esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Using a mathematical model fitted to wind tunnel experimental data, it was possible to perform a complete characterization of several evaporative cooling pads, manufactured with cellulose and alternative materials, in terms of cooling efficiency, Temperature-Humidity Index, dry-bulb temperature drop, relative humidity increment and pressure drop across the pads; as functions of the inlet air velocity, water flow and pad thickness. Pad efficiencies were found between 40% and 70% for pads manufactured with alternative materials for inlet air environmental conditions typical of tropical climate countries like Colombia. The alternative pads with lower particle sizes (higher specific surface area) present cooling efficiencies similar to the cellulose pads (70%) at the expense of a higher pressure drop.  
All pads evaluated can improve the inlet air thermal conditions, providing outlet air with Temperature-Humidity Indices below the external environmental conditions and maintaining the mean thermal comfort zone for a broad range of dry-bulb temperatures and relative humidity combinations, making them useful for several applications.  
The methodology presented here allows the characterization of new alternative materials in order to find those that provide an adequate balance between cooling efficiency and pressure drop across the pad. Additionally, new simulations can be performed using the convective heat and mass transfer coefficients correlations found for particular conditions.  
To decrease the pressure drop across the pads and consequently the electrical power consumption, it is necessary to set the water flow to the minimum value required to maintain the wetted pad, which depends on the evaporation rate, especially when high speeds (above 1.5 m s\({}^{-1}\)) are used.  
Conceptualization: F.A.O.V. and J.A.O.S.; data acquisition: F.A.O.V.; data analysis: F.A.O.V. and A.P.M.R.; design of methodology: F.A.O.V. and J.A.O.S.; writing and editing: F.A.O.V., A.P.M.R., J.A.O.S., F.A.D. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Monitoring of Surgically Treated Upper Arm Fracture by Implanted Antenna at 402 MHz
## 2 The Monitoring Application  
The human's upper arm (humerus) is the long bone that runs from the shoulder to the elbow. The humerus shaft fracture is one that is localized in the mid-portion of the upper arm as shown in Figure 1a. This fracture is a common injury and is always a consequence of a fall with an outstretched hand, or a car accident. The treatment can be with or without surgery according to the fracture pattern and associated injury. A temporary splint extending from the shoulder to the forearm and holding the elbow bent at 90 degrees can be used for the initial management of the fracture. Non-operative treatment usually includes the placement of fracture bracing that will be replaced by a cylindrical brace (Sarmiento brace) three to four weeks later that fits the upper arm while leaving the elbow free. Surgery treatment, on the other hand, usually involves internal fixation of the fragments with plates that are used to hold the fractured bone in its place until healing is completed (Figure 1b), or with screws or a nail [14]. Figure 1c shows the right upper arm after installing the fixation plate surgically.  
The post-surgery follow-up of the fracture is usually conducted by X-ray images, and the patient is monitored at the clinic by serial radiographs at ten days, three weeks, six weeks, and as needed thereafter [15]. The proposed monitoring application in this study saves X-ray exposure and visits to the clinic by monitoring the patient at home. In his health care application, an antenna is implanted within the humerus, and the value of the return loss and transmitted power at its far field is recorded. Human tissue is a lossy medium for electromagnetic waves, and the fracture adds extra lossy tissues. The application considers a safe value of the excitation signal which produces an acceptable level of specific absorption rate (SAR) within the tissue. The difference in transmitted power between the normal and the fracture cases monitors the bone healing status [16].
## 4 Experimental Result Analysis
### Size Effect Analysis  
Size effect was one of the key variables examined in this study. To analyze how reducing specimen size influences the group stud effect and load-bearing capacity, a 1:3 scaled specimen (S-13) was designed and compared with the full-scale specimens.  
The test results indicated that the shear capacity per stud for the scaled specimen was 23.7 kN, which is 16.5% lower than the theoretical value of 28.4 kN, and significantly lower than the reduction observed in the full-scale specimens (23.4% to 27.2%). This suggests that the group stud effect diminishes as the specimen size decreases, likely because the mechanical interaction between studs becomes weaker in smaller structures.  
Moreover, the displacement ductility coefficient for the scaled specimen was 16.6, which was notably higher than that of the full-scale specimens. This demonstrates that reducing the specimen size not only weakens the group stud effect but also enhances the deformation capacity of the structure. This finding is highly valuable for engineering applications, particularly in the design of smaller components. In such cases, reducing the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen** & \(\mathbf{D_{\mathbf{u}}}\) & \(\mathbf{D_{\mathbf{y}}}\) & \(\mathbf{D_{\mathbf{u}}}\)/\(\mathbf{D_{\mathbf{y}}}\) \\ \hline Z-40 & 3.90 & 0.31 & 12.58 \\ Z-32 & 3.50 & 0.29 & 12.06 \\ Z-16 & 3.34 & 0.24 & 13.92 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Displacement ductility coefficient.  
density of stud arrangements could be considered to minimize the negative effects of the group stud phenomenon while simultaneously improving ductility.
# Q-Learning Approach Applied to Network Security
## 4 Results
### Hyperparameters  
To complete the process, we need to set up some parameters used in the learning process. Later, we provide a sensitivity analysis for the changes in \(\gamma\) and \(\alpha\), as the other parameters are required to calculate the Q-functions. \(\alpha\) is the learning rate; its range is between zero and one. It also specifies how often the different Q-values will be updated, based on the feedback that the agent receives from the environment. In network security, a lower rate means that the agent will learn at a slower pace, but its decisions will be more robust and reliable. As the learning rate approaches one, the agent learns faster, which is something that the administrator may prefer in a cyber security set up. The parameter values are provided to be set by the administrator, depending on the goals of the organization and the experience. The discount factor \(\gamma\) sets up the tradeoff between future and present rewards. It also guarantees that the reinforcement learning will converge. In our IR model, we may want to set up \(\epsilon\) closer to one. With this strategy, we would like it to explore the environment, rather than exploiting it and select the actions based on that principle. The environment in intrusion response is exclusively dynamic. A Q-learning agent must identify the most suitable response to all potential scenarios. Epsilon-greedy allows the agent to learn by trying diverse responses, applying this knowledge, and reacting effectively. The high epsilon will encourage more exploration, and later, it can decay. Besides \(\alpha\) and \(\gamma\) and \(\epsilon\) and after various executions of the model with a variety of parameter values, we concluded that the different parameters do not influence the learning capacity of the agent, and ultimately the same optimal policy is selected. We should also mention the action selection process using the \(\epsilon\)-greedy policy. The Q-learning Algorithm 1 shown in Figure 1 begins with classifying network traffic into different categories such as N, DoS, probe, R2L, and U2R, which represent different states. The MDP environment is initialized, and the Q-values for state-action pairs are set to zero. The agent then starts the episode, selects an action using the e-greedy strategy, performs the action, and updates the Q-values based on the reward. This cycle continues until a terminal state is reached, marking the end of the episode. Through multiple episodes, the agent keeps updating its Q-values, refining its policy to learn the best actions to take in different states. The hyperparameters that we applied as an initial set-up are presented in Table 1 and later we provide graphical representations for their change as well as a sensitivity analysis. Figure 1: Q-learning algorithm.
## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Impulse Noise
#### 3.6.2 IN Mitigation Techniques  
The threshold-aided IN technique is defined as a memoryless nonlinear mitigation strategy that comprises blanking [93], clipping [94], and clipping/blanking [95]. In this method of mitigation, the high amplitude and short duration of IN are studied by employing a threshold whose adaptation seems difficult. The authors of [96] describe a threshold optimization strategy considering the Neyman-Pearson criteria. In [95], the authors presented a mathematical solution for IN mitigation utilizing clipping and blanking. In [97], a comparison of numerous analog domain processing strategies for IN mitigation demonstrates that threshold value selection is the most important aspect for enhancing the efficacy of threshold-assisted nonlinear techniques. Once the threshold varies due to channel circumstances, the model gets mismatched. As a result, extremely impulsive environments have a negative impact on the effectiveness of all conventional threshold-based approaches. In [89], the authors have successfully used DL approaches for IN mitigation. Figure 5 presents the DNN performance for IN mitigation in User 1 and compares it with User 2. User 1 uses SIC to reduce inter-user-interference. Thus, it will suffer from IN only. While User 2 is affected by both IN and inter-user interference. Therefore, it has variant BER performance according to SNR values. The results show that the DNN approach can be effectively utilized to overcome IN.  
Figure 4: Sum rate of DPS-CoMP users (Mbps) versus the number of subchannels per cell [82].
# Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites
## 3 Results
### Lake Characteristics  
The 106 lakes finally selected for analysis represented a significant latitudinal and altitude gradient (30\({}^{\circ}\) S to 60\({}^{\circ}\) N; \(-\)2 to 1518 m a.s.l.) across multiple ecoregions (from Boreal to Tropical). The lakes cover a wide range of hydro-morphological characteristics for mean depth, lake surface area, and diverse trophic levels (by Chl-a concentration) (Table 2). According to the OECD trophic classification, 1 lake was oligotrophic, 29 were mesotrophic,  
Figure 2: Lakes from contingency matrix (Table 1) plotted as a function of the catchment area/lake area ratio (horizontal-axis) and the 95 th percentile of SPI (vertical-axis). Lakes in the upper right quarter (95 th percentile SPI \(>\) 1.5 and catchment area/lake area ratio \(>\) 30) for cluster III (limited regular annual burning), cluster IV (significant regular annual burning), and cluster V (limited burning peaks) were selected for further investigation (n = 153).  
58 were eutrophic, and 18 were hypereutrophic (Figure S1A in the Supplementary Materials). Based on the Lake_cci turbidity products for the period 2017-2020, we found that most of the lakes (69%) had mean turbidity between 1.5 and 5 NTU, 14% < 1.5 NTU, 13% between 5 and 10 NTU, 7% in the range 10-20 NTU, and the remaining 3% up to 40 NTU (Figure S1B in the Supplementary Materials).  
Out of a total of 106 lakes, 41 lakes were identified as shallow (mean depth < 7 m), 31 as medium depth (mean depth between 7 and 15 m), and the remaining 34 as deep (mean depth > 15 m). A total of 43% of lakes have a surface < 250 km\({}^{2}\), 41% between 250 and 1000 km\({}^{2}\), 10% up to 3000 km\({}^{2}\), and the remaining six largest lakes reach an area up to a maximum value of 26,734 km\({}^{2}\) (Great Slave Lake, Canada) (Figure S1C in the Supplementary Materials). Lake volume for 25% of the lakes was < 500 km\({}^{3}\), 25% between 500 and 3000 km\({}^{3}\), 28% between 3000 and 10,000 km\({}^{3}\), 21% up to 150,000 km\({}^{3}\), and Great Slave Lake has the maximum value with 1,580,000 km\({}^{3}\) (Figure S1D in the Supplementary Materials). For lake water residence time, 30 lakes had a fast renewal < 2 months, 46 lakes between 2 and 12 months, 15 lakes up to 2 years, 13 lakes had a slower residence time from 4 up to 33 years (Figure S1E in the Supplementary Materials). Finally, our dataset has 17% of lakes at an altitude < 50 m a.s.l., 74% between 50 and 700 m a.s.l., and 9% up to 1800 m a.s.l. (Figure S1F in Supplementary Materials).  
For these 106 lakes, the corresponding BA/A ratios for the period 2017-2020 were distributed as follows: six lakes (located in Africa) had a mean annual ratio BA/A > 0.40, eight lakes had ratios between 0.40 and 0.25, and the remaining lakes had a ratio < 0.25 (Figure 3).
## 6 Results
### Second Scenario--NER Evaluation  
The extraction of information through NER is crucial in specifying the context of a conversation triggered by an intent. Therefore, the evaluation of the NER approach in this scenario is presented in this section. Table 8 displays the overall performance of the NER system implemented in our model. The two groups of entities analyzed in this study are utilized in various contexts within the conversation.  
Based on the analysis of the macroaverage results presented in Table 8, we can draw some inferences regarding the model's performance in detecting entities when inputting a test phrase. The high accuracy value of 0.94 indicates that the model has a greater likelihood of recognizing the correct entity for each inputted testing phrase. The precision and recall value of 0.96 indicates that the model has a low rate of detecting false positives and false negatives for each entity. The F1-score of 0.95 demonstrates a balanced measure between these two metrics. The average macro results suggest that the model accurately recognizes the relevant information from a phrase with a low error percentage. This outcome is significant because it indicates that this method provides the conversational agent with accurate information extraction capabilities when relevant information from a user's entry needs to be extracted to define or change the context of the dialog.  
Figure 3: The displayed figure illustrates the relationship between actual and predicted intents regarding the evaluation of the IR system in the first scenario.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
0.94 & 0.96 & 0.96 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The average results for the second evaluation scenario showcase the performance of the named entity Recognition (NER) model. The figure above provides an overview of the model’s effectiveness in identifying and extracting named entities from utterances.
## 3 Proposed Design and System-Level Development
### Operation Under Shading Condition  
Figure 3b shows the flow of string current when M2 is under the influence of partial shading, while M1 and M3 are under the same condition. Again, the centralized MPPT inverter maintains the MPP operation. It can be seen from Figure 3b that the maximum power point current (\(I_{mpp}\)) of M3 enters into the M2 node through a string switch. Since M2 does not generate the same \(I_{mpp}\) of M3 due to the shading condition, the string switch of M2 is switched off and the buck converter of the M2 module is made operational. It means that the \(I_{mpp}\) of M3 is diverted to the output port of the buck converter. Now, the duty cycle (\(D\)) of the buck converter is adjusted appropriately to ensure that the low input current of M2 is raised to \(I_{mpp}\) of M3 at the output port of the converter. Simultaneously, module M2 operates at its local MPP point. Then,  
Figure 3: Operation and layout formation of proposed architecture under two cases.  
the \(I_{mpp}\) (=\(I_{string}\)) from the output port of M2's buck converter is diverted back to the PV side, as M1 can support the \(I_{mpp}\). Finally, after passing through M1, the common \(I_{mpp}\) (=\(I_{string}\)) reaches the inverter node.
## 6 System Testing and Results
### Web Portal for the Final User  
A Web portal which contains a map of the region where the system is currently implemented, web pages to consult the climatic variables in each one of the RTUs, a downloads zone and general information of the system is stored in the CS located at the Engineering School of the Universidad Aut onoma de Quer daro.  
The main screen of the web portal is shown in Figure 15, where the RTU's location map can be appreciated in the center. The specific location of each one of the RTUs is divided between three wireless stations networks. One is located in the upper left of the map and constituted by the EMA Cerro de las Cruces, EMA Finca la Victoria and EMA Agua Prieta. The second can be found at the center and is formed by the EMA Chanjul and the EMA Pinabeto. Finally a third wireless stations network constituted by the EMA Buena Vista, EMA Piedra Parada and the EMA Vega de los Gatos.  
When the final user clicks on a specific RTU located in the map, the user is redirected to a webpage (Figure 16) where numerical weather information is shown, and also the charge level of the battery is  
Figure 14: Most active protocols during data transmission when using satellite link communication.  
Figure 15: Main screen of the web portal which shows the location of the RTUs in the geographic region “Los Altos de Chiapas”, Mexico.  
shown in order to schedule maintenance or to prevent a power failure. On the left side a photograph of the RTU _in situ_ and a Google's Earth application to visualize satellite pictures of the site can be appreciated.  
Figure 16: Numerical interface oriented to the final user.  
Figure 17: Graphical representation of the acquired data.  
On the same RTU's webpage, a graphical interface is presented. In this, records of the collected data in the last 24 h are shown in a scrolling graph. This is, the last measurement acquired is graphed on the extreme right side and with the arrival of new data it moves to the left side, thus, after 24 h this data will not be shown any more. In Figure 17 an example of the graphical representation of the acquired data is shown.  
In addition to the numerical and graphical representation of the measurements, it is possible to download acquired data from a download zone shown in Figure 18. There are only four steps that the final user needs to follow to download data from the CS: choose the variables to be downloaded in a text file, select the date interval, decide on the specific time interval and click on \"download data\".  
Lastly, in Figure 19 photographs of the RTUs (left and center) as well as the BTU (right) are shown.  
Figure 19: Photographs of the installed RTUs.  
Figure 18: Downloads zone oriented to the final user.
# Acute Effects of Static Stretching on Submaximal Force Control of the Ankle
## 2 Materials and Methods
### Measurement of Maximum Angle of Ankle Dorsiflexion and Passive Resistance Torque  
The maximum dorsiflexion angle of the ankle joint and the corresponding passive resistance torque were assessed using a Biodex System 4 dynamometer (Biodex Medical Systems, Inc., Shirley, NY, USA) in conjunction with the LabVIEW 2013 software (National Instruments, Austin, TX, USA). The measurement position was set with the hip joint at 70\({}^{\circ}\) and the right knee joint at full extension. The participant's right foot was positioned at 0\({}^{\circ}\) of ankle dorsiflexion with the lateral malleolus carefully aligned to the rotational axis of the dynamometer. The foot was stabilized on the footplate by securing straps. An ankle joint angle of 0\({}^{\circ}\) was defined as the orientation in which the footplate stood perpendicular to the floor. The trunk, pelvis, and distal thigh were stabilized using straps. The assessment began with the ankle positioned at 20\({}^{\circ}\) of plantar flexion, followed by passive dorsiflexion at a constant angular velocity of 5\({}^{\circ}\)/s until the torque was reached the preset maximum passive resistance level. The maximum passive resistance torque was identified at the torque which the participant experienced the greatest tolerable stretch without pain. The participants were instructed to remain relaxed throughout the measurement period to avoid any voluntary muscle activation.
## 4 Result and Discussion
### Single Dynamic Impact Test
#### 4.1.1 Stress-Strain Curve
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Static**} & \multirow{2}{*}{\begin{tabular}{c} **Inclence** \\ **Strength** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Train Rate** \\ **(m/s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Peak Strain** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Peak Stress** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Energy** \\ **Absorption** \\ **Property** \\ \end{tabular} } \\ \hline \multirow{4}{*}{U-SF0} & \multirow{4}{*}{100.83} & 4.7 & 37.72 & 0.508 & 65.6 & 0.65 & 0.45 \\  & & 8.1 & 74.94 & 0.362 & 95.0 & 0.94 & 0.74 \\  & & 10.4 & 103.56 & 0.293 & 121.3 & 1.20 & 1.32 \\ \hline \multirow{4}{*}{U-SF2} & \multirow{4}{*}{120.13} & 3.4 & 43.81 & 0.390 & 70.0 & 0.58 & 0.53 \\  & & 7.7 & 71.19 & 0.364 & 90.4 & 0.75 & 0.86 \\  & & 10.2 & 94.41 & 0.750 & 123.1 & 1.02 & 1.73 \\  & & 10.9 & 109.18 & 1.549 & 161.6 & 1.35 & 3.92 \\ \hline \multirow{4}{*}{U-SF4} & \multirow{4}{*}{146.76} & 3.6 & 37.62 & 0.296 & 44.7 & 0.30 & 0.23 \\  & & 7.3 & 82.37 & 0.373 & 79.8 & 0.54 & 0.50 \\ \cline{1-1}  & & 10.1 & 113.58 & 0.448 & 107.1 & 0.73 & 1.28 \\ \cline{1-1}  & & 11.9 & 155.12 & 0.872 & 152.9 & 1.04 & 2.52 \\ \cline{1-1}  & & 12.3 & 189.61 & 2.178 & 195.0 & 1.33 & 4.94 \\ \hline \hline \multirow{4}{*}{U-SF6} & \multirow{4}{*}{135.76} & 4.3 & 66.24 & 0.453 & 58.4 & 0.43 & 0.27 \\  & & 6.9 & 89.69 & 0.839 & 91.3 & 0.67 & 1.09 \\ \cline{1-1}  & & 9.8 & 117.76 & 1.116 & 138.7 & 1.02 & 2.65 \\ \cline{1-1}  & & 11.3 & 166.53 & 1.740 & 186.3 & 1.37 & 4.34 \\ \cline{1-1}  & & 12.5 & 189.85 & 1.852 & 196.4 & 1.45 & 5.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Influence of non-oriented fiber content on impact compression performance. multiple cracks increased. The results showed that under a heavy load and impact load, the higher the steel fiber content, the stronger the energy absorption capacity of the sleeper. The fracture propagation pattern also highlighted that with the increase of the degree of fiber reinforced, the crack resistance of the same width of crack will also increase.