esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Risk Analysis of LoRaWAN and Future Directions
## 3 Introduction to LoRaWAN v1.1
### Session Keys  
To ensure the integrity of the messages emanating from the network servers, MIC (Message Integrity Code) is appended to all uplink-downlink data messages. Besides, for each end-device, there is a specific network session key. All the session keys are derived by using _NwkKey_, _JoinNonce_, _JoinEUI_, and _DevNonce_ value as shown in Equations (1)-(3) (here, note that aes128_encrypt stands for encryption with 128 bit AES Encryption Algorithm). The details are provided below:  
(a) Forwarding Network Session Integrity Key (_FNwkSIntKey_--new)  
_FNwkSIntKey_ shown in Equation (1) is a network session key specific to each end-device that is used for calculating the MIC of all uplink-data messages to ensure data integrity. This key is considered \"public\" and may be shared with a roaming forwarding-Network server (_fN_s). However, it should be kept secret from outsiders (adversaries) by storing it in a secure way.  
\[FNwkSIntKey=aes128\_encrypt(NwkKey,0x01|JoinNonce|JoinEUI|DevNonce|pad16) \tag{1}\]  
(b) Serving Network Session Integrity Key (_SNwkSIntKey_--new)  
_SNwkSIntKey_ shown in Equation (2) is a network session key specific to each end-device used for calculating the MIC of all downlink-data messages to ensure data integrity. This key is considered \"private\" and should not be shared with a roaming _fN_s. _SNwkSIntKey_ should be kept secret from outsiders by storing it in a secure way (to prevent extraction and re-use by malicious parties).  
\[SNwkSIntKey=aes128\_encrypt(NwkKey,0x03|JoinNonce|JoinEUI|DevNonce|pad16) \tag{2}\]  
(c) Network Session Encryption Key (_NwkSEncKey_--new)  
_NwkSEncKey_ shown in Equation (3) is also a network session key specific to each end-device, and used for encryption/decryption of the MAC layer commands (payload). _NwkSEncKey_ also should be kept secret from outsiders by storing it in a secure way.  
\[NwkSEncKey=aes128\_encrypt(NwkKey,0x04|JoinNonce|JoinEUI|DevNonce|pad16) \tag{3}\]  
Figure 2: An overview of LoRaWAN v1.1 key distribution.
# Brain-Inspired Data Transmission in Dense Wireless Network
## 3 Ultra-Dense Wireless Networks and Their Biological Counterparts
### Related Work  
Humans have always been highly inspired by the functioning of our world and nature, and in particular of the human body; these inspirations lead to numerous significant findings and discoveries. In the context of computer science, there are various algorithms and solutions which are motivated by the observation of nature. One can mention for example solutions based on the behavior of bees, bumblebees, or ant-colony, in general, swarm intelligence [22; 23; 24], an algorithm based on findings in genetics, but also solutions such as simulated annealing which mimics the behavior of the fabric processing. Recently, the most popular solutions are related to the application of the neural networks in their various configurations (e.g., convolution neural networks, recursive neural networks, etc.) and implementation contexts (computing, feature recognition, automation, and networking) [25; 26; 27]. It is also worth mentioning that the behavior of bees (swarm) has inspired the design of the whole communication and networking frameworks and protocols, e.g., ZigBee [28]. However, when projecting the brain-inspirations on wireless communications, the number of publications is not that high, which suggests that this domain of research is still unexplored. However, it is worth mentioning some works, e.g., [29] applied a special class of neuromorphic computing (mainly reservoir computing) for signal detection in multiple input multiple output orthogonal frequency division multiplexing systems. In [30], the authors proposed the brain-inspired method for constructing a robust virtual wireless sensor network. Next, in [31], the dynamic spectrum management in cognitive wireless ad-hoc networks is investigated. A very promising approach is presented in [32], where the authors proposed the neural wireless sensor networks inspired by the functioning of the neural network.  
Recently, increased interest is also observed in so-called bio-molecular communications, as presented in [33]. Synaptic transmission and neuro-spike transmission are analyzed in detail in the context of communications in [34; 35; 36].  
Furthermore, the authors is [37] proposed to use the property of neurons to connect to many other neurons (as both an information source and an information outlet) in sensor networks, where a dynamic node grouping mechanism is proposed. In turn, in [38], one can find an algorithm for selecting nodes and bit rates in sensor networks which is based on the immune system.
# Mobility-Multihoming Duality
## 5 Evaluation of Mobility Multihoming Duality  
In this section, we describe the evaluation scenario for our approach. It is an empirical, testbed-based evaluation using unmodified, commercial IPv6 equipment for network connectivity. Commercial workstations were used for hosts, which ran the publicly available Linux implementation of ILNP [50] with the modifications executed as described in Section 4 and summarised below.
## 4 Discussion
* The initial topology, obtained from the processed mesh skeleton, is not changed during refinement. It is possible to enrich the heuristic refinement step with operations by changing the solution graph topology. * The algorithm configuration, such as heuristic refinement parameters, should be replaced with values derived directly from input data, i.e. the initial solution's cylinder radius can be automatically determined using the mean diameter of the input mesh structures. * The presented approach is a fully automated parametrization procedure. On the other hand, when the manufacturing preparation requires increasing minimal thickness, for example, to decrease thermal stresses during the manufacturing process, a parametric model produced by the algorithm can be used to manually apply corrections. Those corrections are then fused back with the original mesh, preserving features that were not captured by the parametric model while improving the properties of the optimized mesh. Additional research is required to evaluate this approach for industrial use. All the described improvement directions are currently under development in ongoing research projects. The development of the complete solution requires a joint research effort in all areas of mechanical design, including structure optimization, numerical feature recognition methods and additive manufacturing. Conceptualization, J.P.; methodology, J.P.; software, J.P.; validation, J.P. and M.N. ; formal analysis, J.P. and M.N. ; investigation, J.P.; resources, M.N. ; data curation, J.P.; writing--original draft preparation, J.P.; writing--review and editing, J.P. and M.N. ; visualization, J.P.; supervision, M.N. ; project administration, M.N. ; funding acquisition, M.N. All authors have read and agreed to the published version of the manuscript. This work was supported by the Polish National Centre for Research and Development under the grant decision No. DWP/TECHMATSTRATEG-III/136/2020. Not applicable. The data presented in this study are available upon request from the corresponding author. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The following abbreviations are used in this manuscript:  
API Application Programming Interface  
CAD Computer-Aided Design  
FDM Fused Deposition Modeling  
PBF Powder Bed Fusion  
LC Load Case
## 1 Introduction
In the aforementioned studies, depending on geological conditions, the height of the water-conducting fracture zone ranged from 22 to 84 m, once again confirming that the parameters of the fractured zone depend on numerous factors. Moreover, the research results were predominantly validated through field monitoring. Thus, there is currently no unified methodology for predicting the height of the fracture zone and, consequently, for assessing the water inrush hazard. To control the water inrush in mining operations, both passive and active control methods can be employed. Passive measures include water table reduction and dewatering, whereas active strategies involve the construction of anti-filtration barriers. Unlike dewatering techniques, anti-filtration barriers do not contribute to the generation of contaminated wastewater or the depletion of groundwater resources. Furthermore, they prevent the additional deformation of geological formations and surface subsidence. The presence of water in coal deposits presents significant challenges for mining and resource recovery. The average water inflow into mining faces typically does not exceed 4-5 m\({}^{3}\) per hour. However, even such volumes lead to a decreased labor productivity, compromised occupational safety, and reduced operational efficiency in coal extraction. Therefore, the identification of effective strategies for mitigating excessive water inflows into mine roadways remains a critical research challenge. The implementation of advanced technological solutions for preventing water inrush necessitates a precise understanding of hazardous zones where preventive measures must be applied. An analysis of recent studies has revealed that the variety of forms of folds and overburden rock structures cannot be taken into account in theoretical calculation methods of the height of the fractured zone, which is their significant weakness. At the same time, numerical simulation allows to predict more accurately the fractured zone parameters in a wide variety of conditions, for example, in the case of a non-monoclinal structure and the complex lithology of overburden rock. Previous articles are mainly focused on assessing the height of fractured zones in monoclinic without taking into account specific geological conditions. The main novelty of this paper is the development of the algorithm for assessing the risk of water inrush from aquifers based on an ANSYS simulation in complex anticline coal seam bedding. The proposed algorithm has been tested for the conditions of the number 817 longwall panel of the Xinhu Coal Mine. Based on the conducted research, the risk of water inrush is evaluated. The water inrush control method by grouting through multiple directional wells is described in this paper. The successful implementation of this method is presented based on the experience of the Xinhu Coal Mine.
## 4 Discussion
Other bacterial strains such as _Serratia plymuthica_ 3 Re4-18, _Pseudomonas fluorescens_ L13-6-12, and _Pseudomonas trivialis_ RE^1-1-14, and fungal species (_Trichoderma gamsii_ AT1-2-4, _Trichoderma velutinum_ G1/8) were also shown to be promising to control _Rtizoctonia solani_[40]. The antagonistic mechanisms can be different: competition for substrate and space, production of inhibitory metabolites, cell wall degrading enzymes, etc. [38; 41]. The conducted research confirmed that microorganisms were able to inhibit or even kill _R. solani_ AG 2-2 IIIB. The study showed that the creation of anaerobic conditions (absence of air and low redox potential) by the microbial community of compost was the most effective to control _R. solani_ AG 2-2 IIIB. The investigation of the antagonistic properties of the isolated microbial strains was also shown to affect the fungus. The collected data confirmed that the application of microbial communities was the most promising approach. It provided the integration of the suppressive environmental conditions created by microorganisms with the antagonistic properties of the strains. These experimental data correspond to those described in literature, such as the synergistic effect of the combination of several strains such as _Gliocladium virens_ and _Burkholderia cepacia_[38; 42]. Another study also confirmed the increase in the antagonistic effect of combining several microbial strains. Moreover, microorganisms could contribute to plant health via the stimulation of root growth, germination rates, biomass yield, resistance to stress, etc. [40]. Although the approach of _Rhizoctonia solani_ control via the application of microorganisms is promising, there are still a lot of gaps in the knowledge of the effective regulation of this process. The conducted research contributed to the understanding of the patterns of the application of microbial communities instead of single strains. Microbial communities were shown to be a promising tool, since they possess integrated mechanisms against _R. solani_ AG 2-2 IIIB, providing the simultaneously acting different pathways to protect a valuable food source: sugar beets.
## 5 Understanding Decentralised Identity Systems
### Hyperledger Indy
#### 5.4.2 Limitations  
While scalability has traditionally been a challenge for Hyperledger Indy due to its consensus mechanism, recent advancements have made significant strides in this area. The introduction of new consensus methods, like the \"Indy Besu\" project--which leverages a modular system compatible with both permissioned and permissionless networks--demonstrates a commitment to improving transaction throughput and flexibility [64]. These developments are promising for handling a larger number of users and transactions. However, some challenges remain, for example, the network's performance can still be affected as usage scales up, particularly due to the voting process required for adding new validators. Additionally, the open-source code for Indy remains restrictive, and the complex deployment process can pose obstacles for enterprises considering its implementation [61; 63].
## 6 Implementation: Case Study
The system detected and reported dead SNs with 100% success in less than 10 s and adjoining SN\({}_{s}\) were able to reconfigure within 20 s. Then, powered off nodes were again turned on, and the network again reconfigured itself to the previous configuration within 10 s.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### Computational Processing  
Although IoT devices' processing capabilities are improving, participating as a node capable of contributing a transaction to a BC is still computationally demanding. IoT devices have limited resources. These devices often have limited computational capabilities, weak network connectivity, low battery capacity, and limited storage. BCs, on the other hand, have their unique set of criteria. Because BC data are large, it is impossible to keep the entire BC in each IoT device, especially since the IoT creates vast amounts of data in real-time, exacerbating the problem [69]. In most circumstances, the devices function normally for a length of time before changing due to a variety of factors such as disconnection, short circuits, and program obsolescence. The following discussion goes over these challenges. Most IoT devices are short of the computing capacity needed to perform the computations required to interact with BC directly [74]. Computational processing in the context of BCs relates to several factors, time latency, power, and transaction throughput. Table 10 highlights the solutions recommended to addressing computational challenges.  
Resource management is a significant concern for the IoT. A matching contract for resource provisioning is utilized in this distributed integration system to couple a resource request with a resource offer. It identifies the characteristics of computing resources related to CPU type, disk space, and RAM, and it executes the pairing with various regulations. Add-on devices that provide specific processing capabilities are increasingly becoming accessible, even if the IoT device itself does not have the requisite computational capability [69]. The Intel Movidius neural computing stick, for example, integrates deep neural networks in hardware for tasks such as filtering and object identification. Moreover, feasible solutions will include data chunking or the detection of indicators such as departures from predicted thresholds. More computational power is necessary for IoT or edge-of-network devices to accomplish such processing [131]. Due to resource limits, consensus mechanisms must be redesigned to be energy-efficient and lightweight, as well as improved energy collecting strategies [85].
### Clinical Parameters  
#### 3.3.1 PD and CAL  
For all studies, the group melatonin had a significant statistical decrease: [PERSON] et al. [34] reported a reduction of 1.27 mm after 6 months, from 3.72 \(\pm\) 0.90 mm to 2.45 \(\pm\) 0.91 mm (\(p\) = 0.02); [PERSON] et al. [30] achieved reduction of 1.4 mm after 3 months, from 4.3 \(\pm\) 0.8 mm to 2.9 \(\pm\) 0.7 mm (\(p\) < 0.001), a decrease of 32.56%; [PERSON] et al. [31] observed a reduction of 2.38 mm, from 4.65 \(\pm\) 1.04 mm to 2.27 \(\pm\) 0.7 mm (\(p\) < 0.001) in 8 weeks; [PERSON] et al. [32] also obtained a reduction in 8 weeks of 1.14 mm, with 4.45 \(\pm\) 0.96 mm at baseline and 2.59 \(\pm\) 1.04 mm; whereas [PERSON] et al. [35] had a decrease of PD in 3 months of 1.9 mm (\(p\) < 0.001) and in 6 months of 2.0 mm (\(p\) < 0.001).  
For the control groups (CG), placebo, also there was a decrease in PD; therefore, the reduction was lower, compared with the test group: [PERSON] et al. [34] reported a similar statistical significance (\(p\) = 0.02) between groups, and the CG had 0.73 mm of reduction after 6 months; [PERSON] et al. [30] had 0.9 mm after 3 months, a decrease of 22.5%; [PERSON] et al. [31] and [PERSON] et al. [32] had a similar result, with the reduction of 0.13 mm (\(p\) = 0.12), with statistical significance found between groups (\(p\) < 0.001) in 8 weeks and with 0.18 mm (\(p\) = 0.1), and with high difference, compared with the test group after 8 weeks; [PERSON] et al. [35] also had significant reduction of PD in 3 months of 1.3 mm (\(p\) < 0.001) and in 6 months of 1.4 mm (\(p\) < 0.001), and compared with a test group, a significant result was found with greater reduction for melatonin group, the difference in 3 and 6 months was 0.7 mm (\(p\) < 0.01).  
CAL was not described by [PERSON] et al. [34]. Nevertheless, this parameter had significant and higher results for the melatonin group, with a reduction in improvement varying by 1.2-2.2 mm (\(p\) < 0.001). The differences found for the control group were lower, but significant, between 0.04-1.3 mm.
# On-Road Driver Emotion Recognition Using Facial Expression
## 4 Data Collection
### Experiment Setup  
The experiment was carried out in Chongqing, China. Benefiting from Chongqing's unique landform, the route selected included abundant terrain and road scenarios (signal lights at intersections, zebra crossings, pedestrian-intensive sections, downtown sections, highways, tunnels, overpasses, bridges, etc.). Figure 2 shows the experimental setup. The experiment was conducted during various periods (morning, midday, afternoon, night). The experiment route involved four districts of Chongqing and the route varied for different participants.  
The experiment's intent was to collect on-road driver facial expressions induced by the road scenario; hence both the driver's face and the road scenario needed to be collected. To record the driver's face as well as the road scenario synchronously, two driving recorders were required. Based on the vehicle condition of this study, the driving recorder needed to meet the following requirements:  
* Mainstream driving recorder power supplies require 5 volts DC. The on-board cigarette lighter of the test vehicle meets the requirement, meaning that the driving recorder needs to support an on-board cigarette lighter port.
* The video recorded by the recorder must be unencrypted (videos recorded by some driving recorders are encrypted) so that the original recorded driving data can be exported for further processing.
* The recorder needs to be small and easy to install to reduce interference with driving.  
Based on the above factors, this study selected Hikvision's D1 Driving Recorder, one for the road scenario recording (Resolution: 1920 \(\times\) 1080, TS) and the other for driver facial expression recording (Resolution: 1920 \(\times\) 1080, TS), with a frame rate of 30 frames per second (fps). This experiment used a TF memory card to store and read the experimental data collected in the recorder.  
In order to reduce interference and maintain the real driving environment, the recorder equipment was installed slightly higher than the steering wheel. Each driver was also required to adjust the driving seat. Furthermore, the cab's interior ceiling lights (except for the dashboard light) and the sunroof were closed to reduce reflection.  
Figure 2: Experimental setup of on-road driver facial expression dataset collection: (**a**) driver facial expression recording, (**b**) road scenario recording, (**c**) experimental vehicle, (**d**) experiment setup, (**e**) navigation of the test road.
## 4 Applications of Graph Attention Networks
### Sentiment Analysis  
Sentiment analysis is a natural language processing (NLP) technique that entails the examination and evaluation of digital textual data to identify and categorize the underlying emotional tone, specifically as positive, negative, or neutral. This approach is widely utilized by organizations to ascertain and classify prevailing opinions concerning a particular product, service, or idea. In pursuit of this objective, the utilization of graph attention networks has been suggested for sentiment analysis and has been applied in diverse categories of sentiment analysis, such as aspect-based sentiment analysis [109; 161; 162; 163; 164; 165; 166; 167], Sentiment Analysis for Multiple Entities and Aspects [168], and Aspect Category Sentiment Classification [6]. Collectively, these endeavors underscore the capacity of graph attention networks as a valuable instrument for sentiment analysis.