esa-sceva/satcom-chunk-collection huggingface dataset:
# Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
## 3 Algorithm Improvement  
### Basic RRT  
RRT constructs a random tree through random sampling. In the space with obstacles, the algorithm starts to explore from the initial node, \(X_{init}\). This process is shown in Figure 3. Firstly, the random point, \(X_{rand}\), is generated. In all the added path nodes of the tree [\(X_{init}\), \(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{nearest}\)], the node \(X_{nearest}\), which is nearest to \(X_{rand}\), is selected. Taking \(X_{nearest}\) as the root node, a fixed step R is added to the direction of \(X_{rand}\) to obtain \(X_{new}\). The collision detection algorithm is used to detect whether the path between \(X_{nearest}\) and \(X_{rand}\) is feasible. If there is no collision, \(X_{new}\) is added to the random tree, T. Alternatively, if there is a collision, \(X_{rand}\) will be regenerated. Repeat the whole process until the distance between the latest node and \(X_{goal}\) is less than the step value, and there are no obstacles between them; at this point it is considered that the algorithm has converged. When the planning algorithm converges, we start from \(X_{goal}\) and trace back along its parent node to find an effective path between the start point and the end point.  
The process of the RRT algorithm is shown in Algorithm 1.  
```
1:\(T\) = init Tree(); // Initialize the random tree
2:\(R\) = init R(); // Initialize the step size
3:\(T[0]\) = Node(\(X_{init}\));
4:for\(i\) = 1 to N:
5:\(X_{rand}\)= Random sampling(); // Random sampling
6:\(X_{nearest}\)= min the distance (\(X_{rand}\))\(Node_{tree}\); // Find the nearest node
7:\(X_{new}\) = extend (\(X_{nearest}\), \(X_{rand}\), \(R\)); // Expand towards random points
8:if not obstacle(\(X_{nearest}\), \(X_{new}\)):
9:\(T[i]\) = add Node(\(X_{new}\)); // Add a new node to the tree
10:endif
11:end for
12:return\(T\)
```  
**Algorithm 1**RRT algorithm  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Joint Number** & \(\alpha\) & \(\alpha\) & \(\theta\) & \(\alpha\) \\ \hline
1 & 90 & 0 & \(\theta_{1}\) & 89.2 \\
2 & 0 & \(-425\) & \(\theta_{2}\) & 0 \\
3 & 0 & \(-392\) & \(\theta_{3}\) & 0 \\
4 & 90 & 0 & \(\theta_{4}\) & 109.3 \\
5 & \(-90\) & 0 & \(\theta_{5}\) & 94.75 \\
6 & 0 & 0 & \(\theta_{6}\) & 82.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DH parameters of the main robot arm.
# Topic-Specific Emotion Mining Model for Online Comments
## 4 Experiments
### Setting Parameters and Determining the Number of Topics  
Our model involves the following parameters: hyper parameters \(\alpha\), \(\beta\), \(\gamma\), the total number of topics \(K\), and the total number of emotions \(E\). Because neutral emotions are few, we ignore neutral emotions and divide emotions into two categories: positive and negative. \(E\) equals 2. \(\beta\) and \(\gamma\) are respectively set to 0.1 and 0.01 by using empirical values.  
The number of topics is critical. Too many topics can lead to high similarity among topics, making it difficult to distinguish different topics. Too few topics will cause granularity of topics, meaning the model cannot calculate topic-level emotion. In a defined dataset, the number of topics should be already determined. Namely, the number of topics in a comment text dataset is certain but unknown. Perplexity [13] is a measure of the quality of the topic model. The smaller the perplexity value is, the better the topic model is. The formula for perplexity is as follows:  
\[perplexity=\exp-\frac{\sum_{t=1}^{T}\ln(\prod_{k=1}^{K}\prod_{e=1}^{E}\prod_{ v=1}^{V}\left(\varphi_{kcv}*\pi_{tke}*\theta_{tk}\right)^{n_{t}^{lev}})}{\sum_{t=1}^{T}n_{t}} \tag{6}\]  
where \(n_{t}^{(kev)}\) is the number of word pairs \(v\) belonging to time \(t\) that are assigned to the topic \(k\) and the emotion \(e\).  
The value of parameter \(\alpha\) is commonly associated with the total number of topics \(K\). Thus, we performed a set of experiments by adjusting the values of \(\alpha\) and \(K\) and calculated the perplexity of each experimental result. The experimental results are arranged as Figure 5.  
In this experiment, the range of \(\alpha\) is 0.1 to 1.0, with step length of 0.1. As for the value of each alpha, \(K\) ranges from 5 to 50, for which the step length is 1. The experiment is iterated 2000 times for each pair of \(\alpha\) and \(k\). Figure 5 shows the smallest perplexity in each value of \(\alpha\). From Figure 5, it is easy to see that the minimum value of perplexity is obtained when \(\alpha\) is 0.6 and \(K\) is 19.  
In order to examine whether the number 19 is in line with the actual number of topics or not, we conducted a questionnaire survey on 20 students who frequently shop online to get aspects they pay close attention to when buying milk or yogurt. The results of the questionnaire are integrated to get the specific contents of the 19 topics. Table 3 shows the key words of 19 topics.
## 2 Theoretical Background
### Tips and Attributes for Designing Cloud-Based Solutions for CH
One of them focuses on the lifecycle management of CH data, facilitating the collection and management of data throughout the restoration process, and providing a powerful tool for visualizing these data in 3D. This capability allows for detailed, interactive views of heritage sites or objects, improving the accuracy and effectiveness of restoration efforts. Another crucial service delivers an advanced search function for CH data that originate from a variety of diverse sources. The platform employs ETL (Extract, Transform, Load) workflows, which are used to gather data from multiple CH providers in different formats, transforming and centralizing it into a unified data repository. This centralized approach not only streamlines data access but also ensures that the information is consistent, easily searchable, and ready for analysis. Furthermore, user-friendly applications are incorporated to increase citizens' and tourists' understanding of CH, making it more accessible to a wider audience. By improving the way data are handled and visualized, the SACHER platform enhances both the preservation of heritage sites and the educational experience for visitors. In summary, the growing adoption of XR technologies in the CH sector is delivering numerous advantages. One of the most notable benefits is the enhanced learning experience that immersive and customizable technologies offer, allowing users to engage more deeply with cultural content [3]. Additionally, devices like Head-Mounted Displays (HMDs) and CAVE systems significantly increase the level of interaction and immersion, providing a more engaging experience. The Bring Your Own Device (BYOD) concept further adds value by allowing visitors to use their own devices to personalize their tours, making the experience more tailored and accessible [46]. Several options also stand out in terms of preferred features and services for enhancing the visitor experience. These include the following [12]: content profiling which leverages user data and preferences to deliver tailored suggestions,thus offering a more personalized experience, decision-making systems to recommend specific content based on individual interests, 3D object and artifact recognition allowing users to interact with heritage objects in a more engaging and informative way, and gamification and storytelling elements to enrich the experience by adding an interactive, narrative-driven dimension that encourages deeper engagement with the cultural context.
# ScienceIoT: Evolution of the Wireless Infrastructure of KREONET
## 3 ScienceLoRa: IoT Infrastructure for Science Research and Education
### Hardware for Device and Gateway  
The ScienceLoRa-Device was developed for accommodating both SX1276 (operated in the sub-GHz ISM bands for low data rate long-range links) and SX1280 (operated in the 2.4 GHz ISM bands for high data rate mid-range links) transceivers. Compared to the sub-GHz LoRa, which has been intensively investigated [15; 16], the 2.4 GHz LoRa has only been developed recently for providing global availability and improving data rate with a wider bandwidth and higher frequency under the same duty cycle regulation [17]. The device consists of two parts: a LoRa module for transmission and the main body, which is a small-sized machine with Linux. The Linux machine provides serial interfaces for receiving data from sensors. The collected sensor data are transmitted through the LoRa module. If the packet transmission is not performed appropriately, the un-trans  
Figure 5: ScienceLoRa network consisting of end device, gateway, network server, and users.  
in the device and retransmitted once the system returns to its normal status. Figure 5(a) shows an example of the ScienceLoRa-Device developed for an environmental radiation monitoring system (see Section 4.1).  
A ScienceLoRa-Gateway comprises four USB-type interfaces for connecting LoRa transceivers. A LoRa communication module (i.e., SX1276 or SX1280) can be plugged into the USB-type interface in the ScienceLoRa-Gateway. The ScienceLoRa-Gateway can be connected via IEEE 802.3 and 802.11 n/ac to send packets to KREONET. Figure 5(b) shows the developed ScienceLoRa-Gateway. The ScienceLoRa-Device and ScienceLoRa-Gateway in Figure 6 are custom-made hardware platforms for accommodating the special requirements in terms of size, interface, security, and connectivity from the users. However, the off-the-shelf LoRa devices can be integrated into ScienceLoRa because the ScienceLoRa-Network Server is built based on the LoRa/LoRaWAN standard.
# Effect of Nano Hydrolcite on the Aging Resistance of a High Binder Content Stone Mastic Asphalt
## 1 Introduction
At the binder level, the aging resistance is evaluated through the evolution of rheological parameters after accelerated aging in the laboratory. The mostly used aging methods of bitumen are the thin-film oven test (TFOT), rolling thin-film oven test (RTFOT), and pressure aging vessel (PAV). The dynamic shear rheometer (DSR) and bending beam rheometer (BBR) are the most frequently used types of testing. Several studies concluded that the modification with LDH enhanced its ultraviolet (UV) aging resistance [35; 36; 37; 31] and flame-retardant properties [34; 38]. In addition to rheological parameters, [PERSON] et al. [36], using Fourier-transform infrared spectroscopy (FTIR) analysis found that LDH can inhibit the oxidation of bitumen during thin-film oven test (TFOT) and UV aging, resulting in smaller content of carbonyl and sulfoxide groups than in the unmodified bitumen. The modification of bitumen with nano hydrotalcite can enhance the aging resistance of the binder and, accordingly, also of the asphalt mixture. Thereafter, such modification can be more effective in the case of asphalt mixtures with high binder content. Furthermore, to full explore the potential of the modification, namely, the UV aging resistance, it must be considered for asphalt mixtures to be applied in surface courses. Currently, there is a gap in knowledge regarding the effects of the modification with hydrotalcite at the asphalt mixture level. The objective of this study was to evaluate the effects of modification with nano hydrotalcite on the surface characteristics and mechanical performance of an asphalt mixture to be used in surface courses. For this objective, two asphalt mixtures were produced: (1) a conventional mixture and (2) a mixture modified with nano hydrotalcite. To better potentiate and explore the effects of the modification, the selected asphalt mixture was an SMA produced with a polymer-modified bitumen (PMB 45/80-65). It is expected that the particularly high binder content (7.5%) can induce a more significant effect of the modification. An experimental program was developed to characterize the asphalt mixtures, including testing for skid resistance, macrotexture, indirect tensile strength, water sensitivity, stiffness, and resistance to fatigue. In addition, to assess possible long-term effects, such as aging resistance, the asphalt mixtures were conditioned for accelerated aging in laboratory, using the long-term oven aging (LTOA) method, and tested afterwards. This paper presents and discusses the results of the experimental program.
## Materials and Methods
### Hyperspectral Images Acquisition and Processing  
Hyperspectral images of _A. thaliana_ leaves were collected by a push-broom hyperspectral imaging system. The system briefly was consisted of a charge coupled device (CCD) camera with 672 \(\times\) 512 pixels resolution (C8484-05, Hamamatsu Photonics, Hamamatsu City, Japan), a spectrograph with 2.8 nm resolution in the range of 379-1023 nm (ImSpector V10E, Spectral Imaging Ltd. Oulu, Finland), two-line lights (Fiber-Lite DC950, Dolan Jenner Industries Inc. Boxborough, MA, USA), a mobile platform (IRCP0076, Isuzu Optics Crop, Taiwan, China), a dark box and a computer installed with V10E software of four different spectral resolutions (1.25, 2.51, 5.03 and 10.08 nm) using the interpolation algorithm. Before the final hyperspectral images acquisition, some key parameters including spectral resolutions, working distance, exposure time, light intensity and scanning speed were optimized with the aim of achieving the best output performance. Two halogen line lights were installed on two sides of the lens above the samples with an angle of 45\({}^{\circ}\) to reduce the shadow. The spectral resolutions were set as 1.25 nm, and the working distance was 28 cm, and the exposure time of the charge coupled device (CCD) camera was adjusted to 40 ms. The _A. thaliana_ leaves were scanned at a speed of 3 mm/s on the sampling platform with a dark background to simplify the image processing procedure. All components were fixed in a dark box to avoid any impact on the hyperspectral images collection.  
Prior to further images processing, the raw hyperspectral images were calibrated by the following equation to reduce the bias caused by the uneven distribution of light.  
\[R=I_{raw}-I_{dark}/(I_{white}-I_{dark}) \tag{1}\]  
where \(R\), \(I_{raw}\), \(I_{dark}\), and \(I_{white}\) were the calibrated images, original images, dark current and reference images, respectively. The calibrated spectral images of first 60 wavelengths were removed due to high noises. As shown in Figure 1, the background of each spectral images was removed firstly. The [PERSON]'s model was then introduced to calculate a global threshold that can segment abnormal tissues (regions of interest, ROIs) from healthy ones from the spectral image [16]. As the spectral images at 750 nm of the infected tissues (Figure 2a) presented a great contrast to healthy ones, it was determined for image segmentation. For healthy samples, the whole leaf area was considered as the ROIs. The mean spectrum of all pixels within each ROI in the range of 450-1023 nm was calculated. In order to further remove the undesirable noise that could impact the extraction of spectral features, and eventually improve the discrimination accuracy, the Savitsky-Golay (SG) smoothing algorithm was also implemented as previously described [10].
# On Vulnerability of Selected IoT Systems to Radio Jamming--A Proposal of Deployment Practices
## 1 Introduction  
One of the first formalized approaches to the problem of electromagnetic (EM) interference affecting Internet of Things and wireless sensor networks can be found in document [1]. It is stressed there that this issue affects the segment between the sensors/meters tier and the Access Points (AP) tier. The topic is treated in general terms in that it introduces potential victims (targets) and consequences that EM interference imposes on the Machine-To-Machine (M2M) communication. The document also presents some recommendable countermeasures.  
The major aim of the narrowband Internet of Things (IoT) systems is to carry the traffic (mainly) from a large number of end-devices attached to utility meters, sensors, detectors, gauges, etc. According to the IoT traffic model presented in [2], once the IoT systems have become fully ubiquitous, the prospective number of the aforementioned sensing devices is expected to be at the level of 40 units per a household. Within the coverage of a single IoT base station (BS), it is assumed that the number of houses is 4275. Such an arrangement, though economical, creates a significant single point of failure when the BS is exposed to intensive interference or jamming, thus disabling the entire IoT network, a situation can be viewed as a form of electromagnetic cyberattack. Figure 1 illustrates this case by presenting a scenario in which readings from multiple sensors (represented by blue arrows, carrying the desirable carrier power, C) are sent towards the base station which at the same time is being jammed by a perpetrator emitting inband interference (I) causing \"carrier to interference and noise\" (CNIR), received by the BS, to drop below a threshold necessary for correct uplink (UL) reception.  
The example presented in this article, albeit relating to Weightless and SigFox, is in fact generic to all kinds of narrowband IoT systems since any system performance can be defined by how its packet error rate (PER) changes with CNIR (e.g., see results for LoRa in [3]). Moreover, the limited width of the ISM (Industrial, Scientific, Medical) frequency bands in which IoT networks operate, i.e., on the order of a few hundred kHz, makes it easy to generate such a jamming signal spanning the entire band at no noticeable degradation of its power density.
# Overview on Different Types of Solar Cells: An Update
## 5 Thin-Film Solar Cells
### Cu(In,Ga)Se\({}_{2}\) Film Solar Cells  
The Cu(In,Ga)Se\({}_{2}\) (CIGS) films are already available in the global solar panel market due to their excellent radiation tolerance, high absorption coefficient (10\({}^{5}\) cm\({}^{-1}\)), suitable band gap value (1.04 eV to 1.65 eV), and long-term stability. However, these films are very expensive because of the indium and gallium (poor abundance). Several deposition techniques have been reported for the preparation of CIGS films and the photovoltaic parameters were studied as well (Table 3). Thin films could be deposited onto rigid glass substrates, and successfully reached power conversion efficiencies of up to 20.3%. However, the current focus of the production of films onto flexible substrates (metal foils and polyimide films) is because of their significantly lower cost. Polyimide films are highly desirable due to electrical insulation and enabling direct monolithic interconnection.
# Ultra-Low-Loss Silicon Waveguides for Heterogeneously Integrated Silicon/III-V Photonics
## 2 Ultra-Low Loss Waveguides for Si/III-V Heterogeneous Integration  
To enable a full design space for lasers and amplifiers in Si/III-V heterogeneous platforms, control of the optical mode confinement in the gain (III-V) layer and passive (silicon) layer should be obtainable by engineering the waveguide widths of the III-V and silicon waveguides. In principle, as described in [27; 28], the ratio between effective indices of the individual III-V guide and that of the silicon guide should be tailorable from above unity (regime a.1 in Figure 1a) to below unity (regime a.3 in Figure 1a). To satisfy that requirement, the thickness of the SOI waveguides needs to be optimized in accordance to the III-V stacks. Since the current is injected vertically through the III-V active region, the III-V stack is typically about 2 \(\upmu\)m thick to separate the contact metal sufficiently far from the optical mode. As shown in Figure 1b, a thickness of 500 nm for the silicon layer results in a good index match with the III-V layer, allowing for a great flexibility in designing the mode hybridization. Other considered thicknesses, such as the widely utilized 220 nm SOI waveguides [2] or multi-micron thick SOI waveguides [6], do not suit well because their indices are fairly far off from that of the III-V.  
The heterogeneous Si/III-V photonic design kit (PDK) developed at University of California at Santa Barbara (UCSB) for 1550 nm wavelength in recent years has been built and optimized for 500 nm thick silicon on 1 \(\upmu\)m buried oxide on silicon substrates. The ultra-low loss (ULL) waveguides in our work here, therefore, are also developed on the same SOI platform.
# Toxic Metals Migration from Plastic Food Contact Materials in Romania: A Health Risk Assessment
## 2 Materials and Methods
### Exposure and Human Health Risk Assessment  
A human health risk assessment involves estimating the likelihood of adverse health effects in people exposed to different chemical contaminants [16]. These risks are usually classified as carcinogenic or non-carcinogenic. Carcinogenic risks assess the likelihood of developing cancer following long-term exposure to a single pollutant or a combination of pollutants, while non-carcinogenic risks focus on exposure-related effects, including genetic and teratogenic outcomes [17].  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Operating Condition** \\ \hline Nebulizer gas flow & 0.93 L/min \\ Auxiliary gas flow & 1.20 L/min \\ Plasma gas flow & 16 L/min \\ RF power & 1000 W \\ Sample uptake flow & 1 mL/min \\ Deflector voltage & \(-9.75\) V \\ Analog stage voltage & \(-1900\) V \\ Pulse stage voltage & 1200 V \\ Torch z position & 0 \\ Analytical mass & 138, 59, 63, 66, 55, 60, 7, 57, 208, 111, \\ Ba, Co, Cu, Zn, Mn, Ni, Li, Fe, Pb, Cd, Cr, Sb & 52, 121 \\ Wash step & Time (45 s), speed (\(\pm\)rpm)—24 \\ Sample flush step & Time (35 s), speed (\(\pm\)rpm)—24 \\ Read delay step & Time (15 s), speed (\(\pm\)rpm)—20 \\ \hline \end{tabular}
\end{table}
Table 2: Instrument operating conditions for metals analysis by ICP-MS.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Element** & **R\({}^{2}\)** & **LOD (\(\mu\)g/kg)** & **r** & **RSD(r)** & **R** & **RSD(R)** & **Recovery (\%) * \\ \hline Ba & 0.9996 & 0.03 & 9.18 & 2.90 & 11.84 & 3.04 & 108.40 \\ Co & 0.9980 & 0.02 & 9.14 & 3.20 & 10.98 & 3.35 & 104.88 \\ Cu & 0.9978 & 0.04 & 9.50 & 2.71 & 10.98 & 3.25 & 100.22 \\ Zn & 0.9986 & 0.04 & 9.84 & 3.77 & 10.47 & 3.98 & 91.50 \\ Mn & 0.9995 & 0.18 & 9.35 & 2.93 & 9.84 & 3.92 & 102.07 \\ Ni & 0.9968 & 0.06 & 9.25 & 2.99 & 10.95 & 3.84 & 95.79 \\ Li & 0.9994 & 0.08 & 9.50 & 3.36 & 10.07 & 4.70 & 102.55 \\ Fe & 0.9962 & 0.06 & 10.52 & 3.86 & 11.25 & 5.63 & 107.10 \\ Pb & 0.9931 & 1 & 9.55 & 2.18 & 12.67 & 8.53 & 93.12 \\ Cd & 0.9978 & 1 & 9.24 & 3.11 & 11.01 & 4.94 & 108.44 \\ Cr & 0.9991 & 1 & 9.10 & 3.24 & 11.57 & 9.94 & 103.85 \\ Sb & 0.9989 & 0.03 & 8.98 & 2.40 & 10.58 & 4.17 & 103.91 \\ \hline \end{tabular} \({}^{2}\)—correlation coefficients; LOD—limit of detection; r—repeatability; R—intermediate precision; *—recovery values obtained at a spike level of 40 \(\mu\)g/L.  
\end{table}
Table 3: Analytical parameters of the method.