esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Water and Dissolved Gas Sampling  
Sampling campaigns were performed in August and December 2020 at Porta Lake and in November 2020 at Massaciuccoli Lake. Sampling activities were carried out between 10:00 and 16:00. Wind speed during sampling campaigns was, on average, around 2.5 m/s (Weather Underground company, San Francisco, CA, USA; [[https://www.wunderground.com](https://www.wunderground.com)]([https://www.wunderground.com](https://www.wunderground.com))). The coordinates of the sampling sites were recorded using a portable GPS (Garmin(r) GPSMAP 62, Garmin(r), Olathe, KS, USA). Water temperature, pH and Eh values were measured directly in the field through a CRISON MM 40+ multiparameter probe. Sampling procedures within the lakes were performed from a small boat, with the exception of few sampling sites at Porta Lake (from LP21 to LP30), which were collected from the lake shores, mostly in areas of dense vegetation and stagnant water (Figure 1a). Water and dissolved gas sampling were carried out in lakes and canals within 5 cm of the water-air interface. Two aliquots were collected for the analysis of water chemical and isotopic composition as follows: (i) one aliquot of unfiltered water was collected in a 125 mL polyethylene bottle for the analysis of major anions, and (ii) one aliquot of filtered (0.45 m) and acidified (with 0.5 mL of Suprapur(r) HCl 30%) water was collected in a 50 mL polyethylene bottle for the analysis of major cations. At Massaciuccoli Lake, two further water aliquots were sampled as follows: (i) in 250 mL polyethylene bottles, containing SrCl\({}_{2}\) and NaOH to precipitate the dissolved carbonate species as SrCO\({}_{3}\), for isotope analysis of total dissolved inorganic carbon (\(8^{13}\)C-TDIC) [51] and (ii) in 15 mL plastic tubes, after filtration (0.45 m), for the analysis of stable isotopes of water at selected sites (SPW, TCW2, TCW3, TCW4, TCW5, LSW4, LSW5, LSW7, LSW8, OMM1, OMW2, OSW1, ECW2, ECW3, ECW8 and ECW9). Dissolved gases were sampled by using pre-evacuated glass flasks with a PTFE stopcock. The flask was submerged down to the sampling depth and the stopcock was opened, allowing the water to enter the flask. The dissolved gases partially exsolved in the headspace due to the pressure drop. The flask was filled by 3/4 of its volume with water, before closing the stopcock [52; 53]. Within Massaciuccoli Lake system, waters and dissolved gases were further collected along vertical profiles at three selected sites from surface to bottom using the single hose method described in [PERSON] et al. [54].
## 3 Results and Discussion
### 2D-PIV Measurement
On the other hand, in panel b, the detachment position is about 90\({}^{\circ}\), indicating that the flow is detached in the same way as it is in the subcritical region. Therefore, even for the same ball, depending on the surface properties of the flow path, laminar and turbulent flow regions coexist in the critical region. Similar results were obtained for the other three types of balls. There was no clear correlation between variation in the separation point and the groove volume. For the soccer ball, the drag reduction in the critical region was slower. Also, the width of the Reynolds number of the drag crisis was wider than it was for the smooth ball. These characteristics are due to the wide range of Reynolds numbers that change from laminar to turbulent flow depending on the position of the ball, as seen in the present results. In addition, in the critical region where laminar and turbulent flows coexist at different positions, the formation of jets is not uniform around the ball. This uneven formation suggests that if the ball is rotating slowly, the direction of the jet would also rotate with the ball and possibly cause an irregular flight path. After the ball is kicked, the drag force reduces the velocity of the flow and decelerates through the critical region to the subcritical region. The non-uniformity of the flow around the ball due to this flow transition is considered to be important to the irregular trajectory of the ball. On the other hand, it was difficult to determine how the grooves drive turbulence from the present results. For example, in the Jabulani case shown in Table 5, figure c does not appear to be driving turbulence at first glance because the surface is smooth. However, it detaches the slowest of the three measured surfaces. Therefore, it is also indicated that the requirement for turbulence in critical region is not just the number of grooves. In \begin{table}
\begin{tabular}{c c c c} \hline \hline No. & Panel Orientation & Subcritical (5m/s) & Critical (12.5m/s) & Supercritical (20m/s) \\  & and Laser Position & \(Re=7.3\times 10^{4}\) & \(Re=1.8\times 10^{5}\) & \(Re=2.9\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The PIV results of Teamgeist with the different panel orientations and flow velocity. order to understand in more detail how the geometry of the groove promotes turbulence, more detailed measurement and analysis is needed, including not only two-dimensional measurements, but also three-dimensional effects. In this study, analysis was based on the flow visualized by PIV and focused on the location of the separation point. In future work, it will be important to quantitatively analyze the wake width of the flow and the flow structure around the groove for a more detailed understanding of flow transitions.
## 5 Conclusions  
The prediction of elevator failures is crucial for maintaining personal safety and preventing economic losses. In this study, we proposed an elevator risk prediction model (RPM-ETC) using deep learning techniques. The model utilizes a multimodal Transformer to capture the temporal relationships in elevator operation data and employs a time compression mechanism to enhance prediction speed. The experimental results demonstrate that the RPM-ETC model outperformed traditional time series prediction models such as the RNN and LSTM in terms of its accuracy and FPS values, achieving an accuracy of 86.3%. Compared to Transformer models without the Self-temporal Compression Mechanism and enhanced PE, the RPM-ETC model exhibited higher precision and prediction speed. This improvement is likely due to the temporal compression mechanism effectively extracting key information while compressing the time series and the enhanced PE maintaining and reinforcing temporal relationships in deep networks. Additionally, we provide a dataset for elevator fault detection to facilitate future research endeavors.  
The data used in this study were collected from real-world scenarios; however, the relatively limited number of faulty elevator samples impacted the model's ability to learn from the minority class effectively. While significant progress has been made in terms of accuracy and processing speed, the results for precision and recall indicate that there is still room for further optimization in reducing false alarms and improving fault detection accuracy. As data collection continues in the future, the increase in fault sample size is expected to enhance the model's learning capacity, thereby further improving its ability to recognize the minority class and achieving better overall performance. To address this challenge, future research will focus on exploring and optimizing data balancing techniques, such as random oversampling and the SMOTE (Synthetic Minority Over-sampling Technique) [42] to further improve the detection capability of the minority class, providing stronger support for model optimization and practical applications.  
Methodology, S.M.; Software, W.X.; Investigation, L.W.; Writing--original draft, H.Y.; Writing--review & editing, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the 2023 Science and Technology Program of the Zhejiang Administration for Market Regulation (ZC2023055).  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Nanjing U-lake Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Visualization of Emergency Evacuation Physical Behavior under Multi-Agent Decision-Making
## 5 Example Analysis
### Improvement Measures and Simulation Results  
In response to the problems that emerged during the evacuation simulation in the previous paper, this study proposes measures such as zoning evacuation, increasing safety exits, optimizing the alarm broadcast system and evacuation signs, strengthening the grid management, and conducting simulation verification for the proposed improvement solutions, expecting to optimize the emergency evacuation efficiency of apartment buildings further.  
Proposal 1: Based on the above-performed evacuation data analysis, the simulation experiments only used distance as the criteria for selecting routes and exits, ignoring the following problems: (a) the internal structure of the building; (b) the uneven utilization of evacuation exits in different evacuation paths; and (c) the internal evacuation channels are too few, leading to heavy congestion, which significantly reduces the evacuation efficiency.  
Option 1: According to the previous simulation experiments, the two exits are not blocked when evacuating the first floor; therefore, the improvement plan is to choose the nearest exit for the upper floor personnel to leave after arriving at the first floor. Option 2: Based on Option 1, the personnel on the second to sixth floors were equally distributed to the two stairways for evacuation. The 92 people on each floor located near the first stairway exit left by this stairway exit, and the remaining 92 people exited by the second stairway exit. Option 3: Based on Option 1, make the personnel on the first, third, and fifth floors choose the nearest access exit to leave, while the personnel on the second, fourth, and sixth floors are distributed equally for evacuation. Option 4: Based on Option 1, the width of its two evacuation staircases is appropriately widened. Option 5: Based on Option 1, the vacant activity room on the right side of the large apartment building is changed into an evacuation safety stairway, and the personnel on each floor above the second floor are evenly distributed to choose the one closer to themselves for evacuation from the three evacuation stairways. According to the proposed five improvement plans, the structure of this experimental model was modified and redistributed according to the number of people, and the experiments were run in turn to collect the data obtained, as detailed in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Floor** & **The Situation of Each Floor** & **Option 1** & **Option 2** & **Option 3** & **Option 4** & **Option 5** \\ \hline \multirow{4}{*}{Sixth floor} & Number of evacuees at No. 1 stair & 79 & 92 & 92 & 62 \\  & Consumed time & 139.68 & 184.37 & 183.64 & 180.87 & 127.85 \\ \cline{1-1}  & Number of evacuees at No. 2 stair & 105 & 92 & 92 & 92 & 61 \\ \cline{1-1}  & Consumed time & 229.15 & 207.38 & 205.42 & 203.58 & 124.49 \\ \hline \end{tabular}
\end{table}
Table 9: Improved program data table.
## 4 Monte Carlo Simulation
### MCS Framework
#### 4.1.2 Event/Impairment Set  
The set of photon events per unit of interaction length can be defined as  
\[\{\alpha_{i}\}=\{\mathrm{moving\ forward/no\ event,\ absorption,\ single\ scattering,\ multi\ -scattering}\} \tag{62}\]The \(\{\alpha_{i}\}\) set represents the extended photon event set (EPES). In Equation (62), the multi-scattering event is a modelling footprint for scintillation and turbulence impairments. Multi-scattering causes different photon times of arrival, leading to multipath optical reception. Hence, when the scattering and multi-scattering events are included in the simulation, attention to modelling the angles (azimuth angle \(\Phi\) and zenith angle \(\theta\)) of photon scattering is critical [125].  
Equation (3) can be used to conclude the weighted probability of randomly selecting an event from the event set defined in Equation (62). Suppose that we include forward scattering, isometric scattering, and backward scattering as three probable instances of single scattering. Then, we can assume a uniform distribution as a basis for the random selection. If the single scattering is selected from the main event set, we branch this into a secondary random selection from the probable instances. The travelled distance between the photon's two events, \(l\) m (can also be referred to as the photon's path length or photon's step size), is given as follows [66]:  
\[l=-\bigg{(}\frac{1}{c(l)}\bigg{)}\ln q_{l} \tag{63}\]  
where \(c(l)\) is the channel attenuation coefficient and \(q_{l}\) is the corresponding survival CDF [66]. Hence, \(q_{l}\) assumes a value range of \(0~{}\leq~{}q_{l}~{}\leq~{}1\).
## 2 Materials and Methods
### Proposed Model Architecture
#### 2.2.3 Control Plane  
The RYU controller uses the OpenFlow protocol to communicate with the OpenFlow switches. The RYU controller was selected, as it encompasses a separate module for QoS and is implemented in Python programming language, which allows rapid prototyping for our proposed intelligent QoS framework [15]. In our proposed framework, the RSUs are SDN-enabled to extend the SDN control towards the OBUs. Thus, RSUs are programmable by the controller. The RYU controller determines the network rules for each slice to fulfill different vehicular applications KPIs and applies them to the data plane. The OpenFlow protocol installs the flow entries between vehicles and application servers to meet the ITS  
Figure 2: Proposed model architecture.  
applications' KPIs. The network policies are defined by the control application,n which is the meter table configured in our proposed adaptive QoS application.
# Haze Removal Based on Refined Transmission Map for Aerial Image Matching
## 2 Physical-Based Image Dehazing and Matching  
Haze is a challenging weather phenomenon for remote-sensing aerial images because, in dehazing, scene transmission depends on unknown depth information. The physical model removes haze based on direct transmission maps. To address block artifacts, a novel method is proposed to estimate a refined transmission map without explicit guided filters. The method consists of two parts. The first is calculation of the DCP, and estimation of the atmospheric light and DCP transmission map. The transmission map of DCP is then refined using the hidden Markov random field and expectation maximization (HMRF-EM) algorithm, which is explained in Sections 2.1 and 2.2. Second, image-matching is performed using the scale-invariant feature transform (SIFT) algorithm to find the valid key-point matches, which is explained in Section 2.3.
# Dynamic Heat Transfer Calculation for Ground-Coupled Floor in Emergency Temporary Housing
## 2 Solving the Heat Transfer Equation
### Heat Transfer Equation
#### 2.1.2 Governing Equation  
As for the two-dimensional ground-coupled floor heat transfer in temporary housing, as shown in Figure 1, the governing equation is  
\[\frac{\partial T(x,z,t)}{\partial t}=\alpha\cdot\
abla^{2}T(x,z,t). \tag{5}\]  
Here, \(z\) is the depth of the soil, m, and positive denotes the downward direction vertical to the ground; \(\alpha\) is soil heat diffusion coefficient, m\({}^{2}\)/s; \(\
abla\) is the Laplace operator. \(\alpha=\lambda/(\rho\ c_{\text{p}})\), where \(\lambda\) is soil heat conductivity, W/(m\(\cdot\)K); \(\rho\) is soil density, kg/m\({}^{3}\); and \(c_{\text{p}}\) is soil specific heat capacity, J/(kg\(\cdot\)K).  
Indoor floor surface (\(x<0\)) is considered as Robin condition, and outdoor ground surface (\(x>0\)) is Dirichlet condition.  
\[\left\{\begin{array}{c}T(x,0,t)-h^{*}\cdot\frac{\partial T(x,0,t)}{\partial x}=T _{\rm ia}(t),\ x<0;\\ T(x,0,t)=T_{\rm os}(t),\ x>0,\end{array}\right. \tag{6}\]  
where \(h^{*}=\lambda/h\). If the indoor floor is attached to insulation, the equivalent convective heat transfer coefficient can be calculated by Equation (3).  
The initial condition is  
\[T(x,z,0)=T_{0}(z). \tag{7}\]  
\(T_{0}(z)\) could be calculated by the one-dimensional soil heat transfer problem.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 4 Heuristic Algorithms for Joint Content Recommendation and Delivery
### Complexity of Heuristic Algorithms  
Let us first consider the time complexity of the two algorithms in resource allocation. Recall that \(K\) is the number of OFDM subcarriers and \(U\) is the number of users. For the sum rate maximization algorithm (i.e., Algorithm 1), the complexity is \(O(UK)\) in the carrier allocation step and \(O(P_{T}K)\) at the bit allocation step. For the minimum rate maximization algorithm (i.e., Algorithm 2), the time complexity is \(O(K^{2})\) in the carrier allocation step and \(O(P_{T}\frac{K}{U})\) in the bit allocation step. In practice, \(K\) is typically much greater than \(U\), so that Algorithm 1 tends to have a lower complexity compared with Algorithm 2.  
Let us now consider the time complexity of the content recommendation algorithms. For the average file size constraint, the time complexity of the two-dimensional cost knapsack algorithm is \(O(\delta FRN^{2})\), where \(R\) is the data rate assigned to the user. Here, \(R\) is related to power \(P_{T}\), channel number \(K\) and user number \(U\). Considering a total of \(U\) users, the total time complexity is \(O(\delta UFRN^{2})\). For the problem with maximum file size constraint, the complexity of the algorithm is \(O(UFN)\).  
We can see that the proposed heuristic algorithms can reduce the exponential complexity of the optimal algorithm to polynomial complexities as the problem size scales. However, the reduced complexity comes at a cost of degraded performance. For example, in a special case where a user with good condition is only interested in small files, the heuristic algorithm may assign a large amount of radio resource to this user to maximize the overall capacity. However, much of the capacity allocated to this user is unnecessary and wasted. In what follows, an analytical framework will be introduced to evaluate the performance of the proposed algorithms.
# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
## 2 Description of the Calibration Methods
### Log-Ratio Calibration Equation (LRCE)  
For the LRCE calibration, the conventional-fractions and its overlapping XRF-scan intensities were first transformed using an additive log-ratio (alr) transformation, in which one of the tested elements was selected as the denominator/additive, to eliminate specimen effects [10]. This resulted in a linear relationship between alr-transformed element fractions and its XRF-scan intensities. Therefore, the alr-transformed element of long-core sediment can be calculated using linear regression equations. As such, the element fractions from our sediment cores were obtained by applying the inverse log-ratio transformation [10].
# Tabu Search Aided Multi-Track Detection Scheme for Bit-Patterned Media Recording
## 2 Proposed Detection Scheme
### Reliability Test  
The proposed detection scheme performs a reliability test, and then conducts the TS detector for the unreliable bits on {\(-1\), \(+1\)} tracks to reduce the complexity of the detector for a priori information of the simplified multi-track detector. If the distance between the readback signal \(r_{i,k}\) with the reconstructed readback signal \(\overline{r}_{i,k}\) on {\(-1\), \(+1\)} tracks is larger than the specific value \(\delta\), we define the corresponding bit \(a_{i,k}\) as an unreliable bit, where \(i\) is the track index and \(k\) is time index. The reconstructed readback signal is calculated by the convolution operation of a three-by-three channel response and the detected data on {\(-2\), \(-1\), \(0\), \(+1\), \(+2\)} tracks from the single-track detector. For example, the calculation of the reconstructed readback signals \(\overline{r}_{-1,k}\) and \(\overline{r}_{+1,k}\) uses the detected data \(\hat{a}_{i,k}\) from the single-track detector, where \(i\) = {\(-2\), \(-1\), \(0\)} and \(k\) = {\(k-1\), \(k\), \(k+1\)}, and \(i\) = {\(0\), \(+1\), \(+2\)} and \(k\) = {\(k-1\), \(k\), \(k+1\)}, respectively. As \(\delta\) increases, an increase in the number of bits performing the TS detector improves the reliability of overall bits, while it accompanies lots of complexities. It is important to properly set the \(\delta\) in consideration of the complexity of detector and the reliability of bits. Based on the reliability test results, we determine that a priori information is set to the APP of the single-track detector or the output of the TS detector.
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 5 Conclusions  
In this paper, a PAP algorithm is proposed to improve the accuracy of indoor positioning systems. The AP proximities were measured through the collected RSSIs to select the positioning technique in the adaptive location estimation step. If one RSSI was higher than the specific threshold, the position was estimated through one AP corresponding to this RSSI. Otherwise, conventional trilateration was applied. We removed the positioning results that cannot be actual positions and corrected them through MAF in the post-processing step. The PAP algorithm addresses the problem of the previous trilateration-based methods, which is unattainable positioning in signal-weak regions, without affecting accuracy in other regions. The static experiment results indicate that the positioning accuracy improves by approximately 21% and 28% in cases of applying the RSSI correction and MP techniques, respectively. The dynamic experiment shows an accuracy improvement of about 45% for the proposed algorithm compared to conventional trilateration. In future research, it is possible to consider a positioning technique through the integration of other technologies in regions where both trilateration and MP techniques exhibit vulnerabilities. First, with proximity identified, the location estimated using the MP technique can be set as the reference location. Dead-reckoning navigation techniques via IMU (inertial measurement unit) sensors can then be added for further tracking. Through this method, consistent positioning performance can be achieved at all measurement locations within the target region.  
Conceptualization, J.-h.Y., H.-j.K. and S.-k.K.; software, J.-h.Y. and H.-j.K.; writing--original draft preparation, J.-h.Y., H.-j.K. and S.-k.K.; supervision, S.-k.K. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (IITP-2024-2020-0-01791, 100%) and by the BB21 plus funded by Busan Metropolitan City and Busan Techno Park.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.