esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data
## 2 Materials and Methods
### Data Preprocessing and Analysis
In Figure 3, correlation coefficients among target pollutants and feature variables are reported. It can be observed that, despite the weak values, most pollutants present a positive trend with the increase of transits. This is especially noticeable with particulate matter (\(PM_{10}\) and \(PM_{2.5}\)), where the correlation is stronger. With reference to the aforementioned aggregation, we must point out that the reported correlations with particulate matter refer to the dataset reduced to daily resolution, while all the other measures refer to the average hourly records. Confirming the statements reported in Section 1, meteorological features appear to be the most correlated with the targets, in particular temperature, wind speed and radiation negatively influence the trend of pollutants, while pressure and humidity present a weak positive correlation. In contrast with the other dependent variables, the Ozone (\(O_{3}\)) shows an opposite behaviour in most cases: it appears for instance strongly correlated with temperature, radiation and wind speed, while also noticeably correlated with humidity in a negative way. Another unexpected result is represented by the complete absence of linear correlation between rain and pollutants. Despite the weak coefficients, we maintained the feature, as the influence of rainfall on air pollutants is seldom immediate and may typically occur in the following hours or days, thus the apparent lack of linear correlation. Likewise, we investigated the autocorrelation for each of the hourly pollutants, with the aim of defining suitable time windows for the estimation procedure. This measure can be defined as the correlation between a signal and its copy delayed over time, thus having the same range \([-1,+1]\). As observable in Figure 4, every pollutant presents a high autocorrelation in a 72-h window, with strong spikes on the 24 th and 48 th mark. Therefore, limited to the target variables with hourly resolution, we augmented the set with lagged features selecting two time windows of 24 and 48 h. While a longer window could have been beneficial, these intervals should still allow for a thorough analysis over the importance of lagged variables in the estimation, and at the same time avoiding a feature explosion in the datasets. We note that this procedure is not required for recurrent models such as Long Short-Term Memory (LSTM) [17], as the inherently sequential architecture allows for time windows of arbitrary lengths, nevertheless we applied the same criteria by forcing the latter to assume the same values of the two intervals chosen. We also point out that this analysis did not take into consideration particulate matter because of the daily resolution of the signals. In this case, we opted for two simpler time windows with a lag of one and two days respectively.
## 3 Results and Discussion
### Resistance to Multiple Bending and Twisting
Subjecting the inserts to bending and twisting, practically, did not affect the value of thermal resistance of the fabric system with a heating insert. In the case of insert A (without an openwork fabric) in the off state, the thermal resistance of the material system remained at the level of approx. 0.10 m\({}^{2}\)K/W before and after 9000 bending/twisting cycles. After switching on the power supply set to 4 V, the thermal resistance of the system increased by approx. 40% to 0.14 m\({}^{2}\)K/W. This value remained unchanged even after 9000 bending/twisting cycles, which indicates a high resistance of the heating insert A to multiple bending and twisting cycles (Figure 9). In the case of insert B (with an openwork fabric), slight fluctuations in thermal resistance after bending and twisting might result from bends created in the heating insert, which, in turn, may have led to the occurrence of small air voids between the layers of the material system (air is a very good heat insulator). However, fluctuations in thermal resistance were small, of the order of max. 0.016 m\({}^{2}\)K/W. The use of an openwork knitted fabric in insert B did not increase its thermal resistance. This parameter was at a similar level as in the case of insert A (without an openwork knitted fabric) both in the off and on states.
## 4 Case Studies and Their Setups
The first mode was a synchronous one where all burners switched their firing mode at the same time, i.e., all burners were either on or off simultaneously. In the second mode, used for setup (b), both tubes were fired asynchronously. Two asynchronous firing modes resulted from the permutations with the two furnace chamber temperatures at the burner-on and-off points, marked \"async-On\" and \"async-Off\" in Table 2, respectively. Together, both the synchronous and asynchronous modes represented the extremes of possible burner firing modes for two neighboring radiant tubes in a single zone of a CGL furnace. Each of the setups in the form of CHT models had different boundary conditions, derived from records of actual CGL furnace operation. In addition to the passing strips, a furnace chamber (as an enclosure), representing the rest of the furnace, needed to be considered in order to calculate a setup's radiation exchange. The enclosure was modeled as a fixed boundary condition, having the same temperature as that of the surrounding furnace zone. The emissivity was set to \(\varepsilon=1\) to eliminate unphysical effects caused by the reflections arising from the enclosure. For nearly all the setups, the strip was assumed to be isothermal, i.e., it had a constant temperature over all the passes. In order to determine the effect of changing strip temperatures across the width of the CGL furnace, setup (c) (Figure 5) had different strip temperatures over its four strip-passes. The temperatures chosen for the strip and the furnace enclosure are given in Table 3.  
Figure 6: P-type radiant tube used for this investigation.
## 6 Conclusions  
In this article, the MHD flow of a blood-gold nanofluid, in a porous medium, near an infinite plate, with ramped velocity and temperature, has been studied. For non-Newtonian nanofluid, the Oldroyd-B model, coupled with the energy equation, has been modeled in terms of the Caputo, CF, and ABC fractional derivatives. The solutions have been developed via [PERSON] and [PERSON]'s numerical algorithms and displayed in numerous graphs. The major findings of this study are as follows:  
* For ramped temperature, the temperature field is higher for the ABC derivative followed by the CF and Caputo fractional derivatives, whereas, for isothermal temperature, the temperature field for the Caputo fractional derivative is higher than the CF and ABC, respectively.
* The velocity field for the ABC derivative is higher than the CF and Caputo fractional derivatives in a ramped velocity case. However, this trend reverses in case of an isothermal velocity case.
* An increase in \(\alpha\) results in a reduction of the thickness of the temperature and velocity boundary layer. Consequently, a decrease in the temperature and velocity fields is observed.
* It was found that the temperature field increases with increasing \(\phi\), and the velocity decreases with increasing \(\phi\).
* The effect of \(\lambda_{1}\) and \(\lambda_{2}\) on the velocity field is reversible.
* An enhance in \(M\) leads to an enhancement in the Lorentz forces, which retarded the velocity, whereas increasing \(K\) and \(Gr\) increases the velocity field.  
**Author Contributions:** Conceptualization, M.S. and I.K.; methodology, M.S. and A.Q.; software, M.S.; I.K. and A.Q. validation, M.S., I.K. and S.S.; formal analysis, M.S.; investigation, M.S.; resources, M.S., I.K. and S.S.; data curation, M.S. and A.Q.; writing--original draft preparation, M.S. and I.K.; writing--review and editing, M.S., I.K., S.S. and Y.-M.C.; visualization, M.S.; supervision, S.S. and I.K.; project administration, I.K., S.S.; and Y.-M.C. and funding acquisition, I.K., K.S.N. and S.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was funded by the Natural Science Foundation of China (Grant Nos. 61673169, 11701176, 11626101, 11601485).  
**Acknowledgments:** The authors would like to acknowledge Ministry of Education (MOHE) and the Research Management Centre, Universiti Teknologi Malaysia (UTM) for the financial support through vote numbers SF278, 5F004, 07G70, 07G72, 07G75, 07G76, 07G77, and 08G33 for this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Computational Simulation Models
### Distributed Energy Resources  
The controllable DER available in the hybrid AC/DC network were modeled according to the block diagrams presented in Figure 9. Given the time intervals involved in the performed studies (few tens of seconds) and the dynamics of the phenomena under study, the following general underlying principles were adopted for the modeling of the controllable DER units:  
* The dynamics of the physical phenomena behind the AC or DC power converter stages used to connect the different DER units to the corresponding grid can be neglected. Given this, the models of the resources to be controlled, namely, the PV panels, EV and storage units, were considered to be operating around a given active power reference (\(P_{sp}\)), the dynamics of the PV panels itself as well as the internal dynamics of batteries used in EV or stationary ESS being neglected.
* According to the DAM approach, the low-level voltage control loop and the modulation mechanisms (e.g., PWM, SPWM or others) can be neglected, the interface to the grid (AC or DC) being represented by controlled current sources.  
Nevertheless, the main control loops of the power converters used to couple these units to the AC and DC networks were considered: an _AC/DC Converter_ (Figure 9A) for the electronic power converter to interface with the LV AC network and a _DC/DC Converter_ (Figure 9B) for the electronic power converter to interface with the DC networks.  
Figure 8: Diagram of the developed computational model for the LV AC network constituting the hybrid distribution grid.  
Figure 9: Block diagram of the developed computational model for the controllable AC (**A**) and DC (**B**) Distributed Energy Resources (DER) units.  
Reactive power is not considered in the DER units available in the LV AC distribution grid, since it is not the object of the study in this paper. Therefore, the generated reference current (\(I_{ref}^{*}\)) represents only the direct current component, the quadrature current component being set to zero. Further, only the positive sequence of the measured voltage is considered for the calculation of the voltage magnitude (\(|V|\)), which is used to calculate the reference current (\(I_{ref}\)) from the active power reference (\(P_{sp}\)). PV generation is assumed to operate at its maximum power point in normal operation conditions.
# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
## 2 Mini-Crack Detection System Based on Laser Excited Thermography  
### Detection Theory  
Figure 1 illustrates the working principle of laser excited thermography for conveyor belt mini-crack detection. The conveyor belt is composed of the upper, intermediate, and lower layers. The rubber in the upper and lower layers is mainly butadiene rubber (BR), and the intermediate layer is a mixture of styrene butadiene rubber (SBR) and natural rubber (NR) to maintain good adhesion to the wire rope [39]. When the mini-cracks appear on the conveyor belt, the intermediate layer rubber will be exposed.  
The transmission peaks of the butadiene rubber in the range of 3.3-3.6 \(\upmu\)m are based on the infrared spectrum of the conveyor belt. The infrared spectrum analysis of the intermediate layer rubber (SBR, NR) shows that the main peak of transmittance is within the range of 6.6-7.1 \(\upmu\)m [40]. In other words, different materials will produce different temperature changes under laser beam irradiation, which emitted different of infrared light intensity [41].  
Therefore, the damage of conveyor belt can be judged by analyzing the intensity of infrared radiation. When the conveyor belt is scanned by the CW laser beam, the thermal  
Figure 1: Working principle of laser excited thermography for conveyor belt mini-crack detection.  
wave propagation characteristics are different of the mini-cracked area and the normal area. Then, the mini-crack can be easily detected by capturing the thermal wave distribution in the conveyor belt. Therefore, the method of laser excited thermography can be used for mini-crack detection. Furthermore, considering sensor installation and feasibility, a mini-crack-detection system based on laser excited thermography is designed. The proposed system can automatically detect mini-cracks when the conveyor belt moves longitudinally.
## 8 RAN Decomposition Scheme
### FWA System and Functional Split Implications
#### 8.5.2 High Bit-Rate TWDM-PON  
The TWDM scheme is a promising solution for different applications and it is capable of delivering efficient bandwidth resources sharing between multiple clients. However, there have been concerns about the TWDM scheme's ability to support services that demand low-latency, as well as near-zero jitter. There are a number of high-bit-rate TWDM-PON alternatives with the capability for supporting more than 25 G per wavelength, that have been presented in the literature. Note that, based on different employable modulation formats such as EDB, ODB, NRZ, and PAM-4, different TWDM-PON variants impose different system requirements on optical amplification and digital signal processing. These will eventually affect the trade-off between system performance and cost. Consequently, the appropriate TWDM-PON variants and their implementation in the future transport network will be contingent on the RAN deployment scenario requirements [8].
## 2 Materials and Methods
### Methods  
#### 2.3.1 PCWA-ResCNN Model Construction  
In the present paper, we propose a new PCWA (Position and Channel Wise Attention) mechanism. This mechanism combines the advantages of channel attention and location attention, as shown in Formulas (1)-(4). This mechanism can focus on the importance of both the channel dimension and spatial dimension of features in data prediction in order to capture key information more effectively and select and enhance important features in a targeted manner. In the PCWA attention mechanism, channel attention is used to add weighting to the channel dimensions of processing features to highlight the importance of different channels. Location attention, in contrast, reinforces the importance of different locations by adding weights to the spatial dimensions of features. Through this fusion, PCWA is able to more effectively capture key information in the data and select and enhance important features in a targeted manner, thereby improving the accuracy and efficiency of data predictions.  
\[C_{att}(c)=\sum_{i=1}^{H}\sum_{j=1}^{W}X(i,j,c) \tag{1}\]  
\[P_{att}(i,j)=\max_{c}X(i,j,c) \tag{2}\]  
\[A(i,j,c)=C_{att}(c)\cdot P_{att}(i,j) \tag{3}\]  
\[Z(i,j)=X(i,j,c)\cdot\left(\sum_{i=1}^{H}\sum_{j=1}^{W}X(i,j,c)\cdot\max_{c}X( i,j,c)\right) \tag{4}\]  
In the formula, \(H\) and \(W\) represent the height and width of the feature graph, \(c\) is the number of channels, \(C_{att}(c)\) represents the attention weight of the \(c\) channel, \(P_{att}(i,j)\) represents the attention weight of the position \((i,j)\), and \(A(i,j,c)\) and \(Z(i,j,c)\) represent combined and applied attention, respectively.  
In addition, a custom residual block is introduced to enhance the performance of traditional convolutional neural networks (CNNs). The residual block alleviates the problem of gradient disappearance in deep networks by introducing skip connections, and it improves the expressiveness and robustness of the model through feature reuse. Combined with the PCWA attention mechanism, the network performs well in feature extraction and selection, it is better able to capture complex features and patterns, and it has greater robustness and generalization ability.  
By combining the PCWA attention mechanism and residual block with the CNN, a new network structure named PCWA-ResCNN is established (Figure 2). With these improvements, the PCWA-ResCNN model can not only extract important features between observations and surface reflectance but also effectively select and reuse these features, thus significantly improving the overall prediction performance. The improvement of this structure induces the network to perform better when handling complex tasks, and it provides strong support for research and application in related fields.  
The loss function \(L_{loss}\) of this model is  
\[L_{loss}=\frac{1}{2}{\sum_{i=1}^{R}{(t_{i}-y_{i})^{2}}} \tag{5}\]  
In the formula, \(t_{i}\) is the target output, \(y_{i}\) is the predicted network value, and \(R\) is the number of responses.
## 3 System Design
### Cache Mechanism Based Hierarchical NRS  
Then, to showed how the hierarchical IP-compatible-ICN cache system works, we designed a specific caching mechanism (As shown in Figure 2) based on an enhanced name resolution system (ENRS) [10; 32] with a hierarchical structure, mainly to provide low-latency resolution services for certain delay-sensitive applications, such as 5 th generation mobile networks (5G) or mobility support [33]. An ENRS divides an HEA into finer grains by quantifying the transmission latency constraints from the user to the resolution node, which means that the same level HEAs have the same resolution latency and the higher the level of HEAs, the longer the latency is.  
To advertise new content, content providers register the EID of the content to a global name resolution service (GNRS) so that users in the network can discover it (steps P1 and P2). A GNRS provides a name resolution service for all network elements in a whole network, which can be regarded  
Figure 1: Overview of a hierarchical cache system.  
Figure 2: A specific caching mechanism based on an enhanced name resolution system (ENRS).  
as the Level 3 RN. To request content, like with mobile terminals, they must firstly attempt to resolve the content name (EID) in the ENRS agent deployed in the point of attachment (POA) of the edge of the internet. To further reduce the resolution latency, the ENRS agent saves the mapping of some popular content EID. If the lookup fails, the NAs of the nearest RN of each level are returned (step 1). As for fixed network users, the Dynamic Host Configuration Protocol (DHCP) is a valuable method to automatically configure the NAs of RHs.  
Source Selection Mechanism  
We propose two simple designs of SSMs. The first approach to select preferable content source is to select the nearest copy. This method firstly judges the distance of each source by a traditional topology-based routing scheme based on shortest path algorithms. However, selecting the nearest source raises communication costs, which increase at an extremely high rate with the growth of the number of the alternative sources.  
The second alternative to investigate is the random selection of the source, which is a simple method to save the latency of judging distance between nodes. Due to strict requirements of the algorithm that divides the network into HEAs with different levels in the ENRS, there cannot be many cache nodes located in the lower HEA that is closest to user. Hence, we inferred that random selection may have a similar performance to that of nearest selection.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 2 Related Work
### Localization without Deep Learning  
Early works on this topic made various attempts at creating models without using deep features. [PERSON] et al. made use of segmentation models and horizon estimation to transform ground-level images to a rectified view to match the top-down view [21]. [PERSON] et al. matched ground-level images with other similar images to average over the features and used that as input to an SVM classifier to locate the image [22]. [PERSON] and [PERSON] used the geometric information of facades in ground-level images to create a probabilistic location map based on a map of the area [23].
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Integrity  
Integrity refers to maintaining the consistency, accuracy, and trustworthiness of data over their entire life cycle. It is a \"property of accuracy and completeness\" according to ISO/IEC 15408-2008 [29].  
Figure 4: Set-up of encrypted communication used in the Encrypted Channels pattern.  
To achieve this, we must be sure that data are not altered in an unauthorized manner at any state. Firstly, data must not be altered in transit; to achieve this, a secure protocol integrating security checks can be used. After reaching their destination, data must not be altered by unauthorized users; file permissions and user access controls are mechanisms that are commonly used towards that direction. However, even authorized users may cause accidental changes. Version control can be the solution to that problem. Finally, changes may occur as a result of events such as an electromagnetic pulse (EMP) or server crashes. Checksums can be used for verification of integrity, and available backups or redundancies are useful for the restoration of affected data.  
Integrity, as was the case for Confidentiality, is considered a property that refers to a high-level, complex problem, thus creating the context for more specific patterns. The relationships of Integrity with the specific patterns are depicted in the graph of Figure 6. Three high-level patterns (Safe Channel, Safe Storage, and Safe Processing) can be utilized for achieving integrity and three lower-level patterns can be used for the implementation of the above, as sketched in the figure.  
Figure 5: Secure multi-party computation ([[https://www.inpherio/](https://www.inpherio/)]([https://www.inpherio/](https://www.inpherio/)) (accessed on 2 December 2020))—overview of the average salary scenario.  
Figure 6: Hierarchical graph of Integrity property and relevant patterns.