esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating LoRaWAN Connectivity in a Marine Scenario
###### Abstract
For particularly remote areas or harsh environments, such as the polar basins, the use of satellite IoT communications is promising, and there is a growing trend of integrating IoT devices with low cost and low power consumption satellite technologies [22; 23]. Satellite IoT technologies can overcome the limited coverage of terrestrial LPWANs: they are not affected by terrain conformation and show destruction resistance, but require specific antennas and proprietary protocols, although a strong effort is put in including innovative specifications for LPWANs suitable also for satellites in terms of multiple access to the channel, latency, collision avoidance, and, more in general, of throughput and service quality. One of the key factors for the use of LPWA networks for marine purposes is to extend their capacity to provide new technological solutions, including hardware, software, and middleware parts, to be applied to platforms and sensors for broad-spectrum marine monitoring. This can support a rapid use of information, and provide innovative services on a basin scale, such as the assessment of water quality or the control of vessels in delimited areas with particular restrictions to navigation (i.e., marine protected areas and off-limits zone). To this aim, some tests have been carried out exploiting the intrinsic capability of Long Range (LoRa) technology to provide reliable communication over the sea for a few miles [24; 25; 26; 27]. A complete LPWA network infrastructure exploiting LoRa transmission technology is presented for the remote monitoring of the sea at a large scale. It was set up to fulfill the objectives of the project entitled \"IoT Technologies for the marine environment\", a strategic research and development action, funded by the Liguria Region, Italy. This network, has been developed with three specific aims: (1) to track pleasure and lightweight boats not equipped with the Automatic Identification System (AIS) in coastal areas with particular restriction to navigation by means of LoRa compliant GPS; (2) to exploit the possibility to use autonomous IoT environmental sensors for monitoring purposes onboard vessels or stand-alone; (3) to verify the use of LoRa for data transmission from existing marine observatories moored offshore in the open ocean. The network performance has been evaluated in operational condition tracking the path of a passenger vessel on which IoT end-nodes have been installed and during a research cruise that took place in the Northwestern Mediterranean Sea in October 2020. Using the developed LoRa network it was possible to track the path of a boat equipped with GPS-IoT end-nodes close to the coast using multiple gateways operating simultaneously on land and to demonstrate the feasibility to transmit meteorological and oceanographic data by means of IoT end-nodes deployed at sea and installed on an existing observatory up to 100 km far from the coast. In Section 2 methods and experimental setup are presented, in Section 3 the results and the discussion are provided, whereas Section 4 summarizes the main findings.
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 5 QAOA Mappings: Orderings and Schedules
### SMS, with Release Dates
#### Encoding and Compilation  
Given the \(\sum_{i=1}^{n}|W_{i}|\) partial mixers, one for each job \(i\) and time \(t\in W_{i}\), we can define simultaneous and sequential mixers as above. Using one-hot encoding, the partial mixer Hamiltonian is:  
\[H_{\mathrm{NS},i,t}^{\mathrm{(enc)}}=\left(\prod_{j\
eq i}\prod_{t^{\prime} \in\mathrm{nbhd}_{i,t}(j)}\frac{I+Z_{j,t^{\prime}}}{2}\right)\left(X_{i,t}X_{ i,b_{i}}+Y_{i,t}Y_{i,b_{i}}\right). \tag{76}\]  
The controlled null-swap can be further compiled using ancilla qubits, as in Section 4.2. While the cost of compilation could be bounded based on the degree of the graph, the overlap of the various partial mixers may be more complicated (with respect to partitioning into disjoint sets) and expensive (with respect to number of gates and ancilla qubits) depending on the SMS instance.  
**Objective function.** As an example objective function, we again considered minimizing the weighted total tardiness, Equation (69). The one-hot encoded phase Hamiltonian takes the form of Equation (70), with \(b_{j}\) included in the summation range of \(t\):  
\[H_{\mathrm{P}}^{\mathrm{(enc)}}=\sum_{i=1}^{n}w_{i}\left[(b_{i}-d_{i})Z_{i,b_ {i}}+\sum_{t=d_{i}-p_{i}+1}^{b_{i}}(t+p_{i}-d_{i})Z_{i,t},\right] \tag{77}\]  
and the corresponding unitary can be implemented with \(\sum_{j}(h_{j}-d_{j}+1)\) single qubit \(Z\)-rotation gates.  
**Initial state.** Any feasible schedule can be used as the initial state. In particular, we used a greedy earliest-release-date schedule. Assume without loss of generality that the jobs are ordered by their release times, i.e., \(r_{1}\leq r_{2}\leq\cdots\leq r_{n}\). Then set \(s_{1}=r_{1}\) and recursively set \(s_{i}=\max\{r_{i},s_{i-1}+p_{i-1}\}\), which is feasible though likely suboptimal.
# Non-Uniform Distribution of Contamination on Composite Insulators in HVDC Transmission Lines
## 2 Natural Pollution Test in the Operating HVDC Transmission Lines  
### The Sample Arrangement and Contamination Distribution  
The contamination samples were typical type suspension composite insulators arranged in different towers in a certain \(\pm\)800 kV HVDC transmission line corridor. The tower views and diagram of test samples arrangement were shown in Figure 1. The sample arrangement and test procedures were based exactly on guideline [18].  
The contamination period was from March 2014 to November 2017. According to the meteorological monitoring statistics near that location, the frequency of wind speed within 0.5-5.4 m/s reaches 95% during test period. And basing on the TSP (total suspended particles) monitoring data near that area, the cumulative probability of particles \(d_{p}\leq\) 50 \(\upmu\)m is about 90%, and PM\({}_{10}\) hold the majority of the TSP. After the contamination period, contamination distribution on insulator surface were obtained, as is shown in Figure 2.  
The following phenomena can be observed. Contamination distribution on insulator surface is non-uniform. Contamination degree on top surface and leeward side is heavier than that on bottom  
Figure 1: Field pollution test of transmission line operating insulators. (**a**) the \(\pm\)800 kV transmission line view; (**b**) Diagram natural contamination insulator string sample arrangement.  
Figure 2: Field pollution test of transmission line operating insulators.  
surface and windward side. Thus, in the DC energized condition, contamination along the string is also non-uniform, and serious pollution occurs mainly in the high voltage terminal and ground terminal. In order to quantify contamination non-uniformity, ESDD (equivalent salt deposit density, mg/cm\({}^{2}\)) and NSDD (non-soluble pollution deposit density, mg/cm\({}^{2}\)) of the samples will be measured.
## 3 Data Compression Technology-Based Intelligent Data Acquisition (IDAQ) System
### Central Monitoring and Control System (CM&Cs)  
To verify the data transmitted from the wireless sensing system regarding the condition of the civil structure, to transmit the controls according to the condition of the civil structure and to link the systems organically to prevent the data loss required for real-time control, a GUI central control systemfor the IDAQ system was developed using LabVIEW (2012) from National Instruments (Austin, TX, USA), running on the host PC as shown in Figure 7d.
# Thermal Effusivity Tester (TET)--A New Device to Determine Thermal Effusivity of Textiles
###### Abstract  
Thermal effusivity tester (TET) is a new device to measure the thermal conductivity and the thermal effusivity (heat dissipation) of textiles under a defined compression, developed at the R&D department of Lenzing AG (Austria). The device performance was tested by comparing its results with results from commercially available devices Alambeta, TCi Thermal Conductivity Analyzer and Kawabata KES-f thermal module. The fabrics tested were typical knit and weave constructions made of different fiber types, including cotton, wood-based cellulosics and polyester. For most of the fabrics, thermal effusivity results show wide agreement among TET, Alambeta and TCi, and strong positive correlation (\(r>0.82\)) with heat flow (Qmax) as obtained from KES. Deviations were observed for some thicker and more resilient fabrics, most probably caused by the differences in the pressure applied by the devices on the fabric surface. The results show that TET offers a reliable and experimentally flexible approach to assessing thermal effusivity on textile structures and emphasizing the role of the dimensional change induced by the measurement conditions on the measured thermal effusivity and conductivity.  
thermal effusivity; thermal conductivity; compression; textile; haptics; measurement device +
Footnote †: journal: Applied Sciences
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## 3 Performance Evaluation
### Simulation Results  
There are two parts of the simulations in this section. The first is the Monte Carlo simulation of the performance of fusion, and the second aims at public test datasets. Monitoring of correlator outputs and the consistency check of Doppler shifts, which have been discussed previously, are employed and fused together in what follows. Their test statistics are denoted as \(T_{1}\) and \(T_{2}\), which correspond to Equations (24) and (35). The corresponding belief functions are denoted as \(m_{1}(\cdot)\) and \(m_{2}(\cdot)\), respectively. Both detectors adopt the same false alarm probability denoted as \(P_{fa}^{(i)}\).
## 4 Experiments and Numerical Results
### Experimental Setup
#### 4.1.4 Implementation Details  
Our code was implemented using the PyTorch (Version 1.13.1, Facebook AI Research, Menlo Park, CA, USA) [49] framework and was built upon the LibKGC [50] library. We utilized a multi-head Transformer architecture in both \(T_{n}\) and \(T_{r}\), which consists of three multi-head Transformers, and \(T_{m}\), which contains six multi-head Transformers. Each multi-head Transformer consists of eight heads with a feed-forward network dimension of 1280, and the hidden layer dropout rate is set to 0.1.  
To train the datasets, we initialized entity- and relation-embedding dimensions to 320 and established a batch size of 256 samples. Table 3 displays the detailed training parameter configurations for the four datasets, while the determination of the parameters relating to the number of multi-hop neighbors was based on the guidelines presented in Section 4.4. We employed a grid search strategy to obtain the optimal configuration of hyperparameters, where \(\alpha\), \(\beta\), and \(\gamma\) were searched within the set \(\{0.1,0.5,0.8,1\}\). Finally, the optimal parameters for \(\alpha\), \(\beta\), and \(\gamma\) were determined to be 1, 0.8, and 0.1, respectively. We applied the Adamax optimization strategy [51] with a learning rate set to 0.01. The rate started at 0, linearly increased during the first 10% of epochs, and linearly decreased in subsequent epochs. Our experiments ran on RTX 4060 8G graphics cards using the above configuration.  
After training RiQ-KGC, we performed additional fine-tuning steps on the model. Firstly, we fixed the entity- and relation-embedding representation and fine-tuned the model with a learning rate of 0.003. Subsequently, we fixed all Transformer module parameters and fine-tuned only the final linear layer using a learning rate of 0.001.
## 6 Discussion
### Shallow Subsurface Structures from Short Wavelength Data  
Shallow subsurface structures for Central Luconia Province are validated from airborne gravity including Tzz, Txz, Tyz and CLP short & intermediate wavelengths data, as well as the first derivatives of aeromagnetic data (TML_RTE_1 vd). Txz, Tyz, and Tzz data represent major NW-SE structural trend, whilst the CLP_short and intermediate wavelength capture NE-SW and broad NW-SE trend. All short wavelength gravity anomalies map also preserved NE-SW structural trend. The NW-SE trend are parallel with the direction of West Baram Line and West Balingian Line, indicating imprint of these two major faults within the sediments of Central Luconia. Some limestone build-ups in Central Luconia, particularly in the central to northern section are also oriented in NW-SE directions (Figure 1C), demonstrating the growth of the build-ups on top of NW-SE oriented structural blocks. The analysis of TMI_RTE_1 vd that represents magnetic bodies shallower than the magnetic basement reveals E-W to NWW-SE orientations, and this is assumed not to be associated with any structural trends interpreted on the short wavelength airborne gravity. Instead, it might represent subsurface structure located at intermediate depth, not too shallow, and not to deep.  
In the northeast section (longitudes 112\({}^{\circ}\) to 113\({}^{\circ}\)), NE-SW to NNE-SSW structural trends follow similar NE-SW trend that has been mapped in both [51; 52]. The NE-SW to NNE-SSW faults that were exposed from the airborne gravity are comparable with the faults' orientation interpreted at the base of carbonate platforms at the southern part of Central Luconia as modelled in [15]. These NE-SW faults are expected to be originated within the brittle upper crust, continues offsetting the sediments in Pre-Cycle and possibly remain active or being re-activated in the same orientation during deposition of sediments in Cycle I as interpreted in seismic line 1004 and 1014 (Figure 11E,F) as well as line 1008 (Figure 12D).  
In the SW section of the airborne gravity coverage area (longitude 111\({}^{\circ}\) to 112\({}^{\circ}\)), N-S to NNE-SSW faults interpreted on Txz and Tzz gravity anomaly map appear to be similar to those mapped by [51]. These faults are marked as NE-SW orientation by [52]. Based on this study, a more precise and detailed faults orientations for the shallow subsurface structure are illustrated especially in the eastern side of the airborne gravity coverage (south of F19 well) and at the section to the west of West Balingian Line. Improvement in mapping of the faults particularly the Cenozoic faults (post-collision faults) are enhanced through interpretation of the airborne gravity maps especially in the central to SW section of the survey area. Apart from that, younger faults, most likely to be smaller scale faults induced by gravitational tectonic from the constant loading of the sediments from onshore to the offshore have been mapped in detail through CLP data for short to intermediate wavelengths (Figure 10).
###### Abstract  
With the development of remote sensing technology and very large-scale integrated circuit (VLSI) technology, the real-time processing of spaceborne Synthetic Aperture Radar (SAR) has greatly improved the ability of Earth observation. However, the characteristics of external memory have led to matrix transposition becoming a technical bottleneck that limits the real-time performance of the SAR imaging system. In order to solve this problem, this paper combines the optimized data mapping method and reasonable hardware architecture to implement a data controller based on the Field-Programmable Gate Array (FPGA). First of all, this paper proposes an optimized dual-channel data storage and access method, so that the two-dimensional data access efficiency can be improved. Then, a hardware architecture is designed with register manager, simplified address generator and dual-channel Double-Data-Rate Three Synchronous Dynamic Random-Access Memory (DDR3 SDRAM) access mode. Finally, the proposed data controller is implemented on the Xilinx XC/VX690T FPGA chip. The experimental results show that the reading efficiency of the data controller proposed is 80% both in the range direction and azimuth direction, and the writing efficiency is 66% both in the range direction and azimuth direction. The results of a comparison with the recent implementations show that the proposed data controller has a higher data bandwidth, is more flexible in its design, and is suitable for use in spaceborne scenarios.  
S 2021 10. 662. [[https://doi.org/10.3390/electronics1006062](https://doi.org/10.3390/electronics1006062)]([https://doi.org/10.3390/electronics1006062](https://doi.org/10.3390/electronics1006062))  
1  
[PERSON],  
[PERSON] and [PERSON]  
Received: 5 February 2021  
Accepted: 9 March 2021  
Published: 12 March 2021  
**Problems:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  
**Copyright:** 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 7 Conclusions and Future Work  
In wireless sensor networks which the bit error rate (BER) is higher than wired network, the energy of sensor nodes is very limited, making it a problem to ensure a low end-to-end Message Fail Delivering Ratio (MFR) without leading a decline in the network lifetime. An efficient way to obtain a reliable data communication is that several sensors transmit sensed data cooperatively, which can be implemented through a technology named Wireless Software Define Networks (WSDNs). In this paper, based on a WSDN where software inside sensors can help them construct clusters to do the data collection task, we focus on the cooperation details, namely, a data collection scheme called Reliability Improved Cooperative Communications (RICC) with adjustable data transmitting power is proposed to achieve high reliability for random network coding based cooperative communications in multi-hop relay WSNs with long lifetime guaranteed. In RICC, high data transmitting power is adopted by nodes in far-sink areas to fully use their residual energy to further reduce the MFR, while the nodes in hotspots area adopt relatively low power to maintain network lifetime, so both of MFR and lifetime of network can be improved. Omnet++ simulator is used to evaluate the effectiveness of RICC. The evaluation results indicate that: with RICC, the utilization ratio of energy is approximately increased by as much as 59.4% to 62.8% compared with NCCC, and the weighted end-to-end MFR is reduced by as least 50%.  
Figure 19: Simulated weighted end-to-end MFR.  
Figure 20: Average reduced weighted end-to-end MFR.  
Although building a reliability cooperative communications network is the key issue for wireless sensor networks, there are many work to do for creating an efficient communication network. With the development of sensing devices for harvest energy from surrounding circumstance, its devices are getting smaller and smaller, and the function is getting stronger and stronger. So for the future work, we plan to seeking new techniques to improve communication reliability as well as lifetime for harvesting energy sensor networks especially in practice applications.  
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61772554, 61379110, 61572526, 61572528), the National Basic Research Program of China (973 Program) (Grant No. 2014 CB046305). [PERSON] designed the algorithms and wrote the manuscript. [PERSON] wrote part of the manuscript. [PERSON] analyzed the experiment results. [PERSON] conceived of the work, designed the algorithms. [PERSON] commented the work.  
The authors declare no conflict of interest.
## 3 Results  
### Evaluation Metrics  
#### 3.1.1 Image Classification Metrics  
Classification accuracy and F1 scores on the validation datasets were used for the evaluation of the trained networks. Classification accuracy is the ratio of results classified as correct for all the classification results and is calculated by using Equation (2). The F1 score is a harmonic mean of precision and recall, an indicator that allows a more accurate evaluation of the networks when the data label is unbalanced. F1 score is calculated using Equation (3). Precision and recall are calculated by using Equations (4) and (5), respectively. The definitions of _TP_, _FP_, _FN_, and _TN_ are tabulated in Table 7.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
\[\text{F1}_{Score}=\frac{2\times precision\times recall}{precision+recall} \tag{3}\]  
\[precision=\frac{TP}{TP+FP} \tag{4}\]  
\[recall=\frac{TP}{TP+FN} \tag{5}\]
# Back to the Metrics: Exploration of Distance Metrics in Anomaly Detection
## 3 Experiments
### Performance of \(k\) Squared Distance Mean Metrics on BTF  
We conducted experiments on computing AS anomaly scores based on \(k\)NN squared distance mean metrics using BTF on the MVTec-3D AD dataset. As shown in Figure 9, when \(k=1\), the performance of P-AUROC and AUPRO is the lowest, and then, with the increase of \(k\), the performance of P-AUROC and AUPRO reaches the maximum at \(k=2\) and \(k=3\), respectively, and then starts to decrease.  
Figure 8: AD (I-AUROC) performance of \(k\) re-weighting metrics on BTF.  
\begin{table}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline  & **Method** & **Bagel** & \begin{tabular}{c} **Cable** \\ **Gland** \\ \end{tabular} & **Carrot** & **Cookie** & **Dowel** & **Foam** & **Peach** & **Potato** & **Rope** & **Tire** & **Mean** \\ \hline \multirow{4}{*}{\begin{tabular}{} \end{tabular} } & M3 DM [44] & 0.981 & 0.949 & 0.997 & 0.932 & 0.959 & **0.925** & 0.989 & 0.995 & 0.994 & 0.981 & 0.970 \\  & FPFH (BTF) [43] & **0.995** & 0.955 & **0.998** & **0.971** & **0.993** & 0.911 & 0.995 & **0.999** & **0.998** & **0.988** & **0.980** \\  & **FPFH (BTM)** & 0.995 & **0.96** & 0.998 & 0.97 & 0.991 & 0.894 & **0.996** & 0.999 & 0.998 & 0.987 & 0.979 \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & PatchCore [44] & 0.983 & 0.984 & 0.980 & 0.974 & 0.972 & 0.849 & 0.976 & 0.983 & 0.987 & 0.977 & 0.967 \\  & M3 DM [44] & **0.992** & **0.990** & **0.994** & 0.977 & 0.983 & **0.955** & **0.994** & **0.990** & **0.995** & **0.994** & **0.987** \\  & RGB iNet (BTF) [43] & 0.983 & 0.984 & 0.980 & 0.974 & 0.985 & 0.836 & 0.976 & 0.982 & 0.989 & 0.975 & 0.966 \\  & **RGB iNet (BTM)** & 0.984 & 0.987 & 0.984 & **0.979** & **0.991** & 0.872 & 0.976 & 0.983 & 0.992 & 0.979 & 0.973 \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & M3 DM \({}^{*}\) & 0.994 & **0.994** & 0.997 & 0.985 & 0.985 & **0.98** & 0.996 & 0.994 & **0.997** & **0.995** & 0.992 \\  & BTF [43] & 0.996 & 0.991 & 0.997 & **0.995** & 0.995 & 0.972 & 0.996 & 0.998 & 0.995 & 0.994 & 0.993 \\ \cline{1-1}  & **BTM** & **0.997** & 0.993 & **0.998** & 0.995 & **0.996** & 0.979 & **0.997** & **0.999** & 0.996 & 0.995 & **0.995** \\ \hline \hline \end{tabular}  
* \({}^{*}\) Denotes results obtained by employing pre-trained parameters provided by the original studies. Unannotated results are directly excerpted from the corresponding literature. The bold method is the method proposed by this paper. The bold number represents the best performance metric in the current modality and category. Because the number of normal pixels overwhelmingly dominates in anomaly detection tasks, the importance of this metric has significantly diminished. Since 2021, AS tasks have primarily referenced the AUPRO metric.  
\end{table}
Table 3: **P-AUROC** score for anomaly detection of all categories of MVTec-3D AD. Our method outperforms other methods in the 3D + RGB setting and achieves a 0.995 mean P-AUROC score.