esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Benchmark LLMs' Performance on Structured Query Generation
#### 4.1.2 Exact Match Validation for Benchmark LLMs  
The performance of ERNIE 3.0, LLaMA-13B, and SQLCoder models in accurately matching 12 types of building information query tasks (presented in Table 1) is illustrated in Table 6.  
Based on the results presented in Table 6 and Figure 2, ERNIE 3.0 demonstrated the best overall performance, achieving a 100% accurate matching rate across 10 of the 12 query categories. However, its performance on query types T4 and T8 was less optimal, with matching rates of 50% and 78%, respectively.  
The LLama-13B model exhibited unstable performance. While it achieved a 100% exact match rate in specific categories (such as T2, T4, and T12), its performance in other categories (including T3, T6, T7, T9, and T10) was significantly worse, with match rates of 0% or very low. The suboptimal performance of LLama-13B in this study is primarily due to its limited linguistic adaptability and domain adaptation capabilities. The model's original training corpus is predominantly in English, with insufficient pre-training in Chinese, leading to weak semantic understanding in Chinese NL2 SQL tasks. This is particularly evident in its failure to model Chinese contextual nuances and to map them to structured language effectively. Furthermore, unlike ERNIE 3.0, which incorporates knowledge-enhancement mechanisms, or SQLCoder, which is optimized for structured language, Llama-13B lacks pre-training or domain adaptation modules specifically designed for the Building Information Modeling (BIM) domain. As a result, it struggles to effectively handle the highly specialized semantic content and structural requirements in BIM queries.  
SQLCoder performed comparably to ERNIE 3.0, achieving a 100% accurate matching rate in 10 categories. However, it exhibited significant weaknesses in problem types T6 and T7, with matching rates of only 13% and 53%, respectively.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.7 Fine-Grained Software Reconfiguration (FiGaRo)  
FiGaRo [63] provides fine grained control of software components, providing support for heterogeneous networks, component dependencies, and version constraints. It is based on Contiki, and has two core constituents: a _component model_ providing support for reconfigurable components with dependencies and versions, and a _distribution model_ that restricts dissemination to a subset of nodes. FiGaRo components are mapped to Contiki _services_, and the Contiki dynamic linking facility is used. Subsets of nodes are selected using attribute/value pairs, and a mesh is created connecting matching nodes. A spanning-tree is selected from the mesh to support software reconfiguration, rooted at the target node closest to the injection point. The current values of a nodes attributes are piggybacked on all transmitted traffic, and is used to populate a simple routing table with hop-based costs. The first stage of reconfiguration is a _marker_ message to identify redundant paths, parents being selected based on minimum hops. Code is distributed in _chunks_ on a hop-by-hop basis using explicit ACKs. Nodes buffer every message, and use overhearing to confirm that their children have in turn transmitted it.  
FiGaRo provides no specific features related to autonomic updates, but the granularity, version and dependency support, and support for groups of nodes (allowing for heterogeneous updates), along with performance improvements, may be useful features in an autonomic environment.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 5 Conclusions  
This work has shown the suitability of OWC for full-duplex, high-speed data transfer in on-axis rotary scenarios. The proposed hybrid lens is able to provide a sufficient RX signal level \(P_{\text{PD FP}}\) of more than \(-16.2\) dBm even for misalignments of up to 3 mm at a communication distance of \(z=12.5\) mm. OWC is therefore able to provide a robust data transfer without the strict mechanical tolerances compared to FORJs. The results show a maximum power penalty resulting from optical crosstalk of 0.9 dB within the FOV. The approach is promising since it allows low-cost fabrication. Besides the electrical components, only a single optical component is required that can be fabricated by injection molding in high volume.
## 4 Discussion  
From the various test results reported in the previous section, the CC1120 wireless module has sufficient radiation tolerance to withstand a space environment. Thus, application to satellite components is feasible as the radiation tolerance of CC1120 is higher than that of the typical microprocessors used in space. In addition, recovery from an SEL is possible through a hardware reset. Therefore, the extremely stable application of the wireless module is possible by resetting it when necessary, which may enable adoption in long-term operation missions. Based on these findings, we developed a satellite communication component. Figure 13 shows the block diagram of the components. In this design, the high-frequency inputs and outputs of CC1120 are switched by relays and connected to a linear amplifier during transmission. Table 10 lists the specifications; Figure 14 shows a photograph of the component.  
Figure 12: Frequency shift tolerance test equipment.  
The developed component was installed as a communication module in CubeSat WARP-01, which was released from the International Space Station (ISS) on 14 March 2021. An orbit demonstration was performed. Figure 15 shows a photograph of WARP-01. Beacon transmission via Morse and downlink and uplink via frequency shift keying (FSK) were successfully achieved. The reset history of the onboard computer showed no evidence of SEL or SEU after deployment. The component continued to operate stably in the space environment. As examples of the reception conditions, Figure 16 shows a waterfall snapshot of a continuous-wave (CW) signal received at a ground station, and Figure 17 shows a waterfall snapshot of an FSK signal.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Detail** \\ \hline Frequency & 435 MHz \\ (transmission and reception) & Ham radio band \\ \hline Transmitter & CW: 0.1 W \\ Output power & FSK: 1 W \\ \hline Modulation & CW/FSK \\  & FSK: 9600 bps, 2400 Hz shift \\ \hline \end{tabular}
\end{table}
Table 10: Satellite communication equipment specifications.  
Figure 14: Communication component for CubeSat installation.  
Figure 13: Block diagram of the transceiver for the satellite.
# Proposal of a Design Procedure for Steel Frames with Viscoelastic Dampers
## 4 Proposed Design Procedure  
The proposed design procedure is based on the assumptions that (i) the response of the building equipped with VEDs is governed by the first mode of vibration and (ii) the addition of the VED-brace subassembly to the frame does not affect its first mode shape. Regarding the first hypothesis, [PERSON] et al. [26] proved that the global response of a viscoelastically damped frame is essentially governed by the first mode o vibration. The second hypothesis derives directly from the application of the Modal Strain Energy method (MSE) as a design criterion [35; 36; 37; 38]. Referring to this latter hypothesis, [PERSON] and [PERSON] [37] demonstrated that the assumptions made in deriving the MSE method may result in an overestimation of the modal damping ratios of viscoelastically damped structures when  
Figure 6: Schematic representation of the brace–VED subassembly.  
the added damping is higher than 20%. Thus, these researchers recommended limiting the value of the added damping to a maximum value equal to 20%.  
Due to its lateral deformability, the insertion of the VED-brace subassembly in the frame does not determine a significant increase in the lateral stiffness of the structure. Owing to this, beams and columns are sized according to capacity design rules established in Eurocode 8 [1] for steel moment-resisting framed (MRF) structures. Thus, members are verified according to Eurocode 3 [55] to resist internal forces determined by means of linear analyses. Once the MRF is fully sized, the VED-brace subassembly is included in the model and sized considering the effects of the higher modes of vibration. Finally, response spectrum analysis is performed to verify that limits on floor displacements are respected. The proposed design procedure is shown in the flowchart diagram reported in Figure 7.  
Beams and columns of the MRF are designed for the Life Safety (LS) limit state (here associated with seismic events with a probability of exceedance of 10% in 50 years). Since limited damage is accepted in these members, the behavior factor \(q\) is taken equal to 1.5, the same value recommended in Eurocode 8 for base-isolated buildings.  
To ensure the functioning of the dissipative system in the worst-case scenario, braces and the columns belonging to the braced bays are designed to remain elastic (\(q=1\)) at the attainment of the Near Collapse (NC) limit state (here associated with seismic events with a probability of exceedance of 2% in 50 years). VEDs are designed to remain undamaged at the attainment of the NC limit state. The elastomer ISD-111H can totally recover from deformation of 400% without damage [56]. However, for the sake of safety (as no statistical  
Figure 7: Flowchart of the proposed design procedure.  
data are available), VEDs are designed to develop a maximum deformation of 200% at the NC Limit State, as suggested by [PERSON] et al. [6].
## 2 Experimental Setup  
Figure 1 presents the schematics of the test setup. The width and height of the wind tunnel duct were 200 mm and 100 mm, respectively, and the mainstream velocity was 10 m/s. Pressure sensitive paint (PSP) (UF-750P, Innovative Scientific Solutions, Inc., Dayton, OH, USA) was sprayed on the test surface and an LED lamp with a 400-nm wavelength (LM2 xLZ-400, Innovative Scientific Solutions, Inc., Dayton, OH, USA) was installed above the test section for PSP illumination. The light emitted from the PSP was captured using a 14-bit cooled CCD camera (pco. 1600, PCO AG, Kelheim, Germany) with a 650-nm long pass filter and stored on a computer. For the film cooling test, air and nitrogen were used as a coolant, and the resulting coolant density ratio (DR = \(\rho_{c}/\rho_{\infty}\), where \(\rho_{c}\) and \(\rho_{\infty}\) are the density of coolant and mainstream flow, respectively) was about 1.0. The coolant was supplied to the slot through the plenum, and two blowing ratios (M = \(\rho_{c}u_{c}/\rho_{\infty}u_{\infty}\), where \(u\) is streamwise velocity) (0.5 and 1.0) were tested.  
Figure 1b,c show the schematic of the slot with the DBD plasma actuator. A function generator (AFG 3021, Tektronix, Inc., Beaverton, OR, USA) and a power amplifier (20/20C, TREK, Inc., Lockport, NY, USA) were used to generate the sinusoidal wave and amplify the signal. A 0.07-mm thick  
Figure 1: Schematic of test setup (not to scale).  
aluminum tape was used for both electrodes. The width of the exposed and insulated electrodes were 3 and 10 mm, respectively. The electrodes were attached to a 3-mm thick dielectric barrier plate (Bakelite), and the distance between the two electrodes was 5 mm. The slot had a 30-degree angle with respect to the mainstream flow, and the width (\(w\)) and length of the slot (\(l_{\mathrm{s}}\)) were 4 and 12 mm, respectively.  
The sinusoidal waveform with a 1-kHz frequency was supplied to the exposed electrode and two amplitudes (6 and 7 kV) were tested. Figure 2 presents the waveforms of the input voltage and current for the case with the 6 kV input voltage. In order to investigate the effects of electrode location, two distances (\(L_{\mathrm{e}}\)) between the slot and the exposed electrode (0 and 10 w) were considered. The coolant density ratio (\(DR\)) was 1.0, and two blowing ratios (\(M\)) (0.5 and 1.0) were tested.
# Instance Hard Triplet Loss for In-video Person Re-identification
## 4 Experiments
### Implementation Details  
We implemented our network using the PyTorch framework. In all experiments, the margin constant \(\alpha\) was fixed to 0.3. We made a clean network design so that the results could be easy to be reproduced, and our code will also be publicly released. Two networks were designed:  
ReID-Head: The input image size was \(300\times 300\). We used an SSD network pre-trained on the PASCAL VOC dataset [63] with an mAP of 77.4% on the VOC2007 Test. To compare different ReID methods with the same detection performance fairly, we fixed the detection part and only trained the proposed ReID-Head. Besides, the relationship between detection and ReID performance had already been fully investigated in [19]. Because the ReID-Head was light-weight, we only trained it for 10 epochs using an Adam [64] optimizer with the learning rate linearly increasing from \(5^{-5}\) to \(5^{-4}\).  
Baseline for cross-camera ReID: To evaluate the efficiency of our proposed instance hard triplet loss on cross-camera ReID task, we compared it with the widely-used state-of-the-art metric batch hard triplet [14] loss on three public cross-camera ReID datasets. As for the network structure, we chose the IDE network [30], which was commonly used as a baseline, like in [7; 30; 65; 66; 67; 68]. The IDE network [30] uses a standard ResNet-50 structure [49] and the pre-trained weights on ImageNet for initialization. After the last convolution layer, a global average pooling follows to get the final ReID feature, which is a 2048-dimension vector for every sample. The input image size we chose was \(256\times 128\), and only random horizontal flipping data augmentation was used. We used an Adam [64] optimizer with an initial learning rate \(5^{-5}\) and linearly increased the learning rate to \(1^{-3}\) within 20 epochs. We kept this learning rate for 60 epochs and then lowered it to \(1^{-4}\) for 20 epochs and \(1^{-5}\) for another 80 epochs. We used a \(PK\) sampling strategy [14] with \(P=32\) and \(K=4\), which meant there were 32 persons and 4 images for each person in a mini-batch. The same network structure was used for instance hard triplet loss. Except for the loss function, all the remaining settings were identical, and the results on three public cross-camera datasets are reported.  
Baseline for in-video ReID: To evaluate the efficiency of the proposed ReID-Head network and instance hard triplet loss on in-video ReID task, we compared it with a state-of-the-art cross-camera ReID method, Part-based Convolutional Baseline (PCB) [2]. For the PCB network, following [69; 70; 71], we used an input image size of \(256\times 128\). The number of horizontal stripes was fixed to 4 accordingly. The \(PK\)-sampling strategy with \(P=12\) and \(K=4\) was used to help the network converge. We trained it on the widely-used DukeMTMC-reID [7] cross-camera ReID dataset and then evaluated its performance on the in-video ReID task.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 3 Experimentation Environments
### Mininet  
Mininet [47; 48] is a network emulation orchestration system. It runs a collection of end-hosts, switches, routers, and links on a single Linux kernel. It uses lightweight virtualization, which permits to emulate network infrastructure in a single host, running the same kernel, system, and user code. Mininet is open source, easy to deploy, and provides a programmable interface to define and build network configurations.  
The Linux container mechanism used by Mininet allows groups of processes to have independent namespaces for system resources (e.g., network interfaces and file systems) running on the same kernel. For each virtual host, Mininet creates a container attached to a network namespace. Each network namespace holds a virtual network-interface connected to a software switch via virtual Ethernet links.  
Mininet presents performance bounds in commodity servers that satisfy the demands of the general network tests. It achieves high aggregated bandwidths and low median round trip times even in demanding scenarios where more than a thousand hosts are included in the emulated topologies [35].  
Another important feature of Mininet is its programmability. In this regard, Mininet integrates on its core a Python API. Hence, Python is used for orchestration, but emulation is performed by compiled C code. The API is well documented, easy to use, and because it is integrated with the Mininet core, everything can be built with it.  
Mininet also integrates several SDN elements. For example, in [49], they combine Mininet and the ONOS [50] controller to present a scalability analysis. Due to the above characteristics, Mininet has become one of the reference emulation tools.
## 7 Discussion
### Future Directions and Research Opportunities
#### 7.2.6 Expansion to Broader Urban Systems  
The future of DT technology extends beyond transportation management and infrastructure maintenance. There is growing interest in applying DTs to larger urban systems, including smart energy grids, environmental monitoring, and logistics [129]. By connecting DTs of different urban subsystems, cities can optimize resource use, reduce environmental impacts, and improve the quality of life for their residents throughout the entire lifecycle of urban infrastructure.  
In summary, DT technology has immense potential to transform transportation infrastructure from a lifecycle perspective. However, its future development will depend on addressing key challenges and exploring new research opportunities. Standardization, advanced analytics, interoperability, policy and privacy considerations, and integration with emerging technologies are critical areas that require further research [26]. Addressing these areas will enable the transportation industry to leverage DTs in building more efficient, resilient, and adaptive infrastructure systems for the future, ensuring value creation at every stage of the lifecycle.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 3 Modeling of UWA Channel Problems and Challenges
### Multipath Modeling  
The multipath propagation of acoustic waves may result in a considerable decline in the acoustic communication signal. It is one of the main causes of the presence of ISI. Vertical acoustic channels exhibit low time dispersion because the multipath geometry is affected by the link architecture. The horizontal acoustic channels, on the other hand, possess large multipath spreads that differ subject to the changes in water depth. Therefore, this multipath geometry determines the response of channel impulse, its refraction, and reflection features that control the number of pathways for propagation, their comparative delays, and intensities.  
Multipath happens from ray bending; however, rays of sound tend to reach the region of deep water with low propagation speed. Sound reflections occurring from the possible direct path of the surface and/or bottom generate the multipath in shallow waters (with a depth of fewer than 100 m) [161]. Figure 16 shows that for propagation from source to receiver, there are four different categories of pathways. This includes (1) the direct path, (2) reflected from the surface alone, (3) only bounce from the seabed (4) and bounce from both single and many times.  
According to [PERSON] and [PERSON], the Ray tracing theory [7] provides a good estimate to determine the coarse configuration for the multipath channel. Ray model considers that sound travels in straight lines in form of rays at a constant speed. These rays reflect or refract with the variation in the speed of sound. Suppose, \(d_{3b}\) is the distance in the upward direction initiating the Eigen-rays with the reflections from the surface denoted by \(\Im\) and \(b\) is the bottom reflections having the time of arrival \(\tau_{3b}\). Therefore, the attenuation is assumed by \(\Gamma_{3b}/\sqrt{A(d_{3b},f)}\). By assessing all possible paths that result in a limited response of impulse, the shallow water acoustic channel is determined using the transfer function given in Equation (27):  
\[H(f)=\frac{1}{A(d_{\infty},f)}e^{-j2\pi f\tau_{\infty}}+\sum_{\Im=1}^{+\infty} \sum_{\Im=-b-1}^{b}\frac{\Gamma_{3b}}{\sqrt{A(d_{3b},f)}}e^{-j2\pi f\tau_{3b}} \tag{27}\]  
where, \(d_{\infty}\) represents the distance along a direct ray. The transfer function of the channel for a finite number of reflections from the surface of the sea and its bottom for practical calculations is given in Equation (28):  
\[H(f)=\sum_{p-1}^{p=0}\frac{\Gamma_{p}}{\sqrt{A(l_{p},f)}}e^{j2\pi f\tau_{p}} \tag{28}\]  
where, \(\tau_{p}\), \(\Gamma_{p}\) and \(A(l_{p},f)\) is the path time arrival \(p\), the loss in total reflection, and the attenuation of the medium, respectively. Therefore, the individual channel path behaves as an LPF that contributes to the general impulse response [7; 162].
## 5 Application of Self-Sensing Materials for
### Structural Health Monitoring (SHM)  
The SHM of transportation infrastructure is essential for enhancing safety, improving service lifespan, decreasing labor involvement by enabling an automatic inspection system, and reducing maintenance costs [237]. Instrumentation and sensor deployment are the initial steps in SHM. The conventional condition monitoring system relies on localized congested instrumentation for short-term and intermittent periods [238]. Moreover, managing, processing, analyzing, and interpreting the vast amount of data collected from deployed congested sensors pose significant challenges in SHM [239]. To address this, the application of self-sensing feature of multifunctional cementitious composites doped with electrically conductive fillers represents one of the recent advances in SHM [240].  
[PERSON] et al. [241] reported the feasibility of using self-sensing cementitious composites containing CFs to evaluate fatigue damage in bridge decks. Similarly, the potential of using self-sensing cementitious composites doped with graphene for the SHM of bridges was assessed in a previous study [242]. Furthermore, the crack detection and localization capabilities of self-sensing cementitious composites have been evaluated using Electrical Impedance Tomography (EIT) [243]. Additionally, EIT has been shown to effectively detect the shape of damage in self-sensing composites, highlighting the technique's utility for SHM [244]. In this context, self-sensing cementitious composites have been utilized in airport pavements to detect cracks and degradation based on electrical resistance tomography (ERT) [245]. The developed electrically conductive concrete pavement successfully detected the formed cracks over loading period based on ERT images. The electrically conductive concrete pavement that was developed effectively detected the formation of cracks during the loading period, as evidenced by the ERT images.
## 3 Results and Discussion
### Molecular Weight (MW) of SPI  
SDS-PAGE analysis of SPI and commercial SPI (COM) is shown in Figure 4. Both samples exhibited a similar electrophoretic profile, showing bands with molecular weight ranging from \(\sim\)110 to \(\sim\)23 kDa. Soy proteins mainly consist of albumins and globulins, and \(\sim\)90% correspond to fractions 7S (\(\beta\)-conglycinin) and 11S (glycinin), which are globulins. Fraction 7S contains the subunits \(\alpha^{\prime}\), \(\alpha\), and \(\beta\) with a MW of 72, 68, and 52 kDa, respectively, while fraction 11S contains two subunits with a MW of 35 and 20 kDa [50].  
The bands in SPI and COM with a MW of \(\sim\)72 kDa, and \(\sim\)52 kDa corresponded to \(\alpha^{\prime}\) and \(\beta\) subunits of fraction 7S, respectively. The subunit \(\alpha^{\prime}\) was observed just in SPI. [PERSON] et al. (2016) reported that there might be a loss of \(\alpha^{\prime}\), \(\alpha\), and \(\beta\)-7S subunits by enzymatic hydrolysis of soybean proteins and complete disappearance of these bands after 3 h of digestion, which can explain the loss of the \(\alpha^{\prime}\) subunit in the commercial sample [51]. Furthermore, a band of \(\sim\)23 kDa was present in both samples that revealed the presence of glycinin (Figure 4).