esa-sceva/satcom-chunk-collection huggingface dataset:
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 2 Related Work  
General simple multi-hop communication networks are widely studied [20; 21; 22]. For those networks, many routing schemes are designed in order to optimize over the minimum count of the hops for the message to get transferred or the minimum cost of the transmission through the links of the network [23]. Those metrics are not necessarily the best to consider. That is due to the fact that the new metric of AoI for the freshness of messages is recently used often to refer to as a good metric for the QoS of the network [24].  
One of the simplest and most natural communication network models is the gossip network model which simply lets the nodes broadcast the messages that they receive to their neighbor set through the available directed links [19; 20; 21]. This model is different than the more typical method of routing that depends on choosing a path to route the message and evaluates the best possible route depending on the metric used, whether the metric is cost or time delay, or another metric. One of those standards is the best worst link in the path of the network. The best worst link is considered to be the link that is the most unreliable (in case of having unreliable links) over the path chosen by the routing scheme. This link is called the bottleneck link. Those routing schemes choose the path that has the best bottleneck link [19; 21; 22].  
In contrast to the criterion of pruning in our work, the research in [25] considered the criterion of pruning to be the simplicity of the network by reducing the number of edges while introducing a trade-off between connectivity and simplicity. In addition, the approach introduced in [26] keeps important edges without losing connectivity by setting a criterion that depends on the best path function standard which assigns a different priority to each edge so that they can prune the edges with low priorities.  
Other pruning methods were introduced before. For example, structural pruning was introduced in [27] which prunes subgraphs of the network while maintaining the hubs and brokers of the network. In addition, descriptive pruning in [27] basically assigns a specific attribute value to each node related to its connectivity and edge degree, and prunes the parts with lower attribute values. This is different than our work here in which we consider the expected AoI of the whole network as the metric for pruning.  
In this work, we consider a transmission model similar to the gossip network model for transmitting messages by flooding the network, in which nodes broadcast the messages to their neighbors. This model has a huge drawback, which is the fact that the messages often are duplicated when they reach the destination node [19; 23; 24]. To the best of our knowledge, this is the first work that studies the effect of pruning the gossip network on the repetition of the messages and the AoI at the same time.
# Single Trace Side Channel Analysis on NTRU Implementation
## 4 Countermeasure
### Implementation of Countermeasure  
#### 4.3.1 Comparison of the NTRUEncrypt  
The computational cost of the proposed countermeasure on NTRU Open Source is similar to the unprotected version, only with additional precomputations. Moreover, it is hard to compare the NTRU Open Source and NTRUEncrypt because of the different process of the private key multiplication. Thus, we only compare the computational cost and memory of the protected and unprotected version of NTRUEncrypt. Table 1 includes the number of initialization, addition, and multiplication costs when the degree is \(N\). Since the computational cost of subtraction is similar to that of the addition, we include both the numbers of subtractions and the numbers of additions. The computation to find the start degree \((i+j)\pmod{N}\) is not included.  
As shown in Table 1, the total number of computational steps necessary to calculate \(f\cdot e\) is reduced when applying our countermeasure. Moreover, the number of multiplications is reduced to square root of the original. Also, the comparison of the memory usage is presented in Table 2. Since NTRUEncrypt use 16 bit as a word size, Table 2 refers to the multiplication of a word size and the number of used arrays.  
Although the number of used arrays is doubled compared to that used in the unprotected NTRUEncrypt, the total computational cost(number of computational steps) is reduced from \(2N^{2}+2N\) to \(N^{2}+6N\), where \(N\) is at least 443. Consequently, considering the computational costs and the memory size, Algorithm 5 is more efficient compared to Algorithm 3 and has side channel resistance.
## 3 A Case Study for Validating the Proposed Model and Process
### Crack Path Predictions and Fatigue Life  
Crack path prediction has posed a challenge in traditional FEM and XFEM methods with multiple theories (maximum potential energy release rate, maximum stress, and minimum strain energy density factor, etc.) providing mixed results in accurate prediction of growth direction. In the experiments with CT specimens by [PERSON] et al. [PERSON] et al. (2018), an auxiliary hole was added to introduce greater variability in growth direction for small adjustments in geometry. It was shown in the book by [PERSON] (2018) that for pure mode I loading, a crack will propagate purely in the horizontal direction (normal to the applied tensile load). By introducing an auxiliary hole, the mode II stress intensity factor, \(K_{II}\), is no longer zero and an unsymmetric crack path results. It is plausible to assert that the crack will propagate towards the upper half of the sample given that the cross sectional area of the sample is reduced in this region by the addition of the tertiary hole. It leads one to predict a crack would propagate mostly horizontal from right to left as described in [PERSON] (2018) with some bias toward the upper half of the specimen where strain energy density is greater.  
In the experiments, the tertiary hole position was varied up to a maximum of 1.5 mm from the CT1 configuration used in this study as shown in Figure 5. Each of the experimental samples generated a unique crack path trajectory which either bypassed the tertiary hole or terminated at the added hole. The crack propagation behavior observed experimentally for the geometry of the CT1 specimen used in this case study was such that the crack initially propagated towards the tertiary hole but bypassed it as the crack advanced. Figure 6 shows the experimental results of the CT1 specimen from [PERSON] et al. [PERSON] et al. (2018).
# A New Methodology for Type Synthesis of Planar Linkages for Exoskeletons up to Five Angular Outputs
## 6 Results  
The proposed methodology has been used to construct a complete database for planar topologies of mechanisms. These topologies are classified according to the number of available angular outputs. A computer-based type synthesis has been developed using Visual C++. The proposed graphical methodology can easily be extended to obtain planar linkages with any number of angular outputs. All results can be summarized in Table 1.  
The detailed results are listed below:  
* A total of three six-bar linkages with up to two angular outputs, illustrated in Figure 3. A total of 15 eight-bar linkages with up to three angular outputs using parallel connection, illustrated in Figure 3 (given in Appendix A).
* A total of 16 ten-bar linkages with up to three angular outputs using series connection, illustrated in Figure 6c.
* A total of 105 ten-bar linkages with up to four angular outputs using parallel connection, illustrated in Figure 3 (given in Appendix A).
* A total of 18 (out of 576) ten-bar linkages with up to four angular outputs using series connection, illustrated in Figure 3 (given in Appendix A).
* A total of 21 (out of 945) twelve-bar linkages with up to five angular outputs using parallel connection, illustrated in Figure 3 (given in Appendix A).  
Exoskeleton design procedures depend on some important factors discussed in Section 2. Generally, the exoskeleton designer should preform deep investigation for all of these factors for each case in a detailed study. The presence of a database of exoskeleton linkages, introduced in this work, reduces design procedure time and facilitates the selection process. Moreover, the resulted topologies are classified according to the number of outputs. Therefore, the exoskeleton designer can select a suitable mechanism directly. Then, all mechanism specifications can be detected, such as number of links, joints, number of actuators and their positions, mechanism weight, and fixation technique. Finally, dimension synthesis,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. of** & **No. of Generated** & **No. of Links** & **No. of Joints** & **Connection Method** \\
**Angular Outputs** & **Linkages** & **No. of Links** & **No. of Joints** & **Method** \\ \hline
2 & 3 & 6 & 7 & Parallel \\
3 & 15 & 8 & 10 & Parallel \\
3 & 16 & 8 & 10 & Series \\
4 & 105 & 10 & 13 & Parallel \\
4 & 576 & 10 & 13 & Series \\
5 & 945 & 12 & 16 & Parallel \\ \hline \hline \end{tabular}
\end{table}
Table 1: Generated linkages with up to five angular outputs.  
simulation, or experimental work should be developed for each individual case to ensure that the selected mechanism can achieve the predetermined trajectory.  
Some previous research proposed mechanical linkage systems for some rehabilitation movement. These linkage systems are included in the proposed results. Two examples are listed below to ensure the validity of the proposed database.
###### Abstract  
The paper presents a hybrid machine learning model for the spatial segmentation of soils by salinity using multispectral satellite data from Sentinel-2 and climate parameters of the ERA5-Land model. The proposed method aims to solve the problem of accurate soil cover segmentation under climate change and high spatial heterogeneity of data. The approach includes the sequential application of unsupervised learning algorithms (K-Means, hierarchical clustering, DBSCAN), the XGBoost model, and a multitasking neural network that performs simultaneous classification and regression. At the first stage, pseudo-labels are formed using K-Means, then a probabilistic assessment of object membership in classes and ensemble voting of clustering algorithms are carried out. The final model is trained on an extended feature space and demonstrates improved results compared to traditional approaches. Experiments on a sample of 33,624 observations (23,536--training sample, 10,088--test sample) showed an increase in the Silhouette Score value from 0.7840 to 0.8156 and a decrease in the Davies-Bouldin Score from 0.3567 to 0.3022. The classification accuracy was 99.99%, with only one error in more than 10,000 test objects. The results confirmed the proposed method's high efficiency and applicability for remote monitoring, environmental analysis, and sustainable land management.  
soil salinity segmentation; remote sensing; ensemble clustering; Sentinel-2; ERA5-Land; XGBoost; multi-task neural network
### Motivations and Contributions  
Motivated by the aforementioned discussions and to break through the limitations of the existing techniques, in this paper, we develop a novel and high-performing training strategy for the estimation of correlated MIMO channels in the presence of colored interference based on the worst-case robustness philosophy. The detailed technical contributions of our work are described below:  
* We propose a general framework for robust training optimization under imperfect channel and interference covariance information at the transmitter. Particularly, we design an optimal training signal for MIMO systems with interference, taking the imperfection of both the channel and interference covariance into account.
* In our proposed framework, the worst-case MSE criterion is used as a performance metric similar to previous works [18; 19; 20]. In contrast to the previous problems, however, the considered design problem is not convex-concave due to the uncertainty in the interference covariance, and consequently the design of the training signal is more complicated. To solve the problem, we take innovative approaches: we initially derive an optimal structure of the training signal, which includes the existing training structures as special cases, and then we solve the training power allocation problem.
* Two power allocation schemes are proposed. First, an optimal power allocation is determined numerically by finding an optimal solution. Next, to reduce the complexity required for optimal power allocation, a closed-form power allocation scheme is proposed by finding a suboptimal solution.
* Based on the latter power allocation strategy, we also propose a suboptimal, yet closed-form, training scheme with low complexity.
* We compare the performance of the proposed schemes with that of the conventional schemes by simulations. Through numerical results, we empirically demonstrate that the proposed schemes substantially surpass the existing schemes with remarkable performance improvements and the proposed suboptimal training scheme provides comparable performance to that of the optimal training scheme.
# Chapter Detection in Robotic Milling Using Entropy Features
## 2 Theoretical Basis and Framework for the Chatter Detection System
### Weighted Refined Composite Multiscale Dispersion Entropy (wRCMDE)
#### 2.2.3 Weighted Refined Composite Multiscale Dispersion Entropy (_wRCMDE_)  
To overcome the shortcoming of inaccurate results when using MDE for short signals, _RCMDE_ is proposed, which is calculated as follows [41]:  
Step 1: The \"coarse-graining\" process of _RCMDE_ can be expressed by:  
\[y_{k,j}^{(s)}=\frac{1}{s}\sum\limits_{b=k+(j-1)s}^{k+js-1}u(b),1\leq j\leq \left[\frac{L}{s}\right],1\leq k\leq s \tag{11}\]  
Step 2: For each scale factor, \(s\), the _DisEn_ of all of the obtained time series \(y_{k}^{(s)},1\leq k\leq s\) is calculated, and the _RCMDE_ is given by:  
\[RCMDE(u,m,c,\tau,s)=\frac{1}{s}\sum\limits_{k=1}^{s}DisEn\left(y_{k}^{(s)},m, c,\tau\right) \tag{12}\]  
Furthermore, kurtosis of the time series, \(KT(u)\), is given by:  
\[KT(u)=\frac{\frac{\frac{1}{L}\sum\limits_{i=1}^{L}(u(i)-\overline{u})^{4}}{ \left(\frac{1}{L}\sum\limits_{i=1}^{L}(u(i)-\overline{u})^{2}\right)^{2}}} \tag{13}\]  
is used for weighting the _RCMDE_. Therefore, the defined _wRCMDE_ is calculated by:  
\[wRCMDE(u,m,c,\tau,s)=KT(u)\times RCMDE(u,m,c,\tau,s) \tag{14}\]  
Step 3: \(s=s+1\); repeating steps (2) and (3) under the same parameters until \(s=s_{\text{m}}\), where \(s_{\text{m}}\) is the largest scale factor.
## Appendix B Some Direction for the Derivation of Full Conditional Posterior  
Appendix B gives an example of a derivation to get the full conditional posterior for one of the MSNBurr-MM parameters. For example, we derived the full conditional posterior for parameter \(\mu_{j},\ j=1,\ 2,..,K\). The full conditional posterior \(\mu_{j}\) for certain \(j\)th cluster is given by:  
\[\begin{split} f\Big{(}\mu\big{|}\sigma_{j},\gamma_{j},p_{j},\mathbf{ z},\mathbf{y}\Big{)}=f(\mathbf{y}|\mathbf{u},\boldsymbol{\sigma},\boldsymbol{ \gamma},\mathbf{z})\beta\Big{(}\mu_{j}\Big{)}=\prod_{i=1}^{N}\Big{(}\frac{ \kappa_{j}}{\sigma_{j}}\exp\!\left(-\kappa\Big{(}\frac{y_{i}-\mu_{j}}{\sigma_{j }}\Big{)}\right)\!(1+\\ \frac{\exp\!\left(-\kappa\Big{(}\frac{y_{i}-\mu_{j}}{\sigma_{j}} \Big{)}\right)}{\gamma_{j}}\Big{)}^{-(\gamma_{j}+1)}\Bigg{)}^{2\tilde{\gamma} _{ij}}\times\frac{1}{\sqrt{2\pi\varphi_{j}}}\exp\!\left(\frac{1}{2}\!\left( \frac{\mu_{j}-\eta_{j}}{\sigma_{j}}\right)^{2}\right)\!.\end{split} \tag{10}\]  
To simplify the derivation, we processed Equation (10) in the form of the logarithm as follows:  
\[\begin{split}&\ln f\Big{(}\mu_{j}\big{|}\sigma_{j},\gamma_{j},p_{j},\mathbf{z},\mathbf{y}\Big{)}=\sum_{i=1}^{N}z_{ij}\frac{\kappa_{j}}{\sigma_{j }}\exp\!\left(-\kappa\Big{(}\frac{y_{i}-\mu_{j}}{\sigma_{j}}\Big{)}\right)\! (1+\\ &\frac{\exp\!\left(-\kappa\big{(}\frac{y_{i}-\mu_{j}}{\sigma_{j}} \big{)}\right)}{\gamma_{j}}\Big{)}^{-(\gamma_{j}+1)}+\ln\!\left(\frac{1}{ \sqrt{2\pi\varphi_{j}}}\exp\!\left(\frac{1}{2}\!\left(\frac{\mu_{j}-\eta_{j}} {\sigma_{j}}\right)^{2}\right)\right)=\sum_{i=1}^{N}z_{ij}\!\left(\ln\!\left( \frac{\kappa_{j}}{\sigma_{j}}\right)-\kappa\Big{(}\frac{y_{i}-\mu_{j}}{\sigma_ {j}}\Big{)}-\left(y_{j}+\right.\right.\\ &\left.\left.1\right)\ln\!\left(1+\frac{\exp\!\left(-\kappa \big{(}\frac{y_{i}-\mu_{j}}{\sigma_{j}}\big{)}\right)}{\gamma_{j}}\right) \right)\!\times\ln\!\left(\frac{1}{\sqrt{2\pi\varphi_{j}}}\right)-\frac{1}{2} \!\left(\frac{\mu_{j}-\eta_{j}}{\sigma_{j}}\right)^{2}.\end{split} \tag{11}\]  
Equation components that do not contain \(\mu_{j}\) can be simplified into constants; therefore, Equation (11) can be written as:  
\[\begin{split}&\log f\Big{(}\mu_{j}\big{|}\sigma_{j},\gamma_{j},p_{j},\mathbf{z},\mathbf{y}\Big{)}=\text{constant}-\sum_{i=1}^{N}z_{ij}\!\left(\! \left(\frac{y_{i}-\mu_{j}}{\sigma_{j}}\right)\!\right)-1\!\left(\frac{\mu_{j}- \eta_{j}}{\sigma_{j}}\right)^{2}+\\ &\sum_{i=1}^{N}z_{ij}\!\left(-\!\left(\gamma_{j}+1\right)\ln\! \left(1+\frac{\exp\!\left(-\!\left(\frac{y_{i}-\mu_{j}}{\sigma_{j}}\right) \right)}{\gamma_{j}}\right)\right)\!.\end{split}\]  
In the same way, a full conditional posterior for parameter \(\sigma_{j}\) and \(\gamma_{j}\) can be obtained.
# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Document Representation
#### 3.2.1 Non-DL Measures  
In this section, the literature is comprehensively reviewed to understand the research attempts and trends in measuring the similarity of STC, including corpus-based measures and knowledge-based measures.  
Bag of Words ModelAccording to [75; 76], BOW is the most traditional text representation method used to simplify the data to be more suitable in the processing stage by considering the text data as groups of words. It is widely used in IR and NLP because of its simplicity and efficiency, where it uses simple words or phrases as features to represent text. The difficulties with text processing stem from the fact that text data's syntactic and semantic content is challenging to quantify. Creating a comprehensive model of all text data is challenging. Thus, the current text analysis approach usually represents a text document by reducing the text structure complexity and simplifying text documents. BOW is a text document representation that treats a written document as a BOW, ignoring word order and grammar. Figure 8 illustrates how BOW can be used to represent two texts. Stop words are removed, such as 'a' and 'is', from practical processing to underline the relevance of other words. In Figure 8, we see a fixed-length vector is represented for each short text. The value assigned to each dimension in the vector represents the term frequency (_tf_) in the corresponding section of the text document. BOW can help text representation by simplifying the text document, but it may not distinguish the difference between two documents with the same bag of words but in different sequences.  
Figure 7: Main taxonomies for short text representation.  
However, the BOW model has several drawbacks. Some corpora, such as social media corpora, include slang and misspelt words, which result in a high-dimensional feature space. Furthermore, these models cannot process complex word meaning differences, such as synonyms and polysemy. BOW has a weak sense of the semantics of the words, or more formally, the distances between words [77].
###### Abstract  
Thailand has recently introduced agricultural policies to promote large-scale rice farming through supporting and integrating small-scale farmers. However, achieving these policies requires agricultural tools that can assist farmers in rice farming planning and management. Crop models, along with remote sensing technologies, can be useful for farmers and field managers in this regard. In this study, we used the AquaCrop model along with moderate-resolution satellite images (30 m) to simulate the rice yield for small-scale farmers. We conducted field surveys on rice characteristics in order to calibrate the crop model parameters. Data on rice crop, leaf area index (LAI), canopy cover (CC) and agricultural practices were used to calibrate the model. In addition, the optimal rice constant value for conversion of CC was investigated. HJ-1A/B satellite images were used to calculate the CC value, which was then used to simulate yield. The validated results were applied to 126 sample pixels within transplanted rice fields, which were extracted from satellite imagery of activated rice plots using equivalent transplanting methods to the study area. The rice yield simulated using the AquaCrop model and assimilated with the results of HJ-1A/B, produced a satisfactory outcome when implemented into the validated rice plots, with RMSE = 0.18 t ha\({}^{-1}\) and R\({}^{2}\) = 0.88. These results suggest that integration of moderate-resolution satellite imagery and this crop model are useful tools for assisting rice farmers and field managers in their planning and management.  
AquaCrop; rice yield; HJ1A/B; LAI; canopy cover; small-scale farmers +
Footnote †: journal: Geo-Informatics
## 6 Conclusions and Discussion  
In this paper, we propose two novel problems, Targeted Propagator Discovery (TPD) and Targeted Customer Discovery (TCD), in the context of location promotion. To the best of knowledge, we are the first to find future potential propagators in location-based social networks. We propose two network embedding methods, LBSN2 vec and PLBSN2 vec, along with a modified random walk mechanism based on the Skip-gram model. We also devise two scoring functions with the estimation of user preference and POI preference for TPD and TCD tasks. Experiments conducted on a large-scale Instagram dataset exhibit superior performance, compared to several well-known network embedding methods.  
While effectively solving both TPD and TCD tasks by the proposed LBSN2 vec and PLBSN2 vec, two implications should be discussed. First, one may concern that the spatio-temporal footprints of users can be obtained and revealed by the POI holders who are performing the algorithms, leading to the issue of privacy leakage. We should highlight that the POI holders can only access the discovered propagators and potential customers. The original data and the footprints of general users are still maintained by the service providers who have an obligation to keep the prediction results confidential. Therefore, every POI holder cannot trace users. Second, one may also think that the POI holder can directly contact the recommended targeted propagators. Such an action could happen indeed. Here our algorithms leave the mechanism design on how to contact propagators and potential customers to the service providers. Whether and how the users react to being propagators or receiving the discount coupons should also be properly devised.  
Conceptualization, Y-C.C. and C.-T.L.; methodology, Y-C.C. and C.-T.L.; validation, Y.-C.C. and C.-T.L.; formal analysis, Y-C.C. and C.-T.L.; investigation, Y-C.C. and C.-T.L.; writing-original draft preparation, Y.-C.C. and C.-T.L.; writing-review and editing, Y-C.C. and C.-T.L.; supervision, C.-T.L.; funding acquisition, C.-T.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Ministry of Science and Technology (MOST) of Taiwan under grants 109-2636-E-006-017 (MOST Young Scholar Fellowship) and 109-2221-E-006-173, and also by Academia Sinica under grant AS-TP-107-M05.  
The authors declare no conflict of interest.  
Figure 3: Results in Recall@N for the TCD task.