esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Roasting and Storage Conditions  
Zambia Kasama Estates green coffee (10 kg from the same bag) was purchased from SweetMaria's.com due to comments regarding its ability to be roasted across a wide range of levels. The coffee was roasted in five batches of 250.0 g \(\pm\) 0.1 g for 16 min for a light roast (\"City\", ejected 20-30 s after first crack ended; average mass loss 14.6 \(\pm\) 0.1% (\(w/w\)) (\(n\) = 5)) and 18 min for a dark roast (\"Vienna\"; ejected after second crack ended; average mass loss 18.6 \(\pm\) 0.1% (\(w/w\)) (\(n\) = 5)) with a home coffee roaster (Hottop USA, Cranston, RI, USA). The color of the two different roasts was measured with a colorimeter (Minolta, Ramsey, NJ, USA; 2\({}^{\circ}\) observer, D\({}_{65}\) illuminant): City (\(n\) = 6); L* = 21.2 \(\pm\) 1.83, a* = 11.2 \(\pm\) 0.251; b* = 23.5 \(\pm\) 0.374; Vienna (\(n\) = 6): L* = 14.3 \(\pm\) 0.522, a* = 10.1 \(\pm\) 0.286; b* = 17.4 \(\pm\) 0.587. After roasting, each coffee batch rested for 15 hours in open paper bags before being combined and transferred to amber glass containers for storage. Room temperature-stored coffees were stored in a dark cabinet, undisturbed until the day before the sensory analysis. Freezer temperature-stored coffees were stored at the bottom of a chest freezer set at \(-\)18 \({}^{\circ}\)C, and were left undisturbed until the day before the sensory analysis. Control samples for both roast levels were roasted, using the same parameters, the day before the sensory testing and rested for 15 h in open paper bags. For all roast batches, oven temperature, fan speed, and heater intensity data were collected every minute to ensure comparable roasting profiles. Time to first crack and first crack duration (all batches) and time to second crack and second crack duration (dark roast batches) were collected to ensure consistency between batches.
###### Abstract  
Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land-atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001-2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation.  
light use efficiency; growing production day; model comparison; remote sensing; carbon cycle +
Footnote †: journal: Remote Sensing, 10, 1008; doi:10.3390/rs10071008
# Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs
###### Abstract  
This paper focuses mainly on the problem of Chinese medical question answer matching, which is arguably more challenging than open-domain question answer matching in English due to the combination of its domain-restricted nature and the language-specific features of Chinese. We present an end-to-end character-level multi-scale convolutional neural framework in which character embeddings instead of word embeddings are used to avoid Chinese word segmentation in text preprocessing, and multi-scale convolutional neural networks (CNNs) are then introduced to extract contextual information from either question or answer sentences over different scales. The proposed framework can be trained with minimal human supervision and does not require any handcrafted features, rule-based patterns, or external resources. To validate our framework, we create a new text corpus, named cMedQA, by harvesting questions and answers from an online Chinese health and wellness community. The experimental results on the cMedQA dataset show that our framework significantly outperforms several strong baselines, and achieves an improvement of top-1 accuracy by up to 19%.  
question answer matching; medical domain; question answering; answer selection; character embeddings +
Footnote †: journal: Applied Sciences
## 3 Theoretical Background and Problem Formulation
### STPA as Component of a Decision Making Tool
#### 3.2.2 State Uncertainty in Markov Decision Processes  
Classic MDPs assume that the current state is completely known and observable. However, in real-life conditions, the discrete state of each system state examined by system-theory applications cannot be fully modeled, since it is not trivial to measure and observe all the parameters that could potentially influence the ecosystem, e.g., the human mood while performing a collaborative task. To this end, each state is considered as partially known. Partial observability of the state is opposed to the MDP principals. To tackle this limitation, we incorporate partially observable Markov decision processes (POMDP) [31], where instead of directly observing the current state, the state is linked to an observation that indicates a clue regarding the state it belongs to. The observations are considered asthe outcome of the selected actions--either CA or UCA--and can also be probabilistic. Therefore, there is a need to specify an observation model that explains the probability of each observation for each state in the system given a specific applied action. It turns out that the maintenance of a probability distribution over all of the states boils down to provide the same information as if we maintained the complete history of the states [32; 33]. In particular, when a selected action is performed and this action provides an observation, the distribution is updated accordingly. POMDPs constitute a convenient representation for tracking the different states of the system and provide selected UC and UCA that produce observations in a probabilistic manner. By associating each selected action with a system state of a particular safety level, we can formalize the STPA products as POMDP problem.
# A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding
## 2 Deep K-SVD Algorithm
### Deep K-SVD Algorithm  
The basic K-SVD algorithm has a shallow architecture and only learns one dictionary level. In this section, the authors proposed to extend shallow dictionary learning into multiple layers, thereby leading to deep sparse coding. Deep dictionary learning is performed by using a greedy approach to continuously find the sparse representation of the coefficient matrix until the last layer is reached. This deep non-negative K-SVD algorithm can extract deeper and more valid UBPs and is more conducive to analyzing and estimating costumer behavior. The schematic of deep UBPs extraction is shown in Figure 2.  
Figure 1: Schematic diagram of sparse coding.  
In the greedy solution, the coefficient matrix learned in the \(i\)th layer \(X_{i}\) can be substituted as \(X_{i}=D_{i-1}X_{i-1}\) until \(i=L-1\) (for L layers), whereas the sparse representation problem in the \((i+1)\)th layer can be expressed as:  
\[\min_{D_{i-1},X_{i-1}}\left\{\|X_{i}-D_{i-1}X_{i-1}\|_{F}^{2}\right\} \tag{3}\]  
Hence, the multi-level dictionary learning problem with non-linear activation can be formulated as:  
\[\begin{split}\min_{D,X}&\left\{\|Y-D_{1}D_{2} \cdots D_{L}X_{L}\|_{F}^{2}\right\}\\ &\forall i,\|x_{i}\|_{0}\leq s_{0}\\ \text{\emph{subject to}}& x_{i,j}\geq 0,1\leq i\leq M,1 \leq j\leq J\\ & d_{j,n}\geq 0,1\leq j\leq J,1\leq n\leq N\end{split} \tag{4}\]  
where \(D=D_{1}D_{2}\cdots D_{L}\) is the DUBPs extracted by deep K-SVD algorithm [29].  
In summary, the deep sparse coding algorithm provides two major improvements in calculating electricity consumption. First, it can obtain a multi-layer overcomplete dictionary, called DUBPs library in this study. DUBPs can be maximally consistent with users' actual electricity consumption behavior through continuous training. Second, the coefficient vector can be optimized \(L\) times to reduce reconstruction error while obtaining an over-complete sparse dictionary. Therefore, the corresponding DUBPs are also more consistent with the actual consumption of users.
# A Numerical Study of the Vibration Characteristics of a Haptic Actuator for a Dial Gear Shifter
## 3 Results and Discussion  
### Characterization of the Haptic Actuator  
Figure 7 shows the mode shapes of the haptic actuator computed via modal analysis. The first three mode frequencies were 194.3, 195.3, and 206 Hz, respectively. While the first two modes were related to the transverse motion of the spring, the third mode was the main vibration mode directly related to the electromagnetic excitation (i.e., the vertical oscillator). The solid blue line with a square symbol in Figure 8 shows the simulated acceleration of the actuator in the frequency range of 160-240 Hz. According to the frequency response, the resonance frequency of the actuator was estimated to be approximately 206 Hz, and the acceleration level was approximately 2766 mm/s\({}^{2}\).  
The test results confirmed the resonance frequency and the corresponding frequency response of the actuator. The solid red line with a circular symbol in Figure 8 shows the acceleration response as a function of frequency, where the maximum peak was found at approximately 206 Hz. The actual acceleration at the resonance was approximately  
Figure 8: Comparison of frequency response functions in the FEA and the experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **(A) (mm)** & **(B) (mm)** & **(C)** & **(D)** \\ \hline
1 & 32 & 17 & 1 & 1 \\ \hline
2 & 32 & 15.5 & 0 & 1 \\ \hline
3 & 32 & 14 & 1 & 1 \\ \hline
4 & 31 & 17 & 0 & 1 \\ \hline
5 & 31 & 15.5 & 1 & 0 \\ \hline
6 & 31 & 14 & 0 & 0 \\ \hline
7 & 30 & 17 & 1 & 1 \\ \hline
8 & 30 & 15.5 & 0 & 0 \\ \hline
9 & 30 & 14 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Design of experiment (L9 orthogonal array).  
Figure 7: Simulated mode shapes of the haptic actuator.  
2786 mm/s\({}^{2}\). The resonance frequency in the test was 206 Hz, which is in good agreement with the simulation results. The discrepancy in the acceleration levels between the test and simulation results was less than 1%. To confirm the FE results of the haptic actuator, the actuation condition was applied to the full model of the dial gear shifter (Figure 5).
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
###### Abstract  
Wireless body area networks (WBANs) have great potential to supply society with vital technical services, but the low power of network nodes severely hampers their development. To solve this problem, Energy-Efficient, a low-power cluster-based routing system intended for precise biological data gathering in WBANs, is presented in this study. This approach comprises three main stages: data aggregation, cluster head (CH) selection, and cluster creation. The suggested approach balances biosensor energy and optimizes energy usage by utilizing the modified snake swarm optimization algorithm (MSSOA) for routing and the adaptive binary bird swarm optimization algorithm (ABBSOA) for cluster formation and CH selection. The suggested technique outperforms the most recent WBAN routing protocols, including MT-MAC, ALOC, DHCO, and M-GWO, by using a power-balancing routing tree and considering biosensor distance and remaining energy. The experimental results demonstrate that the proposed ABBSOA-MSSOA model achieves a jitter protocol value of 0.3 ms at 100 nodes, a buffer occupancy ratio of 2.5%, a cluster lifetime of 600 s, a cluster building time of 12.2 s, an energy consumption of 42 mJ, a communication overhead of 8.3%, a packet delivery ratio of 98.2%, and an average end-to-end delay of 25 ms compared to other existing methods.  
wireless body area network; clustering; routing; adaptive binary bird swarm optimization; modified snake swarm optimization algorithm +
Footnote †: journal: _Journal of the Royal Society Interface_  
0000-0002-4000-3393-2223][PERSON]  
1
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Economic RS-based Proxies  
The nature of economic and business flows associated with functions and processes in urban areas is not directly observable and measurable through RS data. Hence, the disruptions to the functioning of the economic environment are one of the least studied topic in urban DRM. To discuss and explain the proxies for the economic component in details, we differentiate between two main categories--macro, regional, and urban) economics and logistics--that characterize the state of an economy and assure physical flows of economic activities correspondingly.
# A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition
## 1 Introduction
However, as is the case with the system we propose, a system that directly receives video information and recommends it without any prior information about its user has not yet been developed. Therefore, this study applies a deep learning approach to offer personalized advertising videos by capturing new users' facial expressions in real time. Though the proposed procedure follows the principle of CF, we create a dynamic profile of users based on the changes in their facial expressions every moment instead of using their historical records. To achieve this purpose, we developed a recommender system that applied a convolutional neural network (CNN) to predict how a user likes the currently watched advertising video by recognizing their facial expressions. Additionally, to recommend new advertising videos to users, we developed a scale-invariant feature transform (SIFT), an algorithm-based similarity model, to search users with similar preferences in real time. To evaluate the proposed procedure, we compare the performance of three different recommendation approaches, a random system, an average rating-based (best-selling) system, and a typical CF-based system as benchmark systems using eleven food advertising videos. The experiment results indicate that the performance of the proposed procedure is better than that of the benchmark systems in advertising video recommendations to both existing users and new users. The main contributions of this study are as follows:  
* The proposed methodology in this study effectively captures user facial expressions in real-time through a deep learning approach to solve information overload and data sparsity problems. * The proposed methodology can effectively recommend advertising videos without distinguishing between new users (with no history of watching or rating) and existing users (whose previous records or ratings are available). Therefore, the proposed methodology does not suffer from the new user problem. * The study conducts several experiments that use real-world facial expressions data to demonstrate that the performance of the proposed methodology was excellent in the existing recommendation approach. We also found that facial expressions are an important factor for advertising video recommendations. The rest of this study is organized as follows. Section 2 discusses related work on deep learning-based recommender systems, SIFT, and CNN. Section 3 discusses the proposed methodology and its components in detail. Section 4 describes the experimental design and discusses the results of this study. Section 5 summarizes the study, describes its limitations, and presents ideas for future work.
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 7 Conclusions
### Acronyms List  
\begin{tabular}{l l} CAM & Cooperative Awareness Message \\ CH & Cluster Head \\ C-ITS & Cooperative-ITS \\ CM & Cluster Member \\ CSMA & Carrier Sense Multiple Access \\ C-UE & Common User Equipment \\ DL & downlink \\ D2D & Device-to-Device \\ ETSI & European Telecommunications Standards Institute \\ FCD & Floating Car Data \\ FDD & Frequency Division Duplexing \\ HA & Hungarian Algorithm \\ I2V & Infrastructure-to-Vehicle \\ IoT & Internet of Things \\ IoV & Internet of Vehicles \\ ITS & Intelligent Transportation Systems \\ LOS & Line of Sight \\ LTE-A & LTE-Advanced \\ LTE & Long Term Evolution \\ MAC & Medium Access Control \\ MPC & Minimum Rate Oriented Power Control \\ QoS & Quality of Service \\ RB & Resource Block \\
**RBAP** & Resource Block Assignation Problem \\ RRM & Radio Resource Management \\
**RSRP** & Reference Signal Received Power \\ SC-FDMA & Single Carrier Frequency Division Multiple Access \\ SINR & Signal-to-Interference-plus-Noise Ratio \\ SPC & Sum Rate Oriented Power Control \\ SPS & Semi-Persistent Scheduling \\ UE & User Equipment \\ UL & uplink \\ V2I & Vehicle-to-Infrastructure \\ \end{tabular}  
**V2V**: Vehicle-to-Vehicle
**V2X**: Vehicle-to-Everything
**VANET**: Vehicular Ad Hoc Network
**V-UE**: Vehicular User Equipment
**VRU**: Vulnerable Road User
**WI**: Work Item
First, we identify and exploit file access patterns, including temporal, spatial, length, and frequency patterns at different granularity (i.e., file, directory, and file format), for extracting data features reflecting various spatiotemporal and semantic associations. These features are extended with other basic file and request information (e.g., file size, request offset) typically used for this purpose by prior work. The features are then used in real-time to build streaming ML models within a new online learning framework. To show the generality of the framework, models are developed for making two types of predictions. The first one involves predicting the next file offset to be read by a future file request in a particular file. The second one involves predicting the future file hotness of a particular file (i.e., a metric that determines the file's future access rate and is defined concretely in Section 4.1). For evaluation purposes, one regression and one classification model are built for each type of prediction, totaling four models. In summary, this paper makes the following contributions:  
* We show that file access patterns calculated per file, parent directory, and file format are strong features for ML models that predict the presented target variables. * We present and evaluate a set of representative and robust features to be used with streaming ML models for predicting the next offset and future file hotness. * We describe an architecture for an online learning framework for generating and testing training instances efficiently and in real time while the system is running. * We show, using real production workloads, that streaming machine-learning models are vital in supporting the high and continuous number of I/O requests with evolving access patterns present in modern data workloads. In our preliminary work [21], we explored using one streaming classification model to predict the next file offset to be read in a file. In this work, we (1) generalize the application of streaming ML to multiple targets (i.e., next offset and future file hotness) using both classification and regression; (2) formalize the file access patterns as features; (3) present the conceptual architecture of an online learning framework; and (4) provide an extensive experimental evaluation with comparisons to other rule-based, machine-learning, and deep-learning models. The rest of this paper is organized as follows. Section 2 discusses the related work. Section 3 presents the file access patterns, while Section 4 discusses feature extraction and selection. Section 5 presents the online learning framework. Section 6 presents model selection and hyperparameter tuning. Section 7 presents the experimental evaluation. Section 8 discusses the potential limitations of the proposed approach along with future directions. Finally, Section 9 concludes the paper.
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 3 Energy Management Proposal for 5G Network Slicing
### Energy Management Strategies for Adaptive Consumption  
To carry out adaptive energy management in 5G network slicing, five strategies have been proposed, as described below. These strategies are executed by the MNO (see Figure 2), are implemented through algorithmic solutions, as shown in Section 5.2, and they seek offer energy efficiency to the 5G mobile networks through optimization of power consumption (i.e., less and better energy use), while promoting the reduction of OPEX and related environmental impacts. In the energy management process, the customers are aware of the configuration of network slices and/or services performed by the MNO to optimize the consumption, and they are tolerant of the possible modifications/actions on network slices or services. Figure 1 shows an example of the implementation of the proposed strategies.  
Figure 1: Example of application of the management strategies for adaptive energy management in 5G network slicing. (**a**) Application of management strategies for the network slice 3. In this example, the initial distribution of services (initial time, duration, and power demanded) causes inefficient use of energy and allows the execution of only two services (\(S_{3}\) and \(S_{5}\)). The use of management strategies instead allows optimization in the consumption and processing of four services (\(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\)). (**b**) Disaggregated representation of the management strategies applied to services belonging to network slice 3.
# A Novel Approach Proposal for Estimation of Ultimate Being Capacity Based on Pile Loading Test Data
## 1 Introduction
In recent years, there have been many studies on the determination of pile carrying capacity based on artificial neural networks [47; 48; 49; 50; 51]. A feedforward neural network (FFNN) is proposed to determine the ultimate axial load bearing capacity of pre-stressed precast high-strength concrete pile foundations by [PERSON] et al. [52]. It has been found that the proposed FFNN method is consistent with the measurement results and is reliable. In addition to artificial neural network methods, methods developed based on statistical results have also been available in recent years [53]. It has been seen that the pile loading test evaluation methods based on nearly 40 mathematical models in the literature give values that are quite far from the failure load determined by theoretical methods and the failure load determined by graphical methods, even in piles up to failure. In this context, a new method development study has been carried out. In this study, a new method has been proposed to determine the ultimate pile bearing capacity by using eight full-scale pile loading tests. The proposed method has been compared with theoretical methods based on SPT data, methods using SPT data indirectly, full-scale pile loading test evaluation methods, and finite element methods.