esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Patents for Anomaly Detection  
Existing patents and patent applications can also be found in the literature. A patent introduced a real-time anomaly detection method that used a Latent Dirichlet Allocation (LDA) model to analyze groups of annotated time series data [21]. A locality-sensitive hashing function or algorithm determines the number of anomalous groups across the time series data, and this parameter was inputted into the LDA. Another patent introduced a throttling bucket system which limits the rate at which service requests can be fulfilled by a server. The throttle bucket has an associated bucket level, which indicates the amount of service requests that can be fulfilled before restrictions are put into place [22]. Every service request is assigned a cost, associated with its amount, complexity, credentials, and its requested data's sensitivity. Exfiltration attempts and another malicious attacks would theoretically drain the bucket such that the system is shut down to prevent further attacks.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 5 Formal Analysis of the Proposed Scheme
### Analysis of Encryption and Digital Signature Phase  
We use Scyther to verify the post-quantum scheme used in our proposed framework. Scyther is an open-source tool used for automatic verification of security protocols [48]. The verification is based on three main aspects: (1) Logical message components verify whether a key is public, secret, constant, or freshly generated in each run. (2) Message structure includes key pairing, encryption, signature, and hash schemes. (3) Message order verifies the synchronization and involvement of agents.  
To build a model in Scyther, we use _roles_ and _events_ to send and receive messages between two agents. We have also used _claims_ that refers to the intended security properties. We verified two properties, secrecy and authentication. Secrecy analyzes, whether either of the involved agents, communicates to a trusted party on a dubious channel. Authentication addresses the aliveness of agents, synchronization, the commitment of the protocol and, message agreement between two parties [48]. Figure 11 shows the verification results of the proposed scheme. We conclude that using quantum scheme in the SPHINCS-256 algorithm makes our proposed algorithm resistant to classical and quantum threats.  
Figure 10: Probability that EVE measures more than half of the information correctly during Random Substitute Attack.
## 3 Results
### Map of the FSV Estimation in Hunan Province in 2017  
First, the Global PALSAR-2/PALSAR Forest/Non-Forest Map product was used to extract the forest area in Hunan Province. A total of 143,258,710 pixels with a 25 m spatial resolution were defined as forest areas in this product (Figure 9. left), totaling \(8.95\times 10^{6}\) ha of forest area. Then, the selected variables and the best RF model were used to estimate the FSV in the study area. The right of Figure 9 shows that the FSV in Hunan Province ranged from 12.7421 m\({}^{3}\) ha\({}^{-1}\) to 269.649 m\({}^{3}\) ha\({}^{-1}\). The mean FSV per hectare was 39.09 m\({}^{3}\), and the total FSV in Hunan Province was \(3.50\times 10^{8}\) m\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Methods** & \begin{tabular}{c} **Best Model** \\ **Parameters** \\ \end{tabular} & \(R^{2}\).**training** & \begin{tabular}{c} **RMSE.training** \\ **(m\({}^{3}\) ha\({}^{-1}\))** \\ \end{tabular} & \(R^{2}\).**test** & \begin{tabular}{c} **RMSE.test** \\ **(m\({}^{3}\) ha\({}^{-1}\))** \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} Selected \\ variables \\ \end{tabular} } & RF & \begin{tabular}{c} mtry = 5 \\ ntree = 257 \\ cost = 8 \\ gamma = 0.125 \\ epsilon = 0.68 \\ \end{tabular} & 0.91 & 35.13 & 0.58 & 65.03 \\ \cline{1-1}  & RF & \
\begin{tabular}{c} gamma = 0.125 \\ epsilon = 0.68 \\ \end{tabular} & 0.54 & 65.60 & 0.54 & 66.00 \\ \cline{1-1}  & RF &
\begin{tabular}{c} mtry = 7 \\ ntree = 495 \\ cost = 4 \\ gamma = 0.04166667 \\ epsilon = 0.57 \\ \end{tabular} & 0.92 & 34.83 & 0.58 & 66.04 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance comparison of machine learning methods under different variables.
# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 3 Energy-Efficient Game-Theory-Based Spectrum Decision
### Coordination Mode (C Mode)  
#### 3.2.1 Spectrum Sensing Module of C Mode  
The spectrum sensing module of C mode exploits full spectrum sensing (FSS), in which the sensor nodes perform spectrum sensing on entire licensed channels. The sensor nodes sleep for a random duration before they start to perform spectrum sensing. The main purpose of random delay is to support the clustering algorithm. The random delay is brief, and its maximum duration is predetermined.  
To prevent false detection of PU transmissions, all sensor nodes must not perform any data transmission during FSS (quiet period). The sensor nodes obtain the information of whether there is an ongoing transmission on a channel, and record the channel's SNR. If there is no PU transmission detected by sensor node \(S_{i}\) on licensed channel \(A_{x}\), then:  
\[Status(A_{x})_{i}=(available) \tag{1}\]  
otherwise:  
\[Status(A_{x})_{i}=(not\,available) \tag{2}\]  
The outcomes of FSS are a list of all licensed channels, their status, and the variables to calculate SNR. Next, the sensor nodes continue to perform clustering.
## 4 Results  
This scoping review was carried out by analyzing the literature from the MEDLINE/PubMed, Ovid, and Scopus databases using the keywords \"Phelan-McDermid Syndrome 'Facial'\", \"Phelan-McDermid Syndrome 'Face\", and \"22q13.3 Deletion Syndrome 'Facial'\", covering the period from 1990 to December 2024. Through the analysis of these studies, a database was created in which patients were recorded based on gender/age, general characteristics, orofacial traits, and genetic features. The analysis of the data presented in Table 2 reveals a greater involvement of females compared to males, with the age at diagnosis for patients typically occurring within the first decade of life. All patients included in the review exhibited general alterations, such as intellectual disability, speech delay, joint abnormalities, and changes in the hands and feet. In some cases, additional general alterations were present, including hypoplasia of the thyroid cartilage, infantile hypotonia, white matter hypoplasia, torticollis, a waddling gait, and hirsutism. There are no documented cases of lymphedema. Orofacial manifestations are also well documented, being widely reported in all described cases. Facial dysmorphism is characterized by major features, such as dolicothephaly, prominent ears and forehead, widely spaced eyes, strabismus, ptosis, a bulbous nose, and a wide nasal bridge, and minor features, including a round face, epicanthic folds, macroglossia, microcephaly, mild hypoplasia of the central part of the face, frontal hairiness, an arched palate, and an expressionless face. The deletions of the cases examined are predominantly large deletions. An analysis of the mutated genetic sequences reveals that Del(22)(q13.31-q13.33) has been consistently detected, indicating a deletion within this region (chr22: 46,285,592-51,244,566, hg19). Additional variations included del(22)(q13.31q13.33) and del(22)(q13.3), with multiple cases suggesting a potential hotspot for alterations. Some cases also presented del(22)(qter) and del(22)(q13.3-ACR), highlighting a broader spectrum of deletions affecting chromosome 22. Dup(9)(q34.3) was observed in both 46, XX and 46, XY karyotypes, with sequence data showing an increased copy number for the respective genomic regions (139,372,567-140,950,541 for XX and 139,367,413-141,077,092 for XY). There were also instances of combined deletions and duplications, such as del(22)(q13.33) coupled with dup(8)(p23.3p23.2), indicating complex genomic rearrangements. \begin{table}
\begin{tabular}{p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt} p{108.4 pt}} \hline
\begin{tabular}{} \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristics of patients included in this scoping review, categorized by age/gender, general clinical manifestations, orofacial abnormalities, and genetic mutations.
# Modeling and Assessment of GPS/BDS Combined Precise Point Positioning
## 4 Parameter Differences between Combined PPP Models  
In the following, we compare the station clocks, ambiguities and pseudo-range residuals between the traditional and simplified GPS/BDS combined PPP models. Data used is the kinematic solution of station JFNG on DOY 028, 2014.
# Quantitative Study of the Maceral Groups of Laminae Based on Support Vector Machine
## 5 Conclusions  
In this study, we used SVM to quantify some maceral groups and inorganic minerals in the laminae, and the main conclusions are as follows:  
1. Using an appropriate degree of image preprocessing and enhancement can meet geological research needs, and the overall accuracy and kappa coefficient are 82.86% and 0.80, respectively. This method is based on ENVI software and thus is replicable for geologists with little knowledge of machine learning theory. In addition, the map of classification results can illustrate the microscopic components of the source rock to other researchers.
2. Through statistical analysis of the classification results, it is possible to find that the organic matter and mineral areas vary in different laminae. Using this method can provide a new way for subsequent research on the combination of laminae and the difference between laminae in oil generation.  
Figure 8: The kernel density maps of (**a**) pyrite, (**b**) alginite, (**c**) sporinite, (**d**) vitrinite, and (**e**) inertinite.  
3. The spatial distribution of each component can be revealed by the presentation of the kernel density map: pyrite for the multi-core centers distribution, alginite and sporinite for the dotted distribution, and vitrinite for the stripe distribution, respectively; however, intertinite has no noticeable local enrichment. This discovery can assist in the follow-up study of the sedimentary environment of the laminae. However, it is to be noted that there are some limitations in the research, which is a preliminary study of quantitative macareals in laminae. First, we performed analysis on a sample only with lamellar layers because of the lack of examples. Second, the complexity and the fewer numbers of macareals in shale led to some classification results with imperfect accuracy. Although there are limitations, this study serves to provide new approaches to quantify the macareals in laminae, and we will further investigate how to improve the accuracy of computer recognition with few samples and complex macareals in shale.  
Conceptualization, Y.L.; Data curation, Y.W., Y.F. and T.Z.; Formal analysis, Y.T. and X.X.; Methodology, Y.W., Y.F. and Y.L.; Project administration, Z.W.; Visualization, J.T.; Writing--original draft, Y.W.; Writing--review & editing, Y.L., K.L., Y.M. and Y.X. All authors have read and agreed to the published version of the manuscript. This work was funded by National Natural Science Foundation of China (Grant No. 41503034, 41972122, and 42172139), National Science and Technology Major Project of China (Grant No. 2017 ZX05001005-002).  
Not applicable.  
The data that support the findings of this study are openly available in zenodo.org at [[https://zenodo.org/record/7049765#.Y.XXTQHZByjg](https://zenodo.org/record/7049765#.Y.XXTQHZByjg)]([https://zenodo.org/record/7049765#.Y.XXTQHZByjg](https://zenodo.org/record/7049765#.Y.XXTQHZByjg)).  
We are very grateful to three anonymous reviewers for their valuable comments that improved the quality of this paper. The authors declare no conflict of interest.
## 4 Discussion
Additionally, a comparison indicates that the discrete and waveform results are consistent. There are significant differences in CAH (P2 vs. P3), CAK (P2 vs. P4), PAK (P1 vs. P2; P1 vs. P4), knee peak moment (P1 vs. P4), hip peak power (P1 vs. P4), and knee peak power (P1 vs. P3; P1 vs. P4). In addition, significant differences were shown in these periods of stance phase in the discrete data. The results of SPM (1D) analysis clearly provided more information, indicating the period rather than a point of time in the fatigue response. This is also helpful in identifying the key body segments affected by fatigue. Thus, it is important for coaches and players to design a corresponding training program to improve the technique and muscle strength. Moreover, the results of SPM (1D) can provide support for monitoring of training. Considering the key findings of this study, a few limitations should be noted when interpreting the results. First, the participants were male badminton players with at least 8 years of special badminton training. Results may differ for players of different levels, ages, and gender, thus studies of players with a range of abilities and ages, including female players, should be considered. Second, all tests were conducted on a simulated badminton court. Third, the sample size was limited. Fourth, the changing slope of the HR-time curve was used to subdivide the fatiguing process. However, it is not sufficient to explain the status of fatigue. Electromyography (EMG) data might be more suitable and could be used in future work. Finally, although joint moments and power allow further assessment of the functional contribution of the joints, EMG and musculoskeletal system simulations would help understand movement changes during the fatiguing process.
## 5 Conclusions  
The behavior of a structure equipped with a TMD passive controller system is evaluated and identified in this study. A three-story steel frame building and a shaking table were used to simulate the behavior of structure and dynamic load. The accelerometers monitoring system is used to measure the building response equipped with a TMD controller system. Two cases for the weight of employed controller were used, including 1.80 and 2.39 kg of masses. Two nonlinear system identification models were applied to predict the behavior of those case studies including NARX and ANFIS models. In addition, the SISO and MISO input-output parameters are presented. The application of two SI models are designed and developed based on the experimental results. In summary, the following conclusions could be drawn from this study:  
The evaluation of TMD controller system showed that the increase in the mass of the TMD leads to a vibration mitigation of 72% and 84% for the 1.80 and 2.39 kg of the TMD mass, respectively. In addition, the dominant frequency for the 1.80 and 2.39 kg are decreased by 9.4% and 11.8%, respectively. Therefore, the TMD is very efficient controller system for mitigating structures behaviors in both time and frequency domains.  
The results of the NARX model shows that the two time-delays with seismic input parameters have more influence on the performance of the model. The results also indicate that the model 8-NARX with sigmoid function is more suitable for predicting the acceleration response of structures in the training case. Moreover, the results of ANFIS model showed that the highest \(R^{2}\) value and the lowest MSE and RMSE values were obtained for model 3-ANFIS in the training case. Therefore, the developed ANFIS model 3 is more suitable for this study. On the other hand, the comparison between the models 8-NARX and 3-ANFIS shows that the correlation coefficient values were found to be 0.98 and 0.96, respectively. The MAE and RMSE values for model 8-NARX are decreased by approximately 50% relative to model 3-ANFIS. Therefore, model 8-NARX generally outperformed model 3-ANFIS in terms of the training and testing performance of identification for the controlled experimental structures equipped with the TMD passive control system. Finally, this study is still limited as an experimental study and we recommend it should be applied to a full scale control system to improve this study application.  
This work was supported by a 2015 Incheon National University (INU) Research Grant. The authors gratefully acknowledge these supports.  
[PERSON] conceived the study, performed analysis, wrote the paper and interpreted the results; [PERSON] designed the experiments; [PERSON], [PERSON] and [PERSON] reviewed the study plan and edited the manuscript.  
The authors declare no conflict of interest.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture
### Modeling the Parameters
#### 4.1.2 Graph Topology  
A _graph topology_ describes a category of graph shapes with certain common characteristics. In this paper, we focus on relatively simple topologies, namely _random_, _complete_, _star_, _connected_ stars, and _grid_. It is key that the graph is connected--otherwise, each connected component would constitute a separate graph that could be analyzed by itself.  
A _random_ graph is generated here by iterating all pairs of nodes: With a fixed probability \(p\), an edge between each pair is created. Thus, given graph \(G=(V,E)\) and probability \(P(X)=p\) for event \(X\), \(\forall a,b\in V,a\
eq b:P(\{a,b\}\in E)=p\), where \(p\
eq 0\). Therefore, it is not guaranteed that the generated graph is connected. However, by creating a series of \(s\) random graphs, the probability that the graph is connected approaches 1 with \(s\to\infty\).  
A _complete_ graph is a graph in which each pair of nodes is mutually connected. Thus, each node can reach every other node in just one hop.  
A _star_ graph has a designated node called center node to which all other nodes are connected. The non-center nodes are not connected to each other. Communication always has to go through the center node, making it prone to being a bottleneck in the network.  
The _connected-stars_ topology consists of pairwise disjunctive star graphs, each of size \(q\), whose center nodes are connected with fixed probability \(p\). We assume the graph size \(n\) to be a multiple of \(q\). Let \(C\) be the set of center nodes, then \(\forall a,b\in C,a\
eq b,P(\{a,b\}\in E)=p\).  
Finally, in the _grid_ topology, the graph is assumed to be a square shape: Each internal node has four neighbors, the edge nodes have three, and the corner nodes have two. Restricting the shape to squares only means that the graph size \(n\) must be \(n=k^{2}\) for some \(k\in\mathbb{N}\).
###### Abstract  
The space information networks (SIN) have a series of characteristics, such as strong heterogeneity, multiple types of resources, and difficulty in management. Aiming at the problem of resource allocation in SIN, this paper firstly establishes a hierarchical and domain-controlled SIN architecture based on software-defined networking (SDN). On this basis, the transmission, caching, and computing resources of the whole network are managed uniformly. The Asynchronous Advantage Actor-Critic (A3C) algorithm in deep reinforcement learning is introduced to model the process of resource allocation. The simulation results show that the proposed scheme can effectively improve the expected benefits of unit resources and improve the resource utilization efficiency of the SIN.  
Keywords:space information networks; software-defined network; deep reinforcement learning; transmission resource; caching resource; computing resource +
Footnote †: journal:  
_Article_  
Research on Resource Allocation Method of Space Information Networks Based on Deep Reinforcement Learning
# Stabilisation of Soft Clay, Quick Clay and Peat by Industrial By-Products and Biochars
## 4 Discussion
### The Stabilisation Effect for Different Binders in Peat  
Figure 8 shows the stabilisation effect of the biochars and IBPs in the peat. Here, biochar was observed to be far more effective compared to the IBPs, with a strength increase of up to 80% for BC4. These results are similar to previous findings on peat stabilisation [27; 31; 95]. The stabilisation effect can possibly be attributed to the water retention (high absorption capacity) of the biochar and densification [32; 96]. This could be a larger effect in peat with a very high water content and high porosity compared to clays with considerably lower porosity, as indicated by the water content determinations (Table 2).  
In contrast to their positive stabilisation effect on clay, most of the IBPs had a negative effect on the strength of the peat. Adding PSA, FA and BA to CEM yielded strengths that were only \(\sim\)20-50% of that for CEM as a single binder. The reason for this is not known. LS1 and LS2 had a lesser negative effect, with equivalent values of \(\sim\)88-98%. Here, a possible explanation is that the ladle slags were activated by the higher amount of CEM than that in the clays and hence gave a cementitious contribution by pozzolanic reactions, similar to the effects of GGBS [13; 97].
# A Cooperative Control Method for Excavation Support Robot with Desired Position/Posture
## 4 Simulation Calculation of Desired Expected Value for ESR
### Numerical Results and Simulation Analysis  
The lateral AHC parameter settings of the ESR position/posture adjustment control system with fifth order are shown in Table 1. The parameters of the ESR position/posture adjustment controller are as follows: \(C_{1}=100,C_{2}=30,C_{3}=30,C_{4}=10,C_{3}=1.0\).  
Set the most desired position/posture parameters expected by ESR, involving left declination angle \(\alpha_{Le}=0^{\circ}\), left inclination angle \(\gamma_{Le}=0^{\circ}\), left distance \(d_{Le}=1.0\) m, right declination angle \(\alpha_{Re}=0^{\circ}\), right inclination angle \(\gamma_{Re}=0^{\circ}\), right distance \(d_{Re}=1  
Figure 12 analyzes the simulation calculation results of the ESR declination angle adjustment on both sides. With 20 groups of different initial positions/support posture states, based on the coordinated adjustment of the position/support posture control system, the ESR declination angle on both sides drops to about 0\({}^{\circ}\) within 10 s, the collaborative adjustment numerical error is within \(\pm 0.5^{\circ}\), and the minimum deviation between the simulation calculation results and the desired expected value is only within 0.2\({}^{\circ}\).  
Figure 13 shows the simulation results of the scroll angle adjustment of the ESRs on both sides. With 20 groups of different initial positions/support postures, based on the coordinated adjustment of the position/posture adjustment control system, the scroll angle of the ESRs on both sides can also be reduced to about 0\({}^{\circ}\) within 10 s, the numerical error of the coordinated adjustment remains within \(\pm 0.5^{\circ}\), and the minimum deviation between the simulation calculation results and the desired expected value is only within 0.10\({}^{\circ}\).  
Figure 14 demonstrates the simulation calculation results of the distance adjustment between the edge walls on both sides of the ESR. Based on the adjustment of the position/posture adjustment control system, the distance between the edge walls on both sides of the ESR reaches about 1.0 m within 10 s, and the adjustment value error is within \(\pm 0.03\) m. The minimum deviation between the simulation calculation result and the desired expected value is controlled at not more than 0.01 m.  
In summary, from the simulation calculation results in Figures 12-14, aiming at the ESR key position/support posture adjustment control, the desired expected posture adjustment  
Figure 14: Simulation results of edge distances.  
Figure 13: Simulation results of scroll angles.  
control method quickly adjusts the deviation/posture state in a short time, based on the collaborative adjustment proposed in this paper. The control method is effective, the deviation adjustment precision is high, and the support posture stability is reliable.