esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Selection and Modeling  
The ensemble machine learning algorithm AdaBoost.M1 was used to construct multi-class classifiers with a full set of features or the remaining features of the best predictors. Tenfold cross-validation was applied to the models (k = 10, mfinal = 50, coeflearn = Breiman, and maxdepth = 5) to improve reliability of model selection. To model the classification system, a training dataset of input-output pairs of main-joint ROM values and the manual scores was collected. Initially, nine folds were used for training and one fold was used for validation The number of cross-validation folds was changed over each of 10 repetitions. This repeated cross-validation was used to estimate any error due to data partitioning. An automatic scoring system was then constructed and used to predict the most appropriate score for each test. To reduce the number of input variables, we used ordinal logistic regression for subset feature selection. Each test selected up to five statistically significant predictors (\(p<0.05\)) into the model. Feature selection and modeling were performed using RStudio Cloud (Version 4.0.2, RStudio, Boston, MA, USA, 2020).  
Figure 2: The functional movement screen exercises appraised in this study. (**A**) Deep squat; (**B**) hurdle step; (**C**) in-line lunge; (**D**) active straight leg raise; (**E**) trunk stability push-up; and (**F**) rotary stability.
## 2 System Structure and Problem Formulation  
This section is divided into two subsections. In Section 2.1, the system structure is illustrated, consisting of both system model and channel capacity's expression. In Section 2.2, we first updated the expression of channel capacity by introducing the switching loss. The problem to be optimized is further shown in a mathematical manner.
## 4 Conclusions  
This study investigated the feasibility of utilizing excavated soil exclusively as a fine aggregate for the production of soil-based CLSMs. PCE was introduced to soil-based CLSMs to mitigate the water requirements and enhance flowability. The effects of different cement contents, initial water contents and curing times on the mechanical properties and microscopic pore structure distribution of soil-based CLSMs were systematically studied. Based on the experimental findings, the following conclusions can be drawn:  
1. PCE could effectively disperse cement particles and clay minerals, improving the flowability of soil-based CLSMs. With an increase in the cement content, the unconfined compressive strength rises due to a decrease in the volume percentages of extra-large pores and macropores, accompanied by an increase in the volume percentages of small and mesopores. Conversely, as the initial water content increases, the  
Figure 16: Variation in water content and pH under different (**a**) cement contents and (**b**) initial water contents.  
rise in the volume percentages of extra-large pores and mesopores leads to a decrease in the unconfined compressive strength.
2. The \(q_{\text{u}}\) of the soil-based CLSM without PCE (T1) exceeded that of the CLSM with PCE (T2) under different curing time periods. The \(T_{2}\) distribution curves of the soil-based CLSM show bimodal distribution. These curves shifted to smaller transverse relaxation time values with the increasing cement content, while gradually shifting to larger transverse relaxation time values with the increasing initial water content. The addition of PCE had little effect on the \(T_{2}\) distribution curve and the \(T_{2\text{peak}}\).
3. An exponential relationship between the porosity and unconfined compressive strength of soil-based CLSMs was established under different curing time periods.  
These findings contribute valuable insights into the potential applications of soil-based CLSMs. Future research could explore the addition of sacrificial agents to minimize PCE adsorption by clay minerals, ensuring a more economical and efficient application of soil-based CLSMs in geotechnical engineering.  
Conceptualization, Q.G.; Methodology, Q.G.; Resources, Y.C.; Writing--original draft preparation, Q.G.; Writing--review and editing, Q.G., J.X. and B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52178327), the Central University Basic Research Fund of China (No. B200203083), and Graduate Research and Innovation Projects of Jiangsu Province (No. KYCX20_0438).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# A Deep Learning-Based Password Security Evaluation Model
## 2 Related Studies
### Existing Security Evaluation Methods and Their Limitations  
For the password security evaluation indicator, security must be evaluated by considering the password selection tendencies of the user, and this is carried out as shown in Figure 2. Here, the security evaluation score of the password selected by the user is predicted through addition and deduction indicators for security. For example, the most frequently used method at present is lower and uppercase letters, digits, and symbols (LUDS), and it is a method in which the requirement for the number of times letters, numbers, and special symbols are used is reflected. Indicators such as password length and variety of combinations belong to addition indicators since they make the password more complicated, and patterns in which letters continue sequentially or repeat, such as \"AAA\" and \"123\", belong to deduction indicators since they make the password simple [7].  
Like this, the password security evaluation score drawn from the password security evaluation indicators in Figure 2 can be classified into a password with strong security and a password with weak security by determining whether a certain standard is satisfied. For example, the password \"Pass123 word!\" is classified as a password with strong security from the fact that it contains uppercase letters, lowercase letters, numbers, and special symbols and satisfies the LUDS requirement. In addition, if numbers or special symbols are put between words, as in \"Pass123 word!\", it is classified as being very secure in zxcvbn by determining that there is no word that coincides with the words in the dictionary.  
Figure 1: Brute-force password cracking process.  
Figure 2: Existing password security evaluation process.  
However, a security evaluation method like the one in Figure 2 does not check whether the password has been leaked no matter how high its security evaluation score was evaluated to be. In order to check whether a password has been leaked, it is necessary to collect and store existing leaked passwords and compare them with the password selected by the user. However, storing a leaked password list like this can be limiting in low-performance environments, and since new leaked passwords that occur must be added to the list, it is inconvenient. In addition, since the original string cannot be inferred from the hashed data, encrypting and storing the passwords is limiting for methods that evaluate the security of passwords based on the original letter strings such as zxcvbn or Levenshtein distance. In this regard, there is room for an attacker to analyze the stored leaked password list and abuse it to determine the password selection tendencies of the users. In order to solve this problem, a new evaluation method to determine whether a password has been leaked is needed.
# Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites
## 4 ERP Estimation Based on the Combination of Multiple LEO Satellites
### Impact of LEO Number on the ERP Estimation
This further confirms the advantage of the combination of LEO satellites.
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 4 Model Application and Results
### The Standard Rating Curve (SRC) Approach  
Normally, the conventional SRC is the preferred approach to estimate the SSL in rivers. For the development of the SRC model, the total observed data are divided into two phases, a training and testing phase, as explained previously. The SRC approach was employed to develop a relationship between river water discharge, and SSL and _Q_-SSL equations were derived from the USBR method (Equation (2)). By employing these equations, missing SSL was estimated for those days when only water discharge data existed. The effectiveness of this approach is based on the available paired datapoints, which were employed to form SRC. Further, two correction factors, i.e., Ferguson correction factor (FCF) and Smearing correction factor (SCF), were calculated and applied to the derived equations for the estimation of SSL. The sediment rating curves derived for the Pirna and Magdeburg-Strombrucke gauging stations are displayed in Figure 6. The SRC, SRC(FCF) and SRC(SCF) equations derived for the Pirna gauging station (Equations (15)-(17)) and Magdeburg-Strombrucke gauging station (Equations (18)-(20)) are  
\[SSL\ (SRC)=0.0977Q^{1.5002} \tag{15}\]  
\[SSL\ (SRC(FCF))=1.245\ \times(0.0977Q^{1.5002}) \tag{16}\]  
\[SSL\ (SRC(SCF))=1.079\ \times(0.0977Q^{1.5002}) \tag{17}\]  
\[SSL\ (SRC)=1.5481Q^{1.0363} \tag{18}\]  
\[SSL\ (SRC(FCF))=1.248\ \times(1.5481Q^{1.0363}) \tag{19}\]  
\[SSL\ (SRC(SCF))=1.229\ \times(1.5481Q^{1.0363}) \tag{20}\]  
Figure 5: M-test graph for the Magdeburg-Strombrücke gauging station data length.  
Figure 6: Applied SRCs for the stations.  
The performance of different SRC approaches to estimate the SSL in the testing phase for the Pirna and Magdeburg-Strombrucke gauging stations are given in Table 5. Among all the sediment rating curves approaches, the SRC approach (without any correction factor) performed better than the SRC approaches, with a bias correction factor for both gauging stations. However, the overall performance efficiency of all these approaches remains low, because _PBIAS_ results showed that all SRC approaches overestimated the SSL, with a high _RMSE_.  
The observed and predicted SSL, using different approaches, during the test phase for the Pirna and Magdeburg-Strombrucke stations are presented in Figures 7 and 8, respectively. It can be seen from Figures 7 and 8 that SRC was not able to capture the high SSL peaks.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 3 Solving Method
### DRL-Based Method for Optimization  
#### 3.2.1 Markov Decision Process Model  
Deep reinforcement learning is the process of an agent that learns how to make a decision by interacting with the dynamic environment through trial and error. Agents take actions on the environment and achieve positive or negative reward feedback.  
The Markov decision process (MDP) is the fundamental framework of RL for modeling. One agent (a satellite in this research) chooses an action in the current state, then transfers to the next state and receives a reward. This process could be described by a tuple \(\mathcal{M}=\langle\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R},\gamma\rangle\), where \(\mathcal{S}\) is a finite set of states, \(\mathcal{A}\) is a finite set of actions, \(\mathcal{P}\) is a state transition probability matrix. \(\mathcal{R}\) represents the reward function and \(\gamma\) denotes a discount factor (\(\gamma\in[0,1]\)).  
In the EOSSP, the global state \(\mathcal{S}\) includes the task state \(S^{task}\) and the satellite state \(S^{sat}\) is given as the following equations:  
\[S^{task}=\bigcup_{u}^{N}[TWS^{c}_{u},TWE^{c}_{u},ObvS_{u},ObvE_{u},prio_{u},d_ {u}] \tag{17}\]  
\[S^{sat}=\bigcup_{u}^{N}\theta_{u} \tag{18}\]  
where the global state \(s_{t}=\mathcal{S}=[S^{task},S^{sat}]\). The task state \(S^{task}\) is the collection of start and end times of the VTW, start and end times of the OW, priority and the observation duration. The satellite state \(S^{sat}\) is the collection of observation slewing angles.  
The action space \(\mathcal{A}\) is the collection of decision variables of each task, and all of the value range is normalized to \([-1,1]\) as follows:  
\[\mathcal{A}=\bigcup_{u}^{N}a_{u}(a\in[-1,1]) \tag{19}\]  
It should be noted that \(\mathcal{A}\) is not the OW for each task, and the corresponding mapping function is given in the following equation.  
\[\left\{\begin{array}{l}ObvS_{u}=TWS_{u}+\frac{a_{u}+1}{2}(TWE_{u}-TWS_{u}) \\ ObvE_{u}=ObvS_{u}+d_{u}\end{array}\right. \tag{20}\]  
The selected OW for task \(t_{u}\) is \([ObvS_{u},ObvE_{u}]\) and could be seen as the global solution for the EOSSP. It is noted that in a model-free algorithm, it does not need any hypothesis or a prior knowledge \(P\). The VTW-related state \(s_{t}\) will transform to the next state \(s_{t+1}\) after an OW-related action \(a_{t}\) occurs. This process happens continuously within a finite time, and an immediate reward \(r_{t}\) is obtained predictably in each transition step, as shown in Figure 4.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 4 The Future of Farming: 6G-IoT Applications in Agriculture 5.0
### Enabling Secure and Transparent Transactions  
In the Agriculture 5.0 ecosystem, a decentralized system for secure transactions can be developed by combining the strengths of quantum communication, AI, digital twins, and blockchain technology with the 6G-IoT networking infrastructure [124]. In such a system, quantum communication, utilizing quantum mechanics principles, can provide tamper-proof communication channels, while the integration of quantum communication and blockchain facilitates efficient, accountable, and transparent transactions. Moreover, the additional employment of AI and digital twins enables timely identification of threats, enabling proactive measures to be taken.  
In the context of social protection measures intended to mitigate the effects of natural disasters on farmers, agricultural insurance can utilize such decentralized systems to support a variety of security plans. Smart contracts are required to streamline processes and enable direct compensation to affected individuals or groups in order to increase the effectiveness of these plans. The need for third-party data evaluation can be eliminated by automating the collection of data on estimated losses. This will allow for a more precise assessment of damages and the prompt delivery of compensation, reducing the financial burden on affected farmers [51]. In addition, it is essential to point out that a decentralized blockchain network can provide permanent records of agricultural products' journey from point of production to point of sale [63]. This approach can assist in establishing a relationship of trust between the consumer and the producer, as well as providing opportunities and rewards to producers that utilize agricultural resources in a sustainable manner [132].
# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 4 Experiments  
### Experimental Settings  
In our experimental demonstration, we showcase two typical scenes from each dataset to validate the effectiveness of the proposed method.  
The Replica dataset [6], comprising 18 high-fidelity synthetic scenes reconstructed from real-world environments, provides detailed geometric, appearance, and semantic information. Collected through high-resolution RGB-D scanning and refined by manual post-processing, this synthetic dataset allows for precise control over testing variables, making it ideal for isolating specific aspects of the method.  
Additionally, we utilized the ScanNet dataset [7], a large-scale real-world 3D scene dataset. It contains 21 RGB-D scans of indoor scenes, including living rooms, offices, and kitchens. The data was collected using a handheld consumer-level RGB-D camera, with manual annotation to provide semantic labels for over 40 common object categories. This dataset's real-world nature captures the complexity and variability in actual environments, such as occlusions, irregular lighting, and clutter. The combination of synthetic (Replica) and real-world (ScanNet) datasets enables a comprehensive evaluation of SemSLAM across diverse scenarios. Both datasets are equipped with semantic ground truth annotations, enabling a comprehensive evaluation across various scenarios.  
To rigorously evaluate the performance of our proposed method, we selected the mean Intersection over Union (mIoU) and the Absolute Trajectory Error (ATE) as the key evaluation metrics. mIoU, a widely-recognized standard in semantic segmentation, directly gauges the object classification accuracy of our model. By quantifying the overlap between the predicted and ground truth masks, mIoU provides a numerical measure of segmentation quality, where higher values signify superior performance. This metric aligns seamlessly with our overarching objective of enhancing semantic modeling within the SLAM framework. Conversely, ATE serves as a crucial indicator of trajectory estimation accuracy. In the context of SLAM, precise camera pose estimation is indispensable for reliable scene reconstruction and navigation. ATE quantifies the discrepancy between the estimated and ground truth trajectories, with lower values denoting more accurate pose estimation. As such, ATE is instrumental in achieving high-precision SLAM performance.  
To ensure the objectivity of the results, we conducted five independent runs on each scene and calculated the average value. In this study, SNI-SLAM served as our baseline model. We compared its performance with the method presented in this paper. Meanwhile, we also compared it with other existing NeRF-SLAM methods, such as NIDS-SLAM [26].
## 2 Overview of the Experiment
### Measurement Point Distribution and Loading Device  
The loading experiments on the steel pipe specimens filled with steel slag micro-powder UHPC were carried out using a universal testing machine, and the loading setup is shown in Figure 2. Two displacement gauges were placed on the outer side of the steel pipe to measure the vertical deformation. Horizontal and vertical strain gauges were attached alternately at the mid-height of the steel pipe, spaced at one-eighth of the circumference, to determine the longitudinal and circumferential strains during loading. The experimental setup is schematically illustrated in Figure 2(a), and the arrangement of the strain gauge measurement points is shown in Figure 2(b). Prior to the test, two or three preloads (up to 50 kN) [20] were applied to the specimen to ensure consistent readings from the four longitudinal and four transverse strain gauges and to ensure the axial compressive force was uniformly distributed.  
Figure 3: Test layout.  
Figure 2: Loading site.  
The specimen was initially loaded under force control, with the load applied slowly in increments of 5 kN/s until reaching approximately 80% of the predicted peak load. Once 80% of the peak load was reached, the loading mode was switched to displacement control at a rate of 0.5 mm/min. After the peak load was attained, the loading rate was further reduced to 0.2 mm/min. The loading was continued until the specimen exhibited obvious bending failure.
_Algorithms_**2010**, \(3\), 311-328; doi:10.3390/a3030311  
_Article_  
**Univariate Cubic \(L_{1}\) Interpolating Splines: Spline Functional, Window Size and Analysis-based Algorithm**  
**[PERSON]\({}^{1,\ast}\), [PERSON]\({}^{1}\), [PERSON]\({}^{1,2}\) and [PERSON]\({}^{1}\)**  
\({}^{1}\) Industrial and Systems Engineering Department, North Carolina State University, Raleigh, NC 27695-7906, USA; E-Mails: [EMAIL_ADDRESS] (Q.J.); [EMAIL_ADDRESS] (J.L.);  
[EMAIL_ADDRESS] (S.-C.F.)  
\({}^{2}\) Mathematical Sciences Division, Army Research Office, Army Research Laboratory, P.O. Box 12211, Research Triangle Park, NC 27709-2211, USA  
\({}^{\ast}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +01-919-513-1909; Fax: +01-919-515-5281.  
_Received: 11 July 2010 / Accepted: 10 August 2010 / Published: 20 August 2010_  
**Abstract:** We compare univariate \(L_{1}\) interpolating splines calculated on 5-point windows, on 7-point windows and on global data sets using four different spline functionals, namely, ones based on the second derivative, the first derivative, the function value and the antiderivative. Computational results indicate that second-derivative-based 5-point-window \(L_{1}\) splines preserve shape as well as or better than the other types of \(L_{1}\) splines. To calculate second-derivative-based 5-point-window \(L_{1}\) splines, we introduce an analysis-based, parallelizable algorithm. This algorithm is orders of magnitude faster than the previously widely used primal affine algorithm.  
**Keywords:** antiderivative; cubic \(L_{1}\) spline; first derivative; 5-point window; function value; global; interpolation; locally calculated; second derivative; univariate  
**Classification: MSC** 65D05, 65D07
## 3 Results and Discussion
### Effect of Inert Gases Flow  
The main function of inert gas in the process of salt preparation is to protect the process atmosphere and the discharge of water vapor. If the flow rate of inert gas is too small, water vapor cannot be discharged in time. Too much flow will result in the waste of inert gas. The effect of different inert gases (argon and nitrogen) flow rate tests were conducted and the results are shown in Figure 6.  
The test results in Figure 6 further confirm that under the same inert gas flow condition, anhydrous scandium chloride molten salt produced by argon as inert gas was superior to nitrogen as inert. The reason for this was that argon was heavier than air and could carry away water vapor more easily, which helps to improve the quality of molten salt. With the increase of inert gases flow rate, the hydrolysis rate of scandium first decreased and then increased. When the inert gases flow increased to 7.5 L/min, the hydrolysis rate of scandium decreased to 1.23% and the scandium of anhydrous scandium chloride increased to 29.56%. When the inert gases flow continued to increase, and the scandium hydrolysis rate changes little, which indicated that the inert gases flow was too small, the water vapor could not be discharged in time and the interaction time between scandium chloride and water vapor increased, which will lead to the increase of the scandium hydrolysis rate and the unnecessary argon was consumed. Therefore, argon flow rate of 7.5 L/min was more suitable.  
Figure 5: Effect of heating rate on scandium content of anhydrous scandium chloride and hydrolysis rate of scandium.  
Figure 6: Effect of inert gases flow (**a**) argon; (**b**) nitrogen on scandium content of anhydrous scandium chloride and hydrolysis rate of scandium.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 2 Materials and Methods
### Language Model-Based Transfer Learning
#### 2.5.1 Embedding Generation  
In the DDI classification task, knowledge about the interaction type of a drug pair is expressed through text. The sentence text \(t\) is an associated element of the Sentence node, or otherwise contained in \(<v_{S}\), \(sentence\_text,t>\) triplets. By applying a BERT-based LM to \(t\), it becomes possible to reduce the text to a suitable vector representation, in addition to sharing information among the individual nodes of the subgraph \(G\).  
The preparation of \(t\) involves the addition of the special tokens [CLS] and [SEP], which mark the beginning and end of the sentence, respectively. Then, each drug in the sentence is replaced by \(drugi\), where \(i\) is a number. Finally, WordPiece tokenization is applied, through which words outside the BERT vocabulary are broken into individual pieces (subwords) that belong to it.  
Furthermore, the LM is used to initially generate word embeddings \(x_{v_{S}}\) (sentence embeddings) for the entire sentence and a set of \(x_{v_{Tj}}\) (token embeddings) where each one is a representation of a \(Token_{j}\) contained within the sentence. These vectors are assigned to the respective nodes \(v_{S}\) and \(v_{Tj}\), constituting their feature vectors.