esa-sceva/satcom-chunk-collection huggingface dataset:
# 6G Cognitive Information Theory: A Mailbox Perspective
## 4 Distributed Intelligent Network
### Cloud-Edge-Terminal Caching, Communication, and Computing  
Terminals such as smartphones, robots, wearable devices, and other local devices can acquire AI-application data. Due to the relatively low computing and storage capacity of a terminal, when a user's requests are too many, or a computing task is complex, it is not appropriate to process data at the terminal. As an intermediate layer, the edge cloud can handle part of a computing task. The cognitive computing layer at the edge consists of several edge nodes with certain computing power. Edge nodes can be deployed on a gateway, a router, and other servers. However, the computing power of the edge layer is weaker than that of the cloud-computing layer. The cloud focuses more on inputting the computing resources, realizing high-precision computing and analysis, and providing the best computing services, rather than handling all the computing tasks [41].  
Due to the different computing capabilities of terminal devices, edge-computing nodes, and a cloud data center, the order of the computing delay for the same computing task at these three locations in descending order is as follows: local terminal, edge node, cloud center. However, although edge-computing nodes have higher computing power than local devices, there are still computing bottlenecks when they are dealing with many concurrent computing tasks or highly-complex computing tasks. To mitigate a sharp reduction in the edge-computing speed, during 3C fusion and optimization, complex computing tasks can be offloaded to a remote cloud for execution to reduce the computing delay. Besides, when considering the communication delay (data volume and data-transmission rate), it is also necessary to comprehensively select the destination of computing offloading. Therefore, in a cloud-edge-terminal-based 3C fusion scheme [42], not only the performance limits of terminals and edge cloud should be considered, but also the resources cognitive engine and datacognitive engine of a cloud. Elastic matching is also conducted using network resources according to the network resources, interference, energy demand, and load conditions. Moreover, with the help of an historical data record, a prediction is conducted for the edge-network data, such as user behaviors and data traffic, and network resources or data are allocated in advance. This can effectively improve the efficiency of network communication and transmission.
## 2 Performance of IWP with Steel Anchor Beam
### Experimental Program
One end of the jacks was pressed against the reaction wall through a spreader beam and a concrete block, while the other end acts on the steel bracket of the test model through another spreader beam. The test model was anchored to the ground with anchors to prevent from being lifted. The reaction beam on the far left was anchored to the reaction wall with eight 40 mm diameter post-tensioning bars. Concrete supports were placed under the test models and the reaction beam to accommodate the height of the hydraulic jacks. The load was increased up to 2.5P (about 4000 kN), when the vicinity of the loading area yielded. During the experiment, no obvious cracks were observed. Only when the load was around the 1.5P, there were a few muffled sounds heard. As shown in shown in Figure 5, the measurements during the test included strain gauges glued on the steel plates (Figure 5a), embedded rebars and concrete (Figure 5b), as well as linear variable differential transformers (LVDTs) set at the steel-concrete interfaces (Figure 5c). Due to the symmetry of the structure, strain gauges for perforated plates were placed only on the outer side of plates A, B, C, and D, as shown in Figure 5a. Figure 3: Layout of test model: (a) plan view; (b) sections/views (unit: mm). The stress distribution in the perforated plates is a key focus of this experiment. Generally, the shear force on individual connectors can be calculated by placing strain gauges between adjacent holes. However, due to the small spacing between the holes in the test model's perforated plate and the significant stress variation between them, it is not possible to accurately measure the forces on the connectors. Therefore, triaxial strain gauges are not suitable for the study of the stress variation. Figure 4: Test setup. Figure 5: Measurements: (**a**) strain gauges on steel section; (**b**) strain gauges on perforating bars; (**c**) LVDTs (unit: mm). gauges shown in Figure 6 were placed in the central area between four adjacent holes to measure the stress distribution and principal stress direction of the perforated plate. Taking the strain readings from the three gauges as \(\varepsilon_{A}\), \(\varepsilon_{B}\), and \(\varepsilon_{C}\), the maximum stress principal (\(\sigma_{1}\)) and the minimum stress principal (\(\sigma_{3}\)) can be calculated from the following equation:  
\[\sigma_{1}=\frac{E}{2}\left[\frac{\varepsilon_{A}+\varepsilon_{C}}{1-\
u}+ \frac{1}{1+\
u}\sqrt{\left(\varepsilon_{A}-\varepsilon_{C}\right)^{2}+\left(2 \varepsilon_{B}-\varepsilon_{A}-\varepsilon_{C}\right)^{2}}\right] \tag{1}\]  
\[\sigma_{3}=\frac{E}{2}\left[\frac{\varepsilon_{A}+\varepsilon_{C}}{1-\
u}- \frac{1}{1+\
u}\sqrt{\left(\varepsilon_{A}-\varepsilon_{C}\right)^{2}+\left(2 \varepsilon_{B}-\varepsilon_{A}-\varepsilon_{C}\right)^{2}}\right] \tag{2}\]  
Then, the Von Mises stress can be calculated with  
\[\sigma_{miss}=\frac{1}{\sqrt{2}}\sqrt{\left(\sigma_{1}-\sigma_{2}\right)^{2}+ \left(\sigma_{2}-\sigma_{3}\right)^{2}+\left(\sigma_{3}-\sigma_{1}\right)^{2}} \tag{3}\]  
where \(\sigma_{2}=0\) for plane stress state.
###### Abstract  
Automatic object-detection technique can improve the efficiency of building data collection for semi-empirical methods to assess the seismic vulnerability of buildings at a regional scale. However, current structural element detection methods rely on color, texture and/or shape information of the object to be detected and are less flexible and reliable to detect columns or walls with unknown surface materials or deformed shapes in images. To overcome these limitations, this paper presents an innovative gray-level histogram (GLH) statistical feature-based object-detection method for automatically identifying structural elements, including columns and walls, in an image. This method starts with converting an RGB image (i.e. the image colors being a mix of red, green and blue light) into a grayscale image, followed by detecting vertical boundary lines using the Prewitt operator and the Hough transform. The detected lines divide the image into several sub-regions. Then, three GLH statistical parameters (variance, skewness, and kurtosis) of each sub-region are calculated. Finally, a column or a wall in a sub-region is recognized if these features of the sub-region satisfy the predefined criteria. This method was validated by testing the detection precision and recall for column and wall images. The results indicated the high accuracy of the proposed method in detecting structural elements with various surface treatments or deflected shapes. The proposed structural element detection method can be extended to detecting more structural characteristics and retrieving structural deficiencies from digital images in the future, promoting the automation in building data collection.  
column and wall detection; imaging recognition; structural information collection; gray-level histogram +
Footnote †: journal: Applied Sciences
## 1 Introduction
### The Model Based on BPMN Integrated SC Interaction  
To solve RQ1, we propose an interaction model based on Business Process Modeling Notation (BPMN). In Chapter 3 we describe an example, which clearly illustrates the multi-party interaction scenarios when using SCs. Other domain experts who are not developers can understand the logic of SCs with our model and thus integrate their domain knowledge to co-design SCs.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 3 Proposed Framework
### MMTM-RPL Layers
#### 3.1.2 Control Layer  
Because of the low resources of the devices, the control layer (i.e., the fog layer) performs most of the operations and deals with internal attacks (Sybil, rank, and sinkhole attacks in our case). It performs various operations and has several modules (Figure 1), which are discussed in detail below.  
The topology finder module finds the topology and nodes of the underlying device layer (Figure 2). It creates destination-oriented directed acyclic graphs (DODAGs) based on an objective function (OF), for instance, delay, energy used, or hop count [34]. Generally, each RPL instance has an optimization goal that depends on the application goal. The application goal is represented by the OF, such as Zero-OF or MRHOF [35]. The RPL topology is organized into one or more DODAGs, each rooted at a single point, the DODAG root (or sink node). It uses distance vector routing.  
When the process begins, a tree-like structure known as a directed acyclic graph (DAG) is formed, and a parent node is selected by each node [18]. The corresponding parent node is the gateway for its child nodes. Information about the DAG is maintained by each DODAG, which each node receives from its parent. Control messages, such as DODAG information solicitation (DIS) messages, DIO acknowledgments, DODAG information objects (DIOs), and destination advertisement objects (DAOs), are transmitted [36]. All these message types are used to build the topology step by step. The parent broadcasts DIO messages to inform nodes about it. Following the acquisition of a parent node, all nodes rate themselves using the OF. DAO messages are sent from all nodes that have received DIOs. By transmitting DIS control messages, nodes not yet in the DODAG topology can become connected [37; 38].  
When an RPL instance sets up routes in the whole network, the OF is calculated. RPL instances join each DODAG many times. RPL calculates the rank of each node and how many sink nodes there are. The rank of a node indicates its distance from its parent node. Child nodes must have a higher rank than their parent and are announced across the network to prevent loops. To link an LLN to the internet or any other network, a node  
Figure 2: Topology discovery in RPL.  
often uses an IPv6 Border Router/gateway. Nodes can be controlled by one or more RPL instances.  
The topology finder module uses the MRHOF OF of RPL and a trickle timer algorithm. It recovers the topology over time for a wireless sensor network or the IoT [39]. Other than periodic topology updates, every time the location of a node changes (i.e., it leaves a cluster and enters the perimeter or range of another), the topology finder module tells the cluster builder to take the appropriate steps [40].
## 4 Conclusions  
The aim of this paper was to obtain an integrated and automated management and control in buildings. Based on the data of an office building located in Bangkok, the possibility of carrying out a control system using machine learning techniques for the prediction of electrical consumption and mathematical models for the storage management system has been demonstrated.  
The fitting of the SNN model obtained, i.e., the adjustment of the corresponding parameters, was accurate and allowed us to predict instant and subsequent electricity consumption. The results obtained present clear evidence that the combination of the techniques presented establish a model that is more beneficial to the environment and with consequent economic savings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Without Battery** & **With Battery** & **Savings** \\ \hline Electricity expenses & & & \\ (\(\mathbf{\xi}\)) & 97.26 & 39.54 & 57.72 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative of the electricity bills in the study period in the selected zone of the building considering or not the installation of the battery.  
Figure 7: Variation of the electricity costs in a working day in the selected area of the building considering or not the installation of the battery. (**a**) Train/Dev sample division; (**b**) Test sample division.  
Specifically, the SNN with k-fold model established sought to characterize the electricity consumption in the building from the interior and exterior temperatures, the interior lighting and the temporal variables of time of day, day of year and day of week. The determined dataset samples, either modelling or predicting, had errors over 5% and were made up of two hidden layers with 100 and 75 units and with linear and sigmoid activations respectively, being the activations of the output layer sigmoid. In case of considering the management storage system based on the machine learning model presented, the savings in the study zone were considerable, amounting to a reduction of 52.72 \(\xi\) in the electricity bills in the period analyzed.  
Conceptualization, M.C.-C. and D.V.; methodology, D.V. and P.E.-O.; software, M.C.-C.; validation, D.V. and P.E.-O.; formal analysis, M.C.-C.; investigation, M.C.-C.; resources, D.V. and P.E.-O.; data curation, M.C.-C.; writing--original draft preparation, M.C.-C. and D.V.; writing--review and editing, P.E.-O.; visualization, D.V.; supervision, D.V. and P.E.-O.; project administration, D.V. and P.E.-O.; funding acquisition, P.E.-O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data analyzed in this study are presented in [54].  
The authors declare no conflict of interest.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Interference Cancellation-Based Protocols
#### 5.3.4 Multi-Slots Coded Aloha (MuSCA)  
This protocol generalizes the CRDSA protocol. By employing adequate error correction codes given a proper SNIR, multi slot coded Aloha (MuSCA) is able to decode packets even when there are collisions for all the transmissions in a frame [67]. In the example provided for CRDSA in Figure 11, MuSCA would have been able to successfully decode packets sent by users three and four, and received at slots two and five. The MuSCA protocol is designed for uplinks shared among a number of users in satellite systems [67].  
MuSCA achieves a maximum normalized throughput of \(S=1.4\) when the normalized channel load does not exceed \(1.42\). When the \(PLR=10^{-3}\), MuSCA supports an offered load of \(C=1.22\). Considering the very high efficiency of this protocol for normalized offered loads close to one, there are no need for retransmissions. In terms of complexity, MuSCA is very similar to CSA, since the difference between them is mainly in the coding mechanism.  
Figure 11: Example of the operation of contention resolution diversity slotted Aloha (CRDSA) over one frame. A collision-free packet from user two is received in the fourth slot. The packet includes the position of its “twin”, located in slot one. An interference cancellation method is then applied to the packet. In this example, packets from users one, two, and five can be successfully recovered, achieving an \(S=0.6\) for a load of \(C=1\).
## 4 Discussion
A volume-scattering component (HV) can be used to distinguish ice islands from sea ice, which has lower salinity and a lower volume component. The main limitation of full-polarimetric RS-2 data is its small image size compared to the ScanSAR images widely used for sea-ice monitoring. In addition, the polarimetric scattering mechanism may be influenced as temperature increases [12]. The new results on DEM extraction from an RS-2 stereo pair demonstrated the possibilities of the stereo radargrametric technique for EIF applications. However, the stereo SAR data acquisition may require a substantial time difference in acquisition for a stereo pair (one day or more) and, therefore, will be limited for monitoring moving EIFs. In addition, the validation of stereo SAR results over sea ice require validation with more accurate data, such as VHR electro-optical stereo DEM.
# Analysis of Lightweight Cryptographic Algorithms on IoT Hardware Platform +
## 3 Method and Experimental Setup
### Algorithms Used for Evaluation  
Lightweight cryptography of various structures, key sizes, and block sizes were chosen. A wide range of differences in key size, block size, and rounds were realized as essential for analysis goals. A total of 39 different ciphers of 13 families shown in Table 1. Furthermore, a package of 80 algorithms of 32 families presented in NIST round 2 competition [34] are included in an extended study shown in Table 2. In Table 1, a rundown of the elected lightweight block ciphers is provided, arranged in alphabetical order, with their type, structure, block size in bits, key size in bits, and the number of rounds. NIST 2 nd-round candidates in competition are presented in Table 2, with family type, block size in bits, key size in bits, the number of rounds, and some brief information. These candidates are expected to be selected as a replacement of the traditional standards. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Family-Cipher** & **Algorithm** & **Type** & **Structure** & **Block Size (bits)** & **Key Size (bits)** & **Rounds** \\ \hline Relative reference & AES & Block Cipher & SPN & 128 & 128/192/256 & 10/12/14 \\ \hline
1-1 & HIGHT & Block Cipher & Generalized Feistel Structure (GFS) & 64 & 128 & 32 \\ \hline
2-2/3/4 & KATAN & Block Cipher & stream cipher like & 32/48/64 & 80 & 254 \\ \hline
3-5/6/7 & KATANTAN & Block Cipher & Stream cipher like & 32/48/64 & 80 & 254 \\ \hline
4-8/9/10 & LEA & Block Cipher & Generalized Feistel Network (GFN) & 128 & 128/192/256 & 24/28/32 \\ \hline
5-11 & Piccolo & Block Cipher & GFN & 64 & 80/128 only 80 chosen & 25/31 \\ \hline
6-12/13 & PRESENT & Block Cipher & SPN & 64 & 80/128 & 31 \\ \hline
7-14 & PRINCE & Block Cipher & SPN & 64 & 128 & 12 \\ \hline
8-15 & QARMA & Block Cipher & SPN & 64 & 64 & 27 \\ \hline
9-16 & RECTANGLE & Block Cipher & SPN & 64 & 128 & 25 \\ \hline
10-1726 & SIMON & Block Cipher & Feistel & 32128 & 64256 & 3272 \\ \hline
11-2736 & SPECK & Block Cipher &
\begin{tabular}{c} Addition/Rotation/XOR \\ (ARX) \\ \end{tabular} & 32128 & 64256 & 2234 \\ \hline
12-37/38 & TWINE & Block Cipher &
\begin{tabular}{c} Type-2 Generalized \\ Feistel Network (GFN-2) \\ \end{tabular} & 64 & 80/128 & 36 \\ \hline
13-39 & XTEA & Block Cipher & ARX & 64 & 128 & 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thirty-nine algorithms and AES as a relative reference.
# The Efficiency of Large Hole Boring (MSP) Method in the Reduction of Blast-Induced Vibration
## 5 Discussion
### Effects of MSP Method on Vibration Reduction  
According to the results of the test blasting, the MSP method was generally more effective in reducing the blast-induced vibration than the traditional cut method. However, as discussed in the previous section, the CB-2 pattern with the single-MSP method unexpectedly produced a high-amplitude vibration due to the geological characteristics of the rock mass and drilling errors in the stoping area and contour line. The test blasting was performed once for each pattern, and it is difficult to establish the effect of vibration reduction of the MSP method. Based on numerous field applications of the MSP method and the results of the test blasting, a vibration reduction effect of approximately 25-30% was expected compared with the general blasting; thus, additional test blasting must be carried out to establish a tendency of the vibration reduction effect for the MSP method [15; 16].  
When the number of large empty holes was increased (CB-4), the free face in the cut area was expanded to increase the moving space of crushed rock in the stoping area and to reduce the crushing resistance; hence, it was possible to greatly reduce the vibration, and the MSP method with combined CB exhibited a reduction effect of approximately 70%. If maximum vibration occurs in the stoping, contour, or lifter areas rather than the cut area, there is a limit to the effect of reducing the vibration using the MSP method. Therefore, a study of a new blasting pattern is necessary that can be installed separately in each section of a tunnel face to reduce the blast-induced vibration rather than focusing on the cut area. Furthermore, since there are various variables and restrictions in the actual construction site which prevent stringent control over experimental conditions, additional field-scale experiments will be pursued to analyze the effect of precise vibration reduction with respect to the size of the empty holes and demonstrate that the enhancement in vibration reduction efficiency observed is reproducible under homogeneous conditions. These efforts will help establish optimal design patterns using large empty holes to minimize blast-induced vibration depending on the conditions of the site. It is believed that the proper combination of multi-MSP over three holes with other vibration-controlled blasting techniques, such as electronic detonators, can pose as a viable alternative cut method for non-vibration excavation sections close to safety facilities.
## 2 Operation and Control
### Enterprise Architecture
#### 2.6.3 Technical and Non-Technical Losses  
Regarding technical losses, [PERSON] and [PERSON] [150] present an adaptive hybrid genetic algorithm and [PERSON] et al. [151] raise the parameter called \"loss coefficient\" to describe the variation of the load and its effect on the estimation of losses.  
Relating to non-technical losses, [PERSON] et al. [152] and [PERSON] and [PERSON] [153] present non-technical losses (fraud detection) estimator based on statistics and probability, respectively.  
[PERSON] et al. [154] describe some new advances for the detection of non-technical losses in the customers with a windowed analysis (based on the Pearson coefficient).  
Illegal consumers are detected by a procedure based on high performance computing (HPC) algorithms, and this is presented by [PERSON] et al. [155].  
Other authors ([PERSON] et al. [156]) use a non-cooperative model for the detection of non-technical losses, and their accuracy is high.  
It is possible to use expert systems to identify non-technical losses, and in this sense, [PERSON] et al. [157] classify the information of their clients.  
The detection of non-technical losses can be addressed with knowledge-based system (KBS) techniques, in conjunction with ANN and other methods, as shown by [PERSON] et al. [158].  
Continuing with the AI, it is possible to use SVM for the detection of non-technical losses, as they are stated in their work [PERSON] et al. [159] and [PERSON] et al. [160].  
The detection of non-technical losses and their detection can be accelerated through the use of harmony search algorithm and the optimum-path forest classifier, as reported by [PERSON] et al. [161].  
[PERSON] et al. (2019) propose a non-technical loss detection system in two stages, the first is a filter based on text mining and the second is an ANN. The results are optimal and the frauds are detected.  
To summarize, Table 6 shows all the work described in this section. In the rows are shown different areas (database, the exchange between networks and technical and non-technical losses) while in the columns is shown the application rate on enterprise architecture (operation with the external systems and interoperability of large database). As can be seen in the previous table, the interaction of the company with external systems is fundamental, in addition, the interoperability of the databases (with extensive size) is critical for the utility. In this sense, the detection of technical and non-technical losses will be a priority for operators, and the interaction with their customers will be a necessity.