esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion

Table 4: _Cont._About 71.67% of the reviewed research efforts focused on developing building energy consumption forecasting methods for nonresidential, 18.33% focused on residential buildings and only 10% on both (see Figure 5). Within the forecasting categories for building energy consumption, the most studied were whole-building energy with 63.3%. For whole-building energy about 48.3% focused on nonresidential buildings, 13.3% focused on residential buildings and 1.7% on both. The next category studied was cooling and heating energy with 13.2%. For this category about 8.3% focused on nonresidential buildings, 3.3% focused on a residential building, and 1.6% on both. Although the thermal comfort of the occupants is decisive in building energy consumption, a large part of the authors focused on forecasting the energy consumption of the entire building so that they could consider not only HVAC systems but also other systems, which represent a considerable consumption such as lighting and plug loads. On the other hand, the least studied was heating energy with 1.6% and was only considered in residential buildings (see Figure 6). Figure 5: Comparison between building type and forecasting methods. Figure 6: Comparison between building type and forecasting category. For the prediction horizon, about 58.3% of the reviewed research focused on forecasting short-term, 23.3% focused on long-term, 13.4% focused on a very short term, and 5% on medium-term. The explanation could the increased use of demand-side management strategies such as load shifting, which allows shifting the load from on-peak hours to the off-peak hours and requires knowing the building energy consumption in a period of one to 24 h. It should be acknowledged that in recent research the very short-term horizon has grown, since it has been of great help in forecasting the availability of energy resources for the use of renewable energy in buildings (see Figure 7). About 53.4% of the reviewed studies focused on using historical and calendar data on the forecasting methods, 41.7% focused on using historical, weather and calendar data, 3.3% focused on using historical, occupancy and calendar data, and only 1.6% use historical, weather, occupancy and calendar data. This could be because in current BEMS we can count on various sensors to record the energy consumption in the building, but the data related to the occupancy in many cases is not considered due to the complexity that the acquisition of these data can present depending on the nature of the building. On the other hand, having these data can further improve the accuracy of the prediction (see Figure 8). Figure 8: Comparison between building type and input variables. Figure 7: Comparison between building type and prediction horizon.
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 3 5G Networking  
The fifth generation of mobile communication networks (5G) sets some stringent requirements related to ultra-low-latency, ultra-responsive and ultra-reliable services. The only way of accomplishing these requests is to distribute the computational power from the central cloud to a Multi-access Edge Computing (MEC). The MEC approach allows moving storage and relevant computation tasks closer to the access network, reducing the traffic volume and achieving real-time performance for AR applications, together with context-awareness and enhanced privacy protection [3; 26].  
AR and VR technologies are expected to be among the most relevant applications of the 5G/MEC paradigm, with a predicted market of $179 billion by 2021 [27]. On the other hand, this implies a large growth in network traffic. One of the most widely proposed countermeasures consists in local caching at the Edge [28], which comes particularly handy in an assistive maintenance scenario as it allows to keep frequently accessed documents, 3D models and holographic videos directly on the MEC, reducing the load in backhaul links and cloud servers.  
Caching also reduces the end-to-end latency, allowing in our case to upload 3D mesh surfaces at an average speed of 156 MB/s, equivalent to approximately 1.5 M triangles/s. Assuming an average environment mapping composed of 20 K triangles (empirical measurements are reported in Table 1), an entire new map can be uploaded (e.g., while the operator enters a new room) in less than 20 ms. Entering different rooms, along with the associated holographic augmented elements, is perceived seamlessly by the user as the whole procedure is carried out within the HoloLens' framerate. It is worth mentioning that the WiFi transmission between the 5G modem/router and HoloLens is performed with a 802.11 ac connection in the 5 GHz band [29], whose bandwidth is large enough to not introduce any significant delay.  
Once the HMD enters a new room, it starts exploring changes with respect to the previously stored map and periodically transmits the new 3D mesh back to the MEC, where the Unity development platform [30] runs the analyses described in Section 4. Together with HoloLens' data, RGB and thermal images (Section 5) are transmitted to the MEC as well, but their incidence is negligible (around 6.5 MB/s) with respect to the overall data stream.
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 3 Methods for Real-Time Food Monitoring
### Humidity Sensors  
Several humidity sensors have been explored in recent years for real-time packaging monitoring purposes [119, 120, 121, 122]. As described in detail below, these sensors have the advantage of being simple and easily implemented in food packages. For instance, a low-cost wireless humidity sensor, which can be easily introduced inside packages, was developed using a planar inductor and capacitor fixed on a paper substrate [123]. Generally speaking, using a paper substrate ensures a cheap platform, high sensitivity, and a reasonable response time. Indeed, when the humidity within the packaging increases, this substrate adsorbs water, hence, modifying the capacitance of the capacitor [124, 125]. Humidity sensors coupled with a radio frequency identification (RFID) tag have also been reported [126, 127, 128, 129]. These usually consist of two planar inductor-capacitor resonators (Figure 3a), with one of them encoding the ID data and the other one working as the humidity sensor [126]. Figure 3b reports the measured signal of a sensor unit at room temperature: a higher humidity value corresponds to a lower resonance frequency. Alternatively, RFID humidity sensors comprising of a printed spiral inductor working as an antenna can be developed [130]. However, humidity-based sensing materials based on photonic crystals have been widely investigated as a valid system for the real-time monitoring of food packaging owing to their high sensitivity, low cost, and high productivity rate [131, 132, 133]. For example, Figure 3c shows the red-shift of the photonic band-gap of a synthetic opal hydrogel. The opal consists of highly monodisperse polystyrene microspheres self-assembled on a glass substrate and infiltrated with a poly(acrylamide-co-acrylic acid) hydrogel network. By increasing the relative humidity (RH), the hydrogel swells, modifying the lattice periodicity and in turn, the optical response of the opal [134].  
Figure 2: (**a**) Reflectance of a thin polyvinyl chloride (PVC) film before (red line) and after (black line) -5 min in toluene vapor. (**b**) Response of a thin PVC film to water with the spectral position of relative maxima during the exposure. (**c**) Diffusion coefficient (D), Flory–Huggins parameter (x\({}^{\text{H}}\)), and comparison (**v**) between the van der Waals volume of the diffusing species and the spectral shift of the relative maximum. Adapted with permission from [23]. Copyright © 2020 American Chemical Society.  
Humidity sensors, such as those described above, find potential applications in dry food packages, which require the avoidance of any contact with moisture and maintaining a very low humidity level to avoid degradation. Due to their ease of fabrication and cheap cost, such systems are promising candidates for commercial applications on a large scale, however their long-term stability still needs to be optimized.
# Preparation of Magnetically Driven Nickel Phosphide Nanowires and Their Electrochemical Properties
## 3 Results and Discussion
### Electrochemical Tests
The experimental results (Figure 6) suggest that the well-developed nanowire specimen shows relatively good rate characteristics, indicating that the reducing effect of the ohmic overpotential outweighs the increasing effect of activation overpotential. The rate characteristics of samples 18L, 18M, and 18H with different phosphorus contents also exhibit a similar trend, though the difference in charge amount retention is small between samples 18M and 18H. In fact, the surface reaction resistance or activation overpotential might be somewhat reduced because the reaction is more activated when the phosphorus content is high. Nevertheless, overall, sample 18L, which has a well-developed nanowire shape and an open pore structure, showed superior rate characteristics compared to sample 18M and sample 18H, which have relatively closed pore structures with small aspect ratios (Figure 3). Figure 7 show the deterioration trend obtained for five specimens during the repetitive redox reaction cycles. Among the three electrodes with different phosphorus content (samples 18L, 18M, and 18H), sample 18H, which had the highest phosphorus content, showed the most significant deterioration, followed by sample 18M, which had a medium phosphorus content. Among the three electrodes with low phosphorus content (samples 0L, 2L, and 18L), the deterioration of sample 0L, which had the lowest phosphorus content and the least developed nanowire structure, was considerably high compared to the other two specimens (sample 2L and sample 18L) in which the nanowire structures were relatively well-developed. Therefore, it is considered that the higher the phosphorus content or the reaction surface area, the faster the rate of material deterioration due primarily to the greater amount of reacted nickel phosphide. The deterioration rate curve (Figure 7b) tells which phosphorus content or reaction surface area is relatively important degradation  
Figure 6: Rate characteristics of the samples. (**a**) Amount of cathodic charge amount and (**b**) charge amount retention at different cathodic current densities. factor: The robustness of sample 18L, which had the lowest values of both phosphorus content and surface area, was the best. It is noteworthy that the robustness of sample 0L, which had a low phosphorus content and a large response surface area, was significantly low. In contrast, the sample 18M, with a low surface area and middle phosphorus content, showed a relatively high robustness similar to those of samples 2L. This implies that the reaction surface area has a significant effect on the deterioration of the nickel phosphide nanowires compared to the phosphorus content.
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 3 Experiments and Evaluation
### Experiment Data Sets  
The proposed function-level embedding framework was evaluated on the real-world vulnerability data set collected by our previous work Cross-VD [14]. This data set contains manually labeled vulnerable functions and collected non-vulnerable functions from three popular C open source software projects, as listed in Table 1. According to [14], the vulnerability labels were obtained from the National Vulnerability Database (NVD) [35] and the Common Vulnerability and Exposures (CVE) [36] websites. To match the labels with the source code function, the corresponding versions of a project's source code were downloaded from GitHub. Then, each vulnerable function in the software project was located based on the information provided by NVD and CVE websites and manually labeled. For the vulnerabilities that spanned across functions or across files (e.g., interprocedural vulnerabilities), they discarded them. Excluding the identified vulnerable functions and the discarded vulnerabilities, the authors [14] used the remaining functions as the non-vulnerable ones. In this paper, we only chose two open source projects that contain the most number of vulnerable functions, which are FFmpeg and LibTIFF. We also manually labeled another project OpenSSL based on their method.  
The real-world vulnerability data sets aim to test the performance of our framework on practical situations, where there are insufficient labeled data available for training. To perform the experiments, we partitioned the data sets collected from each project as Table 2 shows.
## 3 Experiments and Results  
In this section, we introduce two different datasets applied in our experiments and detail several comparative experiments. Our method mainly compares with the following methods: HOG-SVM [25], ScSPM [27], and deep CNN [31]. In addition to the results from the proposed method, we also quote some results directly from the literature.  
The effectiveness of the proposed method is evaluated from the following experiments. In the following experiments 1 and 2, we apply our method to the Duke dataset to classify the volume data and compare it with the other method. In experiment 3, we apply our method on Beijing clinical datasets and compare with the good-performing ScSPM [27] in traditional methods and the transfer learning-based method, IBDL [34], to evaluate the classification performance of our method on retinal OCT B-scans. In experiment 4, we discuss the efficiency of our method. In experiment 5, we focus on the model structure itself and compare the results of different architectures.  
In our experiments, we utilize cross validation to evaluate the classification performance of the proposed method. The cross validations are repeated with different random seeds to avoid dataset splitting bias. The accuracy, sensitivity, and specificity for each category (AMD, DME, and NOR) are defined by following formula [36]:  
\[accuracy=\frac{TP+TN}{TP+FN+FP+FN} \tag{1}\]  
\[sensitivity=\frac{TP}{TP+FN} \tag{2}\]  
\[specificity=\frac{TN}{TN+FP} \tag{3}\]  
where _TP_ is true positive, _FN_ is false negative, _TN_ is true negative, _FP_ is false positive.  
Our classification algorithm is implemented using Keras and it is conducted on a desktop computer with an Intel (R) Core (TM) i5-4590 central processing unit (CPU), 8 GB of RAM and a Nvidia GeForce GTX 1070 graphics processing unit (GPU). The parameters selected for the proposed method are listed below: 299 \(\times\) 299 for the image size and the pixel value of the images is divided by 255 for normalization; \"mixed8\" for the middle layer name (the output of InceptionD discussed in Section 2.3); 128 for the filters amount, 3 \(\times\) 3 for the kernel size and \"same\" for the padding; the optimizer for training model is adadelta [43] optimizer.
# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 5 Experiment and Results
### Experimental Design and Scenarios  
Microscopic traffic simulations were conducted using SUMO to examine the influence of autonomous vehicles (AVs) on urban traffic composed primarily of human-driven vehicles (HVs). Four scenarios were modeled to reflect different AV penetration rates:  
1. **Scenario 1 (Baseline)**: 100% HVs
2. **Scenario 2:** 10% AVs  
Figure 6: Overview of the simulation and analysis workflow, including data sourcing, preprocessing, SUMO modeling, and emissions estimation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Objective** & **Tool/Module** \\ \hline Behavioral Coordination & SUMO ([PERSON], LC2013) \\ Dynamic AV Logic & TraCI (Python) \\ Network-Level Simulation & OMNeT++ + Veins, Eclipse MOSAIC \\ \hline \hline \end{tabular}
\end{table}
Table 5: Modules for simulating AV communication and coordination.  
* **Scenario 3:** 30% AVs
* **Scenario 4:** 50% AVs.  
Traffic volumes were derived from the Saudi Open Data and TomTom platforms, reflecting weekday peak (7-9 a.m., 4-6 p.m.) and off-peak patterns.
## 3 Advanced Techniques in Remote Sensing of EBC
### Inversion Algorithms  
Various process-oriented models are developed to characterize Earth environments because traditional methods based on simple statistical relationships are often sensor-dependent, and site-specific [84, 85]. These models represent the in-depth understanding of physical processes deriving the Earth system, and are unquestionably useful in Earth observations in support of EBC [85]. Generally speaking, models can be run under two modes, namely inverse mode and forward mode. An inverse mode applies outputs to retrieve inputs that cause them, while a forward mode applies inputs to obtain resulting outputs. For example, [PERSON] and [PERSON] suggested that a remote sensing model can be used to simulate the reflectance of forest canopies [84]. The forward mode treats data on the forest canopy variables as the inputs and the spectral signature as the output but the inverse mode is converse process, _i.e._, the spectral signature is the input and estimates of the forest biophysical variables are the outputs. Obviously, the inverse model is more frequently used in remote sensing. The core of inverse model is inversion algorithms, which mostly follow the physical laws and establish cause-and-effect relationships [85]. In order to understand remote sensing signals and develop practical inversion algorithms to estimate land surface variables, physically-based models are advised to discuss the following three areas [86]: atmosphere (atmospheric radiative transfer modeling), land surface (surface radiation modeling), and sensor (sensor modeling). [PERSON] grouped inversion algorithms into four categories: model simulation and statistical analysis, optimization algorithms, look-up table algorithms, and data assimilation [86]. Several recent examples are provided below to display the applications of inversion algorithms in EBC.  
In order to monitor and model storm-water pollution, [PERSON] and [PERSON] proposed a Bayesian network approach, which falls into the category of model simulation and statistical analysis [87]. A leaf radiative transfer model called the LIBERTY (Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance Yields) was selected and incorporated with three pigments to better understand relationships between leaf biochemical, biophysical, and spectral properties [88]. A look-up table approach was developed to estimate LAI [89]. [PERSON] _et al._ assimilated remotely sensed vegetation index time series, such as MODIS NDVI, into a process-based model BIOME-BGC (Biome-BioGeochemical Cycles) to estimate the gross primary production (GPP) of agro-forestry ecosystems [90]. However, an intrinsic problem in inverse models is the process from inputs to outputs is often non-invertible, _i.e._, more than one combination of inputs results in the same output of spectral signature. [PERSON] stated that, because it is still a nonlinear, ill-posed problem to inverse land surface parameters, further research is required to focus on use of regularization [86].
## 4 Results and Analysis
### A Comparative Analysis: GCN vs. GCN with Betweenness Centrality  
In this subsection, we compare the behavior of our model (GCN with betweenness centrality) with the GCN model. We consider the Zachary Karate Club network and analyze the node embeddings generated by the two models for this network. For this node embedding process, we consider Equations (1), (4), and (5) as defined in Section 2.1.  
Figure (a)a shows the network of the Zachary Karate club, which consists of two classes. In these two classes, there are 34 nodes connected by 78 unweighted and undirected edges. Using this well-known network, we analyze the proposed idea (GCN with betweenness centrality) for classifying nodes in graphs. To empirically test our idea, we train the GCN model by randomly initializing the transformation matrices into the Zachary Karate Club network [45]. Then, we train the GCN with the betweenness centrality model based on selecting the training nodes with high betweenness centrality values. We also draw the node embeddings. When we compare the node embeddings of both models, we find that GCN performs well, but GCN performs better with the betweenness centrality model. Figure (b)b,c show the node embeddings of GCN and GCN with the betweenness centrality model, respectively. Through observations and a comparative study of both embeddings, we show the correctness of our claim that GCN performs better with the betweenness centrality.  
Our idea provides better performance in terms of dispersion and proximity of nodes, while the traditional GCN may consider nodes that do not contribute much to the functions (such as leaf nodes or nodes with lower importance). The main reason for the better performance of the GCN with the betweenness centrality is the consideration of nodes with  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Nodes** & **Edges** & **Classes** & **Features** & **Type** \\ \hline Cora & 2708 & 5429 & 7 & 1433 & Citation network \\ Citeseer & 3312 & 4732 & 6 & 3703 & Citation network \\ PubMed & 19,717 & 44,338 & 3 & 500 & Citation network \\ Wiki-CS & 11,701 & 4,31,726 & 10 & 767 & Wikipedia-based \\ AMZ Computers & 13,752 & 4,91,722 & 10 & 300 & Amazon Computers \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets description.  
Figure 6: Node embedding of GCN and GCN with betweenness centrality.  
high betweenness centrality over random nodes in GCN. Nodes with high betweenness centrality appear in many shortest path node pairs and therefore tend to contribute more to the features during training.
## 3 Network Model and Problem Statement
### Network Model  
This paper assumes that N sensor nodes are randomly scattered in a two-dimensional square field A, and the sensor network has the following properties:  
1) This network is a static densely deployed network. It means a large number of sensor nodes are densely deployed in a two-dimensional geographic space, forming a network and these nodes do not move any more after deployment.  
2) There exists only one base station, which is deployed at a fixed place outside A.  
3) The energy of sensor nodes cannot be recharged.  
4) Sensor nodes are location-unaware, i.e. a sensor node cannot get its location information through other mechanism such as GPS or position algorithms.  
5) The radio power can be controlled, i.e., a node can vary its transmission power depending on the distance to the receiver [5]. For instance, Berkeley Motes [12] have in total 100 power levels.  
The first three properties are typical assumptions about wireless sensor networks. These assumptions are widely adopted by most previous works, such as [3 - 6]. For a sensor node, there are three kinds of methods to get its location information i.e. GPS, directional antenna and position algorithms. However, obtaining location information with a hardware device such as GPS or directional antennae will cause an increase in the cost of a sensor node, and position algorithms that need to exchange a large quantity of messages to compute the node's location information will also result in high energy consumption. As a result, the energy cost and system complexity involved in obtaining geography information may compromise the effectiveness of the proposed solutions as a whole. For this reason, the fourth assumption is reasonable. The fifth assumption mainly deals with the definition of different power levels for intra-cluster and inter-cluster communications. In this way, the node energy consumption can be remarkably reduced so as to further prolong sensor network lifetime. In this paper, for simplicity, we assume the power level is continuous.