esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 AOG Downtime Reduction and Implementation Results
### Holt-Winters Model  
Seasonal adjustments implemented in the Holt-Winters model give a better result than ARIMA in percentage MAPE (7.56%) and RMSE (2.64). Their outlook matches the data recorded, thus allowing it to identify fluctuations related to occasions in AOG incidences. In general, the model can be very effective during the planning of maintenance activities and distribution of resources by predicting periodically recurring fluctuations in rates of incidents. The Holt-Winters model's performance, shown by a MAPE (Figure 20) near 0.456 and an RMSE around 2.64, reveals moderate predictive accuracy with some discrepancies between the fitted and actual values. The green line (fitted data) tracks the general direction of the blue line (actual data) but occasionally diverges notably, indicating that certain shifts or peaks in the real observations are not fully captured.  
The confusion matrix (Figures 21 and 22) reveals that the model predicts every case as \"No AOG\", correctly classifying all 160 \"No AOG\" instances but entirely missing the 40 actual \"AOG\" cases.  
Figure 19: ARIMA model (self-developed).  
Figure 20: ACF and PACF plots.  
Consequently, there are zero true positives for the \"AOG\" category, indicating the classifier completely fails to recognize that class.
## 3 Results and Discussion
### ROC and PR Curves  
The ROC curves of the five models showed that most classes performed well, with nearly all class curves approaching the optimal (0.1) point. However, AlexNet, GoogleNet, and ResNet exhibited poor classification performances for class 5. Nevertheless, performance differences across most categories were not significant.  
In the PR curves, AlexNet, GoogLeNet, and ResNet struggled with classes 4 and 5. This was likely because of the visual similarity of the micrographs from these classes. In contrast, ViT and VGG demonstrated robust performance, remaining unaffected by these similarities and achieving the best results (Figure 6).
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 3 Classification of Over-the-Sea Channel Models
It can be crowded with ships, affected by unique seasonal and weather effects, and affected by huge barriers like islands, rocks, etc. These may cause reflections of the propagated radio waves. This is where multipath wave propagation techniques play an important role by exploiting nLoS and bLoS wave propagation methods. As mentioned earlier, nLoS wave propagation describes a radio link that does not have a clear LoS and that have obstructions in the way. The effect of an obstruction can be negligible to a complete barrier. If the obstruction is smaller than the incident wavelength, it is negligible. Obstructions having the same size of the incident wave length cause diffraction and go to the other end with minor attenuation. If an obstruction is larger than the incident wavelength, the signal is obstructed in varying degrees depending on the materials of the obstructions and their characteristics. Similar scenarios are taken into account in the channel models exploiting nLoS wave propagation techniques. bLoS wave propagation is an especial case of nLoS propagation and is suitable for the applications where very long-distance communication is necessary, and where communication links may get obstructed by huge terrains, rocks, islands, etc. Modeling channels for bLoS propagation over the sea is a great challenge and very few works have been reported to work upon bLoS propagation. Most of the channel models in the literature fall under the classification of LoS propagation. References [5; 9; 10; 11; 12; 17; 19; 21] are the channel models that support LoS propagation only. There are channel models that support both LoS and nLoS propagations. Channel models presented in [8; 14; 16] are such kind of models. Channel models presented in [13; 15] support bLoS propagation. The fifteen channel models discussed in this paper are classified based on the LoS, nLoS, and bLoS propagations in Figure 9.  
Classifying the channel models based on the mobility of the transmitter and receiver is very important because of the Doppler effect. It refers to the apparent shifts in the frequency of the signals  
Figure 9: Classification of channel models based on wave propagation scenarios. due to the motion of the transmitter or receiver or both. This may result in channel fading. Many of the existing channel models dealt with the issues and calculated fade margin for the fading caused by the Doppler effect. They are classified on the basis of the three scenarios found in the literature:  
1. Both the transmitter and receiver are mobile ([10; 11; 14; 15; 17; 18; 21]);
2. Both the transmitter and receiver are fixed ([16]); and
3. The receiver is fixed but the transmitter is mobile ([5; 8; 9; 12; 19]).
## 2 PLC Technologies
### Prime
Current implementations only include the SCP, although the benefits of using the CFP for applications that required certain level of QoS (Quality of Service) are being investigated [51]. PRIME define three types of frames:  
* Beacon PDU (Packet Data Unit): PRIME networks are designed to be plug & play. Therefore, before the SCP, a period of time is reserved for the transmission of beacons, which are used by the concentrator or the switches to advertise themselves, as well as to provide network synchronization. So whenever a new smart meter is switched on, it will listen to the channel waiting for these beacons so that it can join the PRIME network. * Promotion Needed PDU: If a new smart meter is switched on and it does not hear any beacon, it will start sending Promotion Needed PDU, which work as a kind of SOS mechanism, since any smart meter that listen to such a PNPDU will have to ask the concentrator to promote itself to switch, so that the new smart meters can reach the concentrator through it. Figure 2: (**a**) States and transitions of a PRIME Service Node; (**b**) Tree-wise logical topology of a PRIME network. * Generic MAC PDU: The Generic MAC PDU can be either Data or Control packets. Figure 3 shows all the possible control packets. As it can be seen, the control packets include, for instance, the registration messages, which are used to join the PRIME network. As a result of this procedure, the new smart meter is assigned a 22-bit ID which is shorter than its MAC address (48 bits), thus reducing the overhead. Since PRIME communications are connection-oriented, the control packets also cover the connection procedure. The promotion requests which are triggered by the PNPDU are also a type of control packet. Finally, it is worthwhile to highlight also that keep-alives are yet another type of control packet which are used by the concentrator to get an idea of the connectivity status of the network. For the sake of comprehensiveness, although it does not belong to PRIME specification, on top of PRIME or any other NBPLC technology, i.e., at the application layer, DLMS/COSEM is used, where COSME (IEC 62056-61/62) is an energy metering profile of the DLMS (IEC 62056-53) protocol [52; 53] Thus, DLMS/COSEM defines data models for common energy-related parameters together with a communication protocol designed to transport this kind of information.
## 2 Materials and Methods
### 4D-Flow Data Analysis  
All 4D-flow MRI data was pre-processed using in-house program developed in MATLAB 2020b (Mathworks, Natick, MA, USA), and the following tasks were performed: corrections for Maxwell terms, eddy currents, and aliasing, Figure 1A. After pre-processing, a 3D phase-contrast (PC) angiogram (PC MRA) was generated, Figure 1B. This angiogram was used to segment selected anatomical regions of the pulmonary artery, Figure 1B red inset, using in-house MATLAB-based tool \"4D-Flow Analysis Tool\" [13; 14]. Analysis planes were created at the right ventricular outflow tract (RVOT), main pulmonary artery (MPA), the left pulmonary artery (LPA), and right pulmonary artery (RPA) using a specialized visualization software (Ensight 10.2, CEI Inc, Research Triangle Park, North Carolina, USA) to measure peak velocity, net flow, retrograde flow, regurgitation fraction, and forward flow, Figure 1C. Furthermore, the PC MRA was used to measure pulmonary artery diameters (LPA, MPA and RPA), bifurcation distances, and bifurcation angles (Figure 1D) in cvi42 when standard CE MRA was not available in controls (\(n=2\)). Volume sub-regions were created for local advanced 4D-flow analysis. These volume subregions were used to obtain the maximum velocities from the LPA, PA, and RPA using the approach proposed by [PERSON] et al. [15].  
Regional WSS was calculated at peak systole as previously described [16; 17]. Energy loss (EL) was calculated as proposed by [PERSON] et al. [18], which provides the rate of EL in a volume of interest (i.e., power in Watts) at a given time of the cardiac cycle, and peak systole was used in this study. Kinetic energy (KE) was calculated as reported by [PERSON] et al. [13] in each sub-region at peak systole. Vortex size analysis at peak systole was performed as introduced by [PERSON] et al. [10] using the Lambda2 (\(\lambda_{2}\)) method, which is an accepted method to identify 3D vortices. Valid vortex networks were defined as those \(>\)75 mm\({}^{3}\) (considering a voxel resolution of 2.5 \(\times\) 2.5 \(\times\) 3.0 mm) with a shared connectivity neighborhood \(\geq\)4 voxels. This rule reduces the impact of possible incorrect derived vortices due to noise and/or partial derivative errors, which are directly impacted by 4D-flow spatial resolution. Examples for these advanced 4D-flow parameters are presented in Figure 1E.
# Monitoring System of Transmission Line in Mountainous Area Based on LPWAN
## 3 Model and Method
### Network Model Design
Therefore, the network delay function is shown in Equation (9):  
\[D(M_{i,j,k}\,L_{i}\,G_{i}\,O_{i,j})\quad\forall(i,j)\in P,\quad\forall k\in N \tag{9}\]  
\[E(G_{i},L_{i})=\sum_{1=1}^{N}\left(dG_{i}+bL_{i}\right)+\sum_{(i,j)\in P}C_{i, j}O_{i,j}+L_{ps} \tag{10}\]  
where \(E(G_{i},L_{i})\) is the network energy consumption function; \(d\) and \(b\) represent the energy consumption of a single cellular wireless transmission module and LoRa transmission module, respectively; and \(C_{ij}\) represents the communication energy consumption generated on the data link (\(i\),\(j\)). The energy consumption function mainly includes transmission module energy consumption and wireless communication energy consumption, and the path loss function \(L_{ps}\) must be included in the energy consumption of wireless communication. As shown in Equation (10), system energy consumption is the sum of all communication energy consumption used for data transmission during the whole operation period and the energy consumption and path loss of installing the LoRa transmission module or cellular wireless transmission module on the selected tower. According to the above equation, the LoRa dynamic networking model of transmission lines in mountainous areas is shown in Equation (11):  
\[F[D(i,j,k),E(i,j)]\quad\forall(i,j)\in P,\forall k\in N \tag{11}\]
## 3 Key Findings
### RQ1: What Are the Security Challenges of IIoT?
#### 3.1.2 RQ1-2: Multifaceted Connectivity Loopholes of IIoTsec  
It is noteworthy to state that the reality of IIoT dominance over traditional industrialization is based on the evolution of IoT and increasing emerging technologies. As a result, IIoT contains abstraction properties of IoT such as open connectivity, heterogeneity, resource constraint, big data generation, real-time processing, and scalability [56; 57; 58]. These properties open the IIoT ecosystem to greater connectivity challenges. On that discovery, [PERSON] et al. [59] showed the rate at which the IIoT ecosystem is more susceptible to vulnerability as a result of its openness to message queue telemetry transport (MQTT), Modbus TCP, cellular networks, Long-Range Radio Wide Area Network (LoRaWAN), and other TCP/IP-based communication protocols. Additionally, all components in an IIoT ecosystem are Internet operational for seamless intercommunication between various nodes, devices, and systems, which eventually subject storage and sharing to centralized servers. However, the operating costs, delays, and possible exposure to security risks are the fundamental challenges of the centralized architecture [60; 61].  
Such exposure to security risks in an IIoT ecosystem is owed to the multifaceted connectivity (MFC) of IIoT devices [62]. Hence, the security and reliability of the systems become an integral concern. By such consideration, the authors [63] identified the scenario as a CPS-based security challenge that must be urgently identified for prevention. Also, in [6], the scenario is termed industrial CPS-inclined assault, and the authors proposed distributed data storage and management-enabled technology to curtail the effect of MFCchallenges of IIoT. Meanwhile, the study presented by [PERSON] and [PERSON] in [64] is observed applicable to this security challenge. The authors designed a detection model with CNNs, recurrent neural networks (RNNs), and their variants to defend against cyberattacks occasioned by MFC. In the model, CNN was used to extract local attributes, which eventually became inputs to the gated recurrent units (GRU) layer.  
In addition, MFC-tailored assault is also detectable in IIoT smart devices. This portrays more risks to the IIoTsec ecosystem due to the physical layer at which most of these devices are, coupled with the state of device communication [55]. This understanding, therefore, inspired [3] to design a smart contract token-based architecture comprising a token issue contract (TIC), user register contract (URC), and manage contract (MC), for synergetic supervision and management of IIoT ecosystem's events against MFC-like challenges. In the study [65], diverse connectivity of the smart machine operation cycle, better referred to as MFC, accounts for time-lag errors in productivity. A proposal of Operation-Constrained Process Control (OPCC) was instead introduced into the IIoT ecosystem to identify time lags and errors in production under the guise of repeated training-informed-multi-sensor knowledge updates. The experimental analysis showed the efficiency of OCPC.
## 3 Results and Discussion
### Direct Impacts of Gasifier Uptake on Household Livelihoods
#### 3.2.5 Diversification of Fuel Types Used  
Firewood and crop residues were used by the households as cooking fuel. The commonly used fuels and ranking of their qualities when used with the gasifier by 20 householders who participated in a FGD are presented in Table 2.  
From the householders' perspective, the fuels that burned for a longer period and with a hot flame were rated higher. Householders rated neem as the best, with most scores given to the production of good charcoal. Coconut shell was rated as having the hottest flame (Table 2). Coconut shell achieved high scores despite burning for the shortest period, possibly because it is easy to prepare and ignite. Burning for a longer period could probably be the reason mango received the third-highest ranking despite its flame not being very hot (Table 2).  
The survey showed that biomass types varied between the two regions, and that this influenced the frequency of their use (Table 3). For most fuels, the more accessible it was, the more frequently it was used as cooking fuel, as was the case with neem at Ng'ombeni and cashew nut firewood at Matuga (Table 3). However, fuel types such as coconut shell and maize cobs had a lower frequency of use even where they were available. This could be due to their shorter burning period, which necessitatesreloading the stove before meals can be cooked fully an exercise reported to be challenging by the gasifier users.  
The gasifier diversifies the feedstock used by households, as it can use crop residues and firewood. During one of the cooking demonstrations, 0.63 kg of neon tree firewood or 0.7 kg of coconut shell mixed with coconut leaf stalk was required to cook 2 kg of _ugali_, a starch Kenyan dish made by vigorously stirring water and maize (_Zea mays_) flour. Most households using a gasifier sourced their firewood on-farm from tree prunings and crop residues, which can both be used with the gasifier. However, the residues used with the gasifier were leaving airspaces in between the individual pieces for the air flow which is necessary for combustion. They included coconut shells, maize cob, cassava stems, doum palm tree (_Hyphene compressa_) seeds. This confirms previous reports that pyrolytic stoves can expand available feedstock options, as they can use residues to supplement firewood [34; 35]. This can be of benefit because in circumstances where cooking energy is limited, some households are forced to abandon nutritious but cooking-energy-intensive foodstuffs for less-nutritious and faster-cooking foods [36].
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 6 Mimblewimble Implementations
### Beam
#### 6.2.1 Blocks and Transactions  
\(\mathtt{Beam}\) transactions are confidential transactions implemented by the Pedersen commitment scheme. This follows the same approach as our model.  
Figure A4 in Appendix C shows (line 439) how \(\mathtt{Beam}\)'s input stores the commitment, that is, a point over the elliptic curve (class of _ECC::Point_).  
In Section 3 we described how each node maintains a local state. The state keeps track of the UTXO set. \(\mathtt{Beam}\) extends the behavior of that set, supporting the incubation period on a UTXO. This means that Beam sets the minimum number of blocks created after the UTXO entered the blockchain, before it can be spent in a transaction. This number is included in the transaction signature. Figure A5 in Appendix C shows (line 510) how Beam's output stores the number of blocks corresponding to the incubation period.  
In our model (Section 3.5), the predicate _validChain_ should check that every output with certain incubation period on a block was 'lawfully' spent for the entire blockchain (global state). In other words, if we have an output transaction \(o\) with an incubation period \(d\) on a confirmed block \(b\) over the chain and a later confirmed block \(b^{\prime}\) containing \(o\) as an input, then \(b^{\prime}\) should be, at least, \(d\) blocks away from \(b\) on the blockchain.
# From Trees to Barcodes and Back Again: Theoretical and Statistical Perspectives
## 4 Stability of the TNS  
In this section, we investigate the effect of the composition of the TNS and TMD algorithms from a theoretical perspective. Given a strict barcode \(B=\{(b_{i},d_{i})\}_{i=0,\ldots,n}\), we apply the TNS to \(B\), for a fixed choice of the parameter \(\lambda\) (cf. Section 2.4), obtaining a tree \(T_{B}\), and then compute the barcode of \(T_{B}\), TMD\((T_{B})\), which we denote by \(B^{\prime}=\{(b^{\prime}_{i},d^{\prime}_{i})\}_{i=0,\ldots,n}\). To quantify to what extent the TNS acts as an inverse to the TMD, we are interested in determining how similar \(B\) and \(B^{\prime}\) are.  
Expressing the similarity between \(B\) and \(B^{\prime}\) in terms of the bottleneck distance enables us to establish one form of stability for the TNS in the first part of this section. We establish another type of stability for the TNS in the second part, when we show that the probability that the order of two specific bars will be altered upon applying TMD \(\circ\) TNS decreases exponentially with the distance between the death times of the two bars. Together, these stability results imply that the TNS is an excellent approximation to a (right) inverse to the TMD.
# Non-Newtonian Behaviors of Crumb Rubber-Modified Bituminous Binders
###### Abstract  
Crumb rubber-modified bitumen (CRMB) has been utilized in the asphalt paving industry for decades due to its various benefits. The complex interaction between bitumen and crumb rubber as well as the addition of warm-mix additives makes the typical laws of Newtonian fluids insufficient to describe the behaviors of highly modified bituminous binders. To systematically explore the non-Newtonian behaviors of CRMB, a dynamic shear rheometer was utilized to apply shear loading on the samples at various temperatures and shear rates. Results show that the viscosity of different binders are highly temperature- and shear rate-dependent, while highly modified binders exhibit more obvious shear-thinning behaviors at certain temperatures. With the help of zero shear viscosity and yield stress, the shear-thinning behaviors of non-Newtonian binders can be sufficiently characterized. The Arrhenius equation is invalid to describe viscosity-temperature characteristics of bitumen in the non-Newtonian region. A second-order polynomial function was proposed to characterize the viscosity-temperature dependence with a high correlation degree.  
non-Newtonian behavior; viscosity; bitumen; crumb rubber modifier (CRM); warm mix asphalt +
Footnote †: journal: Applied Physics Letters
# Geo-Proxy-Based Site Classification for Regional Zonation of Seismic Site Effects in South Korea
## 3 Development of Site Classification with Geo-Proxies Using Geotechnical Database in Seoul
### Study Areas and Geotechnical Database
In Seoul and nearby Incheon, 10 and 15 rock types, respectively, have developed, with the former being classified into Precambrian, Jurassic and Quaternary and the latter into Precambrian, Jurassic, Cretaceous and Quaternary. In Seoul, the distributive ratios decrease in the order of banded gneiss from the Gyeonggi gneiss complex, Daebo granites and alluvium, which account for 95.5% of the rocks in the area [27]. The DEMs were separated into 5 m mesh areas, which included the extended areas of the Seoul and Incheon administrative areas. In general, the regional geographic information system (GIS) or digital topographic map (DTM) for a specific city utilizes the Transverse Mercator (TM) coordinatesystem, which was transformed into the GRS 80-based world geodetic reference frame. And the ellipsoidal and the projected areas were calculated based on the Geocentric datum of Korea reference. To evaluate the correlations between site response parameters and geo-proxies at the same location, the topographic elevation and slope information at each geo-spatial grid square were extracted from DEM. Based on the geotechnical information calculated for these grid locations, various site effect parameters (\(H\), \(V_{S30}\), \(V_{S,soil}\), \(T_{G}\) and \(V_{S}D_{S}\)) and geo-proxies were rearranged or calculated. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **Thickness of Fill (m)** & **Thickness of Alluvial Soil (m)** & **Thickness of Weathered Soil (m)** & **Thickness of Weathered Rock (m)** \\ \hline
**Seoul** & & & & \\ \hline
**Incheon** & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spatial distribution of geotechnical layer thickness based on geo-spatial grid information in the Seoul and Incheon areas. Figure 2: Geotechnical database (DB) and geo-spatial grid information at 100 m intervals for the Seoul and Incheon areas.