esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Applications
### Internet-of-Things (IoT) Applications  
In IoT applications, more and more intelligent devices are connected to the Internet and produce a large amount of data. Blockchain provides a reliable way to collect data from smart devices. The work in [61] designs a high performance blockchain platform to realize the decentralized autonomy of intelligent devices. [PERSON] et al. used blockchain technology to enhance the security of IoT systems [62]. [PERSON] et al. studied integrating blockchain in sensor networks to achieve security and privacy [9].
* Smart appliances A smart home appliance is a device that connects to the Internet and can help you to monitor and control the device when away from home. Encrypting these appliances on the blockchain can protect the ownership and enable transferability. Walmart has filed a patent application on the management of smart appliances using blockchain. The patent application proposes to manage a system of smart appliances such as computers, kiosks, tabletop devices, gaming devices, laptop computers, and portable media players using blockchain. It also describes a smart environment in a wide range of applications, such as home, media, manufacturing, energy, and healthcare [63]. Energy conservation measurements in buildings are measured as the difference between a predictive baseline model and the actual consumption, and the actual application involves a large amounts of data and often requires a third-party for auditing. The work in [64] develops a prototype for fair and trustable energy performance contracts, which stores the predictive models and the data in an immutable Ethereum based distributed ledger.
* Supply chain sensors Smart sensors provide companies the timely and valuable data of the supplies as they are transported, which can power insightful analysis that promotes improvements in cost, performance, or customer experience. With blockchain, smart sensors can be more secured, and any data-tamper can be tracked and located instantly. Walmart uses blockchain to keep track of sales of pork in China. Its system records where each piece of meat comes from, each processing and storage step, and sell-by-date in the chain [65]. The work in [66] attempts to find blockchain candidates that are able to ensure the integrity of supply chain by evaluating different blockchain alternatives.
# End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control
## 3 Preliminaries  
### Reinforcement Learning  
Reinforcement learning (RL) is suitable for sequential decision problems, in which the previous states and actions have an influence on the following states. Mathematically,this problem can be abstracted as a Markov decision process (MDP). As shown in Figure 1, an agent is defined to interact with an environment in a general RL problem. At each step, the RL-agent receives the observation \(o_{t}\) and the reward \(r_{t}\) from the environment and then produces an action \(a_{t}\) according to a policy \(\pi\), which maps states to a probability distribution over the actions. Under the action \(a_{t}\), the environment shifts from state \(s_{t}\) to \(s_{t+1}\) and gives a new reward \(r_{t+1}\) and the updated observation \(o_{t+1}\). Generally, the states of the environment are partially observable. However, \(s_{t}=o_{t}\) can be assumed for a simple scenario, as well in this paper. The goal for the RL-agent is to interact with the environment and learn the optimal policy \(\pi^{*}\), maximizing the accumulated future reward called return \(R_{t}=\sum\limits_{i=t}^{T}\gamma^{i-t}r(s_{i},a_{i})\), where \(T\) is the terminal time step and \(\gamma\) is the discounting factor representing the influence of the subsequent rewards.  
Based on the expectation of return \(R_{t}\), two types of value function are introduced to evaluate a policy: the state value function \(V_{\pi}(s)=E[R_{t}|s_{t}=s]\) and the state-action value function \(Q_{\pi}(s,a)=E[R_{t}|s_{t}=s,a_{t}=a]\). The commonly used \(Q_{\pi}(s_{t},a_{t})\) is also known as the Q-function, and represents the expected return after taking action \(a_{t}\) in state \(s_{t}\) and thereafter following policy \(\pi\). Then, the optimal policy should satisfy \(\pi^{*}=\underset{a_{t}}{\text{argmax}}Q^{*}(s_{t},a_{t})\).
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 3 Application
### Compression Based on Flight Data  
Due to the huge dimensions of flight data samples, if directly used for modeling, they will increase the computer load and greatly decrease the efficiency of modeling. In addition, the millisecond data collected by the sensors will obscure the information contained in the flight process. Therefore, before fault diagnosis modeling, the sample dimension of the flying parameter data should be compressed. The original data can be packaged using the idea of interval data, retaining the maximum and minimum values of various variables within a sample period of time, grasping the intrinsic characteristics of data objects globally, and compressing massive data. For data with time labels, interval length needs to be determined from the time dimension to ensure the timing of data and ensure that the time represented by each data interval is equal. For data without a time label, interval can be performed by other characteristics of the data. It is important to ensure that each interval has the same meaning in at least one feature of the data.  
\(T_{m\times p}\) is the interval data matrix after compressing the original data; the compression method can be expressed as  
\[T_{m\times p} =(T_{1},T_{2},\ldots,T_{p}) \tag{8}\] \[=\left(\begin{array}{cccc}\left[t_{11},t_{11}\right]&\left[t_{12 },t_{12}\right]&\ldots&\left[t_{1p},t_{1p}\right]\\ \left[t_{21},t_{21}\right]&\left[t_{22},t_{22}\right]&\ldots&\left[t_{2p},t_{ 2p}\right]\\ \vdots&\vdots&\ddots&\vdots\\ \left[t_{m1},t_{m1}\right]&\left[t_{m2},t_{m2}\right]&\ldots&\left[t_{mp},t_ {mp}\right]\\ \end{array}\right)\]  
where \(t_{ij}=\min\Bigl{(}\left[x_{l,j},x_{l+1,j},\ldots,x_{l+k,j}\right]\Bigr{)}\), and \(\overline{t_{ij}}=\max\Bigl{(}\left[x_{l,j},x_{l+1,j},\ldots,x_{l+k,j}\right] \Bigr{)}\). The length of time included in \(t_{ij}\) and \(\overline{t_{ij}}\) is the time period spanned by \(x_{l,j},x_{l+1,j},\ldots,x_{l+k,j}\). After interval compression, the sample size of the original data can be greatly reduced, and using the interval feature that retains the original data, less information is lost, which is beneficial for subsequent modeling and analysis of failures.
## 3 Finite Element Modeling of the Main Tower
### Boundary Conditions  
In the modeling process of the main tower, the constraint conditions can be divided into two categories: (1) constraints corresponding to the main tower; and (2) external construction constraints. External construction constraints refer to the contact between the prestressed steel bundle in the upper and lower beams of the main tower and the concrete entity, as well as the connection constraints between the two ends of the temporary transverse braces and the two sides of the tower wall during construction [14]. The types of constraints are summarized in Table 2. As listed in Table 1, the nodes at the bottom of the main tower were constrained, i.e., the displacements and rotation along X-, Y- and Z-axis were set to be zero. The prestressed steel bundles and concrete were embedded together, i.e., the elements in the model of the steel bundle and concrete shared the same node at the interface. In terms of the boundary conditions of the temporary cross braces, the nodes at the interface between the temporary cross braces and the tower columns were fixed, i.e., different elements adopted the same node at the interface. The diagram of the physical boundary conditions was plotted in Figure 9. In terms of the heat conducting boundary conditions, the governing equation could be expressed by Equation (3) [13]:  
\[\lambda\dfrac{\partial T}{\partial n}+(h_{c}+h_{r})(T-T_{a})=0 \tag{3}\]  
where \(n\) is the normal direction of the boundary surface; \(h_{c}\) is the convective heat transfer coefficient; \(h_{r}\) is the radiation heat transfer coefficient; and \(T_{a}\) is the temperature of the air around the outer interface.  
The governing equation for determining the thermal stress could be expressed by Equation (4) [13]:  
\[\begin{cases}\sigma_{x}l+\tau_{xy}m+\tau_{xz}n=0\\ \sigma_{y}m+\tau_{yx}l+\tau_{yz}n=0\\ \sigma_{z}n+\tau_{zx}l+\tau_{zy}m=0\end{cases} \tag{4}\]  
where \(\tau_{xy}\), \(\tau_{xz}\), \(\tau_{yx}\), \(\tau_{yz}\), \(\tau_{zx}\) and \(\tau_{zy}\) are the stress component; \(l\), \(m\) and \(n\) are the component of the surface unit normal vector in all directions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constraint Object** & **Constraint Type** \\ \hline Bottom of the main tower & All nodes and units are constrained \\ Prestressed steel beam with concrete & embedment \\ Temporary cross braces and tower walls & Multi-point rigid connection \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constraints of the finite element model.
## Materials and Methods
### MTT Cell Viability Assay  
All the test compounds were dissolved in DMSO (cell culture grade, Molar Chemicals, Halasztelek, Hungary) to obtain a stock solution of 10 mM final concentration, and stored at 4 \({}^{\circ}\)C, light protected, until further use. Antiproliferative activity of the synthesized steroids was assessed by MTT reduction assay [21] on human MCF-7 breast adenocarcinoma, PC-3 prostate cancer, A549 lung carcinoma,HeLa cervical adenocarcinoma, U2 Os osteosarcoma cell lines, and on MRC-5 human noncancerous fibroblasts. A total 5000 cells/well were seeded into 96 well plates and 24 h later cells were exposed to the test compounds in 5 \(\upmu\)M concentration for 72 h. Cells receiving no treatment were used as control throughout the experiments. To obtain IC\({}_{50}\) values of the selected compound on various cancerous and noncancerous lines, cells were seeded again in 5000 cells/well density in 96 well plates and treated with the compound in 1, 3, 5, 7, and 9 \(\upmu\)M concentrations or cisplatin in 20, 40, 80, 160, and 330 nM concentrations for 72 h. After the treatments, cells were washed using phosphate-buffered saline (PBS) and incubated with 0.5 mg/mL MTT reagent (SERVA, GmbH, Heidelberg, Germany) diluted in respective cell culture media without fetal bovine serum (FBS) for 1 h at 37 \({}^{\circ}\)C. The formed formazan crystals were dissolved in DMSO and the absorbance was measured using a Synergy HTX plate reader (BioTech-Hungary, Budapest, Hungary) at 570 nm. All the experiments were repeated three times and the data were analyzed by GraphPad Prism 7 software (GraphPad Software; San Diego, CA, USA).
# Guided Spatial Transformers for Facial Expression Recognition
## 3 Methodology
### Masks Generator
#### 3.1.1 Landmark-Based Binary Masks  
Our face changes when we express emotions. Muscles and ligaments adopt different positions when we smile or brown. All these movements help our brain to recognize how people feel in each moment. The landmarks are related to these facial movements as they constitute a set of points that surrounds the key parts of the face as they are the eyebrows, eyes, nose, mouth, and jaw. Once we obtain the landmarks, it is possible to create regions of interest by filling in the enclosed area of the landmarks.  
To detect as many faces as possible, we use a state-of-the-art CNN-based face detector, MTCNN [31]. When it finds a new face, the face passes to dlib library [32] to extract the landmarks. The landmarks consist of 68 (x,y) tuples, which are the coordinates of the points that indicate the position of eyes, eyebrows, nose, and mouth in an image. With these tuples, we generate a binary mask: black and white. In white, we remark the relevant pixels enclosed by the landmarks, and in black, the rest of the background.  
Notice that for some samples, the facial detector fails and does not detect any face. In those cases, it is not possible to generate the landmarks mask of that sample. As the STN always expects an image, we tested several ideas to substitute the landmarks masks on these cases: the first one that appears in tables as v1, consists of a substitute to those samples with full-white images, to indicate to the model that all the pixels are equally relevant; the second option, or v2, was used to introduce the saliency maps instead of the white images; and the third option, or v3, was used to ignore the predictions of the model trained with the landmarks mask and rely on the predictions of another model for those images. In our case, the second model was trained with saliency maps and appears in Tables 1 and A1 as \"STN with saliency masks\".  
For simplicity, the rest of the strategies derived from the landmarks use full-white images (v1) when the face is not detected.  
In the following subsections, we will detail how we extract the saliency maps and how we train each model with the different masks.
# Harmony Search Algorithm for Minimizing Assembly Variation in Non-linear Assembly
## 5 Numerical Illustration
### Stage III--Implementation of HSA
\begin{table}
\begin{tabular}{c c} \hline \hline _Terms_ & **Meaning** \\ \hline Trimming & Remove the components which do not satisfy \(LL_{i}\leq d_{i}\leq UL_{i}\) \\ \hline Partitioning & \begin{tabular}{c} Arranging the components in ascending order based on its di and placing an equal number of components in each bin starting from bin number 1 to \(nb\) \\ \end{tabular}  \\ \hline Mating & \begin{tabular}{c} Selecting one component (_X1_, _X2_, _X3_, and _X4_) randomly from the bin based on the bin combination of cb\({}_{i}\) and making an assembly. Check the assembly’s specification (within _LSL_ and _USL_); if it meets the requirement, then treat it as good assembly (_NoA_); otherwise, treat it as a surplus part \\ \hline _normmd_ & \ \begin{tabular}{c} Matlab function will generate the required number of normally distributed random numbers based on the given mean and standard deviation value \\ \end{tabular}  \\ \hline _randperm (nb)_ & \begin{tabular}{c} Matlab function will generate a permutation combination for the given bin number \(nb\) \\ \end{tabular}  \\ \hline Step 1: A random combination of bins for each component for the size of harmony memory 10 is generated (listed in Table 6). Step 2: The corresponding bin’s components are randomly matched with other components for each harmony number, and the assembly is produced. If the assembly meets the given specification limit, it is accepted as an assembly; otherwise, it is treated as a surplus part. This will be carried out until the component exists in each bin. The accepted assemblies are counted and listed in Table 6 as NoA. Step 3: Table 7 represents the arrangements of harmony, from maximum NoA to minimum NoA. Table 8 illustrates the best bin combination that will produce maximum NoA in the first iteration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline _H.No.(k)_ & \(cb_{X1}\) & \(cb_{X2}\) & \(cb_{X3}\) & \(cb_{X4}\) & \(NoA_{k}\) \\ \hline
1 & 2 5 1 4 3 & 2 5 3 1 4 & 5 3 4 1 2 & 3 1 2 4 5 & 308 \\
2 & 4 2 1 5 3 & 4 2 5 1 3 & 4 2 5 3 1 & 5 3 4 2 1 & 649 \\
3 & 4 2 1 5 3 & 1 5 2 3 4 & 3 4 2 5 1 & 3 1 5 4 2 & 407 \\
4 & 4 3 1 5 2 & 3 2 4 5 1 & 5 2 3 4 1 & 3 5 2 4 1 & 508 \\
5 & 1 5 4 3 2 & 1 3 4 5 2 & 3 2 4 5 1 & 2 4 1 3 5 & 326 \\
6 & 2 1 3 4 5 & 3 2 4 5 1 & 3 1 5 2 4 & 3 5 2 4 1 & 557 \\
7 & 5 2 3 1 4 & 2 5 3 1 4 & 1 2 3 4 5 & 1 5 4 2 3 & 354 \\
8 & 1 5 4 3 2 & 1 3 4 5 2 & 3 2 4 5 1 & 2 4 1 3 5 & 326 \\
9 & 2 5 1 4 3 & 2 5 3 1 4 & 5 3 4 1 2 & 3 1 2 4 5 & 308 \\
10 & 2 3 1 4 5 & 3 1 2 5 4 & 2 5 1 4 3 & 5 4 2 1 3 & 212 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Harmonies after sorting based on NoA.
# Data-Driven Modular Vehicle Scheduling in Scenic Areas
## 4 Experimental Results and Analysis
### Effectiveness Analysis of M2
Table 5 shows that, except for scenario (a), the solution quality measured by the rate of the obtained solution to the optimal solution under a deterministic setting in the rest of the scenarios all exceed 50%. Moreover, scenarios (a), (c), and (e) exhibit notably lower solution quality rates than scenarios (b), (d), and (f), suggesting that M2 achieves a closer approximation to the theoretical optimum when penalties are varied. This aligns with previous findings, indicating that M2's data-driven strategy adapts well to the complexities of real-world operations. In summary, by calculating the average solution quality rate across all scenarios, M2 achieves an average solution quality rate of 0.702. This indicates that, across various scenarios, M2 reaches 70.2% of the theoretical optimum on average. Thus, even without precise future demand, M2 can deliver an effective scheduling strategy for scenic spots at a level approaching 70.2% of the theoretical optimum, demonstrating strong practical applicability. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**(a)**} & \multicolumn{2}{c}{**(b)**} & \multicolumn{2}{c}{**(c)**} & \multicolumn{2}{c}{**(d)**} & \multicolumn{2}{c}{**(e)**} & \multicolumn{2}{c}{**(f)**} \\ \cline{2-11}  & **M2** & **M3** & **M2** & **M3** & **M2** & **M3** & **M2** & **M3** & **M2** & **M3** \\ \hline Average optimization rate (\%) & 1.09 & 2.56 & 1.75 & 2.31 & 1.58 & 3.03 & 3.18 & 3.32 & 2.07 & 3.46 & 3.51 & 3.63 \\ Standard deviation & 0.55 & 1.45 & 0.61 & 0.83 & 0.63 & 1.20 & 1.01 & 1.02 & 0.80 & 1.24 & 1.21 & 1.15 \\ \hline \hline \end{tabular} (In the Tables 4 and 5, (a): \(c=6\) with constant penalty; (b): \(c=6\) with varying penalty; (c): \(c=12\) with constant penalty; (d): \(c=12\) with varying penalty; (e): \(c=18\) with constant penalty; (f): \(c=18\) with varying penalty)  
\end{table} 
Table 4: Statistical analysis of M2 and M3 optimization rates. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** & **Average** \\ \hline Solution quality rate & 0.428 & 0.759 & 0.520 & 0.959 & 0.579 & 0.969 & 0.702 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Solution quality performance of M2.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 4 Simulation Model
### Validation
Finally, the channel is dominated by the third node due to the high values of the interrupt counter (Figure 11f). This behavior implies that DB FBE nodes must operate in synchronous mode (no shifts). Subsequently, we conclude that DB FBE cannot easily coexist with other channel access methods. The results for enhanced FBE, greedy-enhanced FBE, and BITR FBE are shown in Figure 11g-i, respectively. As described in Section 3.2, these variants refrain from fully periodic transmissions by using a backoff mechanism. Therefore, every time one of the nodes dominates the channel, other nodes try to match the starts of their COTs with the dominant one. We illustrate this in the following way. Consider the case of enhanced FBE for a normalized COT of 0.9. When the first node successfully performs backoff, it gains channel access, transmits (when other nodes are checking for ongoing transmissions during ICCA), and then mutes itself for FFP minus CCA. The backoff mechanism in the first node constantly moves its COT forward in time until the other node's ICCA is not interrupted, and its own backoff procedure can be performed. Conversely, greedy-enhanced FBE does not select a new backoff value after unsuccessful ICCA or ECCA, but continuously samples the status of ongoing transmissions and can immediately access the channel, after deducting its own backoff, during the idle period of a previously transmitting node. Meanwhile, the introduction of the idle time reduction in BITR FBE accelerates the COT convergence process. Thus, both greedy-enhanced and BITR FBE achieve better performance, especially for larger COT values. We conclude that introducing backoff in enhanced, greedy-enhanced, and BITR FBE reduces the problem of node domination. Nevertheless, due to the increased probability of collisions, worse performance should be expected than in the case of variants that follow the FFP periodicity and are configured under perfect node coordination. In summary, we have evaluated the performance of all available FBE variants in a specific scenario for varying COT durations. We are able to explain the behavior under all cases and draw specific conclusions regarding further analysis (e.g., that DB FBE nodes must operate in synchronous mode). Thus, having validated our simulator, we proceed to study the upper-bound performance and coexistence capabilities of the FBE variants.
## 4 Experimental Evaluation and Results  
### Experimental Setup  
Dealing with a video sequence requires processing large amounts of data; even a single recording of a person performing an exercise can contain thousands of frames, each of which must be evaluated individually. When the task is expanded to deal with multiple camera calibrations, multiple exercises, etc., this quickly becomes an enormous task of big data that cannot be processed manually. For this, tools that would allow to optimize the video-feed evaluation are required. Our method attempts to simplify the big-data task of human posture evaluation by providing such processing tools. During the training process, we used _MoVi_ and our own created data sets split using the 80:20 rule where 80% of the data set recordings are used for training and 20% of the remaining recordings are used for testing. However, this solution can still bias the results, as the results can be chosen so that the remaining 20% have good results but not necessarily be a generalized solution fit for all cases. Therefore, during these experiments, a new validation dataset consisting of eight videos was prepared that attempted to encompass all edge cases. For example, these edge cases involve determining if the network can detect user actions such as bending sideways, forward/backward, or touching their face. This validation dataset includes these edge cases because our solution relies on monocular images that lose a lot of depth information that can only be inferred from certain lightning conditions, i.e., shadows and based on subjects' orientation to the camera.  
\begin{table}
\begin{tabular}{c c} \hline
**Exercise No.** & **Average Distribution per Subject** \\ \hline Ex. 1 & 99 \\ Ex. 2 & 76 \\ Ex. 3 & 78 \\ Ex. 4 & 87 \\ Ex. 5 & 92 \\ Ex. 6 & 71 \\ Ex. 7 & 70 \\ \hline
**Total** & **592** \\ \hline \end{tabular}
\end{table}
Table 1: Real-world dataset breakdown captured for a single perspective selfie video (average distribution for different subjects).  
Figure 5: Dataset example.