esa-sceva/satcom-chunk-collection huggingface dataset:
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 3 Communication Technologies and Protocols
### Ethernet  
Ethernet (IEEE 802.3) is one of the cheapest, most widely used wired technologies for LAN and Wide Area Network (WAN) connections within buildings which is attributed to its high data rates (ranging from 10 Mbps up to 100 Gbps), distance range (up to 100 m) and high immunity to noise. Ethernet connections are typically based on one of three types of cables, i.e., coaxial, twisted pair or fiber optic, with the cable connecting to a central hub or switch in a star topology and providing the user with the possibility of different data rates, i.e., 10 Mbps, 100 Mbps, 1 Gbps, 10 Gbps and 100 Gbps. In addition, various solutions for real-time applications, (e.g., EtherCAT [82], SERCOS-III [83] and PROFINET [84]) are also presently available to the end user, further enhancing its viability. Although Ethernet provides high immunity to noise and is a fast and secure technology, a significant drawback is the difficulty in making any changes to the network once it is placed. It was also suggested in [85] that Ethernet is not best suited for real-time applications due to a lack of timeliness guarantees, operational flexibility and bandwidth efficiency. To address this limitation, the authors proposed a new protocol, i.e., FTT-Ethernet, which enables online admission control for real-time operation, and data structures and mechanisms designed to support dynamic QoS (quality-of-service) management. More recently, a new time-triggered protocol designed for decentralized control applications has also been presented in [86], which can reduce the node forwarding delay and cycle time during the communication process, with the authors claiming that the proposed protocol has a shorter cycle time when compared with other solutions such as EtherCAT and PROFINET.
# High-Performance Actionable Knowledge Miner for Boosting Business Revenue
## 2 Methods
### Action Rules  
The concept of an _action rule_ was first proposed by [PERSON] and [PERSON] in [17]. The data analysis using the action rule technique is expected to bring a better understanding of data, but also to recommend specific actions aiding in decision-making. By _action_, we understand changing certain values of attributes in a given _information/decision system_. _Action rules_ are extracted from an information system, and a singular action rule describes a possible transition from one object's state to another one, considering the desired change in so-called _decision attribute_[17]. We split attributes into so-called _stable attributes_ and _flexible attributes_, and one distinguished attribute called the _decision attribute_. A single action rule recommends the minimal changes in flexible attributes' values with the given stable state, to achieve a change from one state of the decision attribute to another state. The formal definition of an action rule is as follows:  
**Definition 1**.: _An action rule is a term: \([(\omega)\wedge(\alpha\rightarrow\beta)\rightarrow(\Phi\rightarrow\Psi)]\), with \(\omega\) denoting conjunction of fixed stable attributes, \((\alpha\rightarrow\beta)\) denoting the proposed changes in values of flexible attributes, and \((\Phi\rightarrow\Psi)\) denoting a change of decision attribute, or an action effect._  
Application of Action Rules to Improve NPS  
Within this research, we propose to apply the action rule method to improve the Net Promoter Score metric. The mined actionable patterns should indicate which specific aspects of customer service/products should be addressed so that the predicted \"Detractor\" of a company changes to being a predicted satisfied \"Promoter\" or neutral \"Passive\", at the minimum. We define the _PromoterStatus_ attribute as the decision attribute. This attribute can have three values: satisfied _Promoter_, neutral _Passive_, or unsatisfied _Detractor_. Substituting \(\Phi\) for \"Detractors\" and \(\Psi\) for \"Promoters\", the extracted action rules suggest changes in the flexible attributes leading to a change in the promoter status, with a certain confidence, and under certain defined stable conditions. The flexible attributes here would be any benchmark areas that can be addressed, and the overall goal is overall higher customer satisfaction, as measured by NPS. The Listing 1 presents a sample actionable pattern as extracted from customer survey data:There are two characteristics of each action rule: so-called _support_ and _confidence_. _Support_, in application to customer survey data, indicates the number of customers that matched the rule's antecedent. On the other hand, _confidence_ would be interpreted as the probability of changing the customer's status from Detractor to Promoter.
## 1 Introduction
The ionosphere-free PPP approach is applied for both instant and post-processed scenarios. Our paper is organized as follows: First, the ionosphere-free PPP mathematical model is introduced. Subsequently, the GNSS data collection and processing methodology are presented, along with an evaluation of the quantity and quality of the collected data. The processing results and their analysis are presented and discussed in Section 4. Finally, the conclusion is drawn in the final section.
## 2 Traditional TV Model  
An image could contain many types of noise. According to the distribution condition of probability density function (PDF) of their amplitude, noises are classified into Gaussian noise, Rayleigh noise, uniform noise, exponential noise, impulse noise, gamma noise, etc.  
In this paper, the discussion focuses on impulse noise denoising of images. Impulse noise is additive and is mainly caused by black-and-white bright and dark spots produced by image sensors, transmission channels, decoding processes, etc. In 2004, [PERSON] [11] proposed the use of a L1 data-fidelity term for impulse noise related problems. Since then, many research papers adopted this model in their characterization of this type of noise [35; 36; 37]. The ATV model of impulse noise based on this model is:  
\[\mathbf{F}=\underset{\mathbf{F}}{\text{argmin}}\|\mathbf{F}-\mathbf{G}\|_{1}+\mu R_{ATV}(\mathbf{ F}). \tag{1}\]  
where \(\mathbf{G}\in\mathbb{R}^{M\times M}\) is the image with noise and \(\mathbf{F}\in\mathbb{R}^{M\times M}\) is the denoised image. With \(\|\mathbf{\bullet}\|_{1}\) being the L1-norm, the first term in Equation (1), i.e., \(\underset{\mathbf{F}}{\text{argmin}}\|\mathbf{F}-\mathbf{G}\|_{1}\) is called the fidelity term, and the second term \(\mu R_{ATV}(\mathbf{F})\) is the sparsity regularization term, which includes the prior sparsity information of the image. \(\mu\) is a regularization parameter for weighing between the fidelity and regularization terms. The image restoration problem can then be solved by finding the smallest value of \(\mathbf{F}\) satisfying the conditions, so that Equation (1) is valid. Since the regularization term of the anisotropic total variation model needs to ensure the minimization of both horizontal and vertical gradients, \(R_{ATV}(\mathbf{F})\) can be defined as  
\[R_{ATV}(\mathbf{F})=\|\mathbf{K}_{h}*\mathbf{F}\|_{1}+\|\mathbf{K}_{v}*\mathbf{F}\|_{1} \tag{2}\]  
where * represents the convolution, and \(\mathbf{K}_{h}=[-1,1]\) with \(\mathbf{K}_{v}=\left[\begin{array}{c}-1\\ 1\end{array}\right]\) are the differential operators used for convolution operations in horizontal and vertical directions, respectively.
## 3 Principle of Encryption and Decryption  
In the process of encryption, filtering diffusion and chaotic scrambling are first used to diffuse and scramble encrypted images, respectively. Second, multiple images are superimposed together using the XOR operation. Then, the superimposed image is transformed into the spherical diffraction domain. Finally, the improved equal modulus decomposition (EMD) is employed to obtain ciphertext and private key. The process of decryption is the inverse operation of encryption.
# Task-Driven Real-World Super-Resolution of Document Scans
## 3 The Proposed Framework for Task-Driven Training
### Network Architecture and Loss Function
#### 3.1.1 SRResNet Architecture Overview  
The SRResNet model [9] serves as a backbone to validate our approach. It is composed of an initial feature extraction layer that transforms a three-channel LR input image \(I_{\text{LR}}\in\mathbb{R}^{3\times H\times W}\) into a higher-dimensional representation using a convolutional layer with \(9\times 9\) kernels. This is followed by a deep stack of \(N\) residual blocks, each of which refines the extracted features while maintaining stable training through skip connections. Formally, if \(F_{i-1}\) is the input to the \(i\)-th residual block, then  
\[F_{i}=F_{i-1}+\mathcal{R}(F_{i-1}),\quad i=1,\dots,N, \tag{1}\]  
where \(\mathcal{R}(\cdot)\) denotes the two-layer convolutional transformation with parametric rectified linear unit (PReLU) activations. These residual connections allow the network to focus on learning a high-frequency difference between the HR and LR representations, rather than the entire mapping, thus improving the convergence and final performance.  
After the residual blocks, another convolutional layer merges the learned features into a single tensor, which is then upsampled by successive subpixel convolution (i.e., pixel shuffle) layers. Each subpixel block increases spatial resolution by a factor of \(2\times\) (for a total scaling factor of \(4\times\)), rearranging feature-channel information into finer-grained pixel grids. Finally, a reconstruction layer (with \(9\times 9\) convolutional kernels followed by a hyperbolic tangent activation) maps the upsampled feature maps back to a three-channel RGB image:  
\[\hat{I}=G(I_{\text{LR}};\theta_{C})\in\mathbb{R}^{3\times(4H)\times(4W)}. \tag{2}\]  
In our work, \(\theta_{C}\) denotes all trainable parameters of SRResNet. At first, they are optimized by relying on simulated images from the MS COCO dataset [28] using a mean squared error (MSE) loss function, and the network is subsequently fine-tuned with our multi-task loss function.
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 2 Related Work
### Interaction Styles of Telementoring
#### 2.3.1 Touch-Based User Interface  
A touch-based user interface (UI) is a common and direct interaction style used in many telementoring systems [16; 17; 18; 19; 33]. Most users are already familiar with touchscreens on smartphones and tablets, therefore touch-based UIs do not require much training time. However, in the medical and surgical domains, the use of hands is strictly restricted or prohibited to maintain hygiene. For other domains, touch-based UIs for wearables are popular and considered default. Smartglasses such as Google Glass, HoloLens, and Vuzix Blade all provide on-device touch-based UIs. Figure 1 shows illustrations of touch-based interaction styles where the wearer can control the device by touching it (i.e., touchpad).
## 8 Performance Test
### IP Camera Bandwidth Consumption Performance and Stability  
We have measured all four wireless IP cameras transmitting MPEG-4 codec compressed video over HTTP protocol in order to test the number of bytes per second and the number of messages per second through the network during 15 minutes in order to see the performance of the network and its stability. The video resolution was 320 \(\times\) 240 at 25 fps. The wireless IP camera also transmitted audio at 24 Kbps from the camera to the web page.  
Figure 16 shows the number of bytes per second. There are not more than 800,000 bytes in the wireless network and the mean value was 514,013 bytes. The measurements show that the implemented system can support video streaming from all four Wireless IP cameras without problems because there is not so much bandwidth consumed and the system is stable.  
Figure 16: Number of bytes per second.  
Figure 15: Packets per second when there are four cameras.  
Figure 17 shows the number of packets per second in this case. No packet peak reaches 1,200 packets in the wireless network and the mean value was 798 packets. There are not too many messages through the medium at the same time. As the number of packets sent through the network is proportional to the number of cameras in the wireless network, 34 cameras will send around 40,800 packets per second through the network in this case. On the other hand, we could add one more access point. Taking into account the area covered by the cameras (few cameras are needed to cover large distances) and the number of sensors per each access point (see the research study made in figure 4), the designed system is scalable with the only limitation of the bandwidth of the backbone technology used to join the access points.
## 1 Introduction  
The shallow coal resources in the eastern mining areas of China are gradually depleting, making the western mining areas the main coal resource. Most of the western coal resources occur in the Cretaceous and Jurassic strata, where the roof and floor of the coal seams are mostly composed of weakly cemented rocks, such as mudstone, shale, argillaceous sandstone, and mud-sandstone interbeds. Such rocks are composed of abundant clay minerals with water-absorbent and expansive properties, and have numerous pores and cracks, making them soft, muddy, and they disintegrate easily under interactions with water and rock, causing the strength of the rock to decrease. This renders the roadway to become loose, broken, and unstable, which cannot provide necessary support during mining, increasing high maintenance costs and multiple repairs in the later period, inversely impacting safe and efficient coal production, and increasing the costs of the coal mine.  
Stable support is the key to mining in soft rock roadways [1]. Several studies have been conducted on soft rock roadways in coal mining [2; 3; 4]. The poor mechanical properties of the surrounding rock directly lead to the deformation of soft rock roadways by the development of cracks in the surrounding rock [5] and weathering and oxidation of coal seams [6]. Rocks surrounding the coal seams are generally very weak [3; 7]. Increasing mining depth due to high in-situ stress, high overburden, and tectonic stress causes the surrounding rock to undergo large nonlinear deformation [8], making it soft [9], e.g., the Pingdingshan mining area [10], Yili No. 1 Coal Mine [11], Xiaokang Coal Mine [12], Xin'an coal mine in Gansu Province [13; 14]. Readways experience large deformations, such as layered soft rock roadways [15], cross roadways [16], close coal seam roadways [17], and steeply inclined rock layer roadway [18; 19; 20] due to complicated geological structures. Mining disturbance also contributes to a dramatic deformation of the roadway [21; 22]. Water-based erosion in a water-rich roadway reduces its strength and serviceability [23; 24]. Bottom dilation is a common feature for the deformation of soft rock roadways [25; 26; 27]. Experimental and theoretical analyses have been used to understand the mechanism of large deformation of soft rock [28; 29; 30], based on which a series of combination support schemes have been proposed [31; 32; 33]. Among them, grouting support is widely used because of its good performance [34; 35; 36].  
The surrounding rock of the Jiebangou coal mine roadway is weak, soft, broken, and behaves like mud with water. Initially, the support parameters were selected based on the theoretical calculation results. However, in practical applications, the support system failed, and the surrounding rock is deformed significantly. In this study, a research method, combining field investigation, theoretical analysis, laboratory tests, numerical simulations, and field applications, was adopted to study the deformation and failure characteristics of the surrounding rock of a selected roadway and propose an effective support plan.
# Green Cloud Software Engineering for Big Data Processing
## 4 Results and Discussion
### Performance Evaluation
Figure 13: ESA Violations. In summary, based on the results obtained, it is evident that Combo6 is the best combination of VM consolidation algorithms for our IoT eye tracking big data workload. In order to understand the economic and environmental sustainability implications of Combo6, a projection on the cost of electrical energy for running the compute nodes and carbon emissions for the required energy generation for a period of 30 days is calculated. Viewing the fact that the scope of this research is to study the energy usage of compute nodes, energy cost is calculated only for running the compute nodes while controller and other ICT equipment such as the ethernet switch are not taken into account. A list of 14 countries which are prime locations of hyperscale datacenters [108] and countries that are suitable locations for data centers [109] are chosen. Energy cost in USD and carbon dioxide emission in KgCO2 for a month is calculated using Energy Council (2017) data on electricity-specific energy generation cost and carbon emission of countries [110]. Cost and carbon emissions for wastage during energy generation and transmission are beyond this research scope. A comparison is made between 'When no VM consolidation is applied' and 'Combo6' for processing the same IoT big data workload. Figures 14 and 15 represent the projected energy cost and projected carbon dioxide emission for required energy generation in various countries for a period of 30 days. It is observed that there is a significant decrease in the energy cost and carbon dioxide emission with Combo6 for VM consolidation in each country. In countries such as Denmark that generate energy from renewable energy sources, cost per kWh is as high as 0.34 USD. Applying Combo6 for VM consolidation can save up to USD 25.908 for a small 3-node set up. Countries with colder climates are often preferred locations for data centers as there is no need for additional cooling systems. Countries such as China, India and Japan are becoming popular data center locations due to the availability of labor, connectivity and lower cost of electrical energy. Though the cost of electricity generation in these countries is less, (e.g., China-- USD 0.09 per kWh), the amount of carbon dioxide and other Green House gases (GHG) emitted are very high (e.g., China--1.33 kgCO2/kWh) compared to countries like Finland (0.01 kgCO2/kWh) and Sweden (0.02 kgCO2/kWh) that primarily use renewable sources of energy. Applying energy saving systems and approaches such as the most suitable VM consolidation technique in data centers can save substantial amounts of money and have a great impact on the environment not only by reducing carbon dioxide emissions but also by increasing the lifetime of the computing systems, therefore, less electronic waste [41].
# AU-Guided Unsupervised Domain-Adaptive Facial Expression Recognition
## 3 Methodology
### AU-Guided Triplet Training  
To achieve structure-reliable and compact facial features, we perform AU-guided triplet training (AGT) to further narrow the gap among different domains. The key step is to sample triplets from source and target domains.  
**Triplet selection**. Intuitively, we can select triplets from the union of the source domain and target domain. However, considering that CD-FER is a classification task, we ignore the triplets in the source domain since the ground truths are available. Specifically, we keep only those cross-domain triplets. Given an anchor in the source domain (\(X_{s}^{a}\)) or the target domain (\(X_{t}^{a}\)), we first use it to retrieve the images of the target domain or the source domain that own the same AUs, and then we randomly select a positive sample from the target domain (\(X_{t}^{p}\)) or source domain (\(X_{s}^{p}\)), according to the retrieval samples, and a negative sample \(X_{t}^{m}\) or \(X_{s}^{n}\), according to the rest of the samples from the target or source domain. Thus, we mainly select two kinds of cross-domain triplets: (\(X_{s}^{a}\),\(X_{t}^{p}\),\(X_{t}^{n}\)) and (\(X_{t}^{a}\),\(X_{s}^{p}\),\(X_{s}^{m}\)). We conduct triplet selection in an offline manner. In addition, we also perform hard-negative mining by sorting the similarities between the AU scores of the anchor and all negative samples. We randomly select a sample from these with AU similarities that is larger than a threshold \(\tau_{n}\) (0.5 by default) as a negative sample.  
**AU-guided triplet loss**. After the selection of triplets, we use triplet loss to learn the discriminative and compact features as follows:  
\[L_{tri}=max\{0,\gamma-(\left\|F_{a}-F_{n}\right\|-\left\|F_{a}-F_{p}\right\|)\}, \tag{3}\]  
where \(F_{a}\), \(F_{p}\), and \(F_{n}\) represent the L2-normalized features of the anchor, positive, and negative images, respectively. \(\gamma\) is a margin which can be a fixed hyper parameter or a learnable parameter. We evaluate it in the Experiments section. Training with these cross-domain triplets, we can obtain a cross-domain common feature space which makes similar facial images close and dissimilar ones far away. Considering both pseudo-annotations and triplets, the total loss function is \(L_{all}=L_{c}+\epsilon L_{tri}\), where \(\epsilon\) is a trade-off ratio.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 3 Methodology
### Analysis of Influencing Factors of PIM in Huainan Mining Area
#### 3.2.3 Tangent of Major Influence Angle  
The main influence angle tangent is used to represent the major influence radius at different mining depths, which is generally expressed as the ratio of mining depth to major influence radius. It is related to the roof and overlying strata, mining depth and thickness. According to the collected information in the Huainan mining area, the regression formula between the tangent of major influence angle and the unconsolidated layer thickness could be written as Equation (3):  
\[\tan\beta=0.0044h_{\text{s}}+0.0932 \tag{3}\]  
Figure 4: Fitting curve of horizontal movement coefficient and \(h_{\text{s}}/H_{0}\).  
Figure 3: Fitting curve of subsidence coefficient and \((H_{0}-h_{\text{s}})/H_{0}\).  
The fitting results are shown in Figure 5, The goodness of fit \(R^{2}\) = 0.6546, showing that the size of the tangent of major influence angle is proportional to the thickness of the unconsolidated layer.