esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Experiment Setting  
As a proof-of-concept for our proposed system, we experiment on the publicly available MALib dataset. This comprises 704,128 dependencies between a collection of 56,091 Android GitHub projects to 763 Android third-party libraries. To evaluate recommendation quality, we follow a methodology common in library recommendation research [11; 14; 16]. In particular, we select all projects with at least 10 dependencies as test ones (these are 31,438 in total), by merit of them comprising enough dependencies to be considered high-quality known ground truth. For these projects, we remove \(rm\in\{1,3,5\}\) dependencies to emulate the real-world scenario where not all relevant libraries are used and conduct experiments where we use the remaining dependencies to rediscovering the removed ones.  
For each approach, the following measures assess the quality of the top \(T\in\{5,10\}\) library recommendations. All measures output values in the range \([0,1]\), with higher values indicating recommendations closer to ideal ones.  
_MAP._ The mean average precision of the top \(T\) recommendations. In detail, given the notation \(L_{proj}[i]=\{1\) if the \(i\)-th top library recommendation for project _proj_ is a true positive, 0 otherwise\(\}\), we compute the average precision for each project's top \(T\) library recommendations per  
\[AP_{proj}=\frac{\sum_{i=1}^{T}L_{proj}[i]\sum_{j=1}^{T}L_{proj}[j]/i}{\sum_{i=1 }^{T}L_{proj}[i]}\]  
and report its mean across all projects. Average precision provides a more granular understanding than precision by accounting for recommendation order and is thus able to differentiate between recommendation algorithm quality even for large \(T\).  
_MP._ The mean precision of the top \(T\) recommendations across all projects. Higher values indicate that there are fewer erroneous library recommendations in the list of top ones. Perfect library recommendations yield MP equal to \(\min\{rm/T,1\}\).  
_MR._ The mean recall of the top \(T\) recommendations across all projects. Higher values indicate that there are fewer desired library recommendations (i.e., from those of each project's test set) left out. Perfect library recommendations yield an MR equal to 1.  
_MF1._ The mean F1 score of the top \(T\) recommendation across all projects. The F1 score for a project is the harmonic mean between its precision and recall. Then, the mean of all these scores is obtained.  
_Cov._ The coverage of recommendations is the percentage of libraries that reside in the top \(T\) recommendation of at least one software project. A coverage value of 1 means that all libraries can be recommended, whereas low percentages indicate approaches that prioritize a few well-known libraries--an undesirable outcome when the goal of recommendation is also to discover fitting non-popular libraries.
## 5 Experimental Results
### Comparison with Studies in the Literature
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **Accuracy (\%)** & **Year** & **Ref.** \\ \hline \multirow{6}{*}{ISIC-2016} & \multicolumn{2}{c}{CUMED} & 85.50 & 2016 & [34] \\ \cline{2-5}  & BL-CNN & 85.00 & 2017 & [32] \\ \cline{2-5}  & DCNN-FV & 86.81 & 2018 & [39] \\ \cline{2-5}  & MC-CNN & 86.30 & 2019 & [40] \\ \cline{2-5}  & MFA & 86.81 & 2020 & [41] \\ \cline{2-5}  & AHA-AO & **87.30** & present & Our \\ \hline \multirow{6}{*}{PH2} & ANN & 92.50 & 2017 & [60] \\ \cline{2-5}  & Kernel Sparse & 93.50 & 2019 & [61] \\ \cline{2-5}  & DenseNet201 + SVM & 92.00 & 2020 & [62] \\ \cline{2-5}  & DenseNet201 + KNN & 93.16 & 2020 & [63] \\ \cline{2-5}  & ResNet50 + NB & 95.40 & 2021 & [64] \\ \cline{2-5}  & AHA-AO & **97.50** & present & Our \\ \hline \multirow{6}{*}{Chest-XRay} & DCGAN & 84.19 & 2018 & [65] \\ \cline{2-5}  & DenseNet121 & 86.80 & 2021 & [66] \\ \cline{2-5}  & AHA-AO & **86.90** & present & Our \\ \hline \multirow{6}{*}{Blood-Cell} & CNN + SVM & 85.00 & 2013 & [67] \\ \cline{2-5}  & CNN & 87.08 & 2017 & [68] \\ \cline{1-1} \cline{2-5}  & CNN + Augmentation & 87.00 & 2019 & [69] \\ \cline{1-1} \cline{2-5}  & AHA-AO & **88.60** & present & Our \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with the state-of-the-art methods. The best results for each item are labeled in bold (bold refers to best value). proposed an automatic transfer-learning method based on CNNs using concepts pre-trained with DenseNet121. In order to identify and count basic blood cells in the Blood-Cell dataset, the following identification and counting methods were used. As a result of the CNN's solution, SVM-based classifiers were able to classify data, as proposed in [67]. In addition, a granularity feature and SVM were used in [68]. In order to automate the entire procedure, CNNs were presented as a deep learning method in [69]. The bottom line is that we can remove superfluous features from high-dimensional medical image representations obtained by convolutional neural networks (CNNs) by using our strategy. However, this method's drawback is its complexity in terms of both time and memory. Our next steps will be to decrease the complexity and enhance the performance of the suggested technique. In addition, other augmentation techniques can be studied in the future to optimize the effectiveness of our model.
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 1 Introduction
Climate-oriented skywalk systems regulate the urban thermal environment while enhancing walkability. Cities like Minneapolis, USA, and Calgary, Canada, have adopted fully enclosed aerial walking systems to cope with severe cold [29; 30]. Conversely, subtropical cities like Hong Kong, China and Guangzhou, China, utilize natural ventilation and roof outcroppings to temper heat and solar exposure. In addition, a study on \"urban agriculture\" in Java, Indonesia, has found that incorporating vegetation into the urban environment mitigates the heat island effect and improves the walkability of the city [31]. Finally, the aggregation of multiple urban functions. The skywalk system not only serves as a transportation carrier, but also combines with the landscape, commercial business, leisure and recreation, tourism and sightseeing, and social activities, yielding promising outcomes in Asian countries. Previous studies have mainly focused on case-by-case empirical evidence for a single city or country, typically using single-factor correlation and influence factor analysis. However, there are fewer studies on the comparison and overall coherence between different cities. The research objective of this study is to explore the relationship between skywalk systems and urban environments and the development of coherence law, and at the same time to summarize the construction types and characteristics of existing skywalk systems, thus forming a reference to assist decision making. The Section 2 of this paper is the literature review, screening specific evaluation indexes and constructing the index systems. The Section 3 constructs the research method. This is followed by the results of an empirical analysis in Section 4. Finally, Section 5 conducts a discussion and derives the study's conclusions.
## 3 Experiments
### Analysis on Overlapping Cases  
In order to verify the ability of SRAGNNs model to extract multiple triplets, we conducted further experiments on the CEMR dataset and analyzed the experimental results on different sentence types. Figure 3 shows the experimental results of each model in different sentence types. In the case of entity overlap, the performance of our proposed model outperforms all other methods. This shows that we regard relation extraction as a multi-label classification problem, and it is useful to use relations to guide entity recognition.  
We can see that ETL-Span performs relatively well in Normal class. This is because its decomposition strategy is designed to be more suitable for the Normal cases. At the same time, we found that because the RSAN model is also a relation-guided joint entity and relation extraction model, the RSAN model has a better ability to extract triplets when various types of entities overlap. However, Chinese medical entities and relations are special and complex, and the performance of extracting triplets using the RSAN method is not comparable to SRANNs.  
Figure 2: Correlation between word information and character information in Chinese EMR.  
In Table 8. (1) Sentence S1 is a normal class, and the entity-relation triplets are correctly recognized. (2) Sentence S2 is EPO class. The SRAGNNs model only extracted a triplet of (lead poisoning), (clinical manifestations), (hypertension)\", (hypertension)\", and the relation of ( complications)\" was not extracted. A description such as \"main toxicity\" in the sentence leads directly to this relation of clinical presentation. At the same time, the relation \"complication\" is not directly mentioned in the context of the sentence. Therefore, the SRAGNNs model based on the semantic information of the sentence excludes this complication relation triplet, alleviating the noise problem of remote supervision. This requires the SRAGNNs model to further learn the deep semantic information in this paper. (3) The third sentence is SEO category. In this example, three triplets are involved. Among them, (ADV)( ADV pneumonia)\" and (bronchiolitis obliterans)\" are overlapping entities. The SRAGNNs model identifies these triplets through overlapping relation extraction.
## 2 Materials and Methods  
### Materials  
#### 2.1.1 MOD17A2H GPP Algorithm  
Based on the rationale originally described by [PERSON] (1972) [PERSON] (1972) [PERSON] (1972), the MOD17A2H GPP algorithm can be expressed as:  
\[\text{GPP}=\varepsilon\times\text{FPAR}\times\text{PAR} \tag{1}\]  
where FPAR is the fraction of PAR (MJ\(\cdot\)m\({}^{-2}\)\(\cdot\)d\({}^{-1}\)), and PAR can be calculated from the total shortwave solar radiation (SWRad, MJ\(\cdot\)m\({}^{-2}\)\(\cdot\)d\({}^{-1}\)):  
\[\text{PAR}=0.45\times\text{SWRad} \tag{2}\]  
The magnitude of LUE \(\varepsilon\) (gC\(\cdot\)m\({}^{-2}\)\(\cdot\)d\({}^{-1}\)\(\cdot\)MJ\({}^{-1}\)) in Equation (1) is calculated as:  
\[\varepsilon=\varepsilon_{\text{max}}\times\text{T}_{\text{s}}\times\text{VPD }_{\text{s}} \tag{3}\]  
where \(\varepsilon_{\text{max}}\) (gC\(\cdot\)m\({}^{-2}\)\(\cdot\)d\({}^{-1}\)\(\cdot\)MJ\({}^{-1}\)) is the maximum LUE specified by the Biome Properties Look-Up Table (BPLUT) based on biome type, and T\({}_{\text{s}}\) and VPD\({}_{\text{s}}\) are the attenuation scalars influenced by daily minimum temperature (T\({}_{min}\), \({}^{\circ}\)C) and daytime vapor pressure deficit (VPD\({}_{avg}\), Pa), respectively Monteith (1972); Monteith (1972); Monteith (1972).  
The MODIS images from 2001-2014 were downloaded from the NASA Land Processes Distributed Active Archive Center ([PERSON] et al., 2011). Based on the geographical coordinates of the flux tower sites, 5 km \(\times\) 5 km cut-outs centered over every site location were extracted to represent: (1) land cover classification (MCD12Q1, Collection 5.1, annual product with 500-m spatial resolution); (2) FPAR (MOD15A2H, Collection 6, eight-day product with 500-m spatial resolution); and (3) GPP (MOD17A2H, Collection 6, eight-day product with 500-m spatial resolution).
## 2 Framework of RUL Prediction Based on TCNMABG
### Remaining Useful Life Prediction Based on TCNMABG  
TCN is a neural network structure based on dilated causal convolution, which combines the local receptive field and parameter-sharing advantages of CNN with the long-term dependency modeling ability of RNN to overcome the limitations of traditional time series prediction models. The basic residual module in TCN consists of two convolution layers for nonlinear mapping. In this paper, we use hyperparameters \(k=3\) and \(d=2\) for dilated causal convolution operations, and each layer also adds WeightNorm and Dropout to regularize the network.  
TCNMABG is constructed based on TCN by combining MA and BiGRU, and the structure of TCNMABG is shown in Figure 3. TCNMABG mainly consists of a TCN module, multi-head attention module, bidirectional gated recurrent unit module, and remaining useful life prediction module. The detailed RUL prediction of rolling bearing based on TCNMABG is summarized as follows.  
Firstly, this model extracts the degradation features of device monitoring data through a temporal convolutional network composed of multiple basic residual modules. When constructing the TCN network, the standard convolution is injected with a void processing  
Figure 3: The architecture of the TCNMABG model.  
input time series by setting dilated causal convolution parameters to increase the receptive field and capture longer time dependencies. Introducing a residual connection between each convolutional layer helps solve the problem of disappearing gradients and speeds up the training process. The efficient processing and feature extraction of time series data is realized through dilated causal convolution and residual connection.  
Secondly, after the TCN layer, a multi-head attention mechanism is introduced that can focus on different parts of the input sequence. Each head will calculate the attention weight independently to enhance the model's ability to learn between different feature representations.  
Thirdly, by introducing the BiGRU structure layer, the model enhances the ability to extract features from sequence data at different time scales by processing both forward and backward sequence information.  
Finally, the remaining useful life prediction module converts the high-dimensional input data into one-dimensional vectors through the flatten layer of the neural network model and maps these features to the final RUL as an output result through the fully connected layer.
## 1 Introduction
* What is the impact of concurrent storage accesses on the performance of NVM based storage? This article answers the aforementioned questions through a wide range of empirical studies on systems with HDD and NVM storage emulated by a RAMDisk, which provides NVM performance by stalling DRAM access for the proper duration. To do so, we implement an NVM emulator whose performance can be measured with the setting of appropriate configurations for target NVM storage media. The use of a RAMDisk with no timing delay provides an upper-bound performance estimate of NVM storage, which can be interpreted as the optimistic performance of STT-MRAM. We also set an appropriate time delay for another type of NVM, PCM. Through this measurement study, we make the following findings. * We can decrease the main memory size without performance penalties when NVM storage is adopted instead of HDD. * Buffer caching is still effective in fast NVM storage but some judicious management techniques like admission control are necessary. * Prefetching is not effective in NVM storage. * The effect of synchronous I/O and direct I/O in NVM storage is less significant than that in HDD storage. * Performance degradation due to the contention of multi-threads is less severe in NVM based storage than in HDD. This implies that NVM can constitute a contention-tolerable storage system for applications with highly concurrent storage accesses. Based on these observations, we discuss a new memory and storage configurations that can address the issue of rapidly growing memory demands in emerging applications by adopting NVM storage. Specifically, we analyze the cost of a desktop PC consisting of small memory and fast storage based on Intel's Optane SSD, which is the representative NVM product on the market at this time, in comparison with the traditional PC consisting of large memory and slow storage. We show that such a new memory-storage configuration can be an alternative solution for ever-growing memory demands and the limited density of DRAM memory. The remainder of this article is organized as follows. Section 2 describes the background of this research focusing on the hardware technologies of PCM and STT-MRAM. Section 3 explains the experimental setup of the measurement studies and the performance comparison results on systems with HDD and NVM storage. In Section 4, we summarize the related work. Finally, Section 5 concludes this article.
# Clustering Method of Large-Scale Battlefield Airspace Based on Multi A * in Airspace Grid System
## 5 Experimental Simulation and Analysis
### Evaluation of Airspace Clustering Effect  
This part of the experiment was to explore the effect of airspace clustering on the key link of airspace management and control--airspace conflict detection. According to different clustering requirements, different target cluster numbers \(M\) will be formed. Considering that the process of airspace conflict detection and resolution is affected by multiple factors, this section also selects two key influence parameters; namely, it selects the overall number of airspace \(N\) and the safety margin distance between airspace \(R\) as experimental variables. On the premise that all parameters of airspace use are consistent (airspace use time, airspace use height, and airspace action communication frequency are consistent), the impact factors of the whole experiment mainly focus on the total number of airspace, the number of target airspace clusters and the safety margin distance.
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 3 Lab-in-a-Box (LiB) Architecture
### Encryption Algorithms
#### 3.2.2 XTEA Implementation  
The XTEA algorithm is the improved version of TEA, proposed by [PERSON] and [PERSON] in 1997. It is a Feistel cipher with a block size of 64 bits and a key size of 128 bits. The key schedule is simple and keys for the rounds are scheduled dynamically so it reduces the impact on memory, which is ideal for microcontrollers with low memory.  
Each cycle consists of two Feistel rounds. The number of rounds can be changed for different levels of security. The 128-bit key is broken down to four blocks of 32-bit subkeys. The key for each round is derived from the sum from each round. The rounds are identical and use different multiples of magic constant called delta = (\(\sqrt{5}\)-1)\(\times\)231. The constant number ensures that the subkeys are different for each round. It requires no initialization steps and does not use S-boxes like AES which reduces memory requirement. The code consists of simple operations such as shifts, additions, and XORs. As seen in Figure 4 the data goes through alternating additions and XOR operations. Dual shift ensures the key and data are mixed properly. The 64-bit data block is split into two 32-bit variables and operations such as rotation, addition, and XOR is used to mix the data with the key for each round. The decryption inverts the operations occurred during encryption. Simple operations in encryption and decryption result in low use of RAM space and reduce overall energy consumption. Although XTEA does not provide security level on par with AES-256, its impact on computational time, memory, and energy consumption of resource constraint hardware are low, making it suitable for use in low-power, resource-constrained microcontrollers in various IoT applications where security is not too critical.  
Figure 3: AES encryption and decryption process.
## 4 Numerical Model Validation  
The FE model was verified by comparing the predicted results from the FE analyses with the measured displacement from centrifuge tests (unpublished report results) that were conducted in the Yangtze River research institute in Wuhan, China. The CKY-200 centrifuge of Yangtze River research institute was used in the experiment. Its effective capacity is 200 g-t, its maximum acceleration was 200 g, and its model box was \(1.0\times 0.4\times 0.8\) m. In this experiment, a 100 g model was used, that is the scale ratio was 1:100. In order to conservatively evaluate the stability of the GRS berm in the centrifugal model test, an anti-slide pile was not considered, and the influence of anti-slide pile was taken as a safety reserve. The material of the berm body was taken from the coarse and fine particle mixed soil (excluding the soil particles with particle size greater than 30 mm). The maximum dry density of the filling material was 1.92 kg/m\({}^{3}\), and the optimal moisture content was 15.7 (\(\pm\)2) %. The drainage direct shear strength was 29.5 kPa and the friction angle was 36.4\({}^{\circ}\). The MSW mass was simulated using loose fine sand, which only simulated the earth pressure of the MSW on the berm and ignored the simulation of the mechanical properties of the MSW itself; the thrust generated by the fine sand was slightly higher than that of the actual MSW so as to ensure that the centrifuge test was a conservative assessment of the stability of the refuse retaining dam. The average density of loose fine sand was 1.72 kg/m\({}^{3}\). Through narrow strip tensile and direct shear tests of the reinforcement soil interface, it was determined that a 0.45 mm thick polyester fiber was used to simulate the 2 mm thick geotextile, according to the similar friction and equivalent tensile strength per unit area between the prototype and the centrifuge model. The berm soil was layered by 0.05 m and compacted to a density of \(\geq\)1.85 kg/m\({}^{3}\) by manual hammering at the optimum moisture content. Polyester was laid in a 0.05 m layer, and the reverse wrapping was carried out on the slope surface to simulate the reverse wrapping process of the geotextile in practical engineering.  
Five laser displacement monitoring points were set up on the slope to measure its horizontal and vertical displacement. Figure 5 shows a comparison results of lateral displacement and settlement between measured values from test and predicted from the FE analysis. The measured lateral displacement and settlement of the monitored point in the centrifuge mode were approximated to the values predicted in the FE analysis. As can be seen in Figure 5, the FE and centrifuge models showed good agreement in the displacement field, thus demonstrating the capability of FE modelling to predict the behavior of a GRS berm.
We evaluate the proposed traffic prediction algorithm and slot orchestration mechanism using the real-world traffic datasets and network data. The results demonstrate that a proactive priori knowledge provided by traffic prediction significantly reduces the computation time by up to 46.8%. The rest of the paper is organized as follows. Section 2 provides a review of related work on FlexE and network traffic prediction. Section 3 formulates the problem. Section 4 presents the PMFAN prediction algorithm and GDSO Orchestration algorithm. The simulation results are represented in Section 5, and Section 6 concludes this paper.