esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Findings
### Core Elements Underpinning SCM in the Construction Industry
#### 3.2.2 Logistics  
The two elements included in this subject area are 'planning and control of material flows' and 'integration of materials and information flows'. Transport and logistics of construction materials represent an important share of total costs [118]. Inefficiencies in the planning and control of material flows, such as late deliveries, incomplete orders and quality deficiencies, result in the disruption of construction processes, causing delays and increased costs [47]. Even though time and material buffers are commonly used to mitigate these risks, they may contribute to significant time waste and enormous additional costs [3,21]. Therefore, planning and control of material flows, involving the mechanisms to ensure the availability of materials at the right time, with the required quantity and minimum cost [48], are critical to the success of a project. Accordingly, many models to support decision making for tasks, such as selection of material sources, determination of the number of deliveries and project scheduling, have been proposed by researchers [49; 50; 51; 52; 53; 54]. Moreover, studies indicate efficiency potentials in applying collaborative supply networks [47; 55]. In the traditional practice, each actor coordinates its own materials supply (i.e., storage, packaging and transport) and onsite logistics activities (i.e., material handling activities). An extended scope of coordination, considering joint coordination of materials supply and onsite logistics activities, or even coordination of activities beyond individual sites, including all supply chains directed to them, entails opportunities for improving performance [55].  
The second element, integration of materials and information flows, is crucial for improving the visibility and traceability of materials throughout the construction supply chain. This is not only a way of reducing the uncertainties that affect the above-mentioned planning practices, but also supports progress monitoring and materials management processes as a whole [21; 57]. Different technologies, such as radio frequency identification (RFID) [58; 59], machine learning [60] and smart construction objects (i.e., construction resources augmented with sensing, processing and communication abilities) [61], have been proposed to improve the transparency of information. The central tenet underpinning these technologies is to increase the quantity, rate and quality of information. With accurate information available at the right time and in the right format, project managers can make prompt and informed decisions [59].
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 2 Fair Editions of Naive Bayes
### Fair Naive Bayes Classifier  
[PERSON] et al. [4] proposed a variation of the classical Naive Bayes model to achieve fairness. The authors introduced the concept of discrimination patterns--instances where an individual's predicted outcome is influenced by the presence of sensitive attributes. The algorithm detects discrimination patterns that exceed a defined threshold \(\delta\) using an  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Key Idea** & **Fairness Metric(s)** & **Datasets** & **Remarks** \\ \hline FairBayes III [3]: Latent & Introduces latent variable & & & \\ FairBayes III [3]: L, assumed independent & & & & Mathematically rigorous \\ Latent & of S, with C derived from L & & & and best in theory but \\ Variable Model & via discrimination; trained & & & unstable in empirical tests. \\  & using EM algorithm. & & & \\ \hline Fair Naive & Efficient & & & \\ Bayes & & & & \\ Classifier [4] & discrimination patterns in & & & \\  & Naive Bayes classifiers. & & & \\ \hline Fairness-aware & Extends FairBayes II (2 NB) & Statistical Parity, & US Census & \\ Naive Bayes [5] & to multiple sensitive & & & \\  & attributes (NNB). & Differential & & \\  & & Fairness & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._optimized search strategy. A cutting-plane approach iteratively refines the Naive Bayes model by alternating between parameter learning and constraint extraction, gradually incorporating fairness constraints during training. These constraints are formulated as a signomial programming problem, ensuring that fairness adjustments do not significantly degrade model performance. At each iteration, the algorithm learns the maximum likelihood parameters subject to fairness constraints and identifies \(k\) additional patterns using the updated parameters, which are then added to the constraint set. This process continues until no further discrimination patterns are found.  
This Naive Bayes fairness variation was applied to real benchmark datasets, including the Adult dataset, the German dataset, and COMPAS, achieving better results in terms of both performance and fairness compared to the 2-Naive Bayes model [3].
## 4 Authenticated Multi-hop Join Protocols: \(Djs\) and \(Ijs\)  
In this section, for the sake of completeness and to make the paper self-sufficient, we recall the authenticated multi-hop join protocols that our key establishment protocols are based upon. Protocol \(DJS\) (Direct Join to the Sink), presented in Figure 1, was presented in [11]. It allows new nodes in range of the sink to join the network directly. A new node \(I\) sends a direct request to \(S\) in order to establish a session key with it. Node \(I\) begins the join process by computing the symmetric key \(K_{DH}(I,S)\) with the sink \(S\). Then, node \(I\) generates a nonce \(n_{I}\) and adds its identity in order to form the request {\(n_{I}\), I}. The request is encrypted with \(K_{DH}(I,S)\) and sent to \(S\). Upon reception, in order to decrypt the request, \(S\) computes \(K_{DH}(I,S)\) using the public key of \(I\). Then, \(S\) verifies the identity of \(I\) by checking if the identity of \(I\) belongs to the list of authorized nodes, and generates a new session key \(K(I,S)\). The join response contains \(n_{I}\), the identity of \(S\) and the new symmetric session key. The response is encrypted using \(pk(I)\) and is sent to \(I\). Only \(I\) is able to decrypt the response with its secret key \(sk(I)\). We note that \(n_{I}\) helps \(I\) to authenticate the response message of \(S\).  
Figure 1: \(DJS\): node \(I\) joins the network by communicating directly with the sink \(S\).  
The protocol \(IJS\) (Indirect Join to the Sink), presented in Figure 2, was also presented in [11] and allows a new node to join the network through a neighbour node \(N\). The new node \(I\) sends an indirect request to \(S\) in order to be authenticated and to establish a session key with \(N\). Node \(N\) forwards the request to \(S\) through intermediate nodes. We note that the request and the response are just forwarded by these nodes without being modified. They are unable to decrypt either the request part or the response part of the message; only nodes \(I\) and \(S\) are able to decrypt messages encrypted with \(K_{DH}(I,S)\), and only \(N\) and \(S\) are able to decrypt the messages encrypted with \(K_{DH}(N,S)\).  
Our aim is to establish a shared key between any two authenticated nodes \(I\) and \(R\) of the network (not necessary in range). We propose six different protocols, called \(MKE_{S}\), \(MKE_{S}-light\), \(MKE_{T}-a\), \(MKE_{T}-b\), \(MKE_{T}-c\) and \(MKE_{T}-d\).
## 7 Different Spatial Node Distributions
### Uniform Random Distribution  
Table 14 manifests the results for 49 nodes under random positions along the area. All nodes generate and send traffic to the coordinator. Regarding the retransmissions, MPH has 19.4% less retransmissions than AODV and 18.1% less than DSR. With regard to the CSMA retries, MPH presents an average reduction of 17% against AODV and 12.3% against DSR. Concerning the tries, MPH exhibits 16% less tries by the coordinator than AODV and 13.9% less than DSR. Regarding achievable nodes by the coordinator, MPH has greater self-configuration features, such as 15.2% more achievable nodes than AODV and DSR. Finally, with regard to energy, MPH has energy savings of 18.4% compared with AODV and 16.6% against DSR.  
Table 15 summarizes the percentages of MPH protocol performance against AODV and DSR. This table is intended to explain the contribution of MPH in each of the metrics studied in this work. The MPH protocol remarkably out-performs with respect to AODV and DSR; this may be due to  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Percentage of Better Performance of the MPH Protocol**} \\ \hline \multicolumn{1}{c}{**Retransmissions**} & \multicolumn{1}{c}{**CSMA Retries**} & \multicolumn{1}{c}{**Tries**} & \multicolumn{1}{c}{**Achievable Nodes by the Coordinator**} & \multicolumn{1}{c}{**Energy (J)**} \\ \hline \multicolumn{6}{c}{**Scenario 1**} \\ \hline
**Against AODV** & 18.7\% & 11.4\% & 14.3\% & 13.6\% & 15.5\% \\
**Against DSR** & 16.3\% & 9.4\% & 12.8\% & 13.6\% & 12.7\% \\ \hline \multicolumn{6}{c}{**Scenario 2**} \\ \hline
**Against AODV** & 16.8\% & 13.6\% & 30.2\% & 16.7\% & 16.7\% \\
**Against DSR** & 12.9\% & 11.4\% & 27.5\% & 14.3\% & 11.2\% \\ \hline \multicolumn{6}{c}{**Scenario 3**} \\ \hline
**Against AODV** & 19.4\% & 17\% & 16\% & 15.2\% & 18.4\% \\
**Against DSR** & 18.1\% & 12.3\% & 13.9\% & 15.2\% & 16.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: Better performance of the MPH protocol against AODV and DSR.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Uniform Random Distribution**} \\ \hline \multicolumn{1}{c}{**Retransmissions**} & \multicolumn{1}{c}{**CSMA Retries**} & \multicolumn{1}{c}{**Tries**} & \multicolumn{1}{c}{**Achievable Nodes by the Coordinator**} & \multicolumn{1}{c}{**Energy (J)**} \\ \hline
** AODV** & 2.53 & 3.87 & 2.06 & 39 & 4.13 \\
**DSR** & 2.49 & 3.66 & 2.01 & 39 & 4.04 \\
**MPH** & 2.04 & 3.21 & 1.73 & 46 & 3.37 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Scenario 3.  
its proactive-reactive characteristics, where its mechanisms for updating shorter routes and neighbor tables with parent-child relationships provide better versatility and speedier connection configuration and topology recovery. Moreover, MPH presents greater savings in energy consumption due to the optimization of routes and packet delivery times.
# Numerical Investigation of Jet Angle Effect on Airfoil Stall Control
## 3 Results and Discussion
### Pitching Airfoil Simulations
#### 3.2.2 Flow Control
Figure 18: Lift (cycle-averaged), drag (cycle-averaged), the peak moment and the lift-to-drag ratio (cycle-averaged) of the pitching OA209 airfoil with and without steady-blowing jets. \(Ma=0.3\), \(Re=1.15\) M, \(c_{\mu}=0.06\), \(k=0.05\) and \(\alpha=13^{\circ}\pm 7^{\circ}\).
## 4 Result and Analysis--A Case Study
### Optimization Results and Analysis
The stress value of each cable is less than the allowable stress value of cable material. The optimized cable force increases with the decrease of the inclination angle, and the cable force varies uniformly from the middle of the span to the root of the tower.
# Adaptive Probabilistic Optimization Approach for Vibration-Based Structural Health Monitoring
## 4 Results and Discussion
#### Case 1
Given the extensive attention that this topic has received in recent years, several optimization techniques for optimal sensor placement have been devised and applied to various engineering applications [35, 36]. It is expected that augmenting this algorithm with an optimal sensor location approach could have positive implications, especially when dealing with complex structures. The results produced herein have been collected from a single sensor. Thus, integrating the proposed approach with a data fusion technique to process data from multiple sensors and sources to produce more accurate, consistent, and useful information about incipient damage in structures is necessary to develop a robust tool for SHM systems. Figure 6: Frequency diagrams of (**a**) crack location and (**b**) crack size for Case 3. The adaptive probabilistic algorithm involved employing the SQP to calculate the crack size and crack location in a form of vectors. This step was found to account for the majority of the computational iterations performed by the algorithm, and the average number of iterations for all cases was about 600. The number of iterations was found to be directly related to the level of noise added to the frequency response. In Case 3, the number of iterations reached 830, while the number of iterations for Case 1 was limited to 320. Other signal processes such as those in which SQP was used to minimize the probability of crack location and crack size only accounted for a relatively small percentage of the total computations. Figure 7 shows a summary of the computational time of all the cases considered in the study. The mean and standard deviation of computational time and probability of damage existence is shown in Table 6 and were calculated for ten trials of each case. In Case 1, the computational time of stage 1 was largely consumed by SQP and was moderately higher for stage 2. This may be related to the significant improvement in the results achieved by the algorithm for the probability of damage existence as reported in Table 6. Additionally, there was a substantial reduction in the standard deviation of the probability of damage existence in stage 2 by about 10%. The algorithm, in Case 2, consumed higher computational time in stage 1 and reported a lower probability of damage existence in stage 2. The mean and standard deviation of computational time for Case 3 were reported relatively close, with a slight improvement in the probability of damage existence in stage 2. The analysis presented herein was performed by limiting the number of optimization stages in the algorithm to two. The results strongly suggest that two stages may be sufficient for the application investigated in this paper. However, it may be necessary to consider the proposed algorithm with multiple stages for other structural applications.
# Analytical Investigation of Viscoelastic Stagnation-Point Flows with Regard to Their Singularity
## 3 Analytical Solutions of a Wall-Free Stagnation-Point Flow and Their Singularities
### Case \(k_{2}=0\)
#### 3.1.2 Case \(k_{1}-1=0\)  
In this case, the coefficient of \(T_{11}\) in (19) vanishes. The corresponding solution of \(T_{11}\) is simply given by  
\[T_{11}=\frac{k_{3}-2}{W_{1}}\ln y+F_{1}, \tag{42}\]  
where \(F_{1}\) is an arbitrary function of \(\psi=xy\). The solutions of \(T_{22}\) and \(T_{12}\) remain the same as given in (22)-(24). Again, considering the compatibility condition, the final solutions are obtained depending on the value of \(W_{1}\). All intermediate steps, which are similar to those in the last Section 3.1.1, are omitted.  
1. If \(W_{1}=\frac{1}{2}\), we obtain the solutions: \[T_{11} =2(k_{3}-2)\ln y-F^{\prime\prime}+C_{1}\ln(xy)+C_{2},\] (43) \[T_{22} =-x^{-2}y^{2}F^{\prime\prime}+4x^{-3}yF^{\prime}-6x^{-4}F+C_{3}x ^{-4}+C_{4}y^{4}-\frac{k_{3}}{2}-1,\] (44) \[T_{12} =x^{-1}yF^{\prime\prime}-x^{-2}F^{\prime};\] (45) The logarithmic singularity in \(T_{11}\) caused by \(\ln y\) at \(y\to 0\) can only be avoided when \(k_{3}=2\), i.e., \(W_{6}-W_{7}=\frac{1}{2}\). In this case, the choice (34) for the arbitrary constants \(C_{i}\) (\(i=1,2,3,4\)) and arbitrary function \(F(xy)\) is still suitable to prevent the singularity near the stagnation point. However, for the Oldroyd model, i.e., the case of \(W_{6}-W_{7}=4(1-\beta)W_{1}\) with \(0<\beta<1\) and for the Maxwell model, i.e., the case of \(W_{6}-W_{7}=0\), the logarithmic singularity at the Weissenberg number \(W_{1}=\frac{1}{2}\) is unavoidable. The similar conclusion was also drawn by [11] for Maxwell fluid.
2. If \(W_{1}\
eq\frac{1}{2}\): \[T_{11} =\frac{k_{3}-2}{W_{1}}\ln y+C_{1}\ln(xy)+C_{2},\] (46) \[T_{22} =C_{3}x^{-\frac{2}{W_{1}}}+C_{4}y^{\frac{2}{W_{1}}}-\frac{k_{3}}{ 2}-1,\] (47) \[T_{12} =0.\] (48) The singularity caused by the term \(\ln y\) can only be avoided if \(k_{3}=2\). In addition, \(C_{3}\) also has to be zero to prevent the singularity at \(xy\to 0\). This choice will cause a uniform stress field, which may be physically disputable.
## 4 CxQSIoT-Fog-CloudSERP: An Extension of CxQSCloudSERP Ontology Model
### CxQSIoT-Fog-CloudSERP
#### 4.3.1. Modeling IoT, Fog, and Cloud Services  
Figure 6 shows the extended ontology of CxQSCloudSERP with IoT sensors/actuators and Fog computing concepts.  
Figure 5: A semantic model of different hosts.  
Figure 6: A semantic model of context-aware intelligent services.  
In this model, a _Service_ is divided into three types: context-aware IoT services (CxIService), Fog, and Cloud services which have many inputs and outputs. Inputs/Outputs can be further divided into two types, data flows defined by a \"specific data type and data format\", and an event flow handled by the action of a service. The service is described by an identifier, name, Uri, role, and category, and it is deployed on various hosts (IoT device, Fog server, or Cloud).  
A service is defined by QoS properties such as reliability, availability, and security and a set of context quality properties such as energy. _IoT-Service_ may be either _CxIEventListenerService_ and _CxIActautingService_ to define the data collector and event listener and service's actions, respectively.
# Throughput and Packet Loss Probability Analysis of Long Range Wide Area Network
## 1 Introduction  
Since the emergence of the Internet of Things concept, several communication technologies were proposed to provide various IoT services. LoRaWAN is the one of the LPWA network technologies at present and is considered as the basis of IoT application in many industries [1]. LoRaWAN provides three different classes for EDs to address the various needs of applications: Class-A, Class-B, and Class-C. Class-A is a default mode which should be implemented in all EDs. When an ED has an uplink packet to transmit, it immediately selects a frequency channel randomly and transmits the packet. Uplink transmission is followed by two short downlink receive windows: window RX1 after the receive delay 1 and window RX2 after the receive delay 2, respectively. When a GW has a downlink packet to transmit, it transmits the packet only through these two windows. The GW transmits the downlink packet preferentially through window RX1 over the same channel as the uplink channel. If the GW fails to transmit through RX1, it can re-transmit the packet through window RX2 over another downlink channel dedicated for the downlink receive window. It is noticed that an ED of Class-A sleeps at all times except uplink and two short downlink receive windows, which provides an ED with the highest energy efficiency among all classes of LoRaWAN.  
In LoRaWAN, unlike cellular networks, it is not easy to realize the elaborate access control and QoS support for EDs because of the limited available bandwidth and processing capability. Moreover, as the network size is getting larger, the control of large-scale network becomes difficult. In order to efficiently control the transmission collision of uplink packet originating from numerous uncontrollable EDs in LoRaWAN, it is essential to evaluate the performance of the uplink packet transmission taking into account the characteristics of Class-A of LoRaWAN.  
Many research studies on LoRaWAN have been conducted until now. Some studies measured the performance of LoRaWAN ED considering the distance between an ED and a GW or evaluating performance of LoRaWAN system as a function of the number ofEDs [2; 3; 4; 5; 6]. In addition, various efforts have been made to improve the network system capacity. In Reference [7], considering that the operation of Class-A in LoRaWAN is based on pure-Aloha approach, the author proposed the algorithm to improve the system capacity by employing slotted-Aloha approach in the standard. Analytical models for performance metrics for uplink packet transmission were provided in Reference [8; 9]. To overcome the performance degradation of pure Aloha in LoRaWAN as the network size grows, listen before talk medium access strategy was proposed in Reference [10]. In this paper, we analyze the performance of uplink packet transmission of LoRaWAN Class-A mode using Markov model and evaluate the uplink packet transmission performance in terms of throughput and packet loss probability.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Zone**} & \multirow{2}{*}{**Orogenic Belt**} & \(\mathbf{\Delta f_{\rm b}}\) & \(\mathbf{\Delta f_{\rm m}}\) & \multicolumn{2}{c}{**Rementant**} & \multicolumn{2}{c}{**Magnetic**} & \multirow{2}{*}{**Seismicity**} & \multirow{2}{*}{**Dominant**} \\  & & **(nT)** & & **(nT)** & & & **(10\({}^{-3}\) A/m)** & & **(10\({}^{-9}\) SI)** & \\ \hline A & Vitrim & 269.5 & 603.4 & 2201.0 & 0.5–34,246 & \(-\)1.0–11,762 & More & Magmatic rocks \\ B & Erhotsk & 83.5 & 153.5 & 441.3 & 0.4–1833.7 & \(-\)0.8–4378 & Little & Sedimentary rocks \\ C & Erguna & 107.6 & 151.3 & 1134.5 & 0.8–4726.5 & \(-\)1.3–3275.7 & More & Magmatic rocks \\ D & Southern Mongolia & 15.1 & 39.8 & 215.1 & 1.1–33.4 & 10.4–37.6 & More & Sedimentary rocks \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the lithospheric magnetic anomalies and factors such as the regional geological structure, rock remnant magnetization, susceptibility, earthquake activity, and rock properties among the four regions A, B, C, and D.  
inversion of the magnetic susceptibility of underground media. The results are also based on the basic assumptions that the distribution characteristics of crustal rocks and the depth of the Curie interface are approximately 30 km. The effect of the equivalence principle is also disregarded in the upward continuation method here. It is very difficult to accurately separate the sources of lithospheric magnetic fields at different depths. To obtain the possible magnetic field distribution of sources at different depths, accurate ground surveys and aeromagnetic results at different heights should be combined with the characteristics of the geological structure. In addition, great efforts should be made to investigate further aspects of rock magnetism, such as the Curie temperatures and ferromagnetic materials obtained at high pressures. Such research would be very helpful for separating the sources at different depths and represents an important direction for future research.
## 2 Study Area, Data, and Methodology
### Data  
#### 2.2.1 IMERG Datasets  
IMERG (0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) lat/lon) merges, inter-calibrates, and interpolates MW precipitation estimates from GPM constellation, MW-calibrated IR, SPEs, Global Precipitation Climatology Centre (GPCC) monthly gauge analyses [16]. IMERG provides three sets of precipitation estimates: the IMERG-ER, IMERG-LR, and IMERG-FR. In this study, we analyze IMERG-FR (from now on IMERG). These products have four precipitation estimates based on different data sources and methodologies: IMERG_Cal, IMERG_Uncal, IMERG_HQ, and IMERG_IR (Table 1). In addition, the latest Version 06B of IMERG extends the time range of data back to June 2000 by integrating the precipitation estimates collected during the TRMM era. Please follow the IMERG technical documentation ([[https://gpm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf](https://gpm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf)]([https://gpm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf](https://gpm.nasa.gov/sites/default/files/document_files/IMERG_doc.pdf)), accessed on 15 March 2022) for detailed information. We used daily aggregated IMERG data (i.e., the cumulative sum of 48 values of half-hour precipitation) for all 4 IMERG datasets. The GP stations are mostly equipped with 24 h accumulated precipitation, recorded by a gauge reader using the US Standard (8\({}^{\circ}\) diameter) manual rain gauge [84]. Therefore, we used daily aggregated IMERG data, which allowed us to directly compare with GP station data. A total of 1538 grids (0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) lat/lon) covered the selected study area (Figure 1).