esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hector  
### Algorithm Description  
Each node \(u\) has two sets of coordinates \((V,T)\) as defined in Section 3.  
The routing algorithm combines advantages of both kinds of coordinates : _(i)_ virtual coordinates as in VCost [9] allow the reduction of the path length and _(ii)_ labels as in LTP [10] avoiding reaching a dead end and to guarantee delivery.  
The basic idea is the following. A source node \(s\) holding a packet for a destination node \(d\) performs an energy-efficient greedy routing scheme in a VCost fashion. In order to avoid to be trapped in a local minima, the routing algorithm selects the next hop with regard to not only the virtual coordinates but  
Proof Let us consider a source \(u\) and a destination \(d\). By construction, if a node in \(N_{V}(u,d)\) is chosen as the next hop, this ensures a progress on the \(V\) coordinates. If the next hop is chosen in \(N_{T}(u,d)\) this ensures a progress in the tree toward the destination. The progress will occur since \(N_{T}(u,d)\) is a nonempty set.  
It is worth noting that the progress made on \(V\) is more important than the progress made on \(T\) labels in the geographical space. Indeed, the next hop in the \(T\) labels can have the same \(V\) coordinates and thus more or less the same Euclidean distance to the destination. These lemmas show that the routing protocol HECTOR described in Algorithm 1 always works in a greedy way. The greedy aspect provided by this algorithm makes it simple, memoryless and scalable.  
**Theorem 4**: _The routing algorithm described in Algorithm 1 guarantees delivery._  
Proof Each node has a unique label due to the labeling process described in Section 3. This ensures that the destination of a packet is unique and that at each step of the routing protocol, a next hop closer to the destination can be found. Based on Lemmas 1, 2 and 3, if a path exists (if the network is connected), the routing protocol will find it in a greedy way.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 9 Manufacturing of Hollow-Core Fibers  
### Materials  
Hollow-core fibers of PBGF, Kagome and ARF types are made of one material, usually chlorine-dried pure fused silica, which is strong, dimensionally stable, resistant to humidity, non-toxic, and proven in solid-core fibers. High-purity fused silica tubes for optical fiber production are commercially available [68].  
Alternative materials include chalcogenide soft glasses for mid-infrared applications, and several non-crystalline polymers, in particular, PMMA [64].  
Polymers, however, have the following disadvantages:  
* High gas permeability, allowing for the migration of contaminants into the fiber;
* Outgassing of unreacted monomer, water, etc.;
* Absorption of water from air;
* Lack of stiffness: Young moduli of fused silica and PMMA are 72 GPa and 2.9 GPa, respectively. A 125 \(\upmu\)m or 200 \(\upmu\)m plastic fiber is too soft to manually handle.
# Implementation of Robots Integration in Scaled Laboratory Environment for Factory Automation
## 3 Mobile Robot and Industrial Robot Design
### Hardware of Mobile Robot  
The mobile robot platform, designed in this work, is based on a four wheeled Mecanum drive (Figure 2). The advantage of using Mecanum wheels is the ability to move in any direction without changing orientation. The wheels are actuated by four 100 W BDC motors, powered by Li-ion batteries enabling the robot to operate autonomously for around 16 h. Motors are controlled by motor controllers connected to the main on-board PC. One motor controller can control a pair of brushed DC motors using USB serial, TTL serial, RC or analog inputs. Integrated dual quadrature decoders enable easy, closed-loop speed control system implementation. For this work, a USB serial interface is used to connect the motor controllers with the main computer. The forklift system consists of a linear actuator with a BDC motor and a motor controller. The forklift system is controlled with the set of GPIO signals.  
To detect obstacles and navigate through the environment, the mobile robot is equipped with the laser ranging scanner RPLiDAR-A2, and Intel RealSense D415 depth camera. LiDAR and camera sensors are used for both SLAM and path-planning algorithms, whose implementation is described later. The interface between sensors and the main PC is established using the USB 3.0 communication protocol to ensure fast data transfer, without full bandwidth consumption to prevent data loss.  
The RealSense depth camera has a standard field of view and high resolution [33], which is well suited for applications that require accuracy, such as 3D scanning and mapping. The standard field of view provides higher quality depth per degree. A relatively high range, up to 10 m, provides good perception of surroundings.  
The main PC integrates the LiDAR sensor, depth camera and motor controllers using the ROS framework, which will be explained later. To provide reasonable program execution time, enabling real-time mobile robot control and obstacle avoidance, the main PC has 16 GB DDR4 RAM memory, Ryzen 7 2700U CPU and M.2 NVMe SSD memory.
# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 3 Methodology
### Dataset Preparation
#### 3.1.1 Dataset Collection  
The data collection method of the proposed DPMS is a unique approach that involves the stakeholders' feedback only. It consists of collection strings that contain opinions, appreciation, concerns, complaints, and other types of feedback, which is the sentiment of the stakeholders. Usually, the core components of a university are faculties, departments, library, admission office, the finance and account division, the Human Resource Management (HRM) division, registrar office, and more [20]. The stakeholders' feedback about the quality of services in these university divisions is the dataset of the proposed DMPS. It is an array of variable strings defined by Equation (1), which contains the sentiments of the stakeholders. These feedback are collected using a traditional Opinion Box hanging outside each university section and Google Form.  
\[M[S_{array}]=\sum_{i=0,j=0}^{m,n}(UID,D_{ijk}) \tag{1}\]  
Figure 1: The overview of the methodology developed in this paper.  
In Equation (1), the \(M[S_{array}]\) is the memory location of \(S_{array}\), which is the dataset. The elements of this array are ordered pair \((UID,D_{ijk})\), where \(UID\) is the unique identification key for every instance of the dataset and \(D_{ijk}\) is the string. Here, \(i\) is the university's division, \(j\) represents different individuals, and \(k\) is the length of the string, which varies according to the number of characters in the string. Each instance is labeled through human inspection, which is defined by Equation (2), which is a set of ordered pairs. Here, \(H(S_{array})\) is the human inspection and \(L\) is the label of the data \(D\).  
\[\{(D,L)\}=\{(S_{array},H(S_{array}))\} \tag{2}\]  
The labeled dataset prepared according to Equation (2) contains a'string, label' pair. This labeled dataset is used to train the proposed DPMS. The label of the dataset is a set defined by Equation (3) [21].  
\[\begin{split} L&=\{xx\in\text{Sentiment}\}\\ &\text{where Sentiment}=\{\text{Good, Average, Neutral, Poor}\}\end{split} \tag{3}\]  
The labeled dataset is the raw dataset for the proposed data-driven promotional management system for higher educational institutions. This dataset needs further processing to feed it into the Deep Learning model, to train it and to predict the sentiments of the stakeholders about a particular division of the university [22]. Making promotional decisions based on the predicted sentiment about a particular decision increases the impact of social media promotional posts.
# Superior Mechanical Behavior and Fretting Wear Resistance of 3D-Printed Inconel 625 Superalloy
## 3 Results and Discussion
### XRD Analyses  
The XRD results of the as-received initial Inconel 625 powders and specimens after AM process are shown in the Figure 3. It can be seen that the AM-sample mainly consists of Ni-base austenite phase (\(\gamma\) phase) with crystal size of ~5.61 \(\upmu\)m. Lattice parameter of the as-received powders is 3.5899 A and decreases after AM process to 3.5716 A, which can be demonstrated by a right peak shift on the 2\(\theta\)  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Specimen** & **Method** & **Micro-Hardness (HV)** & **Relative Density** & **Ref.** \\ \hline As-prepared & AM & 335 & 99.7\% & Present work \\ Inconel 625 & Sintering + ageing & 327 & 99.2\% & [16] \\ Inconel 625 & AM & 343 & - & [7] \\ Inconel 625 & Gas tungsten arc deposition & 328 & - & [17] \\ Inconel 625 & Gas tungsten arc welding & 270 & - & [18] \\ Inconel 625 & Sintering & 275 & 91.8 & [19] \\ Milled Inconel 625 & Sintering & 320 & 84 & [19] \\ Inconel 625 & Welding & 252 & - & [20] \\ \hline \end{tabular}
\end{table}
Table 2: Micro-hardness and relative density of the as-prepared specimen and other Inconel 625 alloys fabricated by different methods.  
scale for the AM-Inconel 625 (the inset in the Figure 3). Peak shifts are usually caused by the change of the crystal lattice whereas shifts to higher angles are caused by compression of the crystal lattice. This decrease can be attributed to the depletion of Nb, Cr and Mo in the \(\gamma\)-matrix, since these elements participate in the formation of intermetallic phases [22]. Additionally, a lattice parameter shift in the AM sample compared to the standard alloys 625 fcc \(\gamma\)-Ni phase also indicates that potentially precipitates in the \(\gamma\)-Ni matrix are formed during the AM process. However, the phases precipitated in \(\gamma\)-Ni matrix are not detected by XRD due to a relatively low volume fraction of these precipitates. The result is similar to the previous studies [23]. Only three peaks including \(111_{\gamma}\), \(200_{\gamma}\) and \(220_{\gamma}\) are detected and this reveals the pattern of the columnar dendritic growth [24].
## 5 Research Findings
### RQ1: What Techniques Are Utilized to Abuse the CPLS as C&C Communication Channels?
#### 5.2.1. Prevalent Techniques  
Encoding and the use of botmaster's credentials or hard-coded tokens are the most frequently employed techniques across platforms and over time. Their prevalence could stem from their relatively straightforward implementation and their ability to blend with normal network traffic, which likely make them a preferred choice for many cybercriminals.
## 3 \(V_{oc}\) Losses Analysis
### \(V_{oc}\) Loss Due to Surface Recombination
#### 3.2.2 SRV on a Passivated p\({}^{+}\) Surface  
SRV depends strongly on the processing methods, including oxidation conditions, annealing conditions, surface roughness and contamination. [PERSON] reported a surface recombination velocity of 1640 cm/s on a SiO\({}_{2}\) passivivated p\({}^{+}\) silicon surface, which is independent of injection level [12]. Thermal oxide is used to passivate the heavily doped p\({}^{+}\) epitaxial emitter of the UTSi solar cell. This section tests the SRV of the thermal oxide passivated heavily doped p\({}^{+}\) type surface. Control samples were used at each step to eliminate contamination and extraneous variables. A doping density of \(5\times 10^{17}\) cm\({}^{-3}\) for the rear emitter was selected in this experiment, and these test samples' structures are plotted in Figure 3. Group A2 has 1 \(\upmu\)m, \(5\times 10^{17}\) cm\({}^{-3}\) epitaxial p\({}^{+}\) layers on both surfaces, which are passivated by SiO\({}_{x}\)N\({}_{y}\)[9]; Group B2 has 1 \(\upmu\)m, \(5\times 10^{17}\) cm\({}^{-3}\) epitaxial p\({}^{+}\) silicon layers on both surfaces, which are passivated by SiO\({}_{2}\). These samples were from the same wafer and were processed along with the control ones.  
SRV (\(S_{p}\)) values are extracted in Table 3, using the relation \(J_{0}=qsn_{ie}^{2}/N_{A,sur}\)[12], where \(J_{0}\) is extracted from the above lifetime measurements, which is determined by the surface when the bulk recombination is negligible; \(N_{A,sur}\) is the doping density of surface; \(q\) is the electronic charge; \(n_{ie}\) is the intrinsic carrier concentration in the emitter, and equals to \(n_{io}^{2}\epsilon^{\frac{\Delta E}{kT}}\), where \(n_{io}\) is the intrinsic carrier concentration in the absence of bandgap narrowing and \(\Delta E\) is the bandgap narrowing. \(\Delta E=0.03\) eV for \(5\times 10^{17}\) cm\({}^{-3}\) and \(\Delta E=0.01\) eV for \(2\times 10^{17}\) cm\({}^{-3}\)[12, 13]. According to the calculated results, the SRV values of the SiO\({}_{2}\) passivated p\({}^{+}\) emitter surface are 1551-1677 cm/s, as shown in the table, which is consistent with the value of 1640 cm/s reported by [PERSON] [12].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **Lifetime (\(\upmu\)s)** & \(iV_{oe}\) **(mV)** & \(J_{oe}\) **(A/cm\({}^{2}\))** & **SRV (cm/s)** \\ \hline A2 & 161–221 & 644–658 & – & – \\ B2 & 139–157 & 637–642 & \(1.85\times 10^{-13}\) \(\sim 2.0\times 10^{-13}\) & 1551–1677 \\ \hline \end{tabular}
\end{table}
Table 3: Lifetime, \(iV_{oe},J_{oe}\) and SRV of Group A2 and B2 test samples from Figure 3.  
Figure 3: Test structures for p\({}^{+}\) epitaxial layers and their passivation.
# A Multi-Threading Algorithm to Detect and Remove Cycles in Vertex- and Arc-Weighted Digraph
## 5 Experiments and Analysis
### Experiments with Randomly Generated Digraphs  
First, we generate 100 digraphs randomly to analyze the performance of the above algorithms. Each kind of digraph has different number of vertices and arcs. These digraphs have \(1000\times k\) (\(k=1\), \(2\), \(\cdots\), \(10\)) vertices, and the average out degree of vertices is 1 to 10 in step of 1. For each kind of digraph, we generate 10 different digraphs, and each algorithm runs 10 times for each digraph. The results are average over these 10 runs.  
As shown in Figure 3, the iterative algorithm is significantly better than recursive algorithm because \(R_{1}<1\) and \(R_{2}<1\), and \(R_{1}\) and \(R_{2}\) decrease while the number of vertices increases. \(R_{1}\) and \(R_{2}\) are almost the same when \(|\mathbf{V}|\) is the same. When \(|\mathbf{V}|=1000\), \(R_{1}=0.657\), and \(R_{2}=0.656\). When \(|\mathbf{V}|=10,000\), \(R_{1}=R_{2}=0.29\). Figure 3 also shows that simplification cannot reduce execution time effectively. For a different number of vertices, \(R_{3}\in[0.975,0.997]\), and \(R_{4}\in[0.986,0.996]\). MPIWS outperforms IWS in further due to \(R_{5}\in[0.378,0.558]\).  
Figure 4 shows that the iterative algorithm outperforms the recursive version when the average degree of vertices is the same, and \(R_{1}\) and \(R_{2}\) decrease while the average out-degree of vertices increases. When out-degree is 1, \(R_{1}=0.881\), \(R_{2}=0.981\). When out-degree is 10, \(R_{1}=0.307\), \(R_{2}=0.285\). MPIWS also outperforms IWS in this figure, and \(R_{5}\in[0.323,0.505]\).  
Figure 3: Impacts of number of vertices on execution time of each algorithm.  
Figure 4 shows that simplification has much effect on the performance of these algorithms. Figure 5 shows the numeric results of \(R_{3}\) and \(R_{4}\), \(R_{3}\) and \(R_{4}\) increase rapidly when out-degree increases from 1 to 3, but they change a little when out-degree is larger than 3. Specially, \(R_{3}\) and \(R_{4}\) equal to or are greater than 1 when the average degree is 10, which means that the simplification has no effect on the algorithm's efficiency.  
In fact, when the average degree is relatively small, a large portion of vertices and arcs will be deleted by Algorithm 2, as shown in Figure 6. When the average degree is 1, the residual vertices and arcs are only about 0.7% of original graph. When the degree is 2, this ratio increases to 63% rapidly. Only less than 10% of vertices and arcs can be deleted when the average degree is larger than 3.  
Figure 4: Impacts of average degree of vertices on execution time of each algorithm.  
Figure 5: \(R_{3}\) and \(R_{4}\) under different out-degree.  
Figure 6: Ratio of number of residual vertices and arcs after simplification.
## 4 Evaluation of the Mechanism
### Results
In our opinion, from a practical perspective, the M/G/1 scenario is a versatile choice for operators who want to utilize our mechanism. In order to find limitations of our proposal, we solved an optimization model. We implemented a multi-commodity max-flow problem [10] that shows the potential capacity of the network. For the simulated topology, the maximum potential throughput was 3400 Mbps. Figure 8: Comparison of _CFRI_ for selected scenarios. Figure 7: Comparison of _maxFRI_ for selected scenarios. However, this solution assumed that flows do not change over time, network conditions are stable, and flows can be bifurcated. Such conditions cannot be fulfilled in any real network. This value represents static conditions used as input for calculations which are impossible to achieve: traffic fluctuates, demands are changing, sources generate different patterns of traffic, etc. The gap between the optimal value and the results achieved during the simulations demonstrates how much traffic is transferred inefficiently due to fluctuation of flows.
# Handover-Driven Interference Management for Co-Channel Deployment of Femto- and Macro-Cells
## 7 Conclusions  
In this paper, a novel handover-based interference management framework, called HO-FIM, is presented for two-tier femtocell networks. HO-FIM is composed of three complementary controllers--master, handover, and radio resource controllers--which activate/deactivate the handover  
Figure 11: Number of handovers over iterations (\(M\) = 30, \(F\) = 50).  
Figure 10: Probability distribution of femtocell users’ achieved SINRs when allowing inbound handovers only and when allowing both inbound and outbound handovers (\(M\) = 50, \(F\) = 100).  
controller, make handover decisions, and handle radio resource scheduling. HO-FIM activates the handover controller only when needed and finds a control sequence to minimize handovers while reaching a desired system condition. The handover controller iteratively controls users' handovers in a greedy manner. HO-FIM successfully overcomes the performance limit of existing radio resource coordination approaches and benefits both macro- and femtocell users thanks to the introduction of an additional degree-of-freedom when solving the interference problem.  
The entire work was realized by the author.  
This study was supported by the Advanced Research Project funded by the SeoulTech (Seoul National University of Science and Technology).  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BS & Base station \\ CSG & Closed subscriber group \\ DL & Downlink \\ DSL & Digital subscriber line \\ LTE & Long term evolution \\ MIMO & Multiple input multiple output \\ NGMN & Next generation mobile networks \\ NR & New radio \\ OFDMA & Orthogonal frequency-division multiple access \\ OSG & Open subscriber group \\ QoS & Quality of service \\ RB & Resource block \\ SI & system information \\ SINR & Signal-to-interference and noise ratio \\ UL & Uplink \\ \end{tabular}
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
## 2 Notation  
Natural, real, complex and non-negative numbers are denoted by \(\mathbb{N}\), \(\mathbb{R}\), \(\mathbb{C}\), and \(\mathbb{R}_{+}\), respectively. The set of integers from \(m\) to \(n\) is shown as \([m:n]=\{m,m+1,\cdots,n\}\). The special case \(m=1\) is shortened to \([n]\triangleq[1:n]\). \(\lfloor x\rfloor\) and \(\lceil x\rceil\) denote, respectively, the floor and ceiling of \(x\in\mathbb{R}\). The Galois field GF(q) with \(q\in\mathbb{N}\), \(q\geq 2\), is \(\mathbb{F}_{q}\). The set of matrices with \(m\) rows, \(n\) columns, and elements in \([0:q-1]\) is \(\mathbb{F}_{q}^{m\times n}\).  
A sequence of length \(n\) is denoted as \(x^{n}=(x_{1},x_{2},\cdots,x_{n})\). Deterministic vectors are denoted by boldface font, e.g., \(\mathbf{x}\in\mathbb{R}^{n}\). The \(i^{\text{th}}\) entry of \(\mathbf{x}\) is \([\mathbf{x}]_{i}\). Deterministic matrices are shown by upper-case letters with mathrm font, e.g., A.  
The probability density function (PDF) of a random variable \(X\) is denoted by \(\Pr_{X}(x)\), shortened to \(\Pr(x)\) if there is no ambiguity. The conditional PDF of \(Y\) given \(X\) is \(\Pr_{Y|X}(y|x)\). The expected value of a random variable \(X\) is denoted by \(\mathbb{E}(X)\). The real Gaussian PDF with mean \(\theta\) and standard deviation \(\sigma\) is denoted by \(N(\theta,\sigma^{2})\). The \(Q\) function is \(Q(x)=\frac{1}{2}\operatorname{erfc}\left(\frac{x}{\sqrt{2}}\right)\), where \(\operatorname{erfc}(x)\) is the complementary error function. The binary entropy function is \(H_{b}(x)=-(x\log_{2}(x)+(1-x)\log_{2}(1-x))\), \(x\in(0,1)\).
## 3 Simulation and Verification
### Verification of the Driving Style Recognition Model
Table 1 shows the rates of change in the above variables, and it can be seen that the rates of change in torque, intake pressure, and recirculating fuel injection have some similarity within the corresponding driving style intervals, and the corresponding aggressive, moderate, and calm driving characteristics are defined according to the magnitude of their values. According to the feature selection, even though torque and intake pressure are not included in the selected features, the feature selection and driving style identification results reflect the variation pattern of both. Figure 10: Abnormal data processing process. Figure 9: Schematic diagram of the box plot method. Three typical segments of the vehicle belonging to driver number 47 were selected with 140 consecutive sampling points to explore the differences between the driving styles. Considering the transfer characteristics of the data in the self-encoder-based Bi-LSTM, the driving style segmentation could not be continuously consistent, so the three selected typical segments were divided into aggressive, moderate, and calm driving styles, as shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Torque** & **Inlet Pressure** & **Injected Fuel Quantity** & **Throttle Position** \\ \hline Aggressive & 301.92 & 111.84 & 29.15 & 10.86 \\ Moderate & 186.30 & 72.38 & 19.31 & 5.89 \\ calm & 44.12 & 61.39 & 3.98 & 1.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The average rate of change for each driving style corresponding to the selected characteristics. Figure 11: _Cont._  
Figure 11: Sampling points of vehicle No. 47. Overall, as the driver changed gear and throttle operation, the vehicle's corresponding speed, torque, intake pressure, and cyclic fuel injection produced corresponding rapid changes. Combined with Table 1, even though the values of the indicators were larger for the moderate driving style, the rate of change is low, indicating that the driver maintained a relatively stable driving condition during the driving process. In the case of the calm driving style, the driver did not operate the throttle or gears, but the vehicle still kept moving forward, so it could be assumed that the vehicle was going downhill and there were no obvious sudden changes in the vehicle's state. The driving style recognition model proposed in this paper is effective.