esa-sceva/satcom-chunk-collection huggingface dataset:
# Big Data Perspective and Challenges in Next Generation Networks
## 2 Major Milestones of Next Generation Networks
### Massive MIMO
#### 2.3.2 Challenges with Massive MIMO  
The massive MIMO technique has become an important part of 5G given its promising features [34]. However, several challenges for massive MIMO must be dealt with before the implementation can be realized.  
**Pilot Contamination:**_\"The interference among pilots in different cells is called pilot contamination\"_. Pilot contamination degrades the performance of channel estimation because the channel state information (CSI) in one cell become contaminated due to interference of user of another cell. As the number of BSs antennas increases, pilot contamination also appears, thus posing a problem for massive MIMO design. Although various methods have been proposed for reducing pilot contamination [35], a more suitable design for mitigating pilot contamination is required.  
**Architectural Design:** One of the key challenge for massive MIMO is to design its overall architecture. As in previous technology, there was high power amplifier feeding as a sector of antenna but in massive MIMO there will be thousands of small antennas correspondingly fed by low-power amplifiers and, most likely, there will be a separate power amplifier for each antenna. Thus, coupling between antennas, correlation and scalability are the main issues for designing massive MIMO [2].  
**Full Dimension MIMO:** In existing BSs antennas, the technique of horizontal beam forming is in use. The horizontal beam forming suffers a shortcoming of being able to accommodate only a  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
limited number of antennas. However, in full dimension MIMO (FD-MIMO), azimuth and elevation beam forming can work simultaneously. Thus, FD-MIMO can accommodate much more antennas. Beside this, the vertical beam forming increases power of the signal and reduces the interference in neighboring cells. Hence, the average data rate and edge rate increases in FD-MIMO [2]. Although FD-MIMO has promising benefits, there are some challenges regarding implementation and complexity, which must be considered. The challenges in FD-MIMO are: _Channel estimation for large number of channels_, _Channel estimation complexity_, _Inference between azimuth and elevation domains_, and _MU-MIMO scheduling issues_.  
**Channel Designing:** Besides architectural design issues, the channel modeling for massive MIMO system is also a challenge. For non-ideal behaviors, the actual channel orthogonality needs to be verified. Further road map for channel modeling issues will be materialized in the future with the 3D channel modeling study [36]. Possible channel models that may be adapted for 5G networks have been proposed in [37; 38].  
The challenges and key enablers are shown in Figure 4.  
Figure 4: Challenges and key enablers for next-generation networks (inspired from [8]).
## 3 Fuzzy Evaluation of Surrounding Rock Stability of Ioess Tunnel with Rich Water
### Fuzzy Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation Method
#### 3.1.2 Fuzzy Comprehensive Evaluation  
The basic procedure for Fuzzy Comprehensive Evaluation is as follows [46]:  
(1) Establish the set of factors for fuzzy comprehensive evaluation, denoted as \(U\)  
\[U=\{u_{1},u_{2},\cdots,u_{n}\} \tag{4}\]  
where \(n\) represents the number of evaluation-related factors. The factor indicators correspond to various attributes of the evaluation object, collectively reflecting the quality of the evaluation object.  
(2) Establish the evaluation grade set \(V\).  
For continuous influencing factors, construct a continuous function based on the original data. For discrete influencing factors, assign ratings according to the fuzzy language corresponding to each factor. Describe the qualitative relationships of the influencing factors in quantitative terms, and then construct the evaluation set \(V\) for the influencing factors, which is represented as:  
\[V=\{v_{1},v_{2},\cdots,v_{m}\} \tag{5}\]  
where \(m\) represents the number of evaluation grades. For the classification of surrounding rock stability, the evaluation set is the collection of classification grades for surrounding rock stability.  
(3) Determine the weight matrix and construct the membership relation matrix \(R\).  
For each measured value \(u_{i}\) (where \(i\) = 1, 2,\(\ldots\), n) in the factor set of the object to be evaluated, a fuzzy mapping from the factor set \(U\) to \(V\) can be established:  
\[f:U\rightarrow\Gamma(V),\,u_{i}|\to f(u_{i})\in\Gamma(V),\forall u_{i}\in U \tag{6}\]  
Thus, the single-factor evaluation subset \(r_{i}\) = (\(r_{i1}\), \(r_{i2}\),\(\ldots\), \(r_{im}\)) for \(u_{i}\) is determined. The evaluation subsets of \(m\) related factors form the overall evaluation matrix \(R\):  
\[R=\begin{bmatrix}r_{11}&r_{12}&\ldots&r_{1m}\\ r_{21}&r_{22}&\ldots&r_{2m}\\ \ldots&\ldots&\ldots&\ldots\\ r_{n1}&r_{n2}&\ldots&r_{nm}\end{bmatrix} \tag{7}\]  
where \(0\leq r_{ij}\leq 1\), \(1\leq I\leq n\), \(1\leq j\leq m\) represents the membership degree of factor \(u_{i}\) belonging to the evaluation grade \(v_{i}\).  
(4) Establish a fuzzy evaluation model  
Based on the weight \(W_{i}\) (i = 1, 2,\(\ldots\), k) of each single-factor indicator at this level, the single-level fuzzy evaluation model is constructed as follows:  
\[B_{i}=W_{i}\cdot R_{i} \tag{8}\]  
Based on the calculation results of the single-level fuzzy evaluation model, the fuzzy evaluation matrix for each indicator at the higher level can be obtained. By performing hierarchical calculations in sequence, the relevant membership degrees and weights \(W\) = (\(W_{1}\)\(W_{2}\),..., \(W_{\rm k}\)) for the first-level indicators are ultimately derived, resulting in the final fuzzy evaluation matrix.  
\[B=W\cdot R \tag{9}\]
## 5 Formal Modeling and Analysis of SAPS
### Offline Password Guessing Attacks  
In offline password guessing, the adversary tries to capture the entire communication between party (A) and the TS or between the TS and party (B), but is unable to get the password. The next adversary attack may be possible.  
1. \(\{X_{A},\ P_{A},\ T_{1}\}\rightarrow\{\text{TS}\}\) from party(A) to TS, \(\{ID_{TS},\ Z_{TS}\}\) are TS parameters, \((\{X_{B},\ P_{B}\},T_{3})\) _Party(B) to TS_ and \(\{CP_{A},\ CP_{B}\}\) are the communications between A, B, and the TS known by the adversary. To commence the offline secret password estimating attack, the adversary tries to regenerate \(PW^{\prime}_{p}\), an inverse password of party (A) and computes \(M_{A}{}^{\prime}\). Still, if the adversary gets the IDs of both participants, it is not possible for the adversary to compute \(E_{H(YA)}\) and they are unable to verify the \(P_{A}\) as used in the protocols which contains the message and the identities of the participants.
2. Appropriate to the un-traceability of the Computational Diffie-Hellman assumption (CDH) a difficult adversary does not get \(\{Y_{A}=R_{p}F:=R_{p}dP\to Y_{A}=dX_{A}\ F\}\) and it is impossible for the adversary to get the password.  
3. If an adversary gets party B's password to compute the random \(R_{T_{S}}\) number generated by the TS, as shown in protocol-III, to compute the parameters after the received connection establishing request from party (A), it is not possible for the adversary to compute B's password without knowing \(YB\) because \(Y_{B}=\{d_{B}F\}\) and has ECDLP, as shown in step-3 of protocol-III.  
As a result, the proposed protocol can oppose offline password guessing attacks. To deal with online password guessing attack, the TS detects the encounter during the message validation process and the freshness procedure at the beginning of the communication.
# Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
## 3 Results
### Separate Study Validation Models  
Key results for the Separate Study Validation Models are given in Table 6.  
Differences in accuracy between models were explored, for the Separate Study Validation Models, with Study 2 data being used as the test set (\(n\) = 1440). Exact McNemar tests (two-tailed) were used to make all ten possible pairwise comparisons. To adjust for the number of comparisons, a p threshold of 0.01 was used. There were no significant differences in accuracy between the three most accurate models (voting classifier, XGB, and CatBoost). Likewise, there were no significant differences in accuracy between the two least accurate models (KNN and logistic regression). However, the voting classifier and CatBoost were significantly more accurate than the two least accurate models (KNN and logistic regression). XGBoost was significantly more accurate than KNN, but the accuracies of XGBoost and logistic regression were not significantly different.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Sensitivity** & **F1** & **AUC** \\ \hline
**Voting classifier** & 76\% & 74\% & 79\% & 80\% & 73\% \\
**XGBoost** & 76\% & 74\% & 78\% & 79\% & 73\% \\
**Catboost** & 75\% & 74\% & 76\% & 77\% & 72\% \\ KNN & 71\% & 71\% & 72\% & 72\% & 66\% \\
**Logistic Regression** & 70\% & 71\% & 72\% & 71\% & 65\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance Metrics (mean %s) for the Three-Study Merged Dataset Models. Chance accuracy was 54%.
# The Emerging Roles of RNA Modifications in Glioblastoma
## 3 RNA Modifications in GBM
### RNA m5C Modification in GBM  
5-Methylcytosine (m5C) in DNA has been well described, however, it is not well understood in RNAs. Actually, m5C is not only confined within tRNAs and rRNAs, but also present in mRNA and ncRNAs [118]. RNA m5C usually responsible for stabilization of tRNA molecules via affecting Mg2+ binding, regulating mRNA translation, preventing recognition of endogenous RNAs by the innate immune system, and modulating RNA-dependent inheritance of certain phenotypes [119]. In human beings, NOP2/Sun RNA methyltransferase 2 (NSUN2, Misu), NSUN5 and DNA (cytosine-5-)-methyltransferaseasease 2 (DNMT2) are confirmed as m5C writers, while ALY/REF export factor (ALYREF) is an m5C reader [119].  
As a target of proto-oncogene c-Myc [120], NSUN2 has been shown to play pivotal roles in many neoplasms, such as breast cancer [121], colorectal cancer [122], ovarian cancer [123], head and neck squamous carcinoma [124] as well as esophageal squamous cell carcinoma [125]. In GBM, there is no direct evidences about the biological function of NSUN2. However, \"zipcode\"-like sorting signals in RNA that mediate the enrichment of mRNAs in exosomes of GBM cells [126] may probably be recognized by cytosolic Y-box binding protein 1 (YB-1) and NSUN2 [127]. NSUN5, another RNA methyltransferase that is responsible for m5C in the C3782 position of human 28S rRNA, undergoes epigenetic loss in gliomas, which drives an overall depletion of protein synthesis, resulting in an adaptive translational program for survival under cellular stress and renders gliomas sensitive to bio-activatable substrates of the stress-related enzyme NAD(P)H quinone dehydrogenase 1 (NQO1) [81; 82]. The m5C reader, ALYREF, may be an overexpressed antigen in GBM, according to a study with a nanobody-based anti-proteome approach [83] and is upregulated in recurrent GBM, according to a transcriptome analysis [84], implying it may be a promising drug target for GBM. Theseevidences show m\({}^{5}\)C and its RMPs may also play important roles during the tumorigenesis of GBM (Figure 2, m\({}^{5}\)C part), indicating they are promising pharmaceutical targets for GBM treatment.
## 1 Introduction
While it is expected of irrigated agriculture to increase production, it is generally faced with declining water quality, while new sources of irrigation water are largely limited to unconventional sources and effluents that are mostly not generated in traditional irrigation areas. While the availability of unconventional water sources presents new opportunities to expand irrigation, they often contain constituents that are uncommon in natural waters and, therefore, present challenges in assessing their FFU when using existing guidelines. However, unconventional water sources often originate in areas that are wetter than traditional irrigation areas. This opens the possibility to use them for supplemental irrigation and benefit from the dilution of problematic constituents. Guidelines to assess the FFU of waters for irrigation, which address current and future challenges facing irrigated agriculture, will, thus, have to consider site-specific conditions like variable weather conditions, a wider range of conventional and unconventional water constituents, a range of crop species and soil textures, as well as irrigation management, which may all influence the assessment. While it may not have been possible to fulfil these needs in the past, the availability of powerful electronic computational tools that facilitate fast execution and linking of simulation models, as well as the storage and further processing of large amounts of data, has now made this feasible. This has, at least to some degree, fundamentally changed the question being asked from \"is this water fit for irrigation?\" to \"are there conditions under which this water may be fit for irrigation?\". About a decade ago, based on these insights, the South African Department of Water and Sanitation (DWS) identified the need for the development of water quality guidelines that differ in fundamental ways from those developed earlier [9]. Firstly, the envisaged guidelines had to be risk-based, a fundamental change in philosophy from earlier guide lines. Secondly, they had to provide for much greater site specificity, a widely recognised limitation of previous generic guidelines. Thirdly, they had to be available primarily as a software-based Decision Support System (DSS). This study describes the development and main features of the newly established DSS for irrigation water quality assessment (IrrigWQ), which emanated from a project initiated by the South African Water Research Commission through a directed call with published terms of reference and co-funded by the South African Department of Agriculture, Forestry, and Fisheries [10]. Because IrrigWQ mainly uses internationally accepted cause-effect relationships to assess the effect water quality constituents have on soils, crops, and irrigation infrastructure, and considers site-specific characteristics in producing risk-based FFU assessments of irrigation water quality, it is envisaged that IrrigWQ will find far wider application than only with South African users. The functionality to assess the FFU of irrigation water, which is also used to determine WQRs, will be presented in more detail.
# Thermomechanically Induced Precipitation in High-Performance Ferritic (HiperFer) Stainless Steels
## 3 Results and Discussion
### Comprehensive Correlation of Microstructure and Tensile Properties  
As discussed above, the mechanical properties of HiperFer steels were strongly influenced by the area fraction, particle density, and size distribution of the strengthening Laves-phase precipitates. Area fraction played a minor role, and particle size distribution and homogenous dispersion the major roles in tensile strength. For this reason, mean equivalent circle diameter values of the precipitates were calculated and compared to the key tensile properties.  
The variations in tensile properties can be directly correlated to precipitate microstructures of the model alloys. Figure 10 correlates the individual TMP parameters to the corresponding mean ECD value of the Laves-phase precipitates formed during processing. The general trend towards increased tensile strength with decreasing ECD is obvious in all the diagrams. Decreasing the TMP temperature, increasing the deformation degree, and maintaining the holding time in a reasonable range benefitted size control of the strengthening Laves-phase precipitates and consequently improved the tensile properties that could be obtained.  
Figure 10: Impact of TMP parameters on ambient-temperature ultimate tensile strength and mean ECD of the Laves-phase particles: (**a**) \(\varphi=0.5/t_{\rm H}=60\) s, (**b**) T\({}_{\varphi}=800\)\({}^{\circ}\)C/\(t_{\rm H}=60\) s, (**c**) T\({}_{\varphi}=800\)\({}^{\circ}\)C/\(\varphi=0.5\).  
On the other hand, the ambient-temperature ductility of TMP HiperFer steel presents a negative correlation to the area fraction of Laves-phase particles. Figure 11 displays the evolution of tensile elongation of the TMP material. The tensile ductility obviously dropped with increasing Laves-phase particle area fraction. This was caused by the high hardness and brittleness of the Laves phase. Increasing the area fraction of a brittle phase decreases the ductility of the ferritic alloy matrix [37; 38].
## 8 Algorithm Evaluation  
The traditional DES symmetric encryption algorithm needs to distribute keys in advance. However, obtaining a sufficient number of keys in practical application scenarios is often tricky, which limits this algorithm's wide application. In contrast, the chunked chaotic algorithm does not need to distribute keys in advance and is suitable for systems of all sizes. In addition, the chunked chaos algorithm supports the online upgrade function, and new algorithm modules can be added at any time to meet evolving business needs. For example, more adjustment factors can be added to the rules of chunked chaos to achieve a chaotic effect. The chunked chaotic algorithm inserts the chaotic rules into the generated string in the form of sharing code when generating the ciphertext to achieve the purpose of steganography so that there is no need to distribute the sharing code in advance during the data transmission, which improves the security and applicability of the algorithm. In addition, the MD5 values of the plaintext and post-garbled plaintext are also introduced in the insertion rules for double-checking after the subsequent separation of the post-garbled data by the sharing code to ensure the reliability of data transmission and reduce the probability of success of attackers. As different block sizes and scrambling algorithms are used to reintegrate the plaintext, collisions may occur between the chunking and scrambling processes, resulting in the same data after reintegration. If they happen to be modified during transmission, they may be misclassified as unmodified secure data. Although it is possible to follow the logic of the chunking and disordering algorithm to restore it, data hazards are still involved, thus reducing the algorithm's security. This chunking algorithm adds two MD5 values to the ciphertext to reduce the chance of collision and misclassification and improve the algorithm's security. These two MD5 values are obtained by hashing the plaintext and the chunked data. During the decryption process 5,6, the MD5 values are verified to match, and if they do not match, the ciphertext has been tampered with and needs to be processed accordingly. Therefore, increasing the MD5 value can effectively reduce the chance of collision and false positives and improve the algorithm's security.
## 3 Materials and Methods
### ROI Segmentation  
Image segmentation plays an important role in medical image processing systems. The nuclei and lumen of prostate cancer are the most important components of histopathological images [17]. To identify cell nuclei and lumen from images and carry out systematic processing, a K-means clustering algorithm was applied using MATLAB R2018a (The MathWorks, Natick, MA, USA) [18], where image pixels were partitioned into three clusters (thus, k = 3). The segmented components from the tissue images are: stroma, lumen, and the cell nucleus. However, nucleus and lumen components were selected for feature extraction and SVM classification, as shown in Figure 3[19].  
According to our visual results, the K-means based method is best suited for microscopic biopsy images. K-means segmentation has been applied here to separate the nucleus and lumen tissue components from microscopic biopsy images. The K-means algorithm uses iterative modification to produce a final result. The following algorithm iterates between two steps:  
1. Data assignment step: \[\underset{c_{k}\in\mathbb{C}}{\operatorname{argmin}}\;dist(c_{k},x)^{2}\] (1)
2. Centroid update step: \[c_{k}=\frac{1}{|s_{k}|}\sum\
olimits_{x_{k}\in\;s_{k}}{x_{k}}\] (2) The K-means algorithm is composed of the following steps:  
1. Specify \(k\), number of cluster to be generated.
2. Select \(k\) random points as cluster centers.
3. Assign each instance to its closest cluster center using the Euclidean distance.
4. Calculate the centroid mean for each cluster and use it as a new cluster center.
5. Reassign all the instances to the closest cluster center.
6. Iterate until there is no change in the cluster center.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 4 Experimental Results
### Test Results
#### 4.4.4. Energy Location Prediction Heatmap  
In Figure 9, the location prediction heatmap generated by the ELP-Net model and the actual locations of the tank2 piece in different time periods are presented. The predicted probabilities of enemy locations are normalized to the interval \([0,1]\) to represent their likelihoods. The actual locations of the piece, represented as a green tank on the map, correspond to the true positions in the wargame. As time goes by, the activity range of the tank gradually expands. This is reflected in the heatmap by the increasing number of lighter-colored hexagonal grids, indicating a wider distribution of predicted probabilities. At time \(t_{1}\), the prediction accuracy was the highest as the tank was more stationary and its location was easier to predict. However, as the tank moved further and its operational range broadened, the distribution of predicted probabilities became more dispersed, leading to a decrease in prediction accuracy.  
Despite this, it is worth noting that, while the actual locations of the tank are not always on the hexagonal grid with the highest predicted probability, they are typically in close proximity to it. This proximity allows decision makers to effectively narrow down the potential locations of enemy units. As a result, this reinforces the practical value of using top-x accuracy, such as top 5 or top 10, over top 1 accuracy in real-world  
Figure 8: Top-x accuracy comparison in different models.  
Figure 7: Comparison of the top 1 accuracy among different models for various types of pieces.  
applications as it provides actionable information for decision making in dynamic and uncertain environments.