esa-sceva/satcom-chunk-collection huggingface dataset:
# A High-Utility Differentially Private Mechanism for Space Information Networks
## 4 Experiments
### Datasets and Experimental Setup  
The dataset used in experiments is on 1665 satellites of Starlink in two-line element set, which is collected by CelesTrak. CelesTrak provides SOCRATES-Satellite Orbital Conjunction Reports Assessing Threatening Encounters in Space as a service to the satellite operator community. On 24 March 2005, Air Force Space Command gave CelesTrak the go-ahead to offer TLEs in support of SOCRATES. A list of an object's orbital components is encoded using the TLE (Two-Line Element set) data format. TLE data can be used to estimate the state(position and velocity) of satellites. Therefore, by foreseeing the orbital pathways of space debris in the future, TLE data are utilized to perform risk analysis,close approach analysis, collision avoidance measures, and forensic investigation. The TLE consists of three rows of data, and each row has 69 characters. The first row has a satellite's name; the second and third rows provide information on the satellite, including its orbital inclination, ascending node's right ascension, perigee's argument, orbital eccentricity, semi-major axis, true anomaly, etc.  
The used queries in experiments are the average function and standard deviation function. They were picked because many real-world application situations make use of them. The standard deviation function is non-linear, whereas the average function is linear. The values of privacy budget are from 0.1 to 0.5, which are suitable values in most application scenarios in terms of privacy preservation. The root means square error (RMSE) is used in the experiments in this part as a standard for comparing errors. RMSE is the squared sum of the squared root of the mean difference between the value of the function without privacy protection and the value of the position that received privacy protection. A smaller RMSE indicates better data utility as opposed to a bigger RMSE.
###### Abstract
Furthermore, by predicting high-risk conditions in advance and stopping the vehicle, collisions are avoided even in critical situations. First, in Section 2, the two hazardous speed criteria of the ego vehicle to define the target speed for PBS are described. The first speed criterion is defined as the speed at which the ego vehicle can safely stop when the darting-out occurs, and the second is defined as the speed at which the ego vehicle can escape the intersection before making contact with the darting-out object. Next, in Section 3, PBS provides speed control assistance to prevent the vehicle from entering the hazardous speed region between these speed criteria. Finally, in Section 4, simulations are performed to verify the effectiveness of the proposed PBS for various darting-out conditions and compared the collision avoidance performance between the proposed method and driver assistance systems from the conventional technology and the previous study. Figure 1: Darting-out scenario at intersection right turning.
# Multigap Resistive Plate Chambers for Time of Flight Applications
## 3 Applications in Large Experiments
### CBM Experiment in GSI
#### 3.2.2 High-Rate MRPC  
Towards the particle flux ranges from 1-5 kHz/cm\({}^{2}\) in the intermediate rate region [63], the double-stack MRPC prototype [26] developed at Tsinghua University has been considered. It is built by the low resistive glass plates described above. This MRPC (Figure 7a) has 2 \(\times\) 4 gaps with 250 \(\upmu\)m gap size and 32 readout strips, each of 1 \(\times\) 27 cm\({}^{2}\). The results of SPS beam time in 2015 for the prototypes were presented in [26]. The efficiency and time resolution curves correspond well to the changes of high voltage and PADI threshold. The efficiency is stable at the working point and maintains above 97% for all the run time. The system time resolution tends to improve with time in the beginning and reaches a value of about 85 ps. The first batch of 73 MRPC detectors [71] has been produced and installed into STAR endcap ToF and mini-CBM ToF.  
During the mass production and quality control process, the HV test of MRPC [71] is quite important to ensure its performance. Figure 7b,c shows the monitoring results of the dark current and noise rate after applying the HV. The dark current decreases to around 10 nA with time, and the noise rate finally stays around 1 Hz/cm\({}^{2}\). It can be seen that the noise and dark current are quite low when the particle (here is cosmic ray) rate is rather low, which indicates the low noise and good response of the MRPC detector.
# Cyclic Behaviors of Geopolymeric Recycled Brick Aggregate Concrete-Filled Steel Tubular Column
## 4 Conclusions  
A new type of GRBACFST composite column was proposed to improve the environmental sustainability of CFST column and enhance the reuse efficiency of construction waste and industrial waste. The cyclic behaviors of GRBACFST column were studied experimentally and the influences of the replacement ratio of RBA, steel tube thickness, axial compression ratio and cementitious material types were discussed. Based on the test results, some important conclusions could be summarized as follows.  
1. The proposed GRBACFST column could develop good hysteretic behavior that was comparable to conventional CFST column, in terms of failure process, failure mode and deformation capacity. The plastic hinge always formed at the bottom region of column and the GRBACFST column failed with bulge of steel tube or rupture of steel tube at the corner. The inner GRBAC at the region with serious local buckling occurred was found with serious crush.
2. The hysteretic curves for proposed GRBACFST columns were full without an evident pinching effect and excellent deformation capacity. The incorporation of RBA and increase of axial compression ratio imposed an evidently negative effect on the bearing capacity, while a thicker steel tube was beneficial. The hysteretic behavior was analogous for tested specimens with different cementitious material.
3. The mechanical behavior was similar for all tested columns during the initial stage. However, once the steel tube yielded or buckled, the deference in the degradation stage was obvious. Specimens with larger RBA replacement ratio, higher axial compression ratio and thinner steel tube developed a serious degradation in mechanical behavior.
4. The confinement effect from steel tube could effectively offset the drawbacks of crushed brick aggregate, especially for cases with higher replacement ratios of RBA or under larger deformation. A thicker steel tube could improve the failure process of proposed GRBACFST column and benefit the bearing capacity, ductility as well as energy dissipation ability.
5. The hysteretic behavior was not sensitive to the change of cementitious material, and the geopolymers could serve as an effective alternative to the OPC as an eco-friendly binder for concrete. It was feasible to develop a green building material with industrial waste and construction waste, effectively dealing with the shortage of natural resources and environmental issue.  
Conceptualization, Y.N. and R.L.; Data curation, Y.C. and X.L.; Investigation, Y.C.; Methodology, Y.N.; Project administration, R.L.; Software, X.L.; Supervision, R.L.; Writing--original draft, Y.C.; Writing--review and editing, Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University-Industry Cooperation Project of Fujian Province (Grant No. 2021Y4018) and Fujian Provincial Finance Department (GY-Z21005).
###### Abstract  
This study investigates the effect of salt reduction through the inclusion of seaweed (_Kappaphycus alvarezii_) on the physicochemical and sensory qualities of chicken patties. A control sample (1.5% salt and without seaweed) and four chicken patty formulations were used with two levels of salt (1% and 1.5%) and two levels of seaweed (2% and 4%). Adding seaweed improves water holding capacity and minimized cooking loss in reduced-salt patties. In addition, adding seaweed decreases the shrinkage of the diameter and thickness of chicken patties (\(p>0.05\)). However, adding seaweed made the patties darker, as shown by lower L* values (\(p>0.05\)). Additionally, the incorporation of seaweed significantly increased (\(p<0.05\)) the hardness, chewiness, cohesiveness, and elasticity of patties. Reduced-salt chicken patties with the addition of 2-4% of seaweed showed lower extracted water than 1.5% salt chicken patties with seaweed (\(p<0.05\)), indicating a higher water holding capacity. The sensory evaluation showed that the chicken patty with 1.5% salt and 4% seaweed had the highest overall acceptability. However, the overall acceptability of the chicken patties with 1% salt and 4% seaweed was significantly higher (\(p<0.05\)) than the control. In conclusion, the addition of _Kappaphycus alvarezii_ to reduced salt patties improved textural properties with acceptable taste profiles.  
seaweed; reduced salt; meat emulsion; physicochemical properties; sensory; meat products; patty +
Footnote †: journal: Applied Sciences
## 2 Geological Setting and Data
### Characteristics of Mining Tremors  
#### 2.2.1 Spatial Distribution of Mining Tremors  
Three SMTs occurred in LW 1208 of Shilawusu Coal Mine, all under the condition of goaf mining. The change of mining conditions of LW 1208 was the main reason for the occurrence of SMTs. According to the different mining conditions of LW 1208, the goaf mining area was divided into areas I, II, III, and IV, and the mining push times were September-December 2020, January-April 2021, May-August 2021, and September-December 2021, respectively. Areas I and II correspond to solid coal mining of LW 1208. The area of LW 1208 corresponding to areas III and IV enters into near-goaf mining.  
Figure 3 shows the spatial distribution of mining tremors before and after near-goaf mining, as well as the number and energy proportion of mining tremors in each energy interval. The microseismic events in area I are characterized by a source energy of less than 104 J with relatively dispersed distribution. LW 1208 continues to advance towards the goaf, which is called region II. The number of mining tremors with an energy class of 10\({}^{4}\)-10\({}^{5}\) J increased from 56 to 71 times compared to region I, and a mining tremor with an energy class of over 10\({}^{5}\) J occurred near the goaf. At the stage of solid coal mining in LW 1208, the source of mining tremors was mainly located in the range of 100 m rock strata above the 2-2 coal seam, namely, the J\({}_{1\text{-}2\text{y}}\) formation.  
Figure 2: Formation structure of LW 1208.  
Entering the stage of mining near the goal, the number of mining tremors of all energy levels increased significantly. In particular, the number of mining tremors exceeding 10\({}^{5}\) J in area III was twice that in area II, and in area IV was the same as in area II. These included the \"8.20\", \"8.29\", and \"12.20\" SMTs which caused ground shaking. The source of the SMTs is located in the K1 zh formation. Prior to the occurrence of the SMT, mining tremors occurred frequently in the strata below K1 zh, and the source elevation gradually increased and approached the K1 zh formation. The vertical distribution of mining tremors from J2Z to K1 zh is approximately triangular.  
To summarize, the goaf area increased at the stage of goaf stopping on LW 1208, which leads to the step by step upward strata fracture distribution, and strata fracture often induces mining tremors. Finally, the K1 zh fracture induced the \"8.20\", \"8.29\" and \"12.20\" SMTs.
## 3 Results
### The Influence of Sites  
We then analyzed and compared the uncertainties of the different site locations based on the results of the multi-scheme ground motion simulations. We used the values and weights of the various simulation parameters (Figure 5), weight scheme 12 (Table 4), and the six different site locations (Figure 1b). The obtained average acceleration response spectra of Slip Models 1 and 2 are shown in Figure 8a,b, respectively. The 84 th-percentile results are shown in Figure 8c,d. For Slip Model 1, the average and 84 th-percentile acceleration response spectra of Site 1 and Site 2 are higher than those of Site 3, while the results for Sites 4 and 5 are higher than those for Site 6 (Figure 8a,c). Moreover, for both the average and 84 th-percentile results, the simulated acceleration response spectra of Site 1, Site 2, and Site 3 are higher than those of Site 4, Site 5, and Site 6, respectively. The results show that when the site is located on the hanging wall of the fault, the acceleration response spectrum is higher than that for a site located on the footwall. We can obtain the same conclusion for Slip Model 2 (Figure 8b,d).  
To analyze the reasons for this phenomenon and determine the roles of the different initial rupture points in the results of the ground motion simulations, we further compared the effects of different combinations of sites and initial rupture points on the simulation results yielded by the two source models. We used the same simulation parameters (Figure 5) and Sites 1-3. The average and 84 th-percentile values for Slip Model 1 are shown in Figure 9a,b, respectively. For Slip Model 2, the average value is shown in Figure 9c, and  
Figure 8: Simulation results for Slip Models 1 and 2 at different sites. The average acceleration response spectra for (**a**) Slip Model 1 and (**b**) Slip Model 2; the 84 th-percentile results for (**c**) Slip Model 1 and (**d**) Slip Model 2.  
the 84 th-percentile result is shown in Figure 9d. As shown in Figure 9, we found that point 1 for the different sites is higher than the other points for the same site in Slip Model 1, and points 2 and 3 are higher than points 1 and 4 in Slip Model 2 at the same site. For the different sites, when the initial rupture point is located near a smaller asperity rather than a larger asperity, the simulation results are greater than those for the other schemes. For both source models, the simulation results for Site 3 are lower than those of Sites 1 and 2. The acceleration response spectra of the four initial rupture points for Site 3 in Slip Model 1 are approximately coincident. However, the result for point 4 is significantly lower than the other three initial rupture points for Slip Model 2, which may be due to the longer distance from the largest asperity to point 4.
# Addressing the Algorithm Selection Problem through an Attention-Based Meta-Learner Approach
## 3 Solution Model
### Portfolio of Algorithms  
The portfolio of algorithms used in this work consists of three different configurations of the simulated annealing (SA) meta-heuristic, as stated before. Simulated annealing (SA) is a well-known meta-heuristic with many practical applications. It was initially proposed by [PERSON] et al. [42] in the 1980s. The main idea behind SA is to allow the search to explore low-quality solutions at the initial stages of the search as a means to escape from local optima and, with time, keep only the solutions that improve the current solution. Weachieve this behavior by starting the search with an initial (and high) temperature \(T_{max}\) that decreases throughout the search until it reaches the final (and low) temperature \(T_{min}\). When the temperature is high (at the beginning of the search process), the probability of accepting low-quality solutions is also high. However, the temperature and the probability of accepting low-quality solutions decrease progressively following the cooling schedule, converging into a simple iterative improvement algorithm.  
Moreover, this work uses three different configurations of SA--each differentiated from the other by their neighborhood operator--to conform the algorithm space from which the proposed attention-based meta-learner will choose the specific configuration that best solves each of the VRPTW instances. The pseudocode of the SA algorithm implemented for this work is depicted in Algorithm 1.  
```
1:\(\alpha\leftarrow\) a constant cooling rate
2:\(k\leftarrow\) iterations at a given temperature
3:\(S\leftarrow\) an initial feasible solution
4:\(E_{S}\leftarrow\) the cost of the initial solution \(S\)
5:\(T_{max}\leftarrow\) initial temperature
6:\(T_{min}\leftarrow\) final temperature
7:\(T\gets T_{max}\)
8:while\(T\geq T_{min}\)do
9:for\(iteration=1,\ldots,k\)do
10:\(S^{\prime}\leftarrow\) Neighbor\((S)\)
11:\(E_{S^{\prime}}\leftarrow\) the cost of the neighbor solution \(S^{\prime}\)
12:\(\Delta E\gets E_{S^{\prime}}-E_{S}\)
13:if\(\Delta E<0\) or \(e^{-\Delta E/T}>rand(0,1)\)then
14:\(S\gets S^{\prime}\)
15:endif
16:endfor
17:\(T\leftarrow\alpha\times T\)
18:endwhile
19:return\(S\)
```  
**Algorithm 1** Simulated Annealing Algorithm.
## 4 Experiments and Evaluation
### PM-Based Adaptive Scheduling vs. A Rule-Based Preventive System
The performance of our mechanism is also shown in Figure 11b through the power consumption. The power level is kept almost at the expected level, even slightly below, by changing the speed relatively. The poor reaction times of the other two systems cause a steep increase in the power after the disturbance is introduced. Finally, the efficiency results are shown in Figure 11c and in Table 4. Our mechanism has the best efficiency performance on average. All the systems exhibit a significant efficiency drop caused by an increase in power consumption or a decrease in speed at the beginning of the operation. The significance of our system is visible through restoring the operation relatively well when the disturbance starts dropping, which is directly related to the adaptation skills of our solution. One drawback of our system is the fluctuations in the speed during the speed control, as shown in Figure 11. This is a result of our chromosome design, which takes intervals of allowed speed and power as an operation mode (further analysis are in Section 4.5.2). The smaller the interval is the more the system response approaches to a continuous control. Our mechanism lies at the task-level operational decisions; therefore, a decision is selected only when the system predicts an abnormal condition. Our test conditions are the hardest due to a continuous, large and dynamic disturbance; therefore, a new decision needs to be taken in a real-time frequency for the most efficient operation. Our PM-based adaptive control system is able to provide this behavior; however, the response time of the genetic algorithm introduces a computational limitation. A smaller interval size (more chromosomes) with a more computational resource would compensate the fluctuations as mentioned in Section 4.5.2. In general, we show that incorporating PM as a feedback into automated control mechanisms has significant advantages in terms of increased efficiency while still ensuring the safety of the operation, and that our approach successfully achieves it. By comparing our fine-grained PM-based control with a coarse version of it, that is, a PM-based adaptive control with an allowed operational speed interval of 13 r/min (an approximation to a rule-based system), we demonstrate that a predictive maintenance strategy alone will not be as effective unless it is incorporated into a more sophisticated decision-making algorithm that takes the prognostics, further analyzes the system deviation, and adaptively schedules a new safe and efficient operational decision. Otherwise, a preventive system that schedules for a maintenance intervention still performs similarly with or without a PM analysis at hand.
# MMTD: A Multilingual and Multimodal Spam Detection Model Combining Text and Document Images
## 3 The Proposed MMTD
### Multilingual Text Encoder (MTE)
#### 3.2.1 Transformer Encoder Structure  
The Transformer encoder consists of multiple stacked transformer layers [29], as illustrated in Figure 3. Each transformer layer comprises a multi-head self-attention sublayer and a feedforward network sublayer. The feedforward sublayer consists of two fully connected layers, enabling the extraction of important features. The multi-head self-attention layer incorporates several self-attention mechanisms that capture the correlation coefficients between words in a sentence across multiple dimensions, effectively capturing contextual information within the text.  
Given an input \(x_{0}\in\mathbb{R}^{b\times s\times d_{\text{model}}}\), where \(b\) represents the batch size during training, \(s\) denotes the sequence length, and \(d_{\text{model}}\) represents the word token dimension, the calculation procedure of a transformer layer is as follows:  
\[x_{a}=\text{LayerNorm}(x_{0}+\text{MultiHeadAttention}(x_{0})). \tag{1}\]  
\[x_{1}=\text{LayerNorm}(x_{a}+\text{FeedForward}(x_{a})). \tag{2}\]  
In Equation (1), \(x_{0}\) is added to the output of the multi-head self-attention sublayer, and the result is normalized using LayerNorm to obtain \(x_{a}\in\mathbb{R}^{b\times s\times d_{\text{model}}}\).  
In Equation (2), \(x_{a}\) is added to the output of the feedforward network sublayer, and the result is again normalized using LayerNorm to obtain the final representation \(x_{1}\in\mathbb{R}^{b\times s\times d_{\text{model}}}\).  
Figure 3: Transformer layer structure.  
Figure 2: The framework of the multilingual text encoder.  
The detailed calculation process of the multi-head attention mechanism is as follows:  
\[\text{MultiHeadAttention}(Q,K,V)=\text{Concat}(head_{1},\dots,head_{h})\cdot W^{O}, \tag{3}\]  
\[\text{head}_{i}=\text{Attention}(Q_{i},K_{i},V_{i}), \tag{4}\]  
\[\text{Attention}(Q_{i},K_{i},V_{i})=\text{softmax}\Bigg{(}\frac{Q_{i}K_{i}^{T} }{\sqrt{d_{k}}}\Bigg{)}V_{i}, \tag{5}\]  
where \(Q_{i}=x_{0}\cdot W_{i}^{Q}\), \(K_{i}=x_{0}\cdot W_{i}^{K}\), and \(V_{i}=x_{0}\cdot W_{i}^{V}\) are used to calculate the attention of the \(i\)-th head. Here, \(i=1,2,\dots,h\), and \(W_{i}^{Q},W_{i}^{K},W_{i}^{V}\in\mathbb{R}^{d_{\text{model}}\times d_{k}}\) represent the mapping matrices of \(x_{0}\). Each \(head_{i}\) represents the attention of the \(i\)-th head, and the \(h\)\(head_{i}\) tensors are concatenated along their third dimension \(d_{k}\). Finally, the concatenated result is multiplied by \(W^{O}\in\mathbb{R}^{hd_{k}\times d_{\text{model}}}\) in the third dimension to obtain the value of the multi-head attention.
## 3 Results and Discussion  
### Heating and Cooling Degree Days  
In this study, Bursa, one of the biggest cities located in the second-degree day region (climatic zone) in Turkiye, has been selected for the analysis [35]. Figure 1 reveals substantial differences in the HDD and CDD values from month to month due to variations in temperature and incoming solar radiation. As shown in Figure 3, January, February, and December are the months with the highest HDD values, while June, July, and August have the highest CDD values. The requirement for heating energy during a particular month increases with a higher HDD value. Likewise, a high CDD value signifies a strong need for energy to be devoted to cooling during the corresponding month. For example, August, with a CDD value of 163, needs approximately six times more cooling energy than May, with a CDD value of 26.  
Figure 4 presents the yearly average HDD and CDD values for cardinal directions, which are the north (N), south (S), east (E), and west (W), as well as intermediate directions, including the northeast (NE), southeast (SE), southwest (SW), and northwest (NW). When HDD and CDD values are examined depending on the directions, the north- and northeast-facing walls are notable with their high HDD values, 1465 and 1461, respectively. As mentioned before, the HDD is a unit used to quantify the demand for heating a building in a specific location. Therefore, it is understood that these particular walls necessitate a higher amount of heating energy. When direction-based inspection is examined regarding cooling, it is evident from Figure 4 that the south-facing wall has the maximum CDD value of 473. Since the CDD is a metric used to quantify the amount of cooling needed to maintain indoor comfort in buildings during warm weather, it is determined that the relevant wall needs more cooling energy compared to walls oriented in alternative directions.