esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Investigation
### Test Method  
Figure 3 illustrates the loading devices. A screw jack with a maximum capacity of 500 kN was used to load the testing objects. A pressure sensor was employed to monitor the value of the applied load. C1 and C2 were superposed and subjected to stage loads. The ultimate strength of the prism (\(\sigma_{\mathrm{max}}\)) is approximately 0.78 times of the cubic compressive strength, i.e., 45.24 MPa [29; 30]. The step load (6 kN) was first applied on the sixth day and applied at an interval of five days afterwards until the stress reaching 27 MPa, which equals to around 0.6\(\sigma_{\mathrm{max}}\) (Figure 4). That is to say, the load at each step was kept constant for five days. The baseline of the signal was collected before loading (zero stress state). Then, the aforementioned active monitoring procedures were performed state and at the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Weight (kg/m3)** & **Mix Proportion** \\ \hline Cement & 300 & 1.00 \\ Water & 160 & 0.53 \\ Sand & 875 & 2.92 \\ Stone & 860 & 2.87 \\ Fly ash & 110 & 0.3667 \\ Slag powder & 80 & 0.2667 \\ Admixture & 4.90 & 1.63\% \\ Total & 2390 & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compositional details of the concrete.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{5}{c}{**Cubic Compressive Strength (MPa)**} \\ \cline{2-5}  & **1 Day** & **3 Days** & **7 Days** & **14 Days** & **28 Days** \\ \hline Specim-1 & 26 & 39.2 & 44.1 & 51.2 & 57.5 \\ Specim-2 & 27 & 38.1 & 44 & 49.6 & 57.7 \\ Specim-3 & 25.9 & 39.1 & 45.1 & 49.8 & 58.8 \\ Average & 26.3 & 38.8 & 44.4 & 50.2 & 58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cubic compressive strength of concrete versus age.  
Figure 2: Locations of SA’s (mm).  
day after loaded (ensuring mechanical state of the system kept steady) for each step load case. 60 kHz and 100 kHz harmonic voltage signals with amplitude of 10 V were used to activate the SA to obtain the amplitude information, and pulse drive signals with 5 \(\upmu\)s plus width and voltage amplitude of 10 V were used to capture the velocity of stress wave.  
The selection of the frequency bandwidth was determined through test, which was consistent with that of previous research by [PERSON] on damage detection and concrete strength development monitoring [31]. It should be noted that the recorded signals (Figure 5) were dealt with adaptive threshold method in MATLAB to get rid of environmental noise [32]. Then the treated signal is further fitted as a sine function (see Equation (1)), and the parameter \(A\) is the amplitude of the signal. For velocity, the first arrival time method was used to determine the velocity of stress wave (Figure 6) [22] (see Equation (2)). Additionally, it is worth to mention that this test was carried out in HYHW-12A type walk-in chamber, in which the temperature and relative humidity was kept constant, with 20 \({}^{\circ}\)C and 60%, respectively.  
\[y=y_{0}+A\times\sin(\pi\times(x-x_{c})/\omega) \tag{1}\]  
Figure 4: Loading history.  
Figure 3: Loading device.
## 4 Evaluation and Discussion
### Numerical Example
From Table 1, it can be seen that the limit-state function value obtained by MCs, MDF + SQP, IDF + SQP and MU-DBMRA methods are all positive, that is, the reliability constraint \(g_{1}\) satisfies the reliability design requirements. The iteration times of MDF + SQP, IDF + SQP and MU-DBMRA are 344, 308 and 258 separately. Mu-DBMRA has the highest efficiency. While, compared with MRA considering only aleatory uncertainties, the iteration times are 232 in SMPRA and 258 in MU-DBMRA respectively for test point 1. Obviously, they have the same magnitude and their calculation efficiency are basically equal. This should be mainly attributed to the following three reasons: (1) decoupling the nested multidisciplinary probabilistic reliability and multidisciplinary convex analysis; (2) adopting Lagrange multiplier method and KKT conditions to replace the expensive function extremum analysis, indicating that this method can keep great calculation effectiveness when dealing with multidisciplinary reliability analysis under aleatory and epistemic uncertainties at the same time. Theoretically, when design parameters have both aleatory uncertainties and epistemic uncertainties, the limit-state function value should be an interval. As shown in Table 1, 0.0323 \(\in\) [0.0111, 0.0431], [0.0120, 0.0423], [0.0143, 0.0399], indicating that the single value of limit-state function calculated by SMPRA is in the value range of limit-state function calculated by MU-DBMRA, which, furthermore, verifies the correctness of the proposed method and theory. From studies of three different test points, the variation tendency of the result for limit-state function analyzed by MU-DBMRA method is shown in Figure 7, and the difference is shown in Figure 8. It is thus evident that as the variation degree of epistemic uncertainties decreases, the maximum of the limit-state function also declines gradually, while the minimum increases gradually. In this way, the difference between the maximum and minimum of the limit-state function tends to diminish gradually. This also suggests that engineering designers need to collect information and data about epistemic uncertainties as much as possible, making it less uncertain and enhancing accuracy of multidisciplinary reliability analysis of the limit-state function. In addition, adopting the minimum of the limit-state function as the standard to test its reliability shows that the result of reliability analysis and design optimization by using MU-DBMRA is safe and reliable design result.
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 3 Models and Goals
### System Model  
The system model is comprised of three things, namely the Trusted Authority (TA), RSU, and Vehicles. The depiction of the system model is shown in Figure 2. The TrustedAuthority (or certification authority or Government Authority) is assumed to be honest and will not take part in compromising vehicle location [62; 63]. The vehicle must be registered with the TA before joining the ad hoc network. The TA provides a pseudonym pool to vehicles to be used for a number of days. The pseudonym is used for anonymous broadcast of beacon messages. On the expiry of its pseudonym pool, the vehicle can request another pseudonym pool from the TA. We assume that the vehicle has been registered with TA and is assigned a pseudonym pool. An RSU is a roadside infrastructure fixed on the road to increase the communication range of vehicles. It is a semi-honest entity in the system model, and it may or may not compromise the privacy of a vehicle. RSUs also play a role in the dissemination of data to other entities of the system model. We also assume that the authentication process is performed by each vehicle. The vehicle contains an OBU that is used for communication with other OBUs and infrastructure in the network. OBU records communication events of vehicles. The vehicle has a tamper-proof device that stores the key materials securely, such as anonymous identities and records of all communication events [64]. The vehicle is also equipped with GPS for precise location updates.
## 3 Results  
In this study, data from 83 children and adolescents were included. These children were diagnosed with USCP, bilateral cerebral palsy (BCP), dyskinetic cerebral palsy, spina bifida, and other syndromes. The participants were divided into two subgroups: the USCP group (n = 62), consisting of children diagnosed with USCP, and the Non-USCP group (n = 21), consisting of children with other diagnoses. Table 1 shows the characteristics of the participants.  
The sample size for this study (n = 83) was determined by the availability of participants who met the inclusion criteria. While no formal power analysis was conducted, the sample size is comparable to that in similar studies involving children with neurodevelopmental-optimal and motor disabilities in real clinical situations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Population** & **Number** & \begin{tabular}{c} **Age** \\ **(Mean \(\pm\) Std)** \\ \end{tabular} & \begin{tabular}{c} **MACS** \\ **Distribution** \\ \end{tabular} & \begin{tabular}{c} **GMFCS** \\ **Distribution** \\ \end{tabular} & \begin{tabular}{c} **Sex Distribution** \\ \end{tabular} & \begin{tabular}{c} **Affected Side** \\ \end{tabular} \\ \hline USCP & 62 & \(13.35\pm 2.67\) & \begin{tabular}{c} 1:20 \\ 2:31 \\ 3:9 \\ \end{tabular} & \begin{tabular}{c} 1:48 \\ 2:12 \\ 3:2 \\ 3:2 \\ \end{tabular} & \begin{tabular}{c} Male: 30 (48.4\%) \\ Female: 32 (51.6\%) \\ \end{tabular} & \begin{tabular}{c} Right: 39 (62.9\%) \\ Left: 23 (37.1\%) \\ \end{tabular} \\ \hline Non-USCP & 21 & \(15.86\pm 1.93\) & \begin{tabular}{c} 1:13 \\ 1:16 \\ 2:5 \\ 5:1 \\ \end{tabular} & \begin{tabular}{c} Male: 14 (66.7\%) \\ Female: 7 (33.3\%) \\ \end{tabular} & \begin{tabular}{c} Right: 9 (42.9\%) \\ Left: 12 (57.1\%) \\ \end{tabular} \\ \hline All & 83 & \(13.99\pm 2.73\) & \begin{tabular}{c} 1:61 \\ 1:38 \\ 2:36 \\ 3:9 \\ \end{tabular} & \
\begin{tabular}{c} Male: 14 (53.0\%) \\ 3:3 \\ 4:3 \\ 5:1 \\ \end{tabular} &
\begin{tabular}{c} Right: 48 (57.9\%) \\ Female: 39 (47.0\%) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Population characteristics and distributions. Note: MACS: Manual Ability Classification System GMFCS, classification of hand function in children with CP (I–V, from best to most limited); Gross Motor Function Classification System MACS, classification of gross motor function in CP (I–V, from most independent to most limited).  
The results of the study, measured through the ADL-TTD, provide critical insights into the positional performance, spillage prevention, and strength performance of children with neurodevelopmental disabilities during bimanual lifting tasks across different levels of force control. For spilling in different directions within individual children during the lifting task, we find that most children will not spill water in any direction when the water level is below 3 cm from the top of the box. Figure 6 shows the individual performances.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 1 Introduction
In contrast, this paper addresses machine learning techniques for direct identification of the three mentioned classes (LOS, NLOS, and MP) in a UWB indoor localization system using experimental data collected in seven different environments in two different test scenarios (Section 4). Using the collected real measurement data, we compare three machine learning methods, i.e., support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP), in terms of their prediction accuracy, training time, and testing time. The classifiers are chosen by bearing in mind that the evaluated ML models can be used in a low cost and power efficient real-time system such as microcontroller-based platforms [24]. For the sake of reproducible results and further exploration, we provide all the experimental research data and the corresponding source codes as the Supplementary Data of this manuscript in PUB (Publications at Bielefeld University) [25], which is publicly available. The evaluation of the algorithms is conducted using the open-source Python machine learning library scikit-learn [26].
# Relationships between Surface Properties and Snow Adhesion and Its Shedding Mechanisms
###### Abstract
Wet snow on the other hand has water that fills the gaps between the ice particles, minimizing the amount of air between these particles. Dry snow has a relatively low density, rarely exceeding 100 kg/m\({}^{3}\)[8]. Snow is in so-called pendular regime, when the liquid water content in snow is below \(\sim\)27%. Air and small amounts of water fill the gaps between the ice particles. The so-called funicular regime occurs when gaps between ice particles are mostly filled by liquid water with minimal air [18]. Dry snow can accumulate at wind speeds below 2 m/s and below 0 \({}^{\circ}\)C, in which case air accounts for more than 70% of the total volume and the liquid water content is typically less than 5% [19]. Wet snow results when dry snowflakes rapidly metamorphize at negative air temperatures in supercooled clouds. These snowflakes pass into a positive temperature air layer, allowing melting to occur, thus causing an increase in the liquid water content [20]. Discrepancies exist about at which temperatures wet snow actually forms. [PERSON] claimed wet snow forms in the temperature range of 0 to 2 \({}^{\circ}\)C [2]. However, [PERSON] later claimed that snow accretes at wet bulb temperatures greater than 0 \({}^{\circ}\)C [15]. Next, he modified this claim to wet bulb temperatures greater than \(-0.2\)\({}^{\circ}\)C [21]. [PERSON] and co-workers also suggested that wet snow occurs when air temperatures are between \(-2\) and 5 \({}^{\circ}\)C and the critical percent relative humidity is more than \(85.1-5.3\times\mathrm{T_{a}}\)[22]. Under these conditions snow can adhere to surfaces. At the first stages of snow accumulation, it is relatively easy to remove the snow, but once the temperature falls below 0 \({}^{\circ}\)C, the snow freezes into a hard, dense (300-800 kg/m\({}^{3}\)) layer with strong adhesion [2]. While some studies have aimed at exploring snow formation and adhesion [5; 23; 24; 25] there is no comprehensive study focusing on relationships between surface properties and snow adhesion and its shedding mechanisms. Here, we present a systematic approach to investigate the impact of surface energy and roughness on the adhesion strength and shedding mechanism of snow. An experimental setup is developed to measure snow adhesion strength on various surfaces without altering the properties of snow (i.e., liquid water content). A LWC sensor is used to monitor the snow wetness during the adhesion measurements. We show how infiltration of the snow's free liquid water into the surface roughness depends on temperature and plays a significant role in snow adhesion or shedding. The results of this work can be helpful in identifying snow mitigation strategies on bridge cables, solar panels, and camera lenses of autonomous vehicles.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 2 Methodology
### Contour Mission Flight Planning
#### 2.2.1 S1: User-Provided Parameters  
* Flight area: set the boundary coordinates of the working area.
* GSD: ground sample distance (m/pixel).
* Camera parameters: focal length, image size, sensor size.
* Overlap and sidelap:
* UAV images require a high percentage overlap to generate high-quality 3D point clouds using the overlay information from the photogrammetric process. There is a positive relationship between the image overlap and the accuracy of digital elevation models (DEMs). The accuracy is increased with the increased overlap percentage, and the object's shape is optimized. Photogrammetric software, such as Agisoft Metashape [29], recommends that UAV images be acquired with almost 80% overlap  
Figure 3: Contour route planning flow chart.  
and 60% sidelap. Pix4 DMapper 4.1 suggests at least 75% overlap and 60% sidelap [30]; generally, the overlap is equal to or greater than the sidelap.
## 4 Conclusion and Open Issues
Though there are some security strategies for routing, more attention still needs to be paid to satisfy the application requirement, such as battlefield environment. 3. QoS routing problem. To the video and imaging sensors and real-time applications, QoS routing in WSN should ensure suitable bandwidth (or delay) through the duration of connection as well as providing the use of most energy efficient path. Currently, there is very little research on routing strategy with QoS requirements in resource constrained environment, which is also a challenge task. 4. Intelligent routing mechanism. In WSN, most effort aims to design simple routing strategy, which is easy to realize and energy-aware. Considering those complicated application requirements, the intelligent routing technology is suitable when the sensors have more usable resources. Combining with position information, it's hopeful to design the energy-aware intelligent routing. 5. Node mobility problem. Most of the current protocols assume that the sensor nodes are stationary. However, the moving sink or sensors can not be avoided in some special applications, which make nodes' position change frequently. If the node position is updated frequently or not is a problem. New position-based routing ideas are needed in order to balance the topology update and the energy consumption. 6. Data fusion strategy. As an energy-aware operation, data fusion or aggregation is important and popular in hierarchical routing. Though most position-based routing algorithms are flat routing, it seems that data fusion is hard to use. Considering the grid-based routing algorithms, there are natural clusters making data fusion. How to realize data fusion in one grid is also an interesting problem to explore. Other possible future research for routing protocols also includes achieving global behavior with localized algorithms, routing technology with data compression, etc. Most of these issues can be considered with position information.
# Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features
## 3 Results
### Computational Time  
Another important way to evaluate our approach is computational efficiency. We use Matlab to extract traditional hand-crafted features and deep learning-based features with a 3.40 GHz CPU and 32 GB memory. We also use the Caffe for testing the deep learning-based descriptor extraction time on a PC with NVIDIA TITAN X GPU. The average computational time for different descriptors is listed in Table 3. The reported time here is based on the average of over 400 images, which only contains the feature extraction time. We can find the SCNN descriptors are faster than all hand-crafted descriptors but slower than CNN descriptors on CPU. For the GPU-based extraction, the SCNN descriptors only take 0.029 s per image on average, and it approximates the average extraction time with CNN descriptors.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions
### Evaluation of Performance Metrics  
The essential performance metrics--RMSPE, MAPE, correlation heatmaps, KGE, NSE, and the coefficient of determination (R\({}^{2}\))--collectively provide a framework for the development of these machine learning models. RMSPE and MAPE are fundamental metrics used to assess the difference between observed and predicted values. While RMSPE is sensitive to larger errors and highlights significant deviations, MAPE offers a more straightforward interpretation of average performance by calculating the average magnitude of errors without regard to their direction.
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 3 Load Balancing Based on Symmetric Path Groups (LBSP)
### Handling Asymmetry due to Failures
#### 3.3.2 Modifying the Forwarding Tables  
As previously described, when the network has no failure, upward forwarding in LBSP is performed as predefined. If a switch receives a message about a failure which affects the flows passing through itself, it modifies the forwarding table so that an alternative link group is chosen for packets with the destination addresses within the range indicated in the failure message.  
For instance, consider a flow from Host 15 to Host 115, as shown in Figure 5. Since the LSB of the destination address (115) is 1, Switch T3 forwards packets using output Ports 1 and 3. Similarly, since the second LSB of 115 is 1, Switches A1 and A3 selects output Ports 1 and 3 for the flow. Suppose that the link between the aggregation Switch A31 and the core switch C15 is down. The failure message conveys the information that the affected address range is [112... 127] and the failure type is 3. It means that the flows with the address in the range of 112-127 are affected by the failure and cannot be forwarded through Link Group 1 between an aggregation switch and a core switch. Note that failure Type 3 does not affect the forwarding tables of ToR switches. On receiving the failure message, aggregation Switches A1 and A3 modify their forwarding tables as follows.  
Suppose that Host 15 sends packets to Host 115 at that point. Since the LSB of 115 is 1, Switch T3 forwards packets using Ports 1 and 3 as predefined. However, Switches A1 and A3 select Ports 0 and 2 instead of Ports 1 and 3, because the modified forwarding table indicates that packets whose destination addresses are in the range [112... 127] must be forwarded through output Ports 0 and 2.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 1 Introduction
### Literature Review
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Mathematical** & **Variables** & **Proposed** & **Time** & **Accuracy** \\ \hline [PERSON] et al. [21] & ANN & -Building energy consumption & Fast energy consumption & Cooling/Heating: \(\pm\)10\% (MCD); & Cooling/\(\pm\)10\% (MCD); \\  & & consumption & prediction for complex architecture & Total: \(\pm\)10\% (MSHD) \\  & & -Orientation of the building & Predicting building energy needs using orientation, insulation, and transparency ratio (15\%, 20\%, and 25\%) & Deviation: 3.43\%, Prediction rate: \\  & & -Transparency ratio (15\%, 20\%, and 25\%) & Energy consumption & 94.8–98.5\% \\  & & -Predicted temperature and relative humidity & Prediction for a 2 BHK & \\ [PERSON] et al. [23] & ANN & -Building characteristics & multizone building (two bedrooms, one living room, one kitchen, and two toilets) & 2001–2017 & 95\% coefficient bounds \\  & & -Weather conditions & Modeling permeable coverings for nzEB indoor conditions & The Mean Absolute Error 2019–2020 & 2019–2020 \\  & & -Behavioral groups of indoor ambiences & Forecasting total building energy consumption & 2014–2016 & CV or RMSE: 1.7–7.7\% \\  & & -Areas of building & Household energy demand forecasting using Big Data & RMSPE, MAPE, R\({}^{2}\) \\  & & -Electrical appliance consumption & Hourly building & RF outperformed RT by 14–25\% & RT by 14–25\% and SVR by 5-5.5\% \\  & & -Parameter settings of the & Hourly building energy prediction for 2014–2015 & RF outperformed RT by 14–2015 and SVR by 5-5.5\% \\  & & -Influential features- & Medium-term and & CV of LSBoost: 5.019\% & \\  & & -Weather changes & long-term energy prediction for utilities & 2009 & (summer), \\  & & long-term (LT) predictions & and industrial customers & 3.159\% (autumn), 3.292\% (winter), 3.184\% (spring) \\  & & -Historical data of energy consumption & Predicts actual numerical energy values, then classifies into levels (low, average, high) & Execution time across 3, 5, and 7 energy level cases \\  & & -Heat transfer coefficients & Predicting building energy consumption based on building envelope design parameters & Predicted RMSE is 0.0115, and R\({}^{2}\) is 0.933, which reflect the high accuracy of the RF prediction model \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._