\), \(p<0.05\)), consistent with other studies [26; 37; 40]. Initially, the pH and TTA differed significantly between the control group and both the CTR and MPP-5 doughs (\(p<0.05\)). However, by the end of the fermentation process, there were no significant differences in pH and TTA among the groups (\(p>0.05\)). The pH values at T\({}_{2}\) (approximately 4) align with those obtained for powdered-almond-skin-enriched breads [37].
## 3 Air-to-Air Channel Models  
The A2A communication scenario features cases in which more than two UAVs establish links within a group. These multiple drones communicate with the ground stations (one or more) through a backhaul channel with either one or multiple UAVs. The A2A channel has not been extensively studied in terms of experimental measurements [1], especially considering large UAVs. The channel resembles free space, with a dominant LOS component and regular, low-power multipaths from ground reflections, yet it is directly reliant on the flight altitude and the surrounding area. Usually, the A2A channel exhibits flat fading characteristics at higher altitudes; however, depending on the UAVs' relative velocities and the scattering environment, the channel can be extremely time-variant (i.e., inducing high Doppler spread), which entails a carrier-frequency offset and inter-carrier interference, which both severely affect the communication channel [36].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Refs.** & **Methodology** & **Frequency** & **Equipment** & **Delivered Research** \\ \hline \multirow{2}{*}{[13]} & \multirow{2}{*}{Measurements} & \multirow{2}{*}{18.685 GHz} & Airplane (Dornier 228 & \multirow{2}{*}{Airframe shadowing events} \\  & & & U-CALM) & \\  & & & u = 250 kts & \\ \hline \multirow{2}{*}{[14]} & \multirow{2}{*}{Measurements} & \multirow{2}{*}{L-band} & Aviator 200 UAV & \multirow{2}{*}{Throughput and latency tests} \\  & & & u = 140 km/h & \\ \hline \multirow{2}{*}{[15]} & \multirow{2}{*}{Measurements} & \multirow{2}{*}{1575.42 MHz} & A\(\begin{subarray}{c}\text{Ge}\text{A}\text{I}\text{A}\text{I
# Learning Swimming Techniques by Means of Real-Time Monitoring with Embedded Devices
## 1 Introduction  
The optimal age to acquire swimming skills is during early childhood, as children at this stage of life adapt more easily to water and develop new abilities more effectively.  
Why is the acquisition of swimming skills easier at a younger age than in adulthood [1]? Children commonly engage in learning various new activities, such as reading, writing, dancing, painting, and playing. These activities are often enjoyable and stimulating, particularly when conducted in a group setting among peers of a similar age, where a natural sense of competition may enhance motivation and engagement. As a child, learning new skills is an integral aspect of daily routines.  
Considering an activity such as swimming, young students learn in teams. This has the benefit of learning from each other and can generate competition that pushes an individual to practice more, leading to greater improvements. Undoubtedly, it will have an effect on the swimming technique, which will continue to evolve.  
Learning to swim as an adult is generally considered to be a straightforward process [2]. Adults are expected to grasp the coach's instructions more quickly, facilitate clearer communication, and demonstrate improved coordination of swimming movements. Additionally, they may focus more effectively on required tasks, seek and process feedback more efficiently, and, if necessary, transition to a different coach with greater ease. However, the process is not always straightforward. While adults may benefit from improved communication, financial resources, and comprehension, they often face challenges such as reduced motivation, lack of purpose, and limited time. Additionally, rational thinking can lead to over analyze, creating obstacles to take action. Unlike children, who are moreinclined to engage in activities without extensive risk assessment, adults tend to consider potential dangers, particularly when fear of water is a factor. The apprehension related to water entry or depth is generally more pronounced in adults due to their heightened risk awareness, whereas younger individuals may find it easier to manage such fears in this context.  
In certain situations, traditional approaches may prove ineffective, necessitating the development of new methods. This paper presents an alternative approach to learning or enhancing swimming techniques through the integration of technology. While not all aspects of swimming training can be replaced by technological solutions, incorporating new training simulations or alternative methods can provide cognitive benefits that enhance performance.  
The use of electronic devices, initially as a dryland simulator and subsequently in a real aquatic environment, offers a structured and efficient approach to swimming training. This method has the potential to improve learning outcomes by making training more effective, accessible, and time-efficient while reducing costs and effort.
## 2 Passive Wake-Up Radio
### Discussion of the Passive Wake-Up Radio Parameters
#### 2.4.5 Operating Frequency  
The operating frequency used is also a factor that affects power dissipation. The power consumption is increased when a higher frequency is used, since the power consumption \(P\) is directly proportional to the operating frequency, given as \(P=CV^{2}\)_f_, where \(f\) is the operating frequency, \(C\) is the load capacitance, and \(V\) is the voltage. Therefore, more current will be required in radio circuits operating at a higher frequency to attain equivalent performance as those running at a lower frequency. However, due to other factors the frequency alone does not determine energy consumption. In our table, the highest frequency used is 2.4 GHz and the lowest is 0.433 GHz. The 2.4 GHz, 0.915 and 0.9 GHz frequencies are the most popular in Table 1. However, the lower (sub-GHz) frequencies are now being preferred for the wake-up radios by most designers than the higher frequencies. This is because, the attenuation rates increase at a higher frequency. That is to say the sub-GHz signals last longer than the 2.4 GHz signal before it weakens. Based on the Friis equation [65], at 900 MHz the path loss is 8.5 dB lower than at 2.4 GHz, which means the 900 MHz operating transceiver has a 2.67 times longer range than that operating at 2.4 GHz.
# Low-Cost SDR for GNSS Interference Mitigation Using Spatial Diversity Techniques +
## 1 Introduction
In this work, the suitability of multi-antenna configurations in said devices is analyzed through experimental studies, where two low complexity spatial diversity techniques are implemented and evaluated using a multi-channel software-defined radio (SDR) representative of mass-market devices, the KrakenSDR, together with a Spirent set-up capable of simulating a GPS constellation affected by interference.
###### Abstract  
Cutaneous wound healing is delayed in patients with diabetes. Caffeic acid phenethyl ester (CAPE) has been identified as an effective constituent of propolis with improved wound healing abilities via an oxidative stress decrease. However, its impact on wound healing in diabetic models and its underlying mechanisms remain unclear. Determining the vascular endothelial growth factor (VEGF) contents in a human vascular smooth muscle cell (VSMC)-conditioned medium was assessed using human VEGF immunoassay and vascular reactivity using porcine coronary artery rings. Later, C57 BL/6 or db/db mice were anesthetized, after which a 6-mm biopsy punch was manipulated for perforation via the back skin. Subsequently, CAPE was applied to the wound and changed daily. Furthermore, the injury in each mouse was digitally photographed, and the wound area was quantified. We observed that CAPE increased VEGF levels in human VSMC-conditioned medium, improved endothelium-dependent nitric oxide (NO)-mediated vasorelaxation, inhibited U46619-induced vasoconstriction porcine coronary artery, and enhanced cutaneous wound healing in the diabetic mouse model. Hence, we propose that CAPE improves wound healing in diabetic mice, which is aided by increased VEGF and NO expression.  
caffeic acid phenethyl ester; wound healing; vascular endothelial growth factor; nitric oxide +
Footnote †: journal: Accepted: 21 February 2022  
Caffeic acid phenethyl ester (CAPE) is a principally active component of propolis [17]. It is proposed that CAPE has immunomodulatory, anti-inflammatory, and antioxidant effects, and inhibits lipid peroxidation [17; 18; 19; 20]. However, even though CAPE accelerates cutaneous wound healing via antioxidant effects [21], and enhances wound contraction and re-epithelialization by decreasing oxidative stress [22], it has not been reported whether it improves cutaneous wound healing in a diabetic model. Therefore, in this study, we investigated the effect of CAPE on cutaneous wound healing in a diabetic mouse model, after which we elucidated its possible underlying mechanisms.
## 3 Results and Discussions
### Numerical Analysis  
#### 3.2.1 Flow Field  
A baffle set in the wind's path will obviously make the airflow field change and thus affect the flame shape. FDS was used to calculate the change of flow field when the wind velocity was 1 to 5 m/s, and Smokeview showed the velocity vector slice in the numerical calculation zone. One slice, Y = 0, was set to measure the flow velocity in horizontal directions. Considering that the flow velocity vector fields were similar for different wind speeds, the slice of the flow velocity vector fields of 1 m/s was set as an example in Figure 4 to depict the velocity vector field and the oxygen volume fraction. For a more intuitive representation of the flow field, the velocity vector slice in the wind tunnel was simplified as a schematic illustration, as shown in Figure 5. The shear layer was generated at the top of the baffle as the wind passed the baffle. Meanwhile, two vortex regions formed downstream of the baffle: a recirculation zone near the pool and a much larger recirculation zone downstream of the pool.  
Figure 2: Changes in pool fire flame shape with time at a 3 m/s crosswind and different baffle heights.  
Figure 4: FDS model configuration and simulation results of the flow field and oxygen volume fraction.  
Figure 5: Structure of the flow field.  
Figure 3: Pool fire flame shape under a stable combustion stage at a 0–5 m/s crosswind velocity.  
As the baffle height increased, the recirculation downstream of the pool became larger. When the baffle was placed in front of the pool, it blocked the bottom airflow. The flow field downstream of the baffle was divided into two parts. The airflow above the baffle formed a mainstream zone with higher velocity, while the bottom airflow formed low-speed recirculation zones. The range of the recirculation region is determined by a variety of flow characteristics, the most important of which are the baffle height and the cross-stream turbulence level [18]. The process of flame combustion is influenced by reverse flow in the recirculation zones [19]. The recirculation zone above the pool surface transfers the fresh air to the flame. The recirculation zone downstream of the pool can retain heat and send back unignited fuel vapor to be burned rather than being blown away as is normally the case without a baffle [20; 21].