esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Analysis of Low-Emission Hydrogen Production from a Photovoltaic Carport
## 3 Components of the Low-Emission Hydrogen Generation System
### Photovoltaic Carport with Hybrid Inverter  
In 2024, the latest monocrystalline bifacial panels were proposed to be employed for the construction of a photovoltaic carport [69]. Bifacial cells, which can capture light from both front and back, have higher energy production and efficiency rates than single-sided cells. The active layer is on both sides, which allows the panels to absorb not only the light that falls on them, but also the reflected light that reaches them from the back [70]. When the panels were placed in the standard way, i.e., at an angle of 35\({}^{\circ}\) and with the front side of the module facing south, rather good results were obtained. It is not only extremely effective, but also certainly profitable to position the solar modules at this angle. In this position, the back side can produce up to 25% of the total energy. The height at which the panels are placed is also important. According to the tests conducted, the amount of electricity produced by bifacial photovoltaic panels increases significantly depending on the height of the panels from the ground. Mounting them higher in the frame results in greater electricity production. Therefore, they are ideal for installation on photovoltaic carports. Hence, a horizontal arrangement of three panels in four rows was proposed. The approximate width of the carrot will be 2274 mm \(\times\) 3 = 6822 mm, and the height will be  
Figure 3: Steps in implementing research.  
Figure 2: Green hydrogen generation system powered by a photovoltaic carport.  
1134 mm \(\times\) 4 = 4536 mm. The peak power of twelve panels will be 530 Wp \(\times\) 12 = 6360 Wp. The 3D model of a 6.36 kWp photovoltaic carrot is presented in Figure 4.  
The active surface of the carrot will have the dimensions of approximately 6.8 m \(\times\) 4.5 m. With a spacing of the carrot's legs of 6 m, it will be possible to park two compact-class vehicles under it. The proposed carrot will be able to generate over 6 MWh of electricity per year in Polish climatic and geographical conditions [71]. The carrot should be equipped with a hybrid inverter capable of charging the stationary energy storage.  
In the time of the Internet of Things devices, it is important for the photovoltaic system to have a well-functioning cloud platform for continuous monitoring and effective diagnostics of the entire system [72]. Such a platform should have functions for easy access to the measurement data, and it should allow the data to be saved in standard data exchange formats [73].
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 2 Methodology
### Chi-Square Independence Test  
The chi-square (CHI2) test examines whether there is a relationship between two categorical variables in a single sample. This test assesses the independence or association between two categorical variables [22]. The null hypothesis (\(H_{0}\)) suggests that there is no association between the two variables, implying they are independent. Conversely, the alternate hypothesis (\(H_{1}\)) posits that there is a significant association between them,  
Figure 1: Flowchart of the feature-selection process and interaction between different methods.  
indicating they are not independent. From a contingency table (Table 1) that displays the frequency counts of the joint occurrences \(n_{ij}\) of the two categorical variables, let \(X\) represent one variable with categories \(X_{1},X_{2},\ldots,X_{m}\) and \(Y\) represent the other variable with categories \(Y_{1},Y_{2},\ldots,Y_{k}\).  
The values \(x_{i}\) and \(y_{j}\) are the marginal totals of the rows and columns, respectively, and are calculated as \(x_{i}=\sum_{j=1}^{k}n_{ij}\), \(i=1,\ldots,m\) and \(y_{j}=\sum_{i=1}^{m}n_{ij}\), \(j=1,\ldots,k\). Under the null hypothesis of independence, the expected frequency (\(E_{ij}\)) for each cell in the contingency table is  
\[E_{ij}=\frac{x_{i}y_{j}}{n}, \tag{1}\]  
where \(n=\sum_{i=1}^{m}\sum_{j=1}^{k}n_{ij}\).  
The chi-square statistic (\(\chi^{2}\)) measures the discrepancy between the observed frequencies (\(n_{ij}\)) and the expected frequencies (\(E_{ij}\)) given by (1), and is defined as  
\[\chi^{2}=\sum_{i=1}^{m}\sum_{j=1}^{k}\frac{(n_{ij}-E_{ij})^{2}}{E_{ij}}.\]  
Under certain conditions, such as when the sample size is sufficiently large and the expected frequencies are not too small, the \(\chi^{2}\) statistic follows a chi-square distribution with \((m-1)(k-1)\) degrees of freedom. Typically, each expected frequency \(E_{ij}\) should be at least 5 to ensure the validity of the chi-square approximation.  
To determine whether to reject the null hypothesis, we use the \(\chi^{2}\) statistic. The null hypothesis is rejected if the \(\chi^{2}\) statistic exceeds the critical value from the chi-square distribution at a chosen significance level (such as \(\alpha=0.05\)). Alternatively, the null hypothesis is rejected if the \(p\)-value is less than the significance level.  
It is important to note that the chi-square test is an omnibus test. Therefore, if the test indicates a significant association, post hoc procedures need to be conducted to compare individual conditions.
## 2 Related Work  
For sensor network, a wide range of MAC protocols have been proposed. Among them S-MAC [1] is one of the pioneering mechanism in the contention based MAC protocol specially targeting sensor networks. The basic idea behind S-MAC is that nodes follow a predefined sleep and listen cycle, where nodes wake and sleep together. In T-MAC [3] the adaptive duty cycle is used to improve the energy efficiency of S-MAC.  
TDMA has long been dismissed as an unfeasible solution for wireless _ad hoc_ networks for its lack of scalability and adaptability to varying environments. However, it provides a good energy efficient and collision free communication. Recently several techniques [4] have been proposed for TDMA in sensor networks. But these techniques are not successful in dealing with the fundamental problems that stand-alone TDMA method suffers from.  
In our previous work [5] we introduced the novel idea of combining broadcast scheduling (broadcast TDMA) and link scheduling (pair wise TDMA) together with CSMA in order to gain energy efficiency as well as efficient use of bandwidth.  
In AMAC [6] energy savings is achieved by dynamically changing the schedule of each node depending on the traffic load. B-MAC [7] is the default MAC for Mica2 which adopt LPL (Low power listening) [8] and engineer the clear channel sensing (CCA) technique to improve channel utilization. Z-MAC [9] can dynamically handle CSMA and TDMA together depending on the level of contention in the network. In Z-MAC the knowledge of topology is used to improve MAC performance under high contention situation. The protocol uses DRAND [10], a distributed implementation of RAND [11] in order to assign slots to every node in the network. TH-MAC [12] is a hybrid MAC protocol which uses A-DRAND algorithm for slot assignment. A-DRAND is a modified version of DRAND algorithm for clustered wireless sensor networks. In A-DRAND cluster heads need more slots to relay packets. In [13] a hybrid MAC protocol BAZ-MAC is proposed for _ad hoc_ networks. The protocol uses a bandwidth aware slot allotment technique during the set-up phase, slots are assigned to the nodes according to their bandwidth requirements. In WiseMAC [14] a sender can minimize the length of the preamble by exploiting the knowledge of the sampling schedules of its neighbors during communication and thus reducing the preamble transmission overhead.  
Our proposed EP-MAC also combines TDMA and CSMA. But EP-MAC is totally dissimilar from Z-MAC and other hybrid MAC protocol. Because, where other hybrid MAC protocol uses the broadcast scheduling the EP-MAC uses the link scheduling technique. Link scheduling technique is chosen because of the fact that its performance is better than broadcast scheduling in WSNs, in terms of energy preservation. Moreover, CSMA is used only to offset the drawbacks of TDMA and EP-MAC suitably vary the transmit power with a goal to reduce energy consumption. In addition, EP-MAC minimizes the message delay by introducing parallel transmission which also addresses the fundamental difficulties of using TDMA protocol.
# V2X Communications in Highway Environments: Scheduling Challenges and Solutions for 6G Networks
## 5 Performance Evaluation
This verifies that the newly introduced metric functions effectively, and the temporary denial of resources in vehicles under poor channel conditions is brief due to the fast-changing nature of vehicular communications. Therefore, the vehicles that failed to transmit or receive any packet could be considered outliers or unsupported entities for the network simulator, thus not accounted for in our calculations. It has been observed that the SOVANET+ scheduler outperforms the other two in all three traffic scenarios, in terms of throughput, delay, jitter, and PRR. The new metric also ensures that the performance of vehicles with high and moderate MCS is balanced. Additionally, the overprovisioning of delay-critical services for each vehicle and the temporary  
Figure 8: UL PRR for all UCs. Figure 7: Mean UL jitter for all UCs. rejection of assigning resources to vehicles with low MCS contribute to the needed services' QoS and leads to significantly improved overall performance.
## 3 Experiments and Results
### Data Preparation for CNN Training and Predicting  
The first step encompasses the filtering and normalization of data. The T1-weighted MR, AD, RD, MD, and FA images were all normalized between zero and one to be used as input for the CNN. Prior to that, a percentile filter was applied to eliminate 0.2% of the highest values in each channel, as defined in preliminary experiments. This step is necessary to eliminate any spurious voxel values and keep all input channels within the same normalized range. Failure to follow this step may result in suboptimal performance, as high-value voxels and discrepancies among the maximum values of each map may obscure brain tissue information from the CNN, and any comparison study would not be valid.  
The next step is to generate patches from the images to be used for training. Patches were extracted for the three data views, allowing three corresponding models to be trained and combined. Of the 60 patches extracted for each subject, all were randomly selected; however, 50 were centered inside the thalamus to ensure good balancing of the amounts of voxels inside and outside the segmenting structure. We stress that all input channels and masks must be taken to have exactly the same size and coordinates for each patch to maintain the alignment of input data.  
Data were subsequently allocated among training, validation, fine-tuning, and testing subsets. The testing set comprised the data of 20 subjects, for whom manual segmentation was performed by the specialist. For the training phase, 963 subjects (Table 2) were split among the training and validation sets according to an 80:20 ratio.  
A data augmentation method was also applied during model training. This augmentation was not performed on the raw diffusion or tensorial data, but on the scalar maps computed from the DTI models, as the interpolation of tensors is not as straightforward as that of scalar maps. Two data augmentation techniques were applied to the images: rotation constrained by limits of \(-5^{\circ}\) to \(+5^{\circ}\) with a random distribution, and horizontal reflection. We note that the latter does not apply to the sagittal view, which does not exhibit lateral symmetry. Furthermore, the masks and input channels must undergo identical transformations to keep them aligned. Data augmentations such as translations were not used, as the patching also functions as an augmentation method to make the CNN more generalizable to translations.  
Finally, the training masks were appropriately standardized, as the automated segmentation methods (FSL, FreeSurfer, QuickNAT, and STAPLE) assign different labels to each thalamus with respect to laterality. The standardization consists of setting the background and structures to zero and one, respectively, (we employed a single label for both thalami). Nonetheless, the offered framework supports multiple labels, allowing for multi-structure segmentation as necessary.
# The Efficiency of Binaural Beats on Anxiety and Depression--A Systematic Review
## 3 Results
### Carrier Frequencies  
It is apparent that a broad spectrum of carrier tones exists across different studies, showcasing the variability and adaptability of this therapeutic approach. For instance, in the study by [PERSON] et al. [19], carrier tones with frequencies of 200 Hz for the left ear and 209.3 Hz for the right ear were used, therefore resulting in the generation of binaural beats with a frequency of 9.3 Hz, which aligns with the alpha pattern. In the study conducted by [PERSON] et al. [25], a 432 Hz sound (considered the \"perfect frequency\") was compared with binaural alpha beats. This time, compared to the previously presented study, the authors used carrier tones of 220 Hz and 210 Hz, respectively. [PERSON] et al. [30] used lower carrier tones than other authors, namely 154 Hz for the left ear and 168 Hz for the right ear.
## 4 Reference Scenario
### Production Process and Input Data Analysis  
The case study in this research is about the production process involved in making the TX model, shown in Figure 5, for the 50 kW DC fast charging station, which can charge all electric and plug-in hybrid vehicles. Typical charging times range from 15 to 30 min for DC charging.  
Figure 4: Phases of the manufacturing process.  
The case study of this research focuses on the production process of the \"TX model\" cabinet, shown in Figure 5. This model represents the most significant part of the company's production due to its compatibility with all types of electric vehicles and plug-in hybrids, and is suitable for both indoor and outdoor installations; it is the most requested in the market. Typical charging times range from 15 to 30 min for DC charging. Maximum output power is 50 kW DC and 22 or 43 kW AC. Output voltage for charging ranges from 150 to 500 V in direct current and 400 V \(\pm\) 10% in alternating current.  
The production process leading up to the making of the cabinet has been schematized through a flowchart shown in Figure 4.  
The cabinet emerges from the assembly of macro-elements: base, roof, profiles, doors, and covers. Considering these elements, the production process is divided into two parallel activity flows that converge into a final flow (Figure 6). The first flow is dedicated to the production of the furniture'skeleton' composed of the base, roof, and profiles (blue outline in Figure 6). The activities of the second flow return to the three doors (right, left, and front) and the cover (light blue outline in Figure 6). An assembly activity combines doors and a skeleton to give life to the cabinet (gray outline in Figure 6).  
Figure 5: Cabinet TX model.  
Figure 6: Digital model of the production process.
# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 30 cm Depth in Bangladesh
#### 5.2.3 Five-Day Lead-Time Forecast  
Similar to Section 5.2.2, in order to avoid the repetition of similar tables, we only report the main accuracy results on the testing datasets. For DT forecasts, the MEAN-MAX-MIN input scenario under a four-day time window produced the highest accuracy with the lowest average RMSE of 1.496 \({}^{\circ}\)C and the highest average R value of 0.935. Interestingly, a one-day time window consistently exhibited lower accuracy with the mean input parameters, but the largest time window size did not produce the best prediction. For RF forecasts, the sixth scenario performed the best across all time windows except for the one-day time window, with the lowest average RMSE (1.407 \({}^{\circ}\)C) and the highest average R (0.943) under a five-day time window. The performance of GBT varied across different input scenarios and time windows, with the eighth input scenario demonstrating the highest accuracy, particularly with a four-day time window, resulting in the lowest average RMSE (1.399 \({}^{\circ}\)C) and the highest average R value (0.944). For the hybrid DT-GBT forecasts, the use of the sixth and eighth input scenarios produced the smallest RMSE and largest R values for each time window, with the most favorable results observed with a four-day time window, achieving the lowest average RMSE (1.427 \({}^{\circ}\)C) and the highest average RMSE (1.427 \({}^{\circ}\)C).  
Figure 6: Comparison of observed and optimal forecasted ST30 at four stations.  
average R (0.941) value. The hybrid RF-GBT forecasts consistently favored the eighth input scenario for all time windows, except the four-day time window. The combination of the eighth input scenario and a five-day time window resulted in the lowest average RMSE (1.398 \({}^{\circ}\)C) and the highest average R (0.945) value.  
**Best combination for five-day lead-time forecast:** The hybrid RF-GBT utilizing all input parameters under the five-day window emerged as the optimal forecast model in terms of the lowest average RMSE value (1.398 \({}^{\circ}\)C), and the largest average R value (0.945). This signifies the model's strong ability to capture and predict soil temperature variations accurately. Figure 8 demonstrates that the hybrid RF-GBT under a five-day time window offered a close match with the observed values and exhibited superior accuracy. A closer analysis of the figures reveals that errors mainly occurred in the forecast of extremely high soil temperature.
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 3 Proposed Routing Protocol
### Routing Protocol
#### 3.1.3 Optimization Problem  
Extracting the optimal path that can reduce the sensors' energy in IoT networks can be formulated as follows:  
\[\sum_{i=1}^{ss}E_{n}(L,D)_{i,nx}\times x_{i,nx} \tag{9}\]  
Subject to  
of all nodes participating in communication (ss), subject to the system constraints that have been incorporated in (10). These constraints have been considered to ensure that the solution which is determined by the proposed routing algorithm in each communication process is efficient and accurate. Then constraint ds \(<\) 10 m illustrates that the assumed distance between the two sensor nodes involved in the communication should not exceed 10 m. At each transmission process, the number of sensor nodes should not be more than 15 sensor nodes. There is a threshold for the total amount of energy consumption in each transmission. Moreover, the current sensor node which tries to communicate with the other sensor node in the next hop should also consider the amount of residual energy of this node, i.e., state-of-charge (SoC). The SoC should not be less than a threshold (it is assumed to be no less than 10% of the initial energy of the node to be selected). The reason behind this is to maintain as many alive sensor nodes as possible in the network, which in turn achieves the goal of the study, which is to increase the lifetime of the network.  
In case there is more than one possible path for a message that needs to be sent to the BS, i.e., more than one path has the same cost (energy consumption value), the algorithm should select the path has a higher number of nodes participating in the selected path. The reason behind this is to distribute the level of energy consumption for each path among the largest possible number of nodes, which positively affects the lifespan of the network. The binary decision variable \(x_{i,nx}\) is used to indicated whether the current node (sensor) selects the next node or not, the value of \(x_{i,nx}\) is 1 if a sensor is involved in the next hop, otherwise it is set to 0 as shown in (11).
## 1 Introduction
The GNSS technique can provide accurate information of the ground motion on all three components, but it is limited to point-wise coverage of the study area. Conventional differential SAR interferometry describes the relative spatial deformation of the area (with respect to a reference point) and is limited to the line-of-sight (LOS) direction. GNSS and InSAR can be used jointly on a seismically activated area, to inverse for the parameters of the deformation source and determine the absolute ground deformation field, as well as calculate the E-W and vertical deformation components. On 2 December 2020 10:54:56 UTC, a M\({}_{\text{L}}\) = 4.5 earthquake occurred east of Thiva. According to the Seismological Laboratory of the National and Kapodistrian University of Athens (NKUA-SL), as initially determined, it was located at 38.3140\({}^{\circ}\) N, 23.4663\({}^{\circ}\) E, characterized by a normal focal mechanism with N137\({}^{\circ}\)E, 56\({}^{\circ}\) and \(-37^{\circ}\) for strike, dip and rake, respectively. According to the Geodynamics Institute of the National Observatory of Athens (GI-NOA), the revised location was at 38.3226\({}^{\circ}\) N, 23.4489\({}^{\circ}\) E and the focal mechanism was dip-slip normal, with N111\({}^{\circ}\)E, 35\({}^{\circ}\) and \(-78^{\circ}\) for strike, dip and rake, respectively (see Figure A5 in Appendix B for more details about existing focal mechanism solutions). The aftershocks lasted until 3 January 2021. Even with a moderate magnitude, the deformation field was measurable by synthetic aperture radar interferometry (InSAR), exploiting Copernicus Seninel-1 data in both orbital directions (i.e., ascending and descending). The closest permanent GNSS station from which we were able to acquire data was THIV, located in the city of Thiva. We invert the geodetic data to estimate the rupture plane with uniform slip. We also reassess seismic waveform data to locate all events in order to identify the activated zone, associated with the mainshock and the aftershock sequence. Moreover, we use the rupture plane information from InSAR to better determine the focal mechanism and the mainshock centroid.