esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Validation Period
The variation suggests that there are several pixels which are just at the borderline of a certain drought class, so any overestimation leads the representation to a different class. As an example, the minimum observed SPEI 1 value for February 2017 was \(-1.99\), which reflects severe drought conditions; however, the predicted SPEI 1 value for the same period was \(-2.14\), which reflects extreme drought conditions. Similar observation can be found for January 2017, 2018 and also in March 2018, wherein some pixels with values between 0.96 and 0.99 were overestimated to more than 1, thereby depicting moderately wet conditions instead of near normal. Figure 9: Receiver Operating Characteristic–Area under the Curve (ROC–AUC) curves of drought classification: (**a**) SPEI 1 and (**b**) SPEI 3 for the study region. For SPEI 3, such an observation can be seen in the month of January 2016, in which the observed minimum index value was \(-0.95\), meaning near-normal condition, but the predicted was found to be \(-1.09\), which represents moderately dry conditions. Similar observation can be made in January 2018 and February 2018. However, in March 2018, the minimum index value was underestimated, as the observed minimum value was \(-2.06\), whereas the predicted minimum value was \(-1.92\), thereby representing extremely dry and severely dry conditions, respectively. Therefore, it can be said that, when analyzing at a pixel level, there are situations wherein the observed values are in proximity to the threshold of drought class and the predicted values can overestimate or underestimate it.
# Effects of Smartphone Size and Hand Size on Grip Posture in One-Handed Hard Key Operations
## 2 Materials and Methods  
### Participants  
Smartphone users with more than one year of experience using mainly the right hand for one-handed operations and having no history of visual impairments and musculoskeletal disorders in their hands participated in the grip posture experiment of the present study. Nine participant groups in combination of three hand length and three hand width categories were defined as shown in Figure 1 by considering the hand size distributions of the Korean male and female populations [11] to recruit a representative group of smartphone users in their 20s to 50s in terms of hand size. A total of 45 smartphone users (28 males and 17 females; age = 24.8 \(\pm\) 4.7; hand length = 180.6 \(\pm\) 10.7 mm; hand width = 80.8 \(\pm\) 6.7 mm) who participated in the study were found to be not significantly different from the Korean population (\(n\) = 3921; hand length = 178.4 \(
## 2 Data  
A regional GNSS network consisting of 32 reference stations is considered (see Figure 1). 15 stations are used as the reference network, generating the PPP-RTK products relevant to the user. The other 17 stations then use the generated network products to compute their point positions. All reference stations are part of the EUREF Permanent GNSS network [14].  
Daily observations with a sampling of 30 s of day 293 in the year 2022 are considered. The presented methods here can all be applied to observations with a higher sampling rate. A discussion of problems and opportunities arising when high-rate observations are processed is given in Section 5. A complete list of all considered reference stations is also given in Figure 1. An overview of the processed GNSS and signal-channel combinations is given in Table 1. Note that the receivers can be divided into receiver types A and B based on the tracking mode used to observe the different signals. This will be addressed further in Section 3.1.  
The reference coordinates of all stations have been obtained with the EUREF reference frame solution [15]. Satellite orbits have been fixed to precise values, using the final CODE orbits [16]. The PPP-RTK products generated in the scope of this work are thus satellite clocks and biases. At the user level, the same satellite orbits are used as at the network level.
## 2 Materials and Methods
### Methods
#### 2.2.4 Energy Poverty Mapping  
Once the Energy Expenditure-to-Income Ratio (EEIR) index has been calculated, a GIS system is used to map it on the urban territory. The methodology involves mapping district-level EPC data using a city shapefile within the QGIS environment. The first step in the mapping process involves assessing the energy performance of buildings. The \(EP_{nr,avg}\) consumption is mapped, as this spatial representation allows for the systematic identification of urban areas with the highest average weighted non-renewable energy consumption. Subsequently, the Energy poverty threshold (2M) value and the Energy poverty vulnerability threshold value (see Table 3) are used to classify dwellings according to three levels of energy vulnerability: low vulnerability when EEIR is less than or equal to the energy poverty vulnerability threshold (EEIR \(\leq\) 5%); moderate vulnerability when EEIR is between the energy poverty vulnerability threshold and the energy poverty threshold (5% \(<\) EEIR \(<\) 10%); and high vulnerability when EEIR is equal to or greater than the energy poverty threshold (EEIR \(\geq\) 10%). This mapping process facilitates the identification of areas across the city where energy performance is notably poor, highlighting critical energy vulnerability zones for targeted interventions.  
Based on the results obtained, an analysis of energy efficiency and vulnerability to energy poverty has been carried out. A spatial analysis is carried out to identify districts with the poorest energy performance, focusing on areas with the greatest potential for improvement. It should be noted the limitations of this study. Firstly, it does not consider vulnerability and hidden energy poverty, i.e., those households that reduce their consumption to the minimum in order to be able to afford their energy bills, which is not reflected in the analysis of the 2M indicator.
## 3 Solar Radiation Prediction Time Horizons  
A prediction model essentially consists of a system of linear or non-linear equations that relate the future values of the variable to be predicted with the present and past values of the variable itself and the explanatory variables. Before making a prediction, you must define the prediction horizon on which the model should be applied [14]. The ML applications to predict solar radiations are becoming a trend in the transition of energy generation systems. The models are developed as a time series prediction problem, that will be solved as a classification model. Figure 1 visualizes the most used time horizons in prediction models using ML [23].
## 4 Simulation Results and Analysis
### Dynamic Evolution of Rock Layer Porosity
#### 4.3.2 Porosity of Silstone Interlayer  
To further clarify the integrity and specific damage state changes of siltstone interlayer during multiple mining cycles of the 3 upper and 3 lower Coal Seams, the dynamic monitoring results of thin rock layer porosity are presented in Figure 12.  
As shown in Figure 12, the mining of the 3 upper coal seam has a very limited impact on the siltstone interlayer, with almost no change in porosity, maintaining consistency with the initial state, the average porosity remains stable at the initial level of 0.05, and the maximum value stays fixed at 0.08. After the mining of the 43 lower01 face, significant effects on the roof began to occur, with the porosity of the siltstone interlayer rising sharply. The average value increased to 0.11, an approximately 117.4% increase compared to the upper seam mining, while the maximum value rose to 0.46, an increase of approximately 445.3 percent. However, this effect was limited to the area above the face, with no significant change in the porosity of the siltstone interlayer outside this range, which remained at levels similar to the initial state. After the mining of the 43 lower02 face, the interlayer gauge above it also experienced significant damage, with porosity rapidly increasing. The average value rose to 0.26, an increase of approximately 122.8% compared to the 43 lower01 face, and the maximum value reached 1.00, an increase of approximately 113.7% compared to the  
Figure 12: Porosity variety of the siltstone interlayer after each excavation period.  
43 lower01 face. Meanwhile, the surrounding interlayer gangue remained unaffected, and the siltstone interlayer outside the mining area stayed at the initial state.  
High-stress regions typically depend on the compression and contact between relatively intact rock blocks or strata. Even within fractured regions, interactions between fractured rocks occur, with compressed fractured rocks alleviating excessive stress. This leads to the phenomenon where regions with low porosity exhibit higher stress, while regions with high porosity exhibit lower stress.
# Finite Element Analysis of Combined Energy Piles with Long and Short Heat Exchanger Tubes
## 4 Analysis of the Results  
### Pile Information  
The pile top load-settlement curve under three working conditions, heating, normal temperature, and cooling, is obtained by numerical simulation, as shown in Figure 5, and 5000 kN is taken as the ultimate load value of PHC600 (130) according to the specifications [32]. In order to analyze the thermal-water-mechanical coupling effect of the long-term monitoring process of piles, the load level of the pile top is chosen as 50% of the ultimate load value of PHC600 (130). When the load is 1500 kN, the pile top settlement under the heating, normal temperature, and cooling conditions is 5.40 mm, 10.29 mm, and 12.89 mm, respectively. Under load-heating, the pile top settlement decreased by 47.52%. Under load-cooling, the pile top settlement increased by 25.27%. When the load is 2500 kN, the pile top settlement under the heating, normal temperature, and cooling conditions is 12.00 mm, 17.43 mm, and 20.35 mm, respectively. Under load-heating, pile top settlement decreased by 31.15%. Under load-cooling, the pile top settlement increased by 16.75%. When the load is 4000 kN, the pile top settlement under the heating, normal temperature, and cooling conditions is 23.26 mm, 29.31 mm, and 32.60 mm, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Group**} & **Groundwater** & **Heat Transfer** & **Heat Exchanger** & **Heat Exchanger** \\  & **Seepage Rate** & **Fluid** & **Tube on the Outside** & **Tube on the Inside** \\  & **(m/s)** & **Temperature** (\({}^{\circ}\)C) & **of the Pile (m)** & **of the Pile (m)** \\ \hline
1 & \(2\times 10^{-4}\) & / & 0 & 0 \\
2 & 0 & 50 & 40 & 80 \\
3 & \(2\times 10^{-6}\) & 50 & 40 & 80 \\
4 & \(2\times 10^{-5}\) & 50 & 40 & 80 \\
5 & \(2\times 10^{-4}\) & 50 & 40 & 80 \\
6 & \(2\times 10^{-4}\) & 50 & 40 & 60 \\
7 & \(2\times 10^{-4}\) & 50 & 40 & 40 \\
8 & \(2\times 10^{-4}\) & 50 & 40 & 0 \\
9 & 0 & 5 & 40 & 80 \\
10 & \(2\times 10^{-6}\) & 5 & 40 & 80 \\
11 & \(2\times 10^{-5}\) & 5 & 40 & 80 \\
12 & \(2\times 10^{-4}\) & 5 & 40 & 80 \\
13 & \(2\times 10^{-4}\) & 5 & 40 & 60 \\
14 & \(2\times 10^{-4}\) & 5 & 40 & 40 \\
15 & \(2\times 10^{-4}\) & 5 & 40 & 0 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation test scheme.  
Under load-heating, pile top settlement decreased by 20.64%. Under load-cooling, the pile top settlement increased by 11.22%. This is because the increase in temperature expands the pile body, thereby reducing the settlement of the pile top; the temperature drop causes the pile shaft to contract, which strengthened the settlement at the pile top. The influence of the surface settlement temperature gradually diminished with the increasing load amount. Additionally, the influence of the coupling effect of temperature and force on the settlement of the pile top could be related to the boundary condition, soil layer, and the construction method.
## 5 Enhancements to ShinyAnonymizer Data Encryption Techniques
### Technical Details of Encryption Techniques
#### 5.1.2 Xdes  
Description: One of the processes involved in removing direct identifiers or specific identifiable pieces of data in a dataset. For example, scrubbing a database would involve removing names, social security numbers, and other such direct identifiers.  
Application: This is basically an extension of DES. It deploys multiple phases of DES encryption with different keys, hence improving on the key size and security.
## 7 Experimental Results
### Realtime Implementation  
In a real-time environment, we deployed the end device at different locations of the building. The evacuation path display controller and safety operation controller were also deployed at a different position near the end device to receive the sensor data through 2.4 GHz Zigbee communication. Here, we integrated the flame sensor and DHT sensor with the end device to sense the fire, temperature, and humidity in the indoor building environment. When the data collection node sends a signal of fire breakage, the vision node also sends an information log verifying the same, which is used by the coordinator as an extra layer of information to be used for correlating and examining the current status and preventing any false alarm. The end device with the assistance of LoRa communication transmits the sensor data to the gateway. As the gateway is integrated with the LoRa and Wi-Fi module, it logs the sensor data to the cloud server, i.e., Blynk server. Moreover, Figure 19 clearly illustrates the specifications such as the controller and communication protocol of the hardware that were deployed in the indoor layer and outdoor layer. The Blynk dashboard displays the sensor value received through LoRa communication. After the deployment of all the hardware in real-time, we switched on all the nodes and tested the performance of the nodes in terms of sensing fire, temperature, and visual data in the building. Moreover, communicating this data to the gateway and cloud server was also evaluated in this study.  
Figure 19: Realtime Experimental Setup with Devices and Gateway.  
In Figure 20, the working of the hardware device is visualized, where the end device communicated the sensor data of fire status, temperature, and humidity. As discussed earlier, the end device is the amalgamation of LoRa and 2.4 GHz based Zigbee. The end device communicates the sensor data to the evacuation path display controller through 2.4 GHz based Zigbee and to the gateway via LoRa. The LCD in the end device, evacuation path display controller, and gateway visualize the temperature, humidity, and fire status data. The gateway logs the sensor data on the Blynk server through internet connectivity. Figure 20 presents the Blynk dashboard that visualizes the fire status, temperature, and humidity data. The two different results are visualized in the Blynk dashboard.  
In Figure 21a, the dashboard shows the temperature value as 33 \({}^{\circ}\)C, the humidity level as 66, and fire as not detected. Figure 21b illustrates another instance, where fire is detected, along with a temperature of 51 \({}^{\circ}\)C and a humidity level of 89. This clearly illustrates that the proposed architecture can visualize the real-time sensor data of the building related to fire.  
Figure 20: Visualization of Hardware and Results.
# Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center
## 4 Proposed Multi-Level Traffic Load Balancing MDTLB
### Design Considerations  
The design of the protocol is composed of two parts: an adaptive parameter-setting algorithm, and a rerouting algorithm. The detailed discussion is as below:
## 1 Introduction
Poly(ethylene glycol) (PEG) molecules have been attached onto MSNs' surface in order to enhance the water dispersibility of MSNs and also to tailor the hydrophilicity to control protein adhesion [31; 32]. A combination of MSNs with mixed-block polymeric micelles composed of poly(ethylene glycol)-block-poly(l-lactide) (PEG-PLLA) and poly(ethylene glycol)-block-poly(d-lactide) (PEG-PDLA) enhanced the performance of the controlled drug delivery compared to the polymers alone [33]. PEGylation of MSNs' surface increases the diffusion rate of drug molecules into tumor tissues and also improves their colloidal stability [34]. [PERSON] et al. prepared MSNs coated with a diblock copolymer, consisting of poly(ethylene glycol) methyl ether methacrylate and 2-(tert-butylamino)ethyl methacrylate via the atom transfer radical polymerization method [35]. The results showed that ca. 60% of the loaded DOX-drug was released at pH < 6.5. [PERSON] et al. reported the preparation a pH-responsive nanosystem consisting of PDEAEMA brush grafted on MSNs' surface and modified by cysteine and poly(oligo(ethylene glycol) methyl ether methacrylate) (MSNs-PDEAEMA-Cys-POEGMEMA) and was examined as an anti-cancer drug (DOX) nanocarrier [36]. The DOX was unloaded at basic pH above pH 7 and was successfully released at low pH below 5 due to the protonation process of tertiary amine groups of PDEAEMA. [PERSON] et al. prepared amphiphilic block copolymers made of poly(N,N-diethylaminoethyl methacrylate) and poly(poly(ethylene glycol) methyl ether methacrylate) used to control release of the paclitaxel from MSNs using a pH-trigger. The results showed that about 55% of paclitaxel was released in the intracellular environment at pH 5 [37]. In this current work, we report the synthesis of MSNs with average particle size of 230 nm and average pore size of 5 nm, as estimated by TEM. Surface-initiated activators regenerated by an electron transfer atom transfer radical polymerization (SI-ARGET-ATRP) technique have been used to grow poly(2-(diethylamino) ethyl methacrylate) (PDEAEM A) on the outer surface of MSNs, as pH-sensitive gatekeepers. To enhance the diffusion rate of drug molecules onto tumor tissues and improve their colloidal stability, poly(oligo(ethylene glycol) methyl ether acrylate) (POEGMEA) molecules were then bounded to the outer surface of PDEAEMA brushes via the thiol-Michael addition click reaction. The attachment of POEGMEA was confirmed by XPS and DLS. The synthesized nanosystem was then loaded with Rhodamine B dye (RhB), and the release profile of the dye was investigated in acidic and basic media. The nanocarriers were evaluated for cytotoxic activity against a stem cell line. All nanosystems were found to be nontoxic at concentrations between 10 and 100 \(\upmu\)g/mL for an exposure time of 48 h.
# A Parametric Identification Method of Human Gait Differences and its Application in Rehabilitation
## 2 Methods
### Data Collection and Processing  
The human gait data acquisition experiment was carried out in the Human Gait Capture System Laboratory of Xi'an Jiaotong University, China. All subjects were instructed to walk naturally at a comfortable speed along a walkway for about 5 min and kinematic values of thenlower extremity were obtained through a 10-camera video-based motion capture system (VICON system, Oxford Metrics Limited, UK) operating at 100 Hz. This experiment focused on the right joint rotations (hip, knee, and ankle) in the sagittal plane, i.e., flexion-extension. We hypothesized there would be no difference between the right and left limb. The gait cycle was defined as from the first to second heel strike on the ipsilateral side by the assistance of ground reaction force data recorded using three staggered force platforms, 1500 Hz, embedded in the walkway. The relevant equipment and a participant with markers are shown in Figure 1.  
The raw marker trajectories were pre-processed in Vicon Nexus and Mokka, and then the joints angles were calculated using OpenSIM software (SimTK and Stanford University, USA) through the biomechanical gait model (2392) [41].
# TDLAS Monitoring of Carbon Dioxide with Temperature Compensation in Power Plant Exhausts
## 1 Introduction
[34] combined regularization methods with TDLAS to measure non-uniform temperature distribution, relying on the measurements of 12 absorption transitions of water vapor from 1300 nm to 1350 nm. The results showed that regularization methods are less sensitive to the noise caused by temperature variation. Despite some temperature compensation methods applied in industrial process monitoring, little work is found in practical emission measurements of CO\({}_{2}\) at 1579 nm in power plants. In this paper, TDLAS experiments were conducted in a temperature field simulating the outlet duct of a power plant. Both DA and WMS methods are used to measure CO\({}_{2}\) concentrations at different temperatures. Two temperature compensation methods are used to reduce the measurement errors.
# Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation
## 4 Experimental Validation  
To demonstrate the performance of the proposed acoustic localization approach based on the cluster analysis and iterative interpolation, real-world experiments have been carried out in a practical room. The room is w 9.64 \(\times\) 7.04 \(\times\) 2.95 m\({}^{3}\), where the noise is about 40 dB and the walls are not insulated. The scene and equipment of the experiments are shown in Figure 5. The target area is a rectangular plane with the length of about 6 m and width of about 5 m. The four-channel microphone array is composed of the MPA201 microphones produced by the BSWA Technology Co., Ltd., Beijing, China. The microphones are installed at the four vertices of the positioning area with the height about 1.35 m above the floor. The type of the acquisition card is known as NI9215A from NI company, Austin, USA. The sampling frequency is set as 100 kHz, and the sampling period is 1 s. The sound source is a Bluetooth speaker with the same height as microphone array. A system-provided text tone called \"Popcom\" in iPhone 6 is selected for localization sound signal.  
Figure 4: Virtual RPs generation process.  
In the sampling process, the coordinates of the samples are uniformly distributed in the location area by grid division, and the distance between each samples is 0.593 m. The total number of the samples prepared for database construction is 72, and 13 test points are used for target point estimation.