esa-sceva/satcom-chunk-collection huggingface dataset:
# Supercapacitor-Based Energy Storage in Elevators to Improve Energy Efficiency of Buildings
## 2 Elevator System  
The main components of a traction sheave elevator system are the car, the counterweight, the guides, and the electric drive [27]. The car and counterweight slide on guide rails and are connected by a suspension rope. The suspension rope is suspended from a traction sheave, driven by an electric drive. A typical elevator system is shown in Figure 1. A variable frequency drive (VFD) controls the electric drive [28]. Induction motors or permanent magnet motors are commonly used. The weight of the counterweight is usually selected to be equal to the sum of the weight of the empty car and 50% of the nominal load [27]. Thus, the elevator is in equilibrium when loaded with half the nominal load. The elevator system also includes mechanical safety devices for emergencies.  
The elevator drive can operate in motor mode and generator mode. In motor mode, the electric drive draws energy from the grid, while in generator mode it generates electrical energy that can be fed back into the grid or stored and used as needed. In conventional elevator systems, the regenerative energy is dissipated as heat at the braking resistor.  
The operating mode of the elevator drive depends on the current difference in the total weight between the car and the counterweight and the direction of motion. The conditions for the operating modes are given in Table 1, where \(m_{tc}\) is the total weight of the car, including any passengers, and \(m_{cw}\) is the weight of the counterweight. When the total weight of the car and the weight of the counterweight are equal, the system is in equilibrium. However, the elevator drive operates in motor mode because it must compensate for mechanical losses (e.g., friction).  
Figure 1: Elevator system.  
The energy consumption of an elevator is not easy to estimate, as it depends on several factors: the traffic demand, which is difficult to estimate, the dispatching system, the control system, the standby operation, etc. To estimate the annual consumption of an elevator and its energy efficiency, the most commonly used standards are VDI 4707 and ISO 25745 [29]. These standards are based on short-term energy measurements, where the energy consumption is measured during reference cycles. The reference cycle of an elevator consists of the trip from the ground floor to the top floor and back, starting and ending with an open door. The typical speed profile of an elevator during a reference cycle is shown in Figure 2.  
Elevators usually travel at a constant speed and have an S-shaped speed when accelerating and decelerating to ensure passenger comfort. They enter the station at a very low speed, called the landing speed.
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 7 Mapping of the Georeferenced Data  
In this section, the prototype was examined through a climate walk for georeferenced data collection. As the outdoor environments are beyond human control, the tolerance against these conditions is higher compared to indoor scenarios. A suitable approach would be to map indices like the UTCI to account for a wide range of acceptability in thermal comfort.  
According to the definition, \"UTCI is the air temperature of the reference condition causing the same model response as actual conditions\". The expression includes the meteorological input, the physiological model, and the clothing model to get a dynamic response model. The main factors are air temperature, relative humidity, wind speed, mean radiant temperature, solar irradiance, metabolic rate, and clothing factor. Some supplementary information (i.e., reference to the climate zone, personal background, and physiological data) will enhance the understanding of an individual's perception of comfort in different environmental situations.  
For this assessment, we used the technique from previous research for data plotting on the path. The Grasshopper platform was used for this purpose. The plugins included Ladybug Tools (to calculate the UTCI), @it (to import shapefiles of the buildings and streets, imported from OpenStreetMap and treated with ArcGIS), Lunchbox (to read the files from the data logger), and GHPython (for mathematical calculations and connecting different data types) [30].  
Figure 22: Validation graph for data logger.  
The observation district was Maxvorstadt, Munich. The climate walk route was covered in approximately 35 min. An overview of the selected district is shown in Figure 23. The GPS plot shows precise patterns for geolocation apart from some negligible deviations near the Oscar von Miller Forum. This implies that the GPS readings were accurate enough to ignore the path correction procedures. All the sensor readings were mapped, including the calculations for the MRT and the UTCI (Figures 23-31).  
The UTCI map showed a range between 11 and 21 \({}^{\circ}\)C, indicating no thermal stress. The noise level showed moderation between a few quiet readings of 30 dB to a maximum of 80 dB, while the particulate matter readings for both PM2.5 and PM10 showed a maximum of 16 \(\upmu\)g/ m\({}^{3}\), which translates to good air quality  
Figure 28: (**a**) Solar radiation (w/m\({}^{2}\)); (**b**) noise level maps (dB).  
Figure 26: Transient temperature maps: (**a**) globe temperature (\({}^{\circ}\)C); (**b**) air temperature (\({}^{\circ}\)C).  
Figure 27: (**a**) Relative humidity (%); (**b**) wind speed (m/s).  
Figure 29: (**a**) Mean radiant temperature (\({}^{\circ}\)C); (**b**) universal thermal climate index maps (\({}^{\circ}\)C).
## 3 Results and Discussion
The variation trend of the average BER of the signal OAM state induced by the turbulent atmosphere is opposite to the SNR of the signal OAM state (see Figure 8b). Figure 5: Detection probability of the signal OAM state carried by the non-uniformly correlated high-order Bessel–Gaussian beams in the turbulent atmosphere for the different beam widths analyzed. Figure 8: (**a**) SNR and (**b**) average BER of the signal OAM state carried by the non-uniformly correlated high-order Bessel–Gaussian beams with different beam widths against the strength of the turbulent atmosphere. Figure 6: SNR of the different signal OAM states carried by the non-uniformly correlated high-order Bessel–Gaussian beams in a turbulent atmosphere against the beam width. Figure 7: Average BER of the different signal OAM states carried by the non-uniformly correlated high-order Bessel–Gaussian beams in the turbulent atmosphere against the beam width.
## 5 Methodology
### The Proposed S-2 EC-ERF Framework
* Data Processing: When data arrive at a node, the data are processed to be entered into ERF-LET, and a statement of the data's status is given. Processing includes encoding operations for categorical values and standardization operations. * Attack Detection: The ERF-LET algorithm uses its learning from the training phase to detect patterns associated with security attacks, especially DDoS attacks. If the ERF-LET identifies a specific node's behavior as indicative of an attack, that node is labeled as an \"attack node\" and drops or discards data originating from it. If the ERF-LET detects no attack, no action will be taken on the data. The integration of the ERF-LET algorithm and ECP-LEACH within the S-2 EC-ERF framework creates a holistic approach to WSN security and efficiency. The ERF-LET algorithm focuses on node-level security through real-time threat detection, while ECP-LEACH contributes to network-level energy conservation. This collaborative implementation ensures a balanced and resilient WSN architecture, thereby effectively addressing both security and energy efficiency challenges.
## 4 Numerical Simulation
### Simulation Results
#### 4.2.2 Horizontal Displacement Analysis of the Top of the Enclosure Wall  
The monitoring point W1 was selected in the constructed model for the horizontal displacement analysis of the top of the enclosure wall, and its simulated and measured values were compared and analyzed under different construction stages, as shown in Figure 10. The curve representing the measured values of the monitoring point W1 is denoted as AW1, and the curve representing the simulated values is denoted as BW1.  
As shown in Figure 10, the simulated values do not precisely match the measured values due to the ideal modeling assumptions used in the numerical simulation. In order to improve the calculation efficiency, the soil layer was simplified by assuming that it was uniformly distributed horizontally, while the actual soil layer distribution is not exactly horizontal. The machine tools and other temporary loads that may be stacked around the pit during the construction process are not considered.  
The curves of the simulated and measured results have a similar trend, and the top of the diaphragm wall at the observation point W1 moves in the horizontal direction as the construction proceeds and the displacement increases slowly and finally stabilizes. The simulated values are generally smaller than the measured values, because the modeling process simplifies the actual project and the calculated results are ideal values. However, the maximum values of both do not exceed the limit value of 30 mm, and the excavation process of the foundation pit is in a safe state.
## 1 Introduction
To analyze the terms that describe the relationship of the devices among them and with the network, it is important to define the degree of intelligence that these nodes possess, a term that is included in technological convergence. When talking about heterogeneous environments, we can mention different network topologies so that the devices exist in an environment that has different characteristics. * Cooperative networks use self-configuration capabilities to dynamically adapt on demand, since they can respond to the needs of a specific user, within the policies defined by the operator, while optimizing the general resources of network. * Cognitive networks work on the physical level of the protocol stack, handling emission frequencies and modulation parameters. Cognitive networks exhibit relevant characteristics such as: (1) identification of spectrum access opportunities, (2) selection of frequency bands to be used, (3) coordination among users for access to spectrum, and (4) spectral mobility. The main disadvantage of cooperative networks lies in the shared management and interdependence of networks that are in an environment with common conditions. Cognitive networks have an agile cognitive process with which they can determine the current conditions of the network, in order to plan, decide, and act. The network can learn from these adaptations to make future decisions taking into account the final objectives: to develop communication protocols.
# CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification
## 3 Active Learning
### Analysis of Active Learning for Sentence Classification  
**Euclidean Distance.**  
We measure the Euclidean distance between data points using the last hidden state representation of the model's [CLS] token. We then observe the experimental results of sampling according to active learning strategies: sampling in the order of largest Euclidean distances and smallest Euclidean distances. Since data near the decision boundary may be highly related to those with larger distances, as shown in Figure 2, sampling on the order of largest Euclidean distances alone surpasses random sampling and achieves a similar performance to CAL, targeting data near the decision boundary. Conversely, sampling in the order of smallest Euclidean distances results in much lower performance compared with random sampling.  
**Cosine Similarity.**  
We calculate the cosine similarity between data points using the last hidden state representation of the model's [CLS] token. Similar to Euclidean distance, data near the decision boundary can be considered highly uncertain and heterogeneous. Therefore, when data are sampled in the order of lower cosine similarity, it shows high performance. Conversely, sampling data on the order of higher cosine similarity results in significantly poorer performance; it is a highly similar result with Euclidean distance.  
**CAL (Contrastive Active Learning) and Decision boundary.**  
The definition of CAL [11] is to find data near the decision boundary, which demonstrates good performance. However, if merely finding data near the decision boundary is the aim, similar performance can be achieved with simpler methods that require less computation. Therefore, we conduct experiments using the CAL approach on only the top 20% and bottom 20% of data, based on the sum of Euclidean distances among the 10 nearest neighbors during the KNN process in CAL. The results show that when the Euclidean distance sum is in the top 20%, the performance is similar to applying CAL to the entire dataset. Surprisingly, even for data in the bottom 20% of the distance sum, which are likely at the center of clusters and far from the decision boundary, a certain level of performance is maintained compared with the results of sampling data with low Euclidean distance or high cosine similarity. This means that there can be meaningful distinctions even between clustered data.  
While conducting experiments using the CAL [11], we compare the sum of Euclidean distances among the 10 nearest neighbors for the sampled data with the five dataset in Table 1. Most of the sampled data rank above the 90 th percentile in terms of distance sum, but surprisingly, some rank much lower. This experiment allows us to infer that data can significantly benefit or harm even if they are not highly uncertain or near the decision boundary.
# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 1 Introduction
The main originality of this research is the assessment of the performance scalability of WSNs in the agriculture sector, along with the development of a classification framework for studying IoT in agriculture. The study evaluates performance metrics such as network graph connectivity and power consumption for both stationary and mobile scenarios in 6 LowPAN networks and proposes a new approach for simulating moving animals within the COOJA simulator. Additionally, the paper introduces a novel holistic IoT ecosystem suitable for precision agriculture that satisfies the requirements for both the studied scenarios. This paper presented a framework for integrating IoT systems in precision agriculture. The paper is organized as follows: Section 2 reviews related research works and draws a conclusion on how this work helps to fill the gap in the current state of the art. Section 3 describes the proposed framework that effectively combines an array of sensor measurements, communication technologies, middleware, and applications to provide automated decision making support. Section 4 describes how the proposed framework was experimentally validated and tested through simulation, using two application scenarios: monitoring olive tree farms and stables for horse training. The results obtained through simulation demonstrate the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. Additionally, Section 5 discusses how the results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. Section 6 highlights the several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms.
## 4 Results and Discussion
### Effects of the Higher Sloshing Modes on the Sloshing Response
#### 4.4.4 The Second Loading Condition (S2)  
The Minor Earthquake Horizontal Component  
As with S1, the relationships between the maximum sloshing response of the tank under the seven three-dimensional near-fault ground motions and PGAs, as well as the relationship between the maximum sloshing responses and spectral accelerations at the first five sloshing periods, were studied for the minor earthquake component under the second loading condition (S2). According to Figure 7, minor earthquake components were applied along the longer tank wall by S2. Therefore, the maximum sloshing responses of the tank under the selected ground motions were measured at point \"a\" (Figure 7). In Figure 13, the first graph shows the relationship between PGAs and the maximum sloshing responses, and the five following graphs illustrate this relationship between the maximum  
Figure 12: Accelerations at the first five sloshing vibration periods versus maximum sloshing responses (S2—major components).  
sloshing responses of the tank and the spectral accelerations of the selected records at the first five sloshing periods.  
Similar to the major component in S2, the maximum sloshing response of the tank perpendicular to the direction of the minor earthquake horizontal component was mainly  
Figure 13: Accelerations at the first five sloshing vibration periods and PGAs versus maximum sloshing responses (S2—minor components).  
affected by the spectral acceleration at the first sloshing period with an R\({}^{2}\) of 0.96. In terms of the relationship between PGAs and maximum sloshing response, similar to S1, it can be observed that changing the peak ground acceleration (PGA) had a negligible effect on the sloshing response. In other words, in order to predict which seismic excitations will result in a higher sloshing response in rectangular tanks, the spectral acceleration of the earthquake for 0.5% damping, recommended by [73], is a useful earthquake characteristic. In contrast, PGA is insufficient for this prediction as a single parameter.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 5 Experimental Results and Discussion
### Performance of the Guided Sampling Technique
#### 5.2.2 Performance under Varying Outlier Ratios  
A similar test was performed to assess the performance of the sampling algorithm under various ratios of outliers. To this end, the Church dataset was used. The limited sampling budget was set to 1000 and 5000 sample sets for outlier rates less than or equal to 50% and higher than 50%, respectively. The percentage of outlier-free sample sets among the budgeted sample sets (either 1000 or 5000) and the estimation accuracy (\(\mu_{d}\cdot CP\)) are presented in Figure 5.  
For most of the outlier ratios, the outlier-free sampling rates of the proposed algorithm and those of Multi-GS were very close. RANSAC drew quite less outlier-free sample sets in comparison with the other two methods. For outlier ratios higher than 50%, RANSAC completely failed to detect outlier-free sample sets and to estimate the model correctly. For an outlier ratio of 80%, only the proposed algorithm kept good performance by drawing at least 22% outlier-free sample sets, given the limited sampling budget. As a conclusion, the sampling strategy based on the spatial distribution of correspondences along with the evolutionary search not only increased the speed of reaching an outlier-free sample set, but also decreased the probability of ending up with a degenerate solution.
## 5 Low-Complexity Hybrid Beamforming
### Digital and Analog Beamforming Matrix Solutions
This closed form of the solution can also be seen as \(F_{opt}F_{DD,k}^{H}\) on the feasible set of the simulated beamformer \(\mathcal{F}_{RF}\) on the Euclidean projection. However, this does not affect the complexity of the digital beamformer and modifies Equations (77)-(81):  
\[ \begin{array}{l}\underset{F_{DD,k}}{\text{min}}\Big{\|}F_{opt}F_{DD,k}^{H}-F _{RF}\Big{\|}_{F}^{2}\\ s.t.\ F_{DD,k}^{H}FD_{DD,k}=I_{kN_{c}}\end{array}  \tag{81}\]  
Based on the low-complexity Phase Extraction Alternating Minimization method, we reduce the search range of the digital beamformer. Equation (77) is expressed as:  
\[F_{DD,k}F_{RF}=F_{opt} \tag{82}\]  
In Equation (81), since the combined optimization problem eliminates the need for \(F_{DD,k}\) the other solutions, the complexity of the Phase Extraction Alternating Minimization method can be better reduced. Furthermore, instead of randomly selecting the phase during the initialization of the Phase Extraction Alternating Minimization method, Equation (80) is used to select \(F_{RF}\). Finally, the final solution of the digital beamformer can be obtained by the least-squares correction given in Equation (83). \[F_{DD,k}=\Big{(}F_{RF}^{H}F_{RF}\Big{)}^{-1}F_{RF}^{H}F_{opt} \tag{83}\]  
Applying the projection approximation subspace tracking method to the improved hybrid beamforming optimization method of finding Phase Extraction Alternating Minimization for \(F_{opt}^{H}F_{RF}^{(k)}\), the Singular Value Decomposition calculation, then the specific method based on Phase Extraction Alternating Minimization can be expressed in Algorithm 2. ```
1. Input:\(F_{opt}\), \(\hat{k}\). 2. Initialization: set \(F_{DD,k}\) to a random matrix with orthogonal columns, and \(\hat{k}\) = 0
3. Calculation: \({\text{rg}}(F_{RF})=arg\Big{(}F_{opt}F_{DD,k}^{H}\Big{)}\). Derive the matrix \(F_{RF}\) of the phase. 4. SVD calculation: derived from the projection approximation subspace tracking method; \(F_{opt}^{H}F_{RF}^{(\hat{k})}=\overbrace{V}_{1}^{(\hat{k})}\overbrace{\Sigma}^ {(\hat{k})}\overbrace{V}_{1}^{(\hat{k})}\). 5. Calculation: \(F_{DD,k}^{(\hat{k})}=\overbrace{V}_{1}^{(\hat{k})}\overbrace{V}_{1}^{(\hat{ k})}\)., resulting in \(F_{DD,k}^{(\hat{k})}\). 6. Calculation: \(arg(F_{RF})=arg\Big{(}F_{opt}F_{DD,k}^{H}\Big{)}\), yielding \(F_{RF}\hat{k}=\hat{k}\) +1. 7. Calculation: \(F_{DD,k}=(F_{RF}^{H}F_{RF})^{-1}F_{RF}^{H}F_{opt}\), resulting in \(F_{DD,k}\). 8. Determine the \(k\) when 0\(\leq k\leq\hat{k}\) then proceed to step 4, otherwise proceed to step 9
9. Output: \(F_{RF}F_{DD,k}\). ```  
**Algorithm 2**Improved hybrid beamforming optimization algorithm based on Phase Extraction Alternating Minimization.
## 2 Materials and Methods
### Sensor Signal-to-Noise Ratio  
The signal-to-noise ratios (\(SNR\)) of the spectral channel \(k\) of the RGB and hyperspectral cameras were measured by acquiring the white light reflected at the surface of a calibration target (plain white sheet of paper) during two minutes with an integration time set to 33 ms:  
\[SNR(k)=\frac{\mu_{k}}{\sigma_{k}}. \tag{5}\]  
\(\mu_{k}\) denotes the spectral channel \(k\) mean value obtained by computing the mean value of the temporal intensity averaged over the sensor area. \(\sigma_{k}\) is the spectral channel \(k\) standard deviation value obtained by computing the standard deviation value of the temporal intensity averaged over the sensor area. The illumination source power, quantum efficiency and integration time of the cameras directly impact the SNR values. The SNR values of the RGB and hyperspectral cameras we used in this study are represented in Figure 4. The SNR of the red channel of the RGB camera is 1.1 times greater than the one of the green channel and 1.4 times greater than the one of the blue channel. The mean value of the SNR of the hyperspectral is 3.9 times lower than the SNR value of the red channel of the RGB camera. Note that these values are dependent on our experimental configuration and directly impact the amount of noise and the quantification error in the simulated concentration changes.  
Figure 3: Measure of dissimilarities between the corrected (in orange) or uncorrected (in blue) spectral sensibilities and the ideal spectral sensitivities.