esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 1 Introduction  
Modern societies are exposed to multiple and heterogeneous risks, such as natural disasters, health-related threats, attacks on public spaces, industrial hazards, and household accidents. Some of these threats have become even more prevalent in recent years, where the combined impacts of climate-related disasters and COVID-19 have directly affected hundreds of millions of people around the world [1].  
Disaster management systems provide emergency intervention teams with tools to effectively prepare for, respond to, and recover from any of the aforementioned situations. When being part of such systems, mobile technologies, computer applications, and digital data are able to speed up and increase the effectiveness of the mobilization, preliminary situation assessment, and intervention in disaster-affected areas [2; 3].  
Particularly, the Internet of Things (IoT) paradigm has long proven its suitability in emergency response (ER) operations, thanks to its ability to collect data in real time, from heterogeneous sources, and at a fine granular level [4; 5; 6]. These data are later transmitted wirelessly, thus delivering rich information in a continuous flow to the command and control center. Consequently, IoT technologies may help boost cooperation between organizations, improve situational awareness, and enable complete visibility of the response force and the remaining resources in ER operations [7; 8; 9].  
One of the most important agents in the ER ecosystem are first responders (FRs), who are among the first to arrive to the scene of an emergency and provide assistance or incident resolution. While it is true that some existing IoT-based tools already enhance the operational capacity and increase the safety of FRs in the field [10; 11; 12; 13; 14], there still exist capability gaps, such as \"the ability to know the location of FRs and their proximity torisks and hazards in real time\", as pointed out by the International Forum to Advance First Responder Innovation (IFAFRI) [15].  
To shed more light on this issue, this paper describes the implementation of an indoor positioning system (IPS) based on the ultra-wideband (UWB) technology without pre-existing infrastructure and assesses its performance in a real environment. The main contribution of the current work consists in using GNSS-RTK technology to quickly and accurately self-locate IPS reference devices outside of the target building, which speeds up on-field deployment and therefore makes the whole system suitable for potentially urgent interventions due to its _plug-and-play_ approach.  
The remainder of this document is organized as follows: Section 2 reviews the current IPS systems and technologies, with a special focus on those based on UWB. Section 3 describes the proposed UWB-based IPS and Section 4 elaborates on the system's implementation and the evaluation testbed. Section 5 provides information on the obtained results and, lastly, Section 6 compiles the lessons learned and presents the future work.
# Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation
## 1 Introduction
The estimation result of each iteration process can be compared with the one of the previous iteration process, and the interpolation will end when the different of the estimation results between the two adjacent process is less then the given threshold value. The Selective matching combination of the target point (TP) and the RPs can reduce the matching task, thus improve the positioning efficiency of fingerprinting acoustic localization. Therefore, the positioning database is considered to be divided into a certain number of sub-databases in the offline phase, and then the matching scope can be shrunk to a smaller one through the search of adjacent sub-database [28]. For the database division method, the coordinate space partition is investigated firstly, which is easy to implement and can reduce the influence of outliers. However, this method has the defects of uncertain partitioning results and large positioning error. That is mainly because the division result is greatly influenced by the subjective judgment in the coordinate space partition. The adjacent RPs of the TP may be divided into different sub-databases, which will cause the RPs matching error. In machine learning technology, cluster analysis, as a precursor process of other learning tasks, is often used for classification of unlabeled samples. The RPs with similar features can be assigned to the same sub-database automatically by cluster analysis technique to accomplish the purpose of the database partition [29]. The rest of the paper is organized as follows: In Section 2, the general process of the fingerprinting acoustic localization is briefly introduced. In Section 3, the positioning database partition by cluster analysis and the adjacent RPs searching based on the two-stage matching method are stated, and then the iterative interpolation method is proposed to generate the virtual RPs for ensuring the target position estimation accuracy with few initial RPs. Section 4 presents the implementation details and evaluates the performance of the novel methods from the results obtained. Finally, some conclusions are drawn in Section 5.
## 5 Results and Analyses
### Main Results
#### 5.1.2 Comparison with Large Language Models  
To comprehensively evaluate our approach against recent advances in large language models, we conducted additional experiments comparing CLEAR with state-of-the-art LLMs on the CodRED test set. We evaluated GPT-4 using both zero-shot and few-shot prompting strategies, following the experimental setup from recent cross-document relation extraction studies. The results are presented in Table 4.  
**LLM experimental setup:** For GPT-4 evaluation, we used the following prompt template: \"Given the following document paths connecting entities [HEAD] and [TAIL], determine the semantic relationship between them. Choose from the provided relation types or 'N/A' if no relation exists.\" We provided 3-shot examples for few-shot evaluation and used temperature = 0.1 for consistent results.  
The results demonstrate that CLEAR significantly outperforms all LLM configurations. Specifically, CLEAR achieves a 20.08% higher F1 score compared to the best LLM configuration (GPT-4 fine-tuned) and 17.50% improvement in AUC.  
Cross-document relation extraction requires processing lengthy document paths (average 4900 tokens before filtering). This substantial performance gap shows that while GPT-4 has extended context windows, the model struggles to maintain focus on relevant relational evidence across such extensive contexts, leading to attention dilution and decreased precision.
# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 3 Review Methodology
### Question Formulation and Keywords Definition  
The first step in this review is the definition of the research questions. In this regard, the aim of this research is to examine the existing state of the art dealing with sustainable logistics problems using VNS and to examine in which areas of logistics this metaheuristic has been applied most frequently and successfully, as well as to identify the most commonly used version and understand how it has been implemented.  
Based on the research purpose, we have defined a list of keywords in order to localize and limit this study. In addition to _Variable Neighborhood Search_ and _VNS_, we have considered two sets of keywords: those related to sustainability and those related to logistics. The first set contains the following: _green_, _sustainable_, _environmental_, _closed-loop_, _reverse_, _electric_, _emissions_, _energy consumption_, _waste collection_, and _wind farm_. The second set contains the following: _logistics_, _transportation_, _supply chain_, _location_, _inventory_, and _scheduling_. We have included the keyword _waste collection_ in our search because waste management is a critical aspect of sustainability in logistics. Efficient waste collection systems directly contribute to reducing environmental impact by optimizing waste transport and disposal. In the context of sustainable logistics, the use of advanced optimization techniques like VNS can significantly enhance the efficiency of waste collection processes, making them more environmentally friendly and cost-effective. Regarding the keyword _wind farm_, we have included it because wind farms play a significant role in the transition to renewable energy, which is a key component of sustainable logistics. Optimizing the logistics associated with wind farm operations, such as the transportation and installation of wind turbines, can lead to significant improvements in efficiency and sustainability. The application of VNS to wind farm-related logistics problems helps minimize transport costs, reduce emissions, and enhance the overall effectiveness of renewable energy projects.
# Power Dissipation and Wear Modeling in Wheel-Rail Contact
## 2 Formulation of Wheel-Rail Contact Problem
### Thermal Model  
Due to the work of friction forces, heat energy is generated during the wheel movement. In turn, it generates changes in the rail temperature \(\theta\). We assume that the heat transfer is due to conductivity only. Since the wheel moves along the rail, this heat flow is also not stationary. So, the strip temperature evolution is determined using a nonstationary heat-conductive equation. Let us denote by \(\bar{\kappa}\) a thermal conductivity coefficient. Therefore, temperature \(\theta\) in time interval \((0,T)\) and in the strip domain \(\Omega\) satisfies [6]:  
\[\rho c_{p}\frac{\partial\theta}{\partial t}=\bar{\kappa}\bigtriangleup\theta. \tag{9}\]  
In (9), \(\bigtriangleup\theta\) denotes the Laplacian operator of temperature \(\theta\). Recall [28] that this operator is the sum of the second-order derivatives of temperature \(\theta\). In time interval \((0,T)\), the temperature function \(\theta\) also satisfies the boundary condition along the boundary \(\Gamma\):  
\[\bar{\kappa}\frac{\partial\theta}{\partial n}=Q\ \ \mathrm{on}\ \ \Gamma_{C}\ \ \ \mathrm{and}\ \ \ \bar{\kappa}\frac{\partial\theta}{\partial n}=0\ \ \mathrm{on}\ \ \Gamma_{0}. \tag{10}\]  
Heat flux function \(Q\) is generated due to frictional force. This function is determined using the friction coefficient, friction force and sliding velocity. It also depends on the thermal material resistance coefficient. We shall assume function \(Q\) is stationary. Recall that from (10), it follows that the frictional heat flow occurs along the contact sub-boundary \(\Gamma_{C}\) only. There is no heat exchange along the sub-boundary \(\Gamma_{0}\). The initial temperature of the strip at \(t=0\) is assumed to be the temperature \(\theta_{g}\) of the air, i.e., \(\theta(0,x)=\theta_{g}\).
## 2 Methods
### Hardware Configuration  
Figure 3 shows the optical layout and experimental system to simultaneously measure the thicknesses of multiple points. Considering the measurement principle of spectral interference, a Tungsten/Halogen lamp (KEWLAB, HLS-1, Melbourne, VIC, Australia), which covers a wide wavelength range from 360 nm to 2500 nm with a fiber output power of 5 mW, was used as the light source. An imaging spectrometer (Princeton Instruments, IsoPlane-160, Teledyne Princeton Instruments, Trenton, NJ, USA) with camera pixels of 1024 \(\times\) 1024 was used as a detector. The spectral resolution of the spectrometer is 1.2 nm in the wavelength range from 440 nm to 850 nm. Therefore, according to Equation (3), the film thickness measurement range that the proposed system can achieve was calculated at about 250 nm to 90 \(\upmu\)m. A doublet lens with a focal length of 60 mm was used to construct the detection probe, and the spot size was approximately 1 mm. Linear 1-to-4 fan-out fiber bundles (Xirrui, SUH600, Shenzhen, China) with a core diameter of 600 \(\upmu\)m were used to connect multiple probes to the imaging spectrometer. In particular, the light collected by the fiber bundles imaged on the spectrometer slit could be distinguished by the imaging spectrometer. In the experimental configuration, the slit length was 15 mm, and the slit width was set to 10 \(\upmu\)m to ensure high spectral resolution. While the imaging diameter of the single fiber core on the spectrometer slit was less than 1 mm, the imaging spectrometer could be used to measure the film thickness in up to 15 channels. The film thickness calculation program was run on the MATLAB (R2022b) platform on a personal computer with an Intel(r) CoreTM i5-12400F 2.50 GHz processor (Intel Corporation, Santa Clara, CA, USA) and 16.0 GB of memory.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 4 Our Proposed Routing Schema
### Migration Strategy of the Mobile Sink  
In our proposed schema, the moving trajectory of the mobile sink is a circle, as Figure 6 demonstrates. The mobile sink moves with a constant angular velocity and the current position of the mobile sink can be calculated using its initial position and moving time. Hence, the mobile sink only needs to announce its initial position and angular velocity such that the broadcast message of the network is greatly reduced. We suppose the initial position of the mobile sink is \(P_{0}\), and after a \(\Delta\)t time interval, the mobile sink will have moved to a new position \(P_{\Delta t}\), as is shown in Figure 6. The prediction of the mobile sink demands that the clock of sensors is synchronized and the position information will be calibrated every few rounds. When there is only one mobile sink in the network, the initial position of the mobile sink \(P_{0}\) can be set at any position of the circle with radius \(r\). Once there are multiple mobile sinks, their initial positions are evenly distributed at the edge of the circle with radius \(r\).  
Figure 5: Chain of CHs generation.  
Figure 6: Trajectory of mobile sink.
# Research on Improving Optical Field Distribution of TOSA Based on 3D Defocusing Coupling
## 2 Analysis of the Influence of Optical Field Distribution in TOSA
### Effect of Existing Coupling Model on the Distribution of Optical Field
where \(n_{0}\) is the refractive index of air, \(n_{0}=1\), and \(n_{1}\) are the refractive index of the core, \(n_{1}=1.468\), \(\alpha=8^{\circ}\). After calculation, the value of \(\beta\) is approximately 3.76\({}^{\circ}\). That is, when the incident beam is offset by 3.76\({}^{\circ}\) from the central axis of the fiber, the power coupled into the core of the fiber is the biggest and the coupling efficiency the highest. Figure 6a-d show the distribution of the optical field in the core and cladding of fiber when the fiber moves up along the X-axis. It shows that more light is coupled into the cladding and the distribution in cladding is more irregular with increasing movement. Figure 6e-h show the distribution of the optical field in the core and cladding of fiber when the fiber moves up along the Y-axis. In Figure 6f, there is no discrete optical field in the cladding, which indicates that the incident angle of the end-face of the \(8^{\circ}\) fiber is consistent with the segregation angle when the fiber moves up 0.002 mm along the negative direction of the Y-axis, and the optical field is in the fiber core and around the fiber core. However, with the increasing movement along the positive direction of the Y-axis, the more optical field enters the cladding, and the distribution in the core and cladding is irregularity, which indicates that the incident angle is out of the segregation angle, and the light cannot be fully reflected inside the fiber core, so that the light is refracted into the cladding. Figure 6: _Cont._  
The focal position coordinates are set as (0,0,0), and the 8\({}^{\circ}\) optical fiber moves along the X-axis and Y-axis, respectively. The X (\(\upmu\)m) and Y (\(\upmu\)m) represent the diameter of cladding, and (0,0) is the central point of the fiber core. The color represents the energy density. From the variation trend of the optical field in the fiber, the distribution of the optical field in the cladding is uneven when the fiber moves up along the X-axis or the Y-axis, which makes the power at four directions difference of the TOSA larger, and the power difference tested by jumpers with different concentricity larger also. Finally, the performance of the TOSA is out of specification under different testing conditions, and the yield of the TOSA is low.
## 4 Optical CAM Technology  
In this section, we describe the recent developments towards developing optical CAM architectures, spanning from the first optical Binary CAM cell to a T-CAM cell layout and the design of multi-bit CAM ML architectures.  
Figure 4: Experimental results of RAM cell demonstration: (**a–g**) Time traces (400 ps/div) and eye diagrams (50 ps/div) for the WRITE operation; (**h–j**) Time traces (400 ps/div) and eye diagrams (50 ps/div) for the READ operation; (**k**) BER measurements for the READ/WRITE operation at 5 Gb/s.
## 3 Experimental Results and Discussion
### Effect of Cutting Direction and Joint Spacing on Cutting Performance
#### 3.2.2 Effect of Joint Spacing  
The effect of joint spacing on \(FC\) and \(SE\) was investigated through cutting tests conducted at depths of 6 mm and 9 mm, which were consistently performed across all the specimens. Numerous studies on TBMs have reported that an increase in joint spacing leads to an increase in thrust force [15; 16; 18; 19]. The results obtained in this study show a similar trend. Figure 19 presents the mean values and standard deviations of \(FC_{m}\) and \(FC_{p}\) for intact rock specimens and for different cutting directions in jointed rock mass specimens. Both \(FC_{m}\) and \(FC_{p}\) showed an increasing trend as the joint spacing increased. The mean value of \(FC_{m}\) for a cutting depth of 6 mm increased from approximately 3 kN at a joint spacing of 30 mm to about 4 kN as the joint spacing increased to 90 mm. Similarly, for a cutting depth of 9 mm, the mean value of \(FC_{m}\) increased from 4 kN to approximately 6 kN as the joint spacing increased from 30 mm to 90 mm.  
The mean values and standard deviations of \(SE\) for intact rock specimens and different cutting directions in jointed rock mass specimens are illustrated in Figure 20. Consistent with the findings of [PERSON] et al. [21], \(SE\) also increased as the joint spacing increased. the joint spacing increased from 30 mm to 90 mm, the mean value of \(SE\) for a cutting depth of 6 mm increased from 42 MJ/m\({}^{3}\) to approximately 55 MJ/m\({}^{3}\), while for a cutting depth of 9 mm, it increased from 30 MJ/m\({}^{3}\) to about 40 MJ/m\({}^{3}\).  
Figure 19: Variation in (**a**) \(FC_{m}\) and (**b**) \(FC_{p}\) with \(J_{s}\).  
Figure 20: Variation in \(SE\) with \(J_{s}\).  
It is noteworthy that for a cutting depth of 6 mm, both \(FC_{m}\) and \(SE\) reach the level of intact rock at a joint spacing of 60 mm, while for a cutting depth of 9 mm, they reach the intact rock level at a joint spacing of 90 mm. This suggests that when the joint spacing becomes more than 10 times the cutting depth, the influence of joint spacing can be considered negligible.  
Several researchers have found that the optimal spacing of disc cutters in TBMs decreases as joint spacing increases [35; 36]. [PERSON] et al. [21] reported that in jointed rock mass excavation using disc cutters, cracks generated in closely spaced joint planes cause greater damage to rock blocks, leading to a higher degree of fragmentation, increased crack density, and improved fragmentation efficiency. However, as joint spacing increases, the efficiency of rock breakage decreases, ultimately leading to a reduction in the optimal spacing of the cutting tool. A similar trend was observed in this study. Figure 21 shows the mean values and standard deviations of \(s/p_{\text{opt}}\) with respect to joint spacing. As the joint spacing increased from 30 mm to 90 mm, \(s/p_{\text{opt}}\) decreased from 3.89 to 3.33. Therefore, the influence of joints in excavation using a conical pick can be considered highly similar to their effect in excavation with disc cutters.
## 2 Materials and Methods
### Pinch Data Acquisition System
The output signal of the LVDT sensor was filtered with an analog Low Pass Filter (LPF) of 20 Hz cut off frequency to remove the high-frequency noise. Since the pinching length is only 20 mm, the LVDT sensor was calibrated at 0-, 5-, 10-, 15-, and 20-mm displacement points using a standard digital caliper with 0.05 mm resolution. The calibration process was repeated three times to perform the repeatability test, as mentioned previously for the pinch force load cell. The three voltage-displacement relationships show high linearity of R\({}^{2}\) = 0.9999, as shown in Figure 3.5 The repeatability of the LVDT sensor is \(9.57\times 10^{-5}\%\), which indicates high repeatability. The analog signals of the customized pinch force load cell, pulling force load cell, and LVDT sensors were converted to digital inside the Arduino(r) Due and then recorded in a PC installed with Arduino(r) IDE software. The diagram that illustrates the data acquisition process is shown in Figure 3.6
## 5 Simulation Experiment Analysis
### Jamming Performance
CFAR detection cannot avoid complex clutter and noise environments, so the hypothesis test for detecting the presence of objects in echo signals is uncertain. Thus, \(a\) measure of uncertainty, i.e., entropy is defined as follows:  
\[E(x)=-\int\limits_{-\infty}^{+\infty}p(x)\log_{a}p(x)dx \tag{21}\]  
where \(p\) is the sum of FBMC subcarrier power distributed in the jamming frequency band. Assuming that the interfered radar frequency band is known, the entropy in the interfered frequency band can be maximized to improve the jamming ability to achieve the best jamming effect. As shown in Figure 15, although the proposed joint communication jamming waveform is slightly lower in jamming entropy than the algorithm in [24], our proposed joint waveform requires less jamming power than traditional chirp or other signals. The energy saved can ensure the robust performance of the communication subsystem in the joint communication jamming system. Jamming signal energy mainly affects the jamming signal ratio (JSR). In Figure 16, the JSR gradually increases and saturates as the number of false target jamming increases. When the number of false targets is less than a certain number at the beginning, the JSR level is not high because the jamming effect is not pronounced and the target is not yet covered. Overall, the JSR of our proposed joint waveform based on FBMC chirp is about 20 dB lower than that of the algorithm in [24], requiring less jamming power than traditional chirp or other signals. Figure 15: Jamming entropy for the proposed FBMC chirp vs. algorithm in [24]; the number of false targets is 700.