esa-sceva/satcom-chunk-collection huggingface dataset:
# A LoRa-Based Linear Sensor Network for Location Data in Underground Mining
###### Abstract  
In this paper, we describe a LoRa (from \"Long Range\")-based, linear sensor network we have developed for transmitting location information of personnel and equipment in an underground mine. The system is intended to be used during emergencies when existing communications infrastructure has failed. Linear networks comprise a sequence of relays that forward data to a common destination, the headend. Relays forward location information transmitted from tags carried by personnel or equipment. Relays will usually be put in place as investigators or rescuers enter the mine. LoRa is used both by the tags to communicate to the relays and by the relays to forward messages to the headend. We have implemented and tested this system, and have carried out simulations and analyses to determine its scalability, reliability and fairness. The need for robustness and reliability has led us to use flooding rather than unicast communication. We also use message sequence numbers and time-to-live fields to prevent broadcast storms. Contention is managed using a simplified Carrier Sense Multiple Access (CSMA) scheme. We also address fairness. When the network is under load messages may be dropped by relays making messages generated more hops from the headend more likely to be dropped than messages nearer the headend. We explore the relationship between unfairness, traffic load and number of relays. We also observe that a network of larger numbers of lightly loaded relays performs more effectively than smaller numbers of heavily loaded relays.  
Keywords:internet of things; underground network; wireless linear networks; flooding; LoRa; industrial internet; emergency network +
Footnote †: journal:
# Quantification of Liver Fibrosis--A Comparative Study
## 4 Results and Discussion
### Results from SHG/TPEF Microscopy Methodologies  
[PERSON] et al. [46] compared the obtained diagnostic results with those of [PERSON] red and [PERSON]'s trichrome staining images. The outcomes indicated that features observed in the SHG/TPEF images also appeared in the SR biopsy specimens, confirming that the SHG signals reflected the fibrillar collagen deposition faithfully (Figure 7). In comparison with MT staining, SHG microscopy was able to provide more sensitive and quantitative information about collagen in the liver tissues, having the ability to detect collagen fibers inside the sinusoids.  
In [PERSON] et al. [47], the calculated CPA in a human tissue sample was \(4.21\%\pm 0.67\%\) and equivalent to a Fibro-C-Index value of \(2.78\%\pm 0.44\%\). The semi-quantitative Ishak assessment performed by a histopathologist produced a good agreement, confirming the use of Fibro-C-Index as accurate, if not better than, the conventional staining methods for collagen content quantification.  
When SHG was used as reference, [PERSON] et al. [48] showed fibrosis areas saturated with high collagen content, whereas non-saturated areas corresponded to the non-fibrotic regions of the human  
Figure 7: Localization of 2D collagen patterns in second-harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy imaging. The TPEF image provides red color values for the scanned tissue sample, while SGH signals indicate the areas of collagen deposition in green.  
liver. After the image reconstruction phase, the diagnostic results showed a good linear correlation between the sophisticated method's scoring and the METAVIR (F3, F4) assessment (R\({}^{2}\) = 0.997, 0.9944).  
According to the Beijing classification by [PERSON] et al. [49], progressive, indeterminate, and regressive types of fibrosis were observed in 58%, 29%, and 13% of total patients before Entecavir treatment, versus 11%, 11%, and 78% after treatment. In general, the CPA showed a correlation equal to \(p\) = 0.001 with the semi-quantitative HAI systems. Of the 55 patients who showed regressive changes on post-treatment phase, 29 cases (53%) showed fibrosis recurrence at least one Ishak stage, whereas 25 cases (45%) had significant fibrosis improvements in terms of Laennec sub-stage, CPA quantification and liver stiffness, despite remaining in the same Ishak stage.
# PFDA-FMEA, an Integrated Method Improving FMEA Assessment in Product Design
### Risk Assessment in PD, a Literature Review  
Product Design is a complex process that has multiple different problems to solve during different design phases [48]. Risk analysis is essential during the process, as mentioned in [49; 50; 51; 52; 53; 54]. In the literature, there have been multiple efforts to improve the risk assessment for PD, where different tools are used for this purpose. Recent research [55] examined different methods used for risk analysis, where industry experts were interviewed to identify their points of view for risk assessment methods, like Failure Tree Analysis (FTA), Root Cause Analysis (RCA), Risk index, FMEA, Decision Tree, and more. Further investigations also use multi-agent systems [56], proposing a risk assessment model in complex product design; the authors used venture evaluation and review technique (VERT) and probabilistic risk analysis (PRA) to propose a hierarchical risk assessment for complex design. Another study presents the use of development flexibility to mitigate risks during product launches [57]. The use of Monte-Carlo simulations and historical data to make a better-informed decision based on risks in product design is presented in [58]. Another recent research remarks on the use of Product Lifecycle Management and Computer-Aided Design (CAD) tools to identify product design risks during the design stage, considering changes to the CAD [59].  
Currently there are diverse methodologies applied to the PD process for risk assessment as per the literature reviewed. However, there is no integral model that uses the Pythagorean Fuzzy Dimensional Analysis (PFDA) method [60] to eliminate the gaps brought about by FMEA's uncertainty and vagueness.  
This document aims to improve FMEA risk assessment for the PD process, covering these gaps, as well as helping to achieve a clear and enhanced risk ranking. Likewise, the proposed method offers a practical way to make decisions based on risks during the Product Design process. The PFDA-FMEA proposed method integrates PFDA and FMEA methods. This specific integration is intended to improve FMEA risk assessment and decision-making in the PD process. The PFDA-FMEA method intends to remove the uncertainty and vagueness of human ranking in conventional FMEA risk analysis. The proposed methodology is validated and demonstrated step by step using a real practical example.  
This document is structured as follows: Section 2 contains the basic concepts of the different methodologies used in the proposed PFDA-FMEA method. Section 3 details the PFDA-FMEA methodology step-by-step with the help of a practical real example. Section 4 reveals the details of the calculus performed. The results and discussion are presented in Section 5, and finally, Section 6 concludes the document.
###### Abstract  
Saudi Arabian urban roads and highways have witnessed a large number of traffic crashes. Road lighting is one of the most important factors influencing drivers' safety during the nighttime. Street-lighting design (e.g., spacing and height), visibility (e.g., lane marking and oncoming vehicles), and drivers' satisfaction (e.g., glare effect on eyes and overall ambiance) are primary criteria affecting the performance of an urban street-lighting system (USLS). The present study presents a methodology for the performance appraisal of USLS in Qassim, Saudi Arabia. An online questionnaire survey was developed to obtain drivers' opinions on nine sub-criteria (three under each primary criterion). The responses were translated into a five-scale subjective rating system from very low to very high. Fuzzy synthetic evaluation (FSE) effectively aggregated the statistically diverse (\(p\)-value \(<0.001\)) responses obtained on the three primary criteria. The study found that electronic billboards' positioning, oncoming vehicle lights, and poor lighting in the course of bad weather (mainly dust) are mainly affecting the performance of USLS in the view of road users. The performance levels ranged between \"medium\" and \"high\", with no criteria or sub-criteria achieving a \"very high\" level, suggesting a need for upgrades, such as conversion to LED lights and smart lighting control systems. The proposed methodology benefits the transportation ministries to identify lacking components of USLSs in different regions of Saudi Arabia. The methodology provides the opportunity to include additional or site-specific factors for appraising the performance of USLS before (during planning and design) or after the implementation of improvement actions.  
urban street-lighting; performance appraisal; driver opinion; fuzzy synthetic evaluation; road safety; visibility +
Footnote †: journal: Applied Sociology  
1
# Key Agreement and Authentication Protocols in the Internet of Things: A Survey
## 3 Security Protocols for the Internet of Things
in [134] observed that the development of IoT systems for vehicles, on the one hand, contributed to easing the traffic load and improving travel efficiency. On the other hand, these systems are exposed to security threats in many respects. Therefore, the authors proposed an authentication protocol for such solutions. The proposed protocol uses blockchain technology and a chaotic mapping algorithm. It allows vehicles and roadside units to register to obtain a public identity, which they then use to authenticate and negotiate the key. The authors confirmed the security of their protocol with the Scyther tool. Moreover, they showed that the proposed protocol has lower computation and communication costs than the existing schemes. [PERSON] et al. in [135] focused on IoT solutions that use drones in agriculture. The authors proposed an authentication and key management protocol based on blockchain technology. The authors examined their protocol for its susceptibility to attacks occurring in IoT environments. They showed that the protocol is resistant to MITM attacks, replay attacks, impersonation attacks, privileged-insider attacks, physical IoT smart device and drone capture attacks, and ephemeral capture attacks, secret leakage attacks. In addition, the authors performed a formal protocol analysis by using the ROR model and the Avispa tool. In conclusion, the authors concluded that the protocol has low computational and communication costs. [PERSON] et al. suggested two protocols for IoT drone solutions: a protocol for the authentication process in [136], and a protocol for the key agreement process in [137, 138]. These protocols use AES-CBC-256, ECC, SHA-256 hash functions, and XOR operations. The authors have demonstrated the resistance of these protocols to common attacks occurring in IoT environments, for example, replay attacks and MITM attacks. The authors used the ROM model and the Scyther tool for formal analysis of the protocols. The authors used both proposed protocols in the [139] framework for drones because both are efficient in terms of communication, storage and computing costs compared to similar solutions. [PERSON] et al. in [140] have abandoned the blockchain-based authentication protocol and the hyperelliptic curve cryptography for IoT drones. In this approach, the blockchain is used as a certification authority, and transactions are defined as certificates. Such action is designed to reduce maintenance costs while ensuring a high level of communication security. The authors concluded that the proposed protocol is resistant to common attacks in drone IoT networks and is also cost-effective in terms of computation and communication compared to similar solutions.
## 3 Methodology
### Conversion Algorithm
```
1:\(\alpha=\alpha_{1}\dots\alpha_{n}\) and \(\alpha_{1},\dots,\alpha_{n}\in X\)
2:\(n\gets length(\alpha)\)
3:\(k\gets 1\)
4:while (\(k\leq n\)) do
5:\(\gamma\leftarrow\alpha_{1}\dots\alpha_{k-1}\)
6:\(\delta\leftarrow\alpha_{k+1}\alpha_{k+2}\dots\alpha_{n}\)
7:\(table\gets tables[\alpha_{k}]\) [% selection of appropriate table]
8:\(R\gets rows(table)\) [% number of table rows]
9:\(C\gets cols(table)\) [% number of table columns]
10:for all (\(2\leq i\leq R\)) do
11:if (\(\gamma\in\Gamma_{i}\)) then
12:\(l\gets i\) [% selection of appropriate table row]
13:endif
14:endfor
15:for all (\(2\leq j\leq C\)) do
16:if (\(\delta\in\Delta_{j}\)) then
17:\(J\gets j\) [% selection of appropriate table column]
18:endif
19:endfor
20:\(\beta_{k}\leftarrow table[I,J]\) [% reading phoneme letter from the table]
21:\(k\gets k+1\)
22:endwhile
23:\(\beta\leftarrow\beta_{1}\dots\beta_{k-1}\) [% string of phonemes]
24:return\(\beta\)
```  
**Algorithm 1**. The algorithm for grapheme-to-phoneme conversion (implementation of function \(F(\alpha)=\beta\)).
## 4 Our Model
### Multi-Task Learning Loss Function  
In this section, we frame the rating prediction task as a regression problem. The loss function is represented as follows:  
\[\mathcal{L}_{\text{rating}}=\frac{1}{|\mathcal{X}|}\sum_{u_{i}\in\mathcal{U},v _{j}\in\mathcal{V}}(\hat{r}_{i,j}-r_{i,j})^{2} \tag{13}\]  
where \(\mathcal{X}\) denotes the full training set and \(\hat{r}_{i,j}\) is the actual rating of user \(u_{i}\) for item \(v_{j}\).  
During GCLTE's training, random noise is added to the encoder's convolutional layer. During the embedding-based comparison, the comparison loss is calculated as follows:  
\[\mathcal{L}_{\text{cl}}=\mathcal{L}_{\text{cl}}^{\text{users}}+\mathcal{L}_{ \text{cl}}^{\text{items}} \tag{14}\]  
\[\begin{split}\mathcal{L}_{\text{cl}}^{\text{users}}& =-\frac{1}{N}\sum_{i=1}^{N}\log\frac{\exp\left(\frac{u_{i}\cdot u_{i }^{\prime}}{\tau}\right)}{\sum_{j=1}^{N}\exp\left(\frac{u_{i}\cdot u_{j}^{ \prime}}{\tau}\right)},\\ \mathcal{L}_{\text{cl}}^{\text{items}}&=-\frac{1}{N} \sum_{i=1}^{N}\log\frac{\exp\left(\frac{v_{i}\cdot v_{i}^{\prime}}{\tau} \right)}{\sum_{j=1}^{N}\exp\left(\frac{v_{i}\cdot v_{i}^{\prime}}{\tau} \right)}\end{split} \tag{15}\]  
where \(N\) is the perturbation batch size, \(u_{i}\),\(v_{i}\) are the unperturbed user and item embeddings, and \(u_{i}^{\prime}\), \(v_{j}^{\prime}\), are the perturbed user and item embeddings. \(\tau\) represents the temperature parameter, and \(exp(\cdot)\) denotes the softmax operation.  
For calculating text loss, we employ Negative Log Likelihood Loss as the loss function. It is calculated as follows:  
\[\mathcal{L}_{\text{text}}=-\frac{1}{S}\sum_{t=1}^{S}\log P(y_{t}\mid y_{1},y_{ 2},\dots,y_{t-1}) \tag{16}\]  
where \(S\) denotes the length of the text. \(P(y_{t}\mid y_{1},y_{2},\dots,y_{t-1})\) represents the probability of generating the \(t\)th word based on the first \(t-1\) words in the sequence.  
We then combine the individual losses, weighted appropriately, to form the final loss:  
\[\mathcal{L}=\lambda_{r}\mathcal{L}_{\text{rating}}+\lambda_{t}\mathcal{L}_{ \text{text}}+\lambda_{c}\mathcal{L}_{\text{cl}}+\lambda_{l2}\mathcal{L}_{ \text{I2}} \tag{17}\]  
where \(\lambda_{r}\), \(\lambda_{t}\), \(\lambda_{c}\), \(\lambda_{l2}\) are the weights assigned to each loss and \(\mathcal{L}_{l2}\) represents \(L2\) regularization loss, which helps prevent model overfitting.
## 3 Results
### Description of the Selected Soil Improvement Techniques
#### 3.2.3 Thermal Stabilization  
Thermal stabilization has emerged as a promising and sustainable alternative to address the geotechnical engineering challenges of expansive soils, unlike conventional chemical methods such as lime and cement, which can raise environmental concerns due to the emissions associated with their production. Thermal stabilization leverages heat treatment to modify soil properties [15].  
Scinentifically, the thermal stabilization technique involves exposing expansive soils to elevated temperatures. This process induces fundamental, often irreversible, alterations in the soil's physical, chemical, and mineralogical properties [15]. At the microstructural level, heating causes transformative changes in clay minerals. For example, minerals such as kaolinite can collapse at certain temperatures, while montmorillonite undergoes modifications [15]. Research has shown that heating can transform clay minerals into an amorphous phase at elevated temperatures, which is crucial for understanding the mechanisms underlying soil improvement [15]. The escape of structural water and dehydroxylation during heating also induce chemical changes.  
Historically, modifying clay properties through heat is not new; it dates back to ancient civilizations that employed clay firing at high temperatures (around 800 \({}^{\circ}\)C) to produce durable ceramics and building materials [15].  
Modern thermal stabilization techniques include traditional approaches such as furnace heating or electrical resistance heating and emerging innovations such as microwave heating [15]. Microwave heating is particularly interesting because it can reach high temperatures and couples directly with water molecules, resulting in rapid and relatively uniform heating [15]. The application of microwaves has demonstrated a marked reduction in both the free-shell ratio and the vertical free-swell deformation with increasing heating duration [15].
## 2 Background and Previous Research
The National Oceanographic and Atmospheric Association (NOAA) has already announced the end of the production of traditional PNCs and of the implementation of an online application, which will provide the users with an option to create their own, custom made charts [26]. Hydrographic offices have been considering whether to reduce or altogether discontinue the printing of the PNCs. As a consequence, even for ECDIS back-up arrangements, the world will not be covered entirely with PNCs. Concerning the ENCs world coverage and their features [27], the quality of the ENC data, in particular the depth hydrographic information, may vary, given that the source of surveys is mostly the same as on PNCs. There are approximately 75% of the navigable waters covered with ENCs where seafarers need to exercise caution or a high degree of caution due to the possible existence of uncharted dangers for navigation [25; 28; 29]. Recent efforts to provide a valuable voyage-planning tool for the vessels operating in the Arctic waters [29] has highlighted the importance of a proper ENC coverage, especially in the remote areas. Inevitable reduction in the PNCs' production and the mandatory transfer to electronic means have been further problem-addressing points of the research. Figure 1: Summarized Electronic Chart Display and Information System (ECDIS)-related issues adapted and modified from [13].
# Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector
## 5 Experimental Analysis
### Meta-Learner
#### 5.7.3 Bi-LSTM  
Bi-LSTM or Bi-directional Long Short Term Memory is another variant of RNN which was tested in this meta-learning approach. In a traditional LSTM or RNN, the information propagates sequentially and in forward direction only, enabling future predictions based on past events. Bi-LSTM overcomes this unidirectional problem by training the model in both forward and reverse directions. BiLSTM usually brings together two independent RNNs which enable running input in two directions as future and past [67, 68]. Our goal to using Bi-LSTM was to extract contextual information from every time interval in features like _sessions_ to observe if the accuracy could be increased. The meta-learner included 2 Bi-LSTM layers, each having 50 LSTM units for training with Softmax activation. The Bi-LSTM was trained for 10 epochs.
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 4 Simulation
### Results and Analysis  
Furthermore, in Figure 8, we tend to verify the performance of each device in different network slices. We consider the scenario of the maximum bandwidth \(B_{m}=10\) MHz; the number of devices \(N=15\); the minimum rate requirement for eMBB device \(R_{0}=4\) Mbps; the minimum delay requirement for URLLC device \(T_{0}=20\) ms; and the minimum bandwidth requirement for mMTC device \(B_{0}=0.18\) M. It can be seen from the figure that the proposed AL-SAC algorithm can make all of the devices in various network slices meet their corresponding constraints in three slices, while the Penalty DDPG algorithm cannot. As shown in Figure 8a,b, the device eMBB-3 cannot achieve the required rate,  
Figure 6: The reward achieved by different algorithms during the training process.  
Figure 7: The total bandwidth allocated by different algorithms.  
and the devices URLLC-3 and URLLC-5 exceed the maximum delay in (5). Moreover, comparing the transmission rate, delay and bandwidth constraint in the three sub-figures, it can be seen that the network performance achieved by the device in one slice is more even when the proposed AL-SAC algorithm is adopted. This means a better fairness performance is obtained by our proposed AL-SAC algorithm.  
To see the results affected by different weights, in Figure 9, we also plot the average reward achieved by the proposed AL-SAC algorithm when weight \((w_{\text{eM}},w_{\text{UR}},w_{\text{mM}})=(\frac{1}{3},\frac{1}{3},\frac{1}{3})\), \((\frac{2}{3},\frac{1}{6},\frac{1}{6})\), and \((\frac{1}{10},\frac{3}{5},\frac{3}{10})\) with the maximum bandwidth \(B_{m}=15\) MHz, \(n=10\), respectively. It can be seen that (1) comparing the blue and orange bars in Figure 9a, the total bandwidth allocated to the eMBB slices grows when weight \(w_{\text{eM}}\) increases. (2) Comparing the blue and yellow bars in Figure 9b, the overall delay in the URLLC slice reduces when weight \(w_{\text{UR}}\) increases. (3) Comparing the yellow bars in Figure 9a,c, the overall bandwidth allocated grows proportionally for devices in mMTC slices when weight \(w_{\text{mM}}\). This is because the agent will more attention to the slice with the higher weight.  
Figure 8: Network service constraints for devices in different network slices. (**a**) The achieved transmission rate of devices in eMBB network slice; (**b**) the achieved delay of devices in URLLC network slice; (**c**) the achieved bandwidth of devices in mMTC network slice.