esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 3 Concepts of Functional and Stochastic Atmospheric Constraints
### Methods of Constraint Application
Concurrently, and in the same vein, it is necessary to choose an appropriate process noise which does not dilute the impact of the uncertainties used as constraints. In other words, a large process noise could lead to under-constraining, while a small process noise value potentially over-constrains depending on the GIM delay uncertainty. Similarly, the ZPDs with standard deviations provided in tropospheric products, can be used in constraining the atmosphere in multi-GNSS PPP solutions. The reason for the addition of a ZPD constraint, considering a sequential least-squares adjustment, is to either improve PPP convergence in the first few minutes or to avoid matrix inversion singularities, given poor satellite geometry. A fixed ZPD constraint of 20 cm was used for analyses in the results presented for the multi-GNSS PPP processing in this paper. Given that the ZPD products are made available daily by GNSS site, the choice of a fixed constraint stems from the fact that products were not available for majority of the stations analyzed. Hence, the constraint was empirically chosen based on the consistency of the estimates obtained from the few products made available.
# Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
## 2 Related Work
More detailed evaluations of IAMB, PCMB and IPC-MB algorithms have also been performed, both for CI test efficiency and for sample efficiency [16]. IAMB often performs well with regard to CI test efficiency, requiring fewer tests than the other algorithms, but it performs tests with large conditioning sets, which are inaccurate when there are not enough samples. On the other hand, IPC-MB performs CI tests with smaller conditioning sets than IAMB, making it more accurate, and it also performs fewer tests than PCMB [16]. A more detailed discussion on the use of the statistical G-test by MBD algorithms (IPC-MB in particular) can be found in [10], along with an efficient method of computing the G statistic, relying on computation reuse, called \(dcM1\). Such an optimization aims at alleviating the CI test bottleneck.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 3 Proposed Localization Algorithms
### CSO Optimization Approach
This can be illustrated using the following formulas:  
\[X^{(t+1)}_{(p,\mathcal{A})}=X^{t}_{(p,\mathcal{A})}+\varphi_{1}\times\text{ Rand}\times(X^{t}_{(r1,\mathcal{A})}-X^{t}_{(p,\mathcal{A})})+\varphi_{2} \times\text{Rand}\times(X^{t}_{(r2,\mathcal{A})}-X^{t}_{(p,\mathcal{A})}), \tag{26}\]\[\varphi_{1}=\exp\left(\frac{f_{p}-f_{r1}}{|f_{p}|+\epsilon}\right), \tag{27}\]  
\[\varphi_{2}=\exp(f_{r2}-f_{p}), \tag{28}\]  
where _Rand_ represents a uniform random number in the range \([0,1]\), \(f\) represents the fitness value, \(r_{1}\) represents an index of a roosters and it serves as the partner for the \(p\)-th hen, \(r_{2}\) denotes the index of the selected rooster or hen from the group of individuals, where \(r_{1}\) and \(r_{2}\) are different, and \(\varphi_{1}\) and \(\varphi_{2}\) represent the selection coefficients. In their search for food, the chicks trail behind the mother hen; their behavior is expressed as follows:  
\[X^{(t+1)}_{(p,d)}=X^{t}_{(p,q)}+\text{FL}\times(X^{t}_{(s,q)}-X^{t}_{(p,q)}), \tag{29}\]  
where \(X^{t}_{(s,q)}\) signifies the position of the mother hen of the \(s\)-th chick's mother and \(FL\) is a constant randomly selected from the range of \([0,2]\). Generally, in the realm of 2D WSNs the network localization challenge can be modeled by the following concept. Consider a network denoted as \(Net=\{\text{Sn}_{1},\text{Sn}_{2},\ldots,\text{Sn}_{k+n}\}\) in which there are \(n\) nodes and \(k\) anchors. The location of each sensor is represented by \(A_{i}(x_{i},y_{i})\) and \(U_{i}(xu_{i},yu_{i})\) for \(i=1\) to \(k+n\). In this problem, it is assumed that the \(k\) anchor nodes \(A_{i}\) know their positions and that the objective is to estimate the \(n\) unknown nodes' positions of \(U_{i}\). More precisely, the \(d_{i}\) can be estimated as shown in the second phase of each proposed DV-Hop variant. However, the estimation of this distance value always involves a higher degree of error. In this paper, we present novel localization approaches using chicken swarm optimization to address the localization challenges in WSNs. In this study, the objective function of chicken swarm optimization is defined as follows. \[f(x,y)=\min\left(\sum_{i=1}^{k}\left|\sqrt{(x-x_{i})^{2}+(y-y_{i})^{2}}-d_{i} \right|\right) \tag{30}\]  
Using the objective function defined in Equation (26), the fitness function formula is as follows. \[fitness=f(x,y) \tag{31}\]  
Likewise, all chickens (roosters, hens, and chicks) update their positions using Equations (24), (26), and (29), respectively. The fitness of each particle is assessed using Equation (30). The optimal solution is considered to be the optimized position of the sensor node during this phase. Figure 3 provides the comprehensive localization process utilized in our proposed approach. In the following subsection, the CSO approach is applied to bridge the accuracy gap between DV-Hop and our improved algorithms.
## 2 Design
### Sensing, Control, Communication, and Localization
#### 2.4.5 Software and Protocol  
The main applications run at the main board with a Linux system. The front board and motion controller board deal with the low-level applications, e.g., chassis velocity control, IMU reading, UWB reading, Zigbee data transmission, and docking open/close mechanisms, etc. with non-operating system (OS) firmware and can be called by the main board using the communication protocol. The visual processing board performs different  
Figure 6: The hardware for sensing, control, communication, and localization, the communications between circuit boards and the main input/output pins.  
visual recognition and measurement applications, providing these data to the main board when requested, or it can be set to a timing mode. The protocol defines the frame object, command, and parameters. In addition, the Zigbee board is directly connected to the motion controller board. Therefore, the low-level controller function can be called via remote procedure call (RPC) by another modular robot or an upper-level computer.
## 2 Experiment Setup and Simulation Model  
### Engine Specifications and Measurement Apparatus  
In this paper, a 12V190 natural-gas SI engine was chosen, which was modified from the same type of diesel engine. Its technical specifications are listed in Table 1. It is a V-type arranged engine, and each side has 6 cylinders matched with an independent turbocharger, SJ150-9C. The rated power is up to 600 kW at speed of 1000 rpm.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Specifications** \\ \hline Engine type & 12 cylinders in 60\({}^{\circ}\) V type arrangement \\ Stroke number & 4 \\ Bore \(\times\) Stroke (mm) & 190 \(\times\) 210 \\ Connecting rod length (mm) & 410 \\ Total displacement (Lit.) & 71.45 \\ Compression ratio & 11 \\ TDC clearance height(mm) & 1.8 \\ Firing order & 1-8-5-10-3-7-6-11-2-9-4-12 \\ Rated power (kW) & 600 \\ Rated speed (rpm) & 1000 \\ Turbocharger & Dual, SJ150-9C \\ FMEP@1000 rpm(bar) & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of 12V190 NG engine.  
The engine test setup was illustrated in Figure 1. This engine test was carried out on the 12V190 engine of Shengdong Group. The parameters to be measured mainly include the combustion pressure in the cylinder, the pressure before and after the turbine, the pressure before and after the compressor, the temperature before the intercooler, the intake pipe pressure, the airflow, and the gas flow.
# Salespeople Performance Evaluation with Predictive Analytics in B2B
## 2 Literature Review  
### Salesperson Performance  
Academic studies demonstrate that the success of a salesperson normally has a direct relationship with the company performance, some authors states that: \"When salespeople do well, the organization is likely doing well, and the contrary is normally true as well.\" [2]. When measuring salesperson performance, there are objective data, such as total sales increase, sales commissions or percent of quota, and subjective measures like manager's or peer's assessment of the salesperson [3]. Many companies use a combination of objective and subjective KPI's to make the assessment. A meta-analysis of objective and subjective sales indicators suggests that there is a low correlation identified between objective and subjective sales success indicators, which show that these indicators are not necessarily interchangeable, and the choice of the most appropriate may require trade-off [2].  
The evaluation process of performance varies from company to company [4]. Activities on a job cannot be measured by only one method of objective or subjective measures, as some tasks of a job requires objective method of evaluation, and for others subjective measures are better. [PERSON] examined the impact of objective and subjective measures of evaluation in sales departments, using various methods. For the company being studied, [PERSON] concluded that objective measures were the most relevant factor used in the salesperson evaluation demonstrating the positive impact of the performance [4].  
[PERSON] et al., evaluated the effectiveness of weighted checklist method to appraise the performance of employees on different levels of a bank, based on Self assessment, Competency & demonstration of leadership behaviours, and Skill & knowledge assessment, the achievement classifications were made in 6 levels. The authors of the paper in question concluded that the impact of the method on employees was inevitable and all the financial and non financial benefits were effected due to the method [5].  
[PERSON] et al. defined individual job performance as things people do, and actions people take, that contribute to the organizations goals [6]. In another article [PERSON] et al. mention that performance is what facilitates achieving the organization goals directly [7].  
The performance itself can be measured with judgmental and nonjudgmental measures which are the outcome measures [8]. The outcome measures use objective data, which don't need abstraction from who is collecting the data [9]. There are three predominant methods of measuring the sales performance. These are Outcome measure that are composed by sales volume and its variants, Judgmental managerial ratings and the salesperson Self-evaluation [10]. In the current work only objective measures are available, as the data provided for the current study only contain volume figures among other information related to sales, but none of these are related to subjective measures.
## 2 Related Work
[37] proposed an attack detection method for detecting low-rate DDoS attacks on the SDN controller by combining information distance and generalized entropy. The difference in probability distributions signals a DDoS attack on the network. Experiments with the proposed technique show that combining the information distance with the generalized entropy effectively detects low-rate DDoS attacks. However, it is hard to figure out the dynamic threshold because of the changing attack traffic rates in the traffic flow. [PERSON] et al. [28] proposed a detection technique based on the entropy method. The suggested technique analyzes network traffic flows to determine the likelihood of packets entering the flow. Their technique seems effective and lightweight for detecting attacks in the early stages. However, although the proposed technique aims to detect attacks early, it has some limitations that reduce its effectiveness. An example of such a limitation is using a constant threshold, which is ineffective for detecting different DDoS attack traffic rates and raises the false positive rate. [PERSON] et al. [38] proposed a FADM approach based on traffic flow statistical analysis of network traffic data gathered by the controller's sFlow agent. Furthermore, the proposed technique uses the entropy method that determines the probability of packet features to quantify network traffic changes. In addition, it detects DDoS attacks using a machine-learning algorithm (SVM). However, the false positive rate considerably increases when there is a burst of DDoS attack traffic that targets multiple victims within a short period. Additionally, [PERSON] et al. [39] presented a DDoS defense mechanism (EDDM) based on an entropy method. The proposed approach prevents DDoS attacks on legitimate network users during flash crowd situations by tracking the attack traffic flows. The EDDM has three steps based on traffic statistics collected inside a specific window size and used to determine the entropy value for detecting DDoS attacks. In the sFlow, the entropy value is compared to a static threshold to reveal the traffic packet rate that targets a specific victim. However, there is a latency in processing new packets, and detection accuracy is low for multi-victim attacks. Several techniques for dealing with DDoS attacks that target a single victim have been developed and have high detection accuracy. However, several techniques [37; 38] struggle with multiple-victim attacks. Attackers might exploit this limitation to execute attacks against the controller, disrupting the whole network. Meanwhile, [36; 38] attempted to protect the controller against high-rate DDoS attacks targeting a single victim or multiple victims with a high detection accuracy rate and a low false positive rate. However, most of these techniques only depend on static thresholds. Table 1 summarizes the existing DDoS attack detection techniques by highlighting their strengths and limitations.
###### Abstract  
Based on the flexibility of software-defined radio (SDR) techniques applied to an array of antennas, this article presents a beamforming architecture designed to operate in millimeter-wave bands (28 GHz), with possible applications in radar and 5G systems. The system structure, including its main constituents such as the radio frequency (RF) frontend modules, the radiating elements as well as the baseband processing on the host computer are widely described. Beamforming is achieved by digitally controlling the signals that feed the antennas. The experimental measurements performed in an anechoic chamber validate the proposed approach.  
digital beamforming; SDR; 5G; radar; beam shaping; phased array antenna +
Footnote †: journal: Applied Physics Letters  
_Applied Physics Letters_
# Spinel Magnetic Iron Oxide Nanoparticles: Properties, Synthesis and Washing Methods
## 3 Main Synthesis Methods of Magnetic and Maghemite Nanoparticles
### Thermal Decomposition  
The thermal decomposition is another synthesis method of oxide nanoparticle which is particularly developed since the 2000s [28; 29; 30]. Its principle rests on the decomposition of organometallic precursors (for example iron oleate or iron stearate for the synthesis of iron oxide nanoparticles) [30] within an organic solvent with a high boiling point [30] and in the presence of a surfactant stabilizing agent [30]. A definite interest of this synthesis method is the tight control of the size from 3 to 500 nm. The shape of iron oxide nanoparticles can also be controlled (spheres, cubes, bipyramids, tetrapods,... ) (Table 2). On the other hand, this method requires the use of organic solvents and therefore washing procedures and exchange of complex ligands for applications in aqueous solution.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Advantages** & **Disadvantages** &
\begin{tabular}{l} **Shape and Size** \\ **Saturation Magnetization** \\ \end{tabular} \\ \hline
**Micro-emulsions** & - & Better size control. & - & Low yield. & - & Cube, sphere \\
**Micro-emulsions** & - & Narrow size distribution. & - & Poor crystallinity. & 2-30 nm \\
**[26; 27]** & - & Adjustable size and shape. & - & Difficulties in washing surfactants. & - & Needles 20–80 nm \\  & - & Isolated particles. & & - & Organic solvents. \\ \hline
**Thermal decomposition** & - & Narrow size distribution. & - & High temperature. & - & Cube, sphere, \\
**Thermal decomposition** & - & High crystallinity. & - & Phase transfer required if & triangle, tetrapode. \\
**[28; 29; 30]** & - & Isolated particles. & - & application in aqueous solution. & - & 3–500 nm \\  & - & Low yield. & - & 20 to 82 emu/g \\ \hline
**Polyol method** & - & Reduction of metallic salts. & - & High temperature. & - & Sphere 6–100 nm \\
**[31; 32]** & - & Isolated particles. & - & Difficulties in washing surfactants. & - & 50 to 69 emu/g \\ \hline
**Sol-gel** & - & Soft conditions. & - & Long synthesis time. & - & Sphere 4–200 nm \\
**[35; 36]** & - & Narrow size distribution. & - & The solid matrix can be difficult to & - & 47 to 62 emu/g \\  & - & High crystallinity. & eliminate. & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 2 Separation Formulation Method
### Track Prediction Method  
#### 2.1.1 Clustering and Classification  
The clustering and classification of track data are fundamental to achieving refined track prediction. In this study, after comparing various methods, the K-means algorithm is utilized for clustering historical UAV data. The K-means algorithm is particularly suitable for handling large datasets due to its low computational complexity. An advantage of the K-means algorithm is its minimal parameter requirement, needing only the number of clusters to be set, which significantly reduces the difficulty of parameter selection. In contrast, the DBSCAN algorithm requires setting the neighborhood radius and minimum number of samples, making its parameter selection more complex. Especially in dealing with high-dimensional data, estimating the distance threshold for the DBSCAN algorithm can be challenging and may affect the algorithm's effectiveness. Therefore, the K-means algorithm is a better choice for this study.
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 4 Implementation
### Topology Discovery and HLMAC Address Assignment  
Before routing any traffic, the network topology should be mapped. The controller discovers the network topology by using the Link Layer Discovery Protocol (LLDP) [31]. One the network mapping has finalized, the controller can proceed to assign the corresponding HLMAC to each network node, according to the procedure defined by GA3. This association of HLMAC addresses is only performed internally, in a logical way inside the controller, from which the actual OpenFlow forwarding rules are later on derived. The HLMAC association can be updated if topology changes are detected.  
It is important to highlight that, in this implementation, GA3 has been developed as a centralized function for the sake of simplicity. However, in real networks, GA3 can  
Figure 5: Logical implementation of eTorii.  
perfectly work in a distributed manner and, in fact, networks will benefit from it as the distributed behaviour is potentially much more scalable.
## 4 Results and Analysis
### Comparative Testing of Transmission Mechanisms and Results Analysis
#### 4.2.5 CPU Utilization Analysis  
Optimizing CPU utilization is a core objective in improving the RDMA transmission mechanism's efficiency, particularly under high-load conditions. Therefore, CPU utilization serves as a key indicator of system performance and resource management.  
During the throughput test experiments, the CPU utilization on the sender side was recorded. Figure 15 compares CPU utilization for various RDMA transmission mechanisms at the sending end for different data packet sizes. As shown in Figure 15, the CPU utilization of the status-byte-assisted RDMA transmission mechanism is consistently lower than that of the sliding window RDMA transmission mechanism. This is because the sliding window RDMA transmission mechanism requires additional ACK receiving threads on the sender end to handle ACKs promptly, which results in higher CPU utilization. In contrast, the status-byte-assisted RDMA transmission mechanism eliminates the need for extra ACK receiving threads by leveraging one-sided operations, resulting in a 20% reduction in average CPU utilization.  
Figure 14: Comparison of the throughput of various RDMA transmission mechanisms for different packet sizes: (**a**) small data packet sizes (64 B to 4 KB) and (**b**) large data packet sizes (8 KB to 22 MB).  
Figure 15: Comparison of CPU utilization for various RDMA transmission mechanisms at the sending end for different data packet sizes.  
This reduction in CPU utilization directly supports our research objective of enhancing resource efficiency in RDMA transmission, especially in edge computing environments handling large-scale data transmissions. The findings demonstrate that the status-byte-assisted mechanism not only reduces computational overhead but also significantly enhances system performance under high-load conditions, thereby offering considerable potential for real-world applications.
## 1 Introduction  
In the near future, it can be expected that buildings will be equipped with a range of wireless sensors functioning as part of an overall building management system. Included in this set of sensors will be devices to monitor fire and smoke, allowing detection, localization and tracking of fires. It is expected such information could be used for a variety of purposes, including guiding building occupants to the nearest safe exit, and helping fire fighting personnel to decide on how to best tackle the disaster. Fire/smoke sensors are expected to be programmed to report periodically and also when they detect a sensor input that exceeds a threshold. In the latter case, there is a need for an emergency-adaptive,  
real-time and robust message delivery toward the sink. For example, a fire-fighter relies on timely temperature updates to remain aware of current fire conditions. In addition, as the fire spreads throughout the building, it becomes likely that the sensing devices may become disconnected from the network or indeed be destroyed, so the network routes have to be changed or re-discovered to adapt to these emergency conditions in order for the network to continue operating. Most existing routing protocols consider the energy efficiency and lifetime of the networks as the foremost design factor. The routing mechanisms used in general wireless sensor networks and even routing for forest fire applications are not well suited for in-building disaster situations, where timeliness and reliability are much more critical. For forest fires the focus is on tracking of fires, rather than evacuation or guidance of fire personnel. This combination of real-time requirements coupled with dynamic network topology in a critical application scenario provides the motivation for our research. In this paper, we propose an emergency-adaptive routing mechanism (EAR) designed especially for building fire emergencies using wireless sensor networks (WSN), which provides timely and robust data reporting to a sink. We do not need to know the exact localization of each sensor and also no time synchronization is needed. To the best of our knowledge, this is the first time a real-time and robust routing mechanism adaptive to building fire emergency using WSNs has been proposed. Also, this protocol could be easily used in other similar emergency applications.  
Section 2 presents the related work. In Section 3 we outline the routing problem. We present an emergency-adaptive routing mechanism in Section 4. In Section 5, we present a preliminary analysis. In Section 6, we give ns2 simulation results. Finally, Section 7 concludes this paper.