esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### 5-Fold Evaluation  
The k-fold evaluation is the most employed evaluation scheme in literature [58]. This evaluation considers a training set and a test set made of activity samples performed by all the human subjects. The resulting classifier is subject-dependent and usually exhibits a very high performance. Results of the k-fold evaluation (here we used k = 5) scheme are showed in Table 7 for raw data and magnitude. Overall, the performances achieved using raw data are better than the ones obtained using magnitude as a feature vector. This confirms a result already achieved in previous works [48; 54].  
The AF-17 recognition task is quite challenging with a MAA of about 83% in the case of raw data with KNN, and a MAA of about 66% in the case of magnitude with RF. This means that is quite difficult to distinguish among types of activities especially in the case when magnitude is adopted as feature vector. Figure 5 shows the confusion matrix of the k-NN experiment in the case of raw data.  
The A-9 classification task is quite easy, the MAA obtained by raw data with RF is about 88% while the MAA obtained by magnitude with SVM is about 79%. This means that is quite easy to distinguish between types of activities. Looking at the confusion matrix in Figure 5, the most misclassified pairs of activities are Standing up from laying and Standing up from sitting, Lying down from standing and Sitting down, Going upstairs and Walking, Going downstairs and Walking, Jumping and Going downstairs.  
The F-8 recognition task is quite challenging: the MAA is about 78% and 57% in the case of raw data with KNN and magnitude with RF respectively. This result suggests that distinguish among falls is very complicated The most misclassified pairs of falls are Falling with protection strategies and Generic falling forward, Syncope and Falling leftward, Generic falling backward and Fallingbackward-sitting-chair, Falling rightward and Falling with protection strategies, Falling rightward and Syncope.  
In contrast, the AF-2 recognition task is very easy for all the classifiers and for both raw data and magnitude with an MAA of about 99% achieved with raw data and SVM. These results are similar to those obtained by previous researchers on a similar classification task performed on different datasets [29; 54]. This means that it is very easy to distinguish between falls and no falls.  
To summarize, the F-8 and AF-17 are quite challenging classification tasks. The use of this dataset for those tasks will permit researchers:  
* to design and evaluate more robust feature representations as well as more robust classification schemes for human activity recognition.
* to study more robust features to deal with accelerometer samples of different types of falls.
# A Mixed Clustering Approach for Real-Time Anomaly Detection
###### Abstract
Then the overlapping lifetimes are kept in a compact form using a set superimposition [40; 41]. At any stage of merging, two superimposed intervals associated with two clusters are superimposed based on the non-empty intersection of their cores. Consequently, each resulting cluster will have an associated superimposed time interval, which produces a fuzzy time interval [42; 43]. The algorithm stops when no further merger is possible. The algorithm supplies a set of clusters where each cluster will have an associated fuzzy time interval as its lifetime. One of the most challenging issues of partitioning clustering approach is to specify the value of \(k\) and there are several methods developed for this purpose. The proposed algorithm has addressed this issue very nicely by producing the same number of stable clusters irrespective of the number of input clusters (\(k\)) because the number of clusters will be reduced during the merging phase. Clearly, the number of output cluster will be less than or equal to \(k\), and the output cluster set is more stable and invariant with respect to the number of input cluster. The data instances, which either belong to sparse clusters or does not belong to any of the clusters or fall in the fuzzy lifetimes with very low membership values will be considered as anomalies. Thus, whether a data instance can be an anomaly or not also depends on its time of occurrence or generation. Then the proposed algorithm's time complexity is evaluated. Finally, an exhaustive evaluation with two datasets, KDDCup'99 Network anomaly dataset [44] and Kitsune Network Attack dataset [45] has been performed along with a comparative analysis against some of the well-known clustering-based approaches of both static and dynamic nature. The results decently demonstrate the efficacy of the proposed algorithm. The article is arranged as follows. The recent developments in this area are discussed in Section 2. In Section 3, the terms, notations and definitions that have been used here are discussed. In Section 4, the proposed system using algorithm and flowchart is explained. The complexity analysis is given in Section 5. The experimental analysis and the results are given in Section 6. Finally, the paper is winded-up with Conclusions, Limitations and lines for future works in Section 7.
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 3 The Proposed Signature Scheme
### The Proposed Scheme
#### 3.1.1 Signing Process  
In the signing process, we need to go through the encryption process, as the cipher text will be used in the verification process to extract the original message and ensure the data integrity. To sign a message \(m\), the sender encrypts \(m\) as:  
\[c=Enc_{pkr}(m) \tag{3}\]  
Then, the sender signs the message \(m\) by calculating \(s_{1}\) and \(s_{2}\), which contain their private keys \(k_{s}\), \(r_{2}\), and \(p\) as follows:  
\[s_{1}=m^{k_{s}}\mod n^{\prime} \tag{4}\]  
and  
\[s_{2}=m^{r_{2}\times p}\mod n^{\prime} \tag{5}\]  
The digital signature to be sent to the receiver is \(sig_{m}\):  
\[sig_{m}=(s_{1},s_{2}) \tag{6}\]  
The workflow of a signing process in the proposed DS technique is shown in Algorithm 1.  
```
0:message \(m\), secret keys \((k,r_{2},p)\), public keys \((n,n^{\prime},pk_{r})\)
0:\(sig_{m}\)
1:functionSig
2:compute \(s_{1}:s_{1}\gets m^{k_{s}}\mod n^{\prime}\)
3:compute \(s_{2}:s_{2}\gets m^{r_{2}\times p}\mod n^{\prime}\)
4:\(sig_{m}\leftarrow(s_{1},s_{2})\)
5:return\(sig_{m}\)
6:endfunction
```  
**Algorithm 1** Signing algorithm.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results  
### Relationship between \(\Delta\)SM and GPM Data  
Precipitation is the most important hydrological variable that influences the soil wetness condition, particularly for the near-surface. Both the GLDAS and SMAP soil moisture products effectively respond to atmospheric conditions [61, 79]. Hence, a strong relationship should exist between \(\Delta\)SM values and the intensity and spatial pattern of rainfall events. While \(\Delta\)SM\({}_{GLDAS}\) will follow this relationship, earthquake impacts on soil moisture may change this template for the observed \(\Delta\)SM\({}_{SMAP}\). Figure 5 shows \(\Delta\)SM values from SMAP (\(\Delta\)SM\({}_{SMAP}\)) and GLDAS (\(\Delta\)SM\({}_{GLDAS}\)), as well as the amount of accumulated precipitation in the SMAP event window for some of the target earthquakes at a 25 \(\times\) 25 km resolution, i.e., after resampling SMAP and GPM data to coarser grids (similar maps for other seismic zones are shown in Figure A2 of the supplement). In three of the study zones, including Meinong in Taiwan, Accumoli in Italy, and Samos in Greece, no significant rainfall was reported in the SMAP event window. While \(\Delta\)SM\({}_{GLDAS}\) followed GPM, in which soil moisture decreased in these regions, the SMAP data showed considerable increases in soil moisture content for some grids. These increases were readily observed for many grids in Palu, Indonesia and Illapel, Chile as well. These results, in turn, suggest that SMAP can register earthquake-induced soil moisture increases.  
Figure 4: (a) Soil moisture change of Croatia region reported by SMAP (9 km \(\times\) 9 km). (b) Resampled soil moisture change of Croatia region reported by SMAP with coarser grid (25 km \(\times\) 25 km). (c) Accumulated precipitation of Croatia region in the SMAP event window (10 km \(\times\) 10 km). (d) Resampled accumulated precipitation of Croatia region in the SMAP event window with coarser grid (25 km \(\times\) 25 km).  
Figure 5: Soil moisture change reported by SMAP and GLDAS and accumulated precipitation in the SMAP event window on 25 km \(\times\) 25 km grids.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 5 Implementation
### Implementation Considerations and Optimization  
The choice of the frequency band and the allocation strategy depends on regulatory requirements and the specific application. The system must ensure minimal interference with other communications, and comply with local spectrum regulations.  
The efficiency of the system is influenced by the power requirements of the RPU and the overall system. Optimizing the power usage while maintaining high security is crucial, especially in portable or battery-operated devices.  
Real-time generation and processing of random numbers and frequency hopping require efficient algorithms and hardware. The system must ensure low latency to avoid delays in communication.
## 3 Numerical Setup and Results
### Parameters Optimization
This phenomenon was because the weight attenuation caused numbers of the values in the \(W_{out}\) matrix to be 0 or close to 0, making the model to be in an under-fitting state and reducing the accuracy. It had excellent performance around \(reg=1\times 10^{-11}\). It should be noted that the optimal value of \(reg\) is affected by SNR. When the SNR is large, the optimal \(reg\) will decrease.
## 2 Related Work  
Artificial intelligence (AI) algorithms have been proposed to address a wide variety of questions in medicine; e.g., for prostate Gleason score classification [9], renal cancer grading [10], breast cancer molecular subtyping [11] and their outcome prediction. Moreover, AI-based methods have been applied to the segmentation of various pathological lesions in the fields of neuropathology [12], breast cancer [13], hematopathology [14], and nephrology [15].  
The above-cited studies have been conducted mainly on the most common tumors (i.e., breast or prostate), while AI-based methods have been scarcely adopted to deal with other types of cancer, despite their high incidence and mortality rates, as the Oral Squamous Cell Carcinoma (OSCC). The analysis of a recent systematic review by [PERSON] et al. [16] shows that still few applications of automatic WSI analysis algorithms are available for OSCC. In particular, the survey reports 11 records about the employment of AI-based methods for the analysis of specific histological features of oral lesions: out of 11, only four papers refer to OSCCs, namely [17; 18; 19; 20], one paper is about oral epithelial dysplasia [21], five about oral submuc fibrosis, i.e., [22; 23; 24; 25; 26], and one paper is about oropharyngeal squamous cell carcinoma [19]. Another recent application of machine learning algorithms on oral lesions histopathological is based on immunohistochemistry (IHC) positivity prediction [27].  
Segmentation methods on WSI images have been developed mainly for nuclei segmentation [28; 29; 30], epithelium segmentation [19; 24], microvessels and nerves [31], and colour-based tumour segmentation [17]. Recently, [PERSON] et al. [32] proposed an indirect segmentation method, through small tiles classification, with an accuracy of 95.12% (sensitivity 88.69%, specificity 97.37%).  
To the best of our knowledge, there are no published results on direct OSCC segmentation using deep learning and none employing the TCGA as a source of histopathological images. This workSegmentation results obtained using the different pre-processing functions are discussed in Section 4.
## 3 Results
### Steering Angle Estimation
#### 3.2.2 Average Steering Angle Estimation  
In the validation process, the hidden Markov model Viterbi algorithm was used for the estimation. A total of 39,534 original alterations were used, and the course-altering angle and average steering angle for each alteration were calculated in the validation dataset.  
Figure 8: Random-walk simulated course alterations.  
Figure 9: Trajectories of random-walk simulated course alterations.  
Using the ten-fold cross-validation method, the validation dataset was separated into a \"train dataset\" and a \"test dataset\" only to calculate the estimation accuracy. The estimation accuracy for the exact corresponding value without allowing angle error was less than 0.5 accuracy, but when allowing error angles from one to ten degrees, the accuracy rose to 0.9789. The estimation accuracy with allowing angle error is arranged in Table 1.  
The estimation accuracy was calculated upon cross-validation by separating the training and test datasets for objective results. The estimated average steering angles vary over course alteration angles and time. Figure 10 shows the populated experts' course-altering and steering angles in elapsed times without outliers for clear visualization.
## 4 POMDP Model Formulations
### CSPOMDP Model
#### 4.1.4 Reward  
Rewards are formulated as the sum of all maintenance-related costs for a given action taken under a specific pipeline state. Corrosion-mitigating activities discussed above incur a different amount of cost to implement. These costs include production downtime, spare parts acquisition, repair operation, and any flow compensation costs that are computed using organizational data. [PERSON] et al. [5] considered the cost associated with the risk of failure in estimating the reward. It is computed as the product of the failure risk and the respective cost of the failure. Expert options and historical data can serve as a source of input for this computation. When multiple inspections are considered in the POMDP model, the inspection cost can be added to the cost function.  
The total cost (\(TC_{S,A}\)) was defined as the total reward obtained for a given state and action (Equation (3)). It was computed as the sum of the maintenance operation cost (\(MC_{S,A}\)--for a given state and action), the failure cost (\(FC_{S}\)--for a given state), and the inspection cost. Failure costs aggregate the cost of different failures (leaks and raptures) based on their respective probability.  
\[TC_{S,A}=MC_{S,A}+FC_{S}+I \tag{3}\]  
where \(TC_{S,A}\)--the total cost (reward);  
\(MC_{S,A}\)--the cost of maintenance;  
\(FC_{S}\)--the failure cost = \(\sum_{f\in Failures}Prob_{f}\times Cost_{f}\);  
\(I\)--the inspection cost.
# Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism
## 3 Main Model
### Obtain Meta-Path Embedding Based on the Attention Mechanism  
After obtaining a single meta-path embedding, the traditional meta-path aggregation method is to average the pooling operation to obtain the context representation of the meta-path \(c\). This simple average pooling operation does not take into account the impact of the users and items involved on the meta-path, and the lack of meta-path has no consideration of semantic information in the interaction.  
After obtaining a single meta-path embedding, the traditional meta-path aggregation method is an averaging pooling operation to obtain the contextual representation \(c_{u\to i}\) of the meta-paths. This simple averaging pooling operation does not consider the influence of the involved users and items on the meta-paths and lacks the consideration that meta-paths have different semantic information in the interaction. In practical recommendation tasks, different meta-paths have different semantics among the same set of users and items, and different users have different preferences for meta-paths. Thus, our model learns the attention weights of users and items on meta-paths in response to interactions and given an initial representation of \(x_{u}\) users and an initial representation \(y_{i}\) of items, a two-layer architecture is used to implement this attention mechanism as:  
\[\alpha_{u,i,\rho}^{(1)}=f\Big{(}W_{u}^{(1)}x_{u}+W_{i}^{(1)}y_{i}+W_{\rho}^{(1 )}c_{\rho}+b^{(1)}\Big{)} \tag{5}\]  
\[\alpha_{u,i,\rho}^{(2)}=f\Big{(}W^{(2)}\alpha_{u,i,\rho}^{(1)}+b^{(2)}\Big{)} \tag{6}\]  
where \(W_{u}^{(1)},W_{i}^{(1)},W_{\rho}^{(1)}\) denote the first layer weight matrix, \(b^{(1)}\) is the first layer bias vector, \(W^{(2)}\) and \(b^{(2)}\) denote the second layer weight vector and bias vector, respectively, and \(f(\cdot)\) is the \(ReLU\) activation function. After that, using the \(Softmax\) function to normalize the attention scores of all the above meta-paths, the final meta-path attention weights are obtained as  
\[\alpha_{u,i,\rho}=\frac{exp(\alpha_{u,i,\rho}^{(2)})}{\sum_{\rho^{\prime}\in M }exp(\alpha_{u,i,\rho}^{(2)})} \tag{7}\]  
The meta-path attention weight can be interpreted as the contribution of the meta-path to the interaction between \(u\) and \(i\). After obtaining the attention value \(\alpha_{u,i,\rho}\) of the meta-path, the enhanced meta-path-based context is calculated according to the following weighted summation:  
\[c_{u\to i}=\sum_{\rho\in P}\alpha_{u,i,\rho}\cdot c_{\rho} \tag{8}\]  
where \(c_{\rho}\) is the representation of a single meta-path obtained from Equation (4), \(\alpha_{u,i,\rho}\) is the attention weight generated by each interaction, and \(P\) is the set of meta-paths.
# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 3 Combining Knowledge Graph and Collaborative Filtering Recommendation Algorithm
### Knowledge Graph Similarity Calculation  
#### 3.1.1 Construction of Knowledge Graph  
In the realm of education, the course knowledge graph stands as a significant application of knowledge graph technology in course content. Constructing the course knowledge graph enables the structured representation of course knowledge points, culminating in the formation of a cohesive knowledge system. Among the various approaches to knowledge graph construction, manual methods excel in precisely determining knowledge points and relationships. Consequently, this paper employs manual construction methods to establish the knowledge graph.  
During the construction phase, data are sourced from online learning platforms, encompassing course materials such as slides, micro-videos, and text materials. These teaching materials define the course structure, organizing the knowledge graph into three  
Figure 2: Algorithm framework diagram.  
layers. The first layer, denoted as 'L,' represents course modules and is established based on the selected courses. The second layer, 'C,' signifies chapter modules and is structured around the chapter names extracted from online courses. The third layer, 'K,' represents knowledge point modules. Each chapter necessitates a clear definition of relevant knowledge points to ensure accuracy. Within the course resources, fundamental chapters and knowledge points exhibit clear logical order relationships. Based on the course's characteristics and content, five types of relationships--namely \"belongs to, associates with, precedes, contains, and synonym\"--are defined. Figure 3 illustrates the conceptual structure of the knowledge graph.  
Knowledge extraction constitutes the central step in the manual construction of the course knowledge graph. Entities and relationships are meticulously extracted, guided by predefined knowledge points. In the entity extraction phase, a thorough review of video and text resources from each chapter is conducted to identify concepts closely related to the chapter's content. Knowledge point entities are then extracted and linked to the previously defined knowledge points, encompassing concepts such as \"process\" and \"scheduling\". In the relationship extraction phase, knowledge graph relationships among knowledge points are deduced based on their logical connections. For instance, the knowledge point \"scheduling algorithm\" may entail a predecessor relationship, such as the \"scheduling concept\". To ensure the precision of the knowledge system, knowledge extraction encompasses the acquisition of knowledge points from various textbooks and data sources. Due to the disparities among sources, the same knowledge point entity may exhibit variations, necessitating entity alignment to eliminate ambiguities. For instance, \"process control\" in one textbook and \"process management\" in another can be harmonized into a standardized entity: \"process control\".
# Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming
## 4 Approach
### Linear Quadratic Optimization
Finally, the control sequence of \(u_{k}\) minimizing the cost can be represented as:  
\[u_{k} = -\mathbf{K}\mathbf{x}_{k}\]  
where the obtained \(\mathbf{K}\) represents the optimal values for \(K_{P}\) and \(K_{I}\) for the basic LQ ABR scheme. Although our controller is based on the feedback control based on the level of playback buffer, it needs to estimate expected mean throughputs, which can dynamically fluctuate in the mobile environment. To predict mean throughput, our controller applies Holt-Winters time-series forecasting algorithm [16], which is known to be more accurate than formula-based predictors [17], on throughput samples measured while downloading each chunk.