esa-sceva/satcom-chunk-collection huggingface dataset:
# Event-Based Probabilistic Embedding for POI Recommendation
## 4 Experiments
### Baselines for Comparison  
We compare TARE with the following six state-of-the-art methods with well-tuned parameters.  
* **LRT:** LRT [20] is a time-enhanced MF model based on observed temporal properties that characterize each user by different latent vectors for different time slots.
* _GeoMF:_ GeoMF [4] is a geographical Weighted Matrix Factorization (WMF) model which integrates geographical influence by modeling users' activity regions and the influence propagation on geographical space.
* **JIM:** JIM [24] is a joint probabilistic generative model that mimic users' check-in behaviors by integrating the factors of temporal effect, content effect, geographical influence and word-of-mouth effect.
* **TRM:** TRM [25] is a unified probabilistic generative model that simultaneously discovers the semantic, temporal, and spatial patterns of users' check-in activities, and it models their joint effect on users' decision making for the selection of POIs to visit.
* **PACE:** PACE [8] is a general and principled combination of CF and SSL based on neural networks that model user preference for POIs. PACE considers both social effect and geographic effect. On the other hand, PACE is an embedding algorithm based on the node2 vec algorithm.
* **Geo-Teaser:** Geo-Teaser [10] is a temporal POI-embedding model that captures the check-ins' sequential contexts and the various temporal characteristics on different days. On the other hand, Geo-Teaser is an embedding algorithm based on the word2 vec algorithm.
# A Survey of Electromagnetic Techniques Applied to Cultural Heritage Conservation
## 2 EM Techniques Applied to CH
### Ionizing Radiation Risks  
Apart from the failure risk associated with the improper control of the two parameters mentioned in the previous subsection, the major risk in the use of ionizing radiation are secondary effects like damages or unacceptable changes in the artifact due to modifications induced by the radiation on the object. The eventual modification of the object may be a direct consequence of the interaction with the radiation, or an indirect one produced by the free radicals induced by the irradiation process. The worse side effects are those that bring changes to the constituent polymers of the artifact, i.e., cellulose, lignin, and proteins.  
Cellulose is the main component of paper, textiles made of cellulosic fibers and wood, which also contains lignin. The principal cause of cellulose deterioration is oxidation, and the second one is acidification. Radicals are a determining factor in the oxidation process, and they may be induced by ionizing radiation [75; 76; 77; 78], but cellulose stands doses of up to 10 kGy. Details of the levels of irradiation used to evaluate changes in the artifact, and the results obtained are reported in Section 3.  
Lignin has an antioxidant effectiveness; for this reason, wood, which contains lignin in addition to cellulose, is less prone to oxidative degradation than paper or cotton textiles; hence, ionizing radiation is particularly suited for wood, which can stands repeated treatments.  
Concerning protein fibers, silk is less stable than wool, but both typologies of textiles resist exposure to doses of up to 10 kGy of gamma rays, without the loss of essential properties such as elasticity and mechanical resistance [30].  
The possible side effects of irradiation treatments may be summarized as follows [79]:  
* Color changes and surface erosion;
* Compromising of the mechanical integrity of material. These side effects typically appear in the following ways for three of the most common typologies of CH materials:
* Transparent materials tint easily;
* Opaque materials do not manifest visible changes;
* Paper may structurally collapse. As is clear from previous subsections, the risks associated with the use of radiation technologies for the artifact depend on the radiation dose absorbed by the object, but it must be highlighted that, although side effects may occur, they are usually far less damaging than the deterioration process.  
In next section, we will go through the EM irradiation techniques summarized in Table 2. References [14; 35; 48; 74; 80; 81; 82; 83; 84; 85; 86] are the core references for the fundamentals of the EM techniques presented; the other references will be addressed as needed.
## 2 Methodology
### Data and Data Quality Requirements (Sources and Geography)
#### 2.2.4 End-Of-Life Stages  
In Malaysia, 95% of collected wastes are disposed of in landfills, and various reports mentioned that dumping and landfilling are the main disposal methods in many countries [28]. The after-use of green biocomposite takeout food containers produced organic wastes in nature because of their fully biobased elements, and therefore, composing would be an excellent option. [PERSON] et al. [28] reported that composting is one of the most cost-effective and sustainable waste management options as it enables the recycling of potential plant nutrients. Composing gives benefits to many agricultural activities such as farming productions, horticulture, and in some cases, for land restoration. Nevertheless, composting is not carried out on a large scale and is rarely practiced in Malaysia, even though compost is priced from RM180 to RM1000 per ton, depending on its quality [28]. Organic waste produced from the analyzed SPF-reinforced sago starch bioccomposite takeout food container is assumed to have great potential for high-quality compost and generates revenue. For this reason, this study took into consideration both landfills and composting for end-of-life scenarios. Due to the data constraint of the composting practice that was relatively uncommon in the past LCA study [8], this analysis was extrapolated data from a study by [PERSON] et al. [29], where 17% of the disposed containers were sent to composting and another 83% to landfill. The overall data used to analyze the cradle-to-grave of the SPF-reinforced sago starch bioccomposite takeout food container are shown in Table 1.  
Figure 3: Transportation of raw materials to biocomposite manufacturing gate: (**a**) routes travelled for sago starch: Mukah-Kuching-Klang-Shah Alam and (**b**) route travelled for SPF (Kampung Kuala Jempol-Shah Alam; sources: google maps.com and ports.com).
# High-Precision Pointing and Tracking System Design for Near-Space Balloon-Based Optical Observation
## 4 Conclusions  
In this experiment, the primary platform pointing system and secondary precise tracking system on the balloon-based observation platform worked normally, providing a guarantee for the coronagraph's stable tracking and detection of the sun. The primary platform pointing system can realize \(\pm 1^{\circ}\) pointing accuracy. However, some other issues were also exposed in this experiment. Firstly, the experimental platform did not store real-time position and attitude information of the secondary precise tracking system, and it is currently unable to analyze its specific angle, attitude, and other information in this experiment. In subsequent works, we will upgrade and optimize the whole system. Firstly, we will improve the storage of real-time angles, attitudes, and other information. Secondly, we will further improve the performance of pointing and tracking accuracy to provide support for the coronagraph to obtain better imaging quality images. In addition, the next flight experiment is planned to be conducted in 2024 or 2025. Before conducting the flight experiment, we will perform multiple ground observation experiments to verify the overall performance of the system.  
Conceptualization, M.H.; methodology, L.Q.; resources, W.Z.; writing--original draft preparation, Z.W. and Z.Z.; writing--review and editing, Y.S. and G.W.; visualization, X.L. and Y.Z.; project administration, M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the strategic priority research program of the Chinese Academy of Science (Grant No: XDA17020603), the Space Debris Research Project (KJSP2020020201), and the Youth Innovation Promotion Association CAS.  
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.
# Age- and Sex-Adjusted Reference Intervals in Tear Cytokine Levels in Healthy Subjects
## Appendix B Centile Curves Estimation  
This appendix shows the estimations of centile curves for each evaluated cytokine based on the fitted generalized additive models for location, scale and shape (GAMLSS) models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Age** \\ **(Years)** \\ \end{tabular} } & \multicolumn{5}{c}{**Percentile**} \\ \cline{2-5}  & **2.5 th** & **5 th** & **5 th** & **95 th** & **97.5 th** \\ \hline
18 & 32.5  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Percentile for Women**} & \multicolumn{4}{c}{**Percentile for Men**} \\ \hline
**2.5 th** & **5 th** & **50 th** & **95 th** & **97.5 th** & **2.5 th** & **5 th** & **50 th** & **95 th** & **97.5 th** \\ \hline
19.25 & 26.64 & 145.21 & 791.44 & 1095.20 & 25.93 & 35.88 & 195.53 & 1065.69 & 1474.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Percentiles of Fractalkine levels estimated with the fitted GAMLSS model. Family distribution Normal with sex dependence for location parameter (\(\mu\)).  
_Appl. Sci._**2021**, _11_, 8958  
_Appl. Sci._**2021**, _11_, 8958  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Age** \\ **(Years)** \\ \end{tabular} } & \multicolumn{6}{c}{**Percentile for Women**} & \multicolumn{6}{c}{**Percentile for Men**} \\ \cline{2-10}  & **2.5 th** & **5 th** & **5  
_Appl. Sci._**2021**, _11_, 8958  
_Appl. Sci._**2021**, _11_, 8958  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Age** \\ **(Years)** \\ \end{tabular} } & \multicolumn{4}{c}{**Percentile**} \\ \cline{2-5}  & **2.5 th** & **5 th** & **5 th** & **95 th** & **97.5 th** \\ \hline
18 & 13.65 & 21.2  
_Appl. Sci._**2021**, _11_, 8958  
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Age**} & \multicolumn{5}{c}{**Percentile**} \\ \cline{2-5} \multicolumn{1}{c}{**(Years)**} & **2.5 th** & **5 th** & **50 th** & **95 th** & **97.5 th** \\ \hline
57 & 33.20 & 51.60 & 515.22 & 5144.75 & 7994.96 \\ \hline
58 & 33.97 & 52.79 & 527.10 & 5263.32 & 8179.23 \\ \hline
59 & 34.75 & 54.00 & 539.25 & 5384.63 & 8367.74 \\ \hline
60 & 35.55 & 55.25 & 551.68 & 5508.73 & 8560.60 \\ \hline
61 & 36.37 & 56.52 & 564.39 & 5635.70 & 8757.90 \\ \hline
62 & 37.21 & 57.82 & 577.40 & 5765.59 & 8959.75 \\ \hline
63 & 38.07 & 59.16 & 590.71 & 589
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 3 Proposed Algorithm
### Data Encryption Process
#### 3.2.3 Caesar Cipher Algorithm  
Whenever plain text is transmitted, such plain text may contain special symbols, letters, and alphanumeric cases. In the Caesar cipher encryption algorithm, a decimal value is converted to equivalent ASCII, and the formula \"E = D + N\" is used on the ASCII. \"E\" is an encrypted text that is retrieved from \"D + N\". \"D\" means ASCII value, while \"N\" means the number of shifts. The number of shifts means a constant value added with \"D,\" and the result is achieved.
# Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems
## 2 System Model and Optimization Problem
### Optimization Problem  
EE is defined as the ratio of the total information transmission rate to the total power consumption for all links, expressed as  
\[\eta=\frac{R_{SD}}{P}=\frac{\frac{1}{2}\log_{2}\bigg{(}1+\sum\limits_{i=1}^{N} \gamma_{i}\bigg{)}}{P_{S}+2P_{C}+\frac{N}{i=1}\big{(}P_{i}+P_{Q}-P_{H}^{i}( \rho_{i})\big{)}} \tag{7}\]  
Figure 2: Comparison of energy harvesting in different EH models [7; 10; 16].  
This paper studies how to jointly optimize the relay transmit power and the PS ratio to maximize the EE. The optimization problem can be expressed as  
\[\begin{array}{rl}(\mathrm{P1})&\underset{P_{i}\rho_{i}}{\text{max}}\ \eta\\ s.t.C1&:0\leq\rho_{i}\leq 1\\ \mathrm{C2}&:0\leq P_{i}+P_{Q}\leq P_{H}^{i}(\rho_{i})\\ \mathrm{C3}&:\gamma_{i}\geq\gamma_{\min}\\ \mathrm{C4}&:P_{th}^{i}\leq\rho_{i}P_{S}h_{i}\leq P_{th}^{i+1},l=0,1,2,\ldots,L\\ \mathrm{C5}&:\sum\limits_{i=1}^{N}P_{i}\leq P_{\max}\end{array} \tag{8}\]  
where \(\gamma_{\min}=2^{R_{\min}}-1\) and \(R_{\min}\) is the minimum required rate for \(S-R-D\), \(P_{\max}\) is the threshold of the relay nodes' total transmit power, constraint C2 means the energy causality, C3 is the constraint of the quality of information transmission, C4 is the constraint that the energy harvester works in the \(l-th\) linear regions and C5 is the constraint of the total transmit power of all relays, and it is a requirement to consider interference across the network [18].
# Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning
## 2 Materials and Methods
### Estimation of Inter-Frequency Clock Bias
The BDS or GPS multipath can be extracted using the ionospheric-free geometric linear combination, and the ionospheric-free combination as well as multipath signals mostly varies due to variability of satellite elevation angles and receiver's antenna height [25]. According to [PERSON] and [PERSON] [26], the multipath signals are more sensitive to the satellite elevation angle variations than the receiver's antenna height changes. The ED IFCB estimates derived from DIF measurements, which consist of two different ionospheric-free carrier phase combinations, will suffer from multipath effects. Since the physical surface formation surrounding the ground station receivers used in this paper does not change during a certain time span, the impact of variability of receiver's antenna height should not be considered. [PERSON] et al. [27] illustrated the elevation angle dependence of phase multipath error. The satellite signals with low elevation angles exhibit large multipath, especially for those below \(35^{\circ}\) elevation. Therefore, the elevation-dependent weighting shown in Equation (7) can significantly weaken the effects of multipath. The IFCB can be obtained through an accumulation of ED IFCB estimates. In addition, a reference epoch with an assumption of IFCB value should be selected. The IFCB for satellite \(s\) at epoch \(t\) can be written as:  
\[\delta^{s}(t)=\delta^{s}(t_{0})+\sum_{p=t_{0}+1}^{t}\Delta\delta^{s}(p,p-1) \tag{8}\]  
where \(\delta^{s}(t_{0})\) is the IFCB for satellite \(s\) at the reference epoch \(t_{0}\), and \(\Delta\delta^{s}(p,p-1)\) is the ED IFCB for satellite \(s\) between the adjacent epochs \(p\) and \(p-1\). The IFCB value at the epoch 00:00:00 (GPS time) is set to zero for each day, and this epoch is selected as the reference epoch in this study. The IFCB estimates at all epochs of a day will contain a common bias. In parameter estimation process, the common bias will be absorbed by the phase ambiguity term, and thus has no effect on PPP float solutions. Since the phase bias \(b_{k}\) is station independent, it is eliminated in between-receiver single-difference. Therefore, the triple-frequency signals can be directly used in precise relative positioning and the issue of IFCB is absent. As the PPP usually adopts undifferenced model, the phase bias \(b_{k}\) cannot be eliminated by between-receiver single-difference. In addition, phase bias \(b_{k}\) is time-varying and satellite-dependent, and cannot be absorbed by phase ambiguity or receiver clock parameters. Thus, the issue of IFCB should be carefully handled when the triple-frequency signals are simultaneously used in PPP.
## 1 Introduction
### Transient Steady-State Response Analysis of Underdamped Stochastic Resonance System
#### 3.2.1 Transient Response Theory Derivation
When the difference between the transient value and the steady-state value falls within a 2% range, the system can be considered to have entered a steady-state process. When \(\gamma\in(0,1)\), the response speed graphs of the SUUBSR system and the FUBSR system are depicted in Figure 3.  
In Figure 3, it can be observed that when both systems are in the transient phase, the system response values deviate from their respective steady-state values, leading to partial energy loss in the OFDM signal. After the transient process begins, the SUUBSR system curve rises rapidly, first entering the steady-state phase at time \(T1\). After a period of decaying oscillation within the potential well, the system fully enters the steady-state phase at time \(T2\). The FUBSR system's response curve rises smoothly, performing non-steady periodic motion between two potential wells, and enters the steady-state phase at time \(T3\). The response speed of the SUUBSR system is significantly better than that of the FUBSR system. After entering the steady state, the range of particle motion in the SUUBSR system is \([-x1,x1]\), and the range of particle motion in the FUBSR system is \([-x2,x2]\). This indicates that the presence of damping coefficients can improve the system's utilization of noise, resulting in a wider range of particle transitions and higher amplitude values of the system's output signal. Before the system enters the steady state, the response generated by the particle's motion within the trap is composed of two parts: the system's transient response and the excitation response of the input signal. Among them, the system parameters, damping coefficient, signal frequency, and the number of subcarriers all affect the size of the system's transient response. In addition, according to Equation (36), the time required for particles to transition between the left and right potential wells, as well as the transient response time, can be calculated. When the SUUBSR system processes OFDM signals symbol by symbol, due to the influence of the transient response, each OFDM symbol will lose part of its energy, affecting the resonance effect of the system. The method adopted in this paper to remove the transient response is to splice the current OFDM symbol being processed with the latter segment of length \(T_{d}\) of the previous symbol period's OFDM symbol, thus forming an OFDM data segment with a data length of \(T_{s}+T_{d}\). This ensures that the data segment of the current symbol period is not destroyed when the system has a transient response.
## 1 Introduction
### Datasets Background
A single dataset is produced from 180 days of data: 90 days were used to extract features for training and 90 days to extract features for labeling. Thus, we produced many datasets using the sliding window approach for different 180-day windows. For setups where true testing data are used, deriving from a 90-day period, a total of 270 days of data is involved. The attributes of the dataset prepared for the experiments are listed in Table 2. The daily data were calculated for the following parameters:  
1. The number of calls;
2. The sum of the minutes from all calls;
3. The payment amounts;
4. The sum of the costs of customer payments;
5. Activity metric (provided by company). The parameters presented above were calculated for the last 90 days of the data, resulting in 450 features in total. To describe the customer, we calculated widely used features for churn prediction--the Recency, Frequency, and Monetary (RFM) features for the five key parameters mentioned above. For each parameter, RFM features were computed over four specific time intervals: days 1-90 (\(t\in[1,90]\)), days 1-30 (\(t\in[1,30]\)), days 31-60 (\(t\in[31,60]\)), and days 61-90 (\(t\in[61,90]\)). This approach resulted in 12 RFM features per parameter (3 features \(\times\) 4 intervals), summing to a total of 60 RFM features across all parameters. By utilizing multiple time intervals, we aimed to capture both recent and historical customer activities, allowing the model to detect changes in behavior. This aggregation enhances the robustness of the predictive model, especially in the context of clients who often display irregular usage patterns.
## 2 Related Work  
### Deep Learning for Traffic Flow Prediction  
The rapid development of deep learning provides a new research method for the spatial-temporal prediction of traffic flow [31; 32; 33]. Stacking autoencoders (SAEs) deepen the network layers to learn the latent traffic spatial-temporal relationships and employ a greedy layerwise unsupervised algorithm to pretrain the model [34]. CNN-MLP can extract local spatial correlations and is less sensitive to data noise [18]. By abstracting network traffic into images, CNNs can be employed to learn large-scale spatio-temporal information [19]. Some scholars extracted traffic spatial-temporal information through Conv-LSTM and then used a Bi-LSTM to obtain temporal periodicity features of traffic flow [21]. The Conv-LSTM conforms to the attention mechanism optimization module to extract spatio-temporal features and then uses the BiLSTM module to efficiently capture daily and weekly periodic features [23]. T-GCN adopts a GCN to obtain the spatial dependence of urban roads and then introduces a GRU to obtain the temporal dependence of dynamic changes [35]. ST-ResNet uses convolutional and residual networks to extract spatial correlations, we well as residual networks to extract temporal features [36]. FCL-Net uses Conv-LSTM network to extract spatial-temporal correlation, as well as an LSTM network to extract external weather features [37]. However, a CNN is usually suitable for grid data. In reality, traffic flow data are often non-Euclidean structural data, so a GCN is introduced into traffic prediction. The STGCN employs the GCN and temporal gated convolutions to model spatial and temporal dependencies [38]. The DCRNN adopts the bidirectional random walk mechanism on the graph to model the spatial dependence, uses the Gated Recurrent Units (GRUs) to capture the temporal dependence, the overall model adopts the encode-decode structure, and the scheduled sampling technique to improve the prediction accuracy [39]. Further, a study used advanced deep learning models to accomplish multitask learning and prediction on missing traffic data [40].
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 4 Evaluation
### Cost Model
#### 4.1.4 Analysis  
Since skew-join records occur often in reducer 1 and other skew keys are insignificant, reducer 1 determines the execution time of the algorithms. We compared the costs of join computation between the three algorithms.  
* From (*) and (**), \(\Rightarrow\)\(C_{reduce(HBP)}>C_{reduce(RBP)}\).
* From (*) and (**), \(\Rightarrow\)\(C_{reduce(HBP)}>C_{reduce(MDRP)}\).
* From (**) and (***), \(\Rightarrow\)\(C_{reduce(RBP)}>C_{reduce(MDRP)}\).  
Therefore, we have Equation (5).  
\[C(J)_{HBP}>C(J)_{RBP}>C(J)_{MDRP} \tag{5}\]  
A range-based partition performs join operations with skewed data more efficiently than a hash-based partition thanks to the fragment-replicate mechanism. A multi-dimensional range partition is more efficient than a range-based partition thanks to the thorough skew processing of both datasets.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 3 BDS-3 Integrity Failure Model and Effect Analysis Algorithm
### BDS-3 Integrity Risk Factors Analysis
#### 3.3.2 Anomaly Monitoring Missing Alarm  
Anomaly monitoring missing alarms can also be divided into missing alarms in space segments and missing alarms in ground control segments.  
According to Section 2.2, the system transmits integrity information to users by broadcasting integrity parameters. Therefore, if the relevant integrity parameter information was a broadcast error, it leads to the occurrence of missing alarms. In addition, considering the integrity monitoring ability completeness, especially SAIM ability, it is still relatively limited and unable to realize the slow change accumulation monitoring of satellite clock and navigation message anomalies. Therefore, once the above anomalies occur, the SAIM cannot be monitored temporarily.