esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation of Titanium Implants and Laser Irradiations  
SLA (sandblasted and acid-etched) titanium implants (10 mm in diameter and 2 mm thick; Dentis, Daegu, Korea) were used. The reason the titanium disc was SLA treated prior to the experiment is to reproduce the surface of a commercial dental implant. The SLA of the titanium disc was performed by the implant manufacturer (Dentis, Daegu, Korea). A total of 10 titanium implants were prepared. Nine out of 10 discs were designated as experimental groups and divided into three groups based on the laser used: group 1 was irradiated using the K2 mobile laser, group 2 was irradiated using the Epic 10\({}^{\text{TM}}\) laser, and group 3 was irradiated using the Saeshin diode laser. The remaining one titanium disc was designated as a control without laser irradiation.  
Three titanium implants in each experimental group were irradiated with lasers at different power settings. The first disc of each experimental group was irradiated at 1.0 W power setting for a period of 15 s with continuous wave mode. The second disc of each experimental group was irradiated at 2.0 W, and the third disc was irradiated at 3.0 W for the same period in the continuous wave mode.  
Titanium disks were sterilized before irradiation and stored individually in sterile microtubes after irradiation. Titanium implants were irradiated at three spots, and the titanium implants were irradiated with just a single \(\pi\) (0.4/2)\({}^{2}\) area of the beam within a 1.5 \(\times\) 1.5 mm square (Figure 1). Within the range of each spot per specimen was irradiated under the same conditions at the same power setting with the same type of laser. Before using three types of dental diode lasers, the temperature of implant surface was confirmed during irradiation using a calibrated thermometer system (mid LOGGER GL240; GRAPHTEC, Yokohama, Japan). The laser fiber tip has a circular shape, and the same fiber tip was used for three dental diode lasers. The laser fiber tip was unmoved, and in contact vertically with the titanium disk surface while irradiation was performed for a period of 15 s. After cooling completely, the laser was irradiated at the next spot. All diode lasers were irradiated by the same investigator (H.-K.K.), and when irradiating the spots, the laser was irradiated carefully within spot area to prevent irradiation of other spots. The energy-dispersive spectrometry (EDS) was analyzed immediately after laser irradiation, considering the contamination of the sample.
## 3 Dataset
### Ground Measurements  
PM\({}_{2.5}\) concentrations in Malaysia have been measured since April 2017. The Department of Environment, Malaysia (DOE) increased the number of air quality monitoring stations across the country from 52 to 65 stations in 2017 (Figure 1). Furthermore, these stations also measure meteorological parameters (e.g., ambient temperature, TEMP; RH, wind speed, WS; wind direction, WD) and gaseous pollutants (e.g., nitrogen dioxide, NO\({}_{2}\); carbon monoxide, CO; sulphur dioxide, SO\({}_{2}\); ozone, O\({}_{3}\)). The stations are strategically distributed to represent urban, industrial, suburban and rural areas [92]. PM\({}_{2.5}\) measurements were performed via the TEOM 1405 DF, which is a continuous dichotomous ambient air monitoring system with two Filter Dynamics Measurements Systems [93], able to measure PM\({}_{2.5}\) and PM\({}_{10}\). SO\({}_{2}\), NO\({}_{2}\), CO and O\({}_{3}\) are measured using Thermo Scientific model 43i, model 42i, model 48i and model 49i, respectively [93; 94]. RH and TEMP were recorded using a Climatronic AIO 2 Weather Sensor (Climatronic Corporation) [95]. All the ground data were obtained on an hourly basis covering the period from January 2018 to December 2019.  
All air quality and meteorological measurements went through quality assurance and quality control (QA/QC) procedures. Instruments for the detection of gases were manually calibrated once a fortnight. Flow verification for PM\({}_{10}\) and PM\({}_{2.5}\) measurements using TEOM was conducted once a month. The data removal during the QC check was predominantly due to insufficient measurements and instrument failure, while some perturbed data were also excluded as outliers in a second-level QC check [95].
## 2 The Elephant in the Room: Great Diversity
### Nb-IoT
#### 2.3.2 Low-Power Communication Technology  
The NB-IoT stands out in the realm of low-power wireless communication technologies due to its exceptional capabilities that are specifically tailored for the modern IoT. Standardized by the 3 GPP in Release 13 back in 2016, the NB-IoT was meticulously crafted to support the burgeoning demand for a network that can efficiently connect a vast number of devices across extensive areas without the burden of high energy consumption. By leveraging a narrow bandwidth of around 200 kHz for its transmissions, The NB-IoT is not only able to ensure low-speed data exchange, but also significantly extends the battery life of IoT devices, making it an indispensable technology for scenarios where devices are expected to operate for years without battery replacement.  
The NB-IoT's design focuses on minimal energy usage, while still providing robust and reliable connectivity. This is particularly advantageous for a wide array of IoT applications that necessitate prolonged device longevity. Its enhanced coverage and deep penetration capabilities make it perfectly suited for monitoring environmental factors in remote or difficult-to-access locations [36].  
The advent of the NB-IoT as a low-power communication technology marks a pivotal advancement in the IoT ecosystem. Its ability to offer extensive coverage, superior indoor penetration, and ultra-low energy consumption while leveraging existing cellular network infrastructures makes it an ideal candidate for a plethora of IoT applications. This positions the NB-IoT as a cornerstone technology in the ongoing evolution of smart cities, agriculture, environmental monitoring, and beyond, heralding a new era of connectivity that is both efficient and sustainable.
## 1 Introduction  
According to the theory of new economic geography [1], regional advantage is the initial condition of industrial agglomeration. Economic activity is initially concentrated in a geographical area that generates a large number of agglomeration externalities by reducing costs and boosting firm productivity. The positive feedback effect and economies of scale of industrial agglomeration attract more enterprises to gather in the same region to take advantage of the positive externalities brought by proximity, leading to the self-reinforcement of industrial agglomeration [2, 3]. When a particular industry gathers in a particular region, the core competitiveness of the industry is enhanced, and the regional advantage is further enhanced.  
Transportation infrastructure is the key factor that determines regional advantages [4, 5]. Transportation infrastructure reduces transaction costs and expands cargo transport capacity. The improvement in transportation infrastructure may change the location advantage [6, 7]. Transport infrastructure affects separate industries completely differently [8, 9]. After the improvement of transportation infrastructure such as the highway network and high-speed rail network, some industries expanded as transportation costs fell, while other industries contracted as economic activities shifted. More convincing empirical evidence is needed to characterize the relationship between transportation infrastructure and industrial agglomeration.  
This paper took the Yangtze River Delta urban agglomeration as the research area and examined the impact of transportation infrastructure on industrial agglomeration from the perspective of urban agglomeration transportation integration. The influence mechanism of urban agglomeration transportation integration on industrial agglomeration was also studied. The rest of this paper is organized as follows. In Section 2, we re-view relevant literature and propose relevant research hypotheses. Section 3 introduces the relevant background, including the rapid development of transportation infrastructure and the trend of industrial agglomeration in the Yangtze River Delta urban agglomeration. Section 4 describes how we prepared the data and our methodology for empirical research. Section 5 investigates the impact of urban agglomeration transportation integration on industrial agglomeration in the Yangtze River Delta, examines the impact mechanism of urban agglomeration transportation integration on industrial agglomeration, and reports our analysis results. Section 6 discusses the model estimation results based on Section 5. Section 7 summarizes the main findings of the study and provides a conclusion. In the final section, we also discuss the contributions, impacts, limitations, and opportunities for future work in the study topic.
## 4 Evaluation Metrics
### Goodput  
Goodput is a metric that quantifies the rate at which valuable data are successfully transferred over the network and reach their intended destination, ensuring data integrity. Instantaneous goodput is calculated for a specific time interval [55]. The formula for goodput is given by  
\[Goodput=\frac{\text{Size of the transmitted file}}{\text{Time taken to transfer the file}} \tag{4}\]  
Goodput focuses on relevant and meaningful data, excluding data retransmission, protocol overheads, or any other extraneous information. It also excludes the packets that were delivered but, later on, discarded because they were corrupted or not delivered on time to their intended destination. In the context of IoT-enabled NBMS, goodput refers to the useful data rate that is actually processed, acted upon, and contributes directly to the performance, functionality, efficiency, and security of the system [56]. Hence, goodput is a measure of the network's deliverable capacity for useful data.  
Figure 13: Simulation setup.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 6 Conclusions and Future Scope  
In conclusion, this research has presented a comprehensive exploration of Canonical Correlation Analysis variants, namely PCCA and MCCA, as powerful techniques in fusion with SVM for hyperspectral image semantic segmentation. Through rigorous experimentation and evaluation on benchmark datasets, the efficacy of these CCA variants in capturing meaningful spectral relationships has been demonstrated. The approach exhibits notable performance in preserving fine-grained details and accurately delineating land cover classes within hyperspectral scenes. The Proposed Technique's ability to mitigate the curse of dimensionality, effectively exploit spectral information, and adapt to varying scene complexities positions it as a promising solution for HSI segmentation challenges. The fusion of PCCA and MCCA facilitates the capture of pairwise and collective spectral correlations, offering a more holistic representation of hyperspectral data. Furthermore, the scalability of the approach has been evidenced through experiments on datasets with varying spatial and spectral resolutions. The consistent performance across different scenarios underscores the robustness of the CCA-based technique. As the research community endeavours to advance remote sensing capabilities, the suggested future directions emphasize the potential for further refinement and extension of the Proposed Technique. Integration with deep learning models, adaptation to larger datasets, exploration of unsupervised learning scenarios, and incorporation of multimodal data present exciting opportunities for enhancing the accuracy and versatility of hyperspectral image segmentation. In essence, the findings presented in this research contribute to the evolving landscape of remote sensing applications, providing a foundation for future innovations and improvements in hyperspectral image analysis. The demonstrated capabilities of CCA variants pave the way for more sophisticated and effective techniques, fostering advancements in environmental monitoring, agriculture, and resource management.  
R.G.; Conceptualization, Methodology, Result analysis, Visualization, Writing--original draft. G.K.; Investigation and Supervision. S.S.K.; Investigation and Supervision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found at [[https://rslab.ut.ac.ir/data](https://rslab.ut.ac.ir/data)]([https://rslab.ut.ac.ir/data](https://rslab.ut.ac.ir/data)) (accessed on 8 August 2023).  
The authors declare no conflicts of interest.
## 5 Light-Weight Distributed Security Scheme
### Node Addition  
Notice that after deletion of the master key from the network, we will encounter a problem for node addition, node movement, or when a path condition changes during a local repair or rediscovery process. In all these cases, we cannot establish a new connection between two nodes that they did not communicate before without having the appropriate shared security keys between them. And in order to generate these keys, the nodes should know the master key that is already deleted from their memory.  
One solution to this problem is to assume that the base station is secure and trusted at any time during the network operation, so that there is no need to delete the master key from its memory. In the case of node addition, a new node has the master key but the existing nodes do not have. So, the new node first generates the unique-node key from the master key and waits until it hears broadcast messages (CH-Msg) from other CHs, then it selects one of them, or more depending on the proposed routing protocol, as its CH or parent toward BS. The new node then sends a request message authenticated with the MAC tag using the unique-node key to the selected CH asking the base station for the appropriate keys (pair-wise and unique-cluster keys) for the new connections. The selected CH forwards the request message as it is (without verifying the MAC) toward BS. Then, BS verifies the MAC of this message, generates the requested security keys using the master key, and sends them back to the requesting node and its CH or parent. The new node then establishes the new connections and deletes the master key from its memory.  
For node movement or when the path conditions have changed, the same process of adding new nodes will be followed except that these nodes do not have the master key as well but they have already generated the unique-node keys.
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## 4 Simulation Settings and Results  
In this section, simulation results are provided to show the performance gain of our proposed GAN-based channel estimation scheme. In particular, the OFDM channel dataset containing \(N=20,000\) samples is generated by following the TDL-C channel model [28], which characterizes the Rayleigh fading channel environment in the urban macrocell scenario under the non-line of sight (NLOS) condition. The ratio of the training samples to the total samples is set as 0.8. Power normalization is operated for all samples. The number of subcarriers and symbols within each OFDM block are set as \(N_{f}=48\) and \(N_{s}=14\), respectively. The carrier frequency is set as \(f=3.5\) GHz, and the bandwidth of each subcarrier is set as \(B_{f}=30\) kHz. The time interval of an OFDM block is set as \(T=0.5\) ms. To avoid the inter symbol interference, the cyclic prefix accounting for 6% of the symbol length is added to the front of each symbol. In addition, to ensure that the maximal propagation delay is not larger than the duration of cyclic prefix, the root mean square (RMS) delay spread is considered to be set as \(T_{d}=300\) ns. Moreover, the scenario that the terminal user moving with high speed is also considered, in which two speed cases are simulated, which are \(v_{1}=150\) km/h and \(v_{2}=300\) km/h, respectively.  
As shown in Figure 6, three types of pilot configurations with different numbers of pilots are considered in the evaluation of the channel estimation performance. To reduce the cost of pilots and ensure the efficiency of data transmission, the maximum number of pilot elements is limited to 96. In particular, the pilot positions are designed on the same columns for all three configuration schemes, which are the 3, 6, 9, and 12-th columns. The aim of configuring the sparse pilot pattern is to capture the fast-changing trend of the channel environment. The only difference between these three schemes is the row positions of pilots. Specifically, in the cases of pilot configurations (a), (b) and (c), the pilots are inserted every two rows, every four rows, and every eight rows, respectively.  
To evaluate the accuracy of the channel estimation, the performance metric needs to be determined at first. The normalized mean square error (NMSE) that has also been used in [6; 7; 23] is selected as the metric in this paper, and its definition can be expressed as  
\[\text{NMSE}_{h}=\frac{1}{N}\sum_{n=1}^{N}\frac{\|\mathbf{h}_{\text{ideal},n}-\text {G}(z_{n}^{*};\theta_{g}^{*})\|^{2}}{\|\mathbf{h}_{\text{ideal},n}\|^{2}}, \tag{11}\]  
where \(n\) denotes the index of test sample.  
Next, the architecture of the GAN model used in this experiment is shown in detail. The subsequent subsection gives the simulation results.  
Figure 6: Pilot configuration schemes.
## 2 Materials and Methods
### Data  
#### 2.2.1 Remote Sensing-Based Vegetation Growth Products  
Two remote sensing-based vegetation growth products were used in our study: the enhanced vegetation index (EVI) and gross primary productivity (GPP). The EVI has been widely used in ecological research [26; 27; 28], and furthermore, compared with the commonly used normalized difference vegetation index (NDVI), it has improved performance over high biomass regions such as forests [29] and reduces the influence of water vapor and clouds. We used the MODIS 500 m 16-day EVI (MOD13A1 Version 6, [[https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1](https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1)]([https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1](https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13A1)), accessed on 26 March 2021) here. A MODIS GPP product (MOD17A2H collection6, [[https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A2H](https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A2H)]([https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A2H](https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD17A2H)), accessed on 27 March 2021) with a 500-m spatial resolution and 8-day temporal resolution was employed as the GPP data source. This data was generated from an improved light use efficiency (LUE) model with the MODIS FPAR and PAR data [30].  
Both of the MODIS datasets were mosaiced and extracted via the Google Earth Engine (GEE) platform [31]. To make the time resolution comparable to the drought index, we recalculated the datasets for each month using the days as weights. For example, the first record of the EVI in 2001 was on 1 January; the second record was 16 days later, on 17 January; and the third record was on 2 February. To obtain the monthly record, we defined the day weight of the first record as 16/31 in January, the second record as 15/31 in January, and the third as 1/28 in February in a non-leap year (1/29 in February in a leap year). This is also an official processing procedure in the MODIS group for producing the monthly NDVI [32].
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
###### Abstract  
This paper presents the design and development of miniature coils for wireless power and data transfer through metal. Our coil has a total size of \(15\,\mathrm{mm}\times 13\,\mathrm{mm}\times 6\,\mathrm{mm}\). Experimental results demonstrate that we can harvest \(440\,\mathrm{mW}\) through a \(1\,\mathrm{mm}\)-thick aluminum plate. Aluminum and stainless-steel barriers of different thicknesses were used to characterize coil performance. Using a pair of the designed coils, we have developed a through-metal communication system to successfully transfer data through a \(1\,\mathrm{mm}\)-thick aluminum plate. A maximum data rate of \(100\,\mathrm{bps}\) was achieved using only harvested power. To the best of our knowledge, this is the first report that demonstrates power and data transfer through aluminum using miniature coils.  
Keywords:inductive coupling; wireless power; inductive power transfer +
Footnote †: This journal article is an extended version of the paper “Miniature Coil Design for Through Metal Wireless Power Transfer” presented in the 2021 IEEE Wireless Power Transfer Conference, San Diego, CA, USA, 1-4 June 2021.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 8 Trust Model Design Case Study: Factory 4.0 vs. Agriculture 4.0
### Network Environments  
In this section, we discuss the Factory 4.0 and Agric 4.0 environments, which are generic setups/networks that apply to many but not all Factory and Agric 4.0 environments, respectively.  
Factory 4.0 networks, in general, be in a smaller area than Agric 4.0 and are mostly indoors. The sensors have access to the infrastructure of the factory they are placed in. This can potentially give the sensors access to power sources and wired communication connections (this can all be achieved together using PoE or a similar technology). The sensors are difficult to access for unauthorized personnel due to the access and physical controls on the factory floors. Given these factors, we assume that power and communications are very reliable, and the probability of the sensors being taken down by physical attacks is low.  
Agric 4.0 networks, on the other hand, have a more challenging network environment. The area of an Agric 4.0 network may be the entire farm (which, in large farms, will be thousands of acres of land), and the sensors may also have to deal with harsher environ ments. Agric 4.0 networks can be referred to as SAGUIN (Space-Air-Ground-Undersurface Integrated Networks). Sensors may be placed in all these harsh environments to monitor things such as soil/water/air condition and livestock. Along with these challenges, sensors may also have trouble accessing power, with options such as wireless power transmission, distributed wireless power transmission and ambient energy harvesting being considered to provide power to the sensors.  
Communications in different areas of the farm need to be compatible with one another. For example, communications in the milking parlor or greenhouse may use Zigbee, or Bluetooth, as opposed to the communications in soil quality monitors throughout the land, which may need to use technologies such as LoRa for longer-range communications. These two networks most likely have to be combined in a central system for AI data analyses [53].  
Table 2 gives a comparison of the Factory and Agric 4.0 network environments.