esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Discussion and Open Issues
### Open Issues in Application-Defined IoT Protocols  
The IoT applications can be developed by using various protocols across different components of the aggregate solution. Several techniques are used that combine good attributes of application-, physical-, and network-layer protocols [9].  
1. IoT-based sensing and acquisition technologies are connected to data stores on the back end; these applications need to support big data using MongoDB, Hadoop, Postgres, or ScaleDB, a variant of MySQL for high velocity storage. These need accessing techniques development and addressing.
2. MQTT and AMQP are different technology stacks or protocols. They both use messaging bus. It is, however, possible to take MQTT and then go from service to server, but AMQP is more popular for server-to-server scenarios. Therefore, applications are to be developed by swapping MQTT and AMQP.
3. If there is an anomaly in the data or an event that requires more than a single poll, then it is possible to instantiate a streaming scenario that generates a time series or a stream of data using MQTT. Thus, bandwidth needs to be optimized for carrying such sessions.
4. IoT can be distributed in geographically separate regions and the decision making can be time consuming. A need for modular connectivity is experienced in the applications where the data from one region is in real time, as well as in delayed use in another region.
5. With continuous sessions of information exchange, the algorithms must be adaptive if battery-powered nodes are considered. Such nodes should be then treated with battery-constrained accessing protocols, such as CoAP with slight modifications.  
* Mobility, reliability, security, privacy, energy efficiency, scalability, management, availability, and interoperability issues are in the rack toward the development of smart cities.
* Recent additions of blockchain technology, AI, machine learning, non-fungible tokens (NFTs), augmented reality, massively multiplayer online gaming (MMOGs), real-time video summarization, and decision marking through big data analytics must be well-addressed in the development of smart cities.
* Certain security risks are involved with the deployment of IoT while expanding smart cities. These risks may include but are not limited to data breaches at local networks, networks being compromised by intruders, eavesdropping, network congestion by unwanted traffic, distributed denial of service (DDoS) attacks, etc. Irradicating and limiting such risks should be kept as the focal point of advanced research in expanding smart city applications and the development of Industry 4.0 applications, as well as equipment.
* In addition, big data can be collected and sold back as analytics, and yields can be increased through smart monitoring, and so on.
## 3 Results and Discussion
### Spatial Analysis of Carbon Sinks in Natural Semic Areas
The warm and humid climate in southern regions is conducive to vegetation growth, and the scenic areas are larger and more concentrated in distribution. Northwestern regions such as Tibet, Xinjiang, Qinghai, and Ningxia have higher elevations and dry and cold climates, unfavorable for vegetation growth. The scenic areas in these regions are smaller, less in distribution, and have a lower carbon sink quantity. Although Beijing, Tianjin, and Shanghai are located in the eastern coastal region with a favorable vegetation growth climate, most of their economically developed scenic areas are focused on shopping and consumption, with smaller ecological areas and vegetation coverage, resulting in a lower carbon sink quantity. Figure 2: (**a**) Nuclear density analysis diagram of scenic spots of A level and above in China; (**b**) Nuclear density analysis map of national natural scenic spots. In 2020, the vegetation carbon sink quantity in scenic areas increased by 76.6271 million tons compared to 2019. Due to reduced human activities, most provinces witnessed an increase in vegetation carbon sinks in scenic areas in 2020 compared to 2019. Regions with positive differences in vegetation carbon sinks between the two years are mainly concentrated in the central and southern regions, where natural scenic areas are prominent and heavily impacted by the COVID-19 pandemic. Among them, Henan Province, Hubei Province, and Guangxi Zhuang Autonomous Region showed the most significant increase in carbon sink quantity in their natural scenic areas. In the northwestern region, where human activities were reduced, there was also an increasing trend in the carbon sink quantity of scenic areas. However, the magnitude of the increase was relatively small. In contrast, the northern and southwestern regions experienced a negative growth in carbon sinks due to temperature, precipitation, and the carbon sink quantity required for economic development. Among them, Zhejiang Province, Inner Mongolia Autonomous Region, and Jilin Province witnessed the largest decrease in carbon sinks.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.4 Resistance to Insider Attacks  
Assuming that the \(CS\) registers adversary \(\mathcal{A}\) as a legal user, \(\mathcal{A}\) can establish authentication with \(IoT_{SL_{j}}\) and the \(CS\). In this scenario, \(\mathcal{A}\) seeks to compute the session key of another legitimate user. However, \(\mathcal{A}\) cannot obtain the credentials \(UI_{i}\) or the random numbers of other users from the messages transmitted over the public channel. Hence, our scheme provides resistance to insider attacks.
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
###### Abstract  
Invasive aquatic plant species can expand rapidly throughout water bodies and cause severely adverse economic and ecological impacts. While mechanical, chemical, and biological methods exist for the identification and treatment of these invasive species, they are manually intensive, inefficient, costly, and can cause collateral ecological damage. To address current deficiencies in aquatic weed management, this paper details the development of a small fleet of fully autonomous boats capable of subsurface hydroacoustic imaging (to scan aquatic vegetation), machine learning (for automated weed identification), and herbicide deployment (for vegetation control). These capabilities aim to minimize manual labor and provide more efficient, safe (reduced chemical exposure to personnel), and timely weed management. Geotagged hydroacoustic imagery of three aquatic plant varieties (_Hydrilla_, _Cabomba_, and Coottail) was collected and used to create a software pipeline for subsurface aquatic weed classification and distribution mapping. Employing deep learning, the novel software achieved a classification accuracy of 99.06% after training.  
Keywords:autonomous vehicles; robotics; machine learning; deep learning; image preprocessing; hydroacoustic sensing +
Footnote †: journal: Applied
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## 4 System Implementation
### Software
#### 4.2.1 Prerequisites  
As shown in Table 5, a Windows 10 PC with IAR Embedded Workbench for 8051 and Visual Studio 2017 Community (Enterprise and Professional version are also supported) are leveraged to develop applications for CC2530 ZigBee nodes, MT3260 development kits and Windows desktop respectively. Azure Sphere software development kit (SDK) Version 18.11 [46] and Microsoft NET Framework Version 4.7.0 are also installed as indispensable extensions for Visual Studio 2017 Community. In order to establish connection with Azure IoT Hub, Microsoft.Azure.Devices and WindowsAzure.ServiceBus NuGet packages are included in the windows-form based project.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Value** & **SFR 1 or Function** \\ \hline Radio Channel & 2405 MHz & FREQCTRL.FREQ[6:0] \\ Receiver Sensitivity & \(-85\) dBm & CCACTRL0[7:0] \\ Transmit Power & 4.5 dBm & TXPOWER[7:0] \\ Network Topology & Star & GenericApp.Init \\ Light AD Channel & AIN6, P0\_6 & ADCCON3[7:0] \\ Gas AD Channel & AIN7, P0\_7 & ADCCON3[7:0] \\ UART Selection & 0 & POSEL, U0 CSR \\ UART Baud Rate & 9600 & U0 GCR, U0 BAUD \\ \hline \hline \end{tabular}  
* Special Function Register.  
\end{table}
Table 4: Key settings for ZigBee device.
# CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
## 3 Method  
### Network Architecture  
As presented in Figure 2, the proposed network consists of four parts: feature extraction, spherical cost volume construction, cost regularization, and cost regression. A three-stage cascade architecture is leveraged to construct the multi-scale spherical cost volume for a coarse-to-fine depth estimation. Furthermore, the depth range of spherical sweeping is adjusted adaptively based on the prediction and uncertainty from the previous stage.  
**Feature Extraction.** We extract input image features using ResNet50 [30] and FPN [31], specifically the top-level FPN features with shape \(\frac{1}{2}H\times\frac{1}{2}W\times C\), where \(H\) and \(W\) are input height and width.  
**Spherical Cost Volume Construction.** The sweeping parameters in stage \(t\) are determined using Equations (10)-(13), where we project input image features onto equirectangular features based on the hypothetical spheres and camera parameters, to construct the initial spherical cost volume. As the stages progress, the resolution of equirectangular features increases, while the interval and number of hypothetical spheres decrease, as shown in Table 1.  
**Cost Regularization.** We regularize the initial spherical cost volume at each stage using the cost regularization network from [11]. Each stage has a separate cost regularization network.  
**Cost Regression.** We use soft-argmin to predict the proper sphere index \(s_{t}^{pred}\), which is determined as  
\[\begin{split}\mathbf{p}_{t}&=softmax(SC_{t}^{*})\\ &=\{p_{t,min},\dots,p_{t,j},\dots,p_{t,max}\}\\ s_{t}^{pred}&=\sum_{j=S_{t,min}}^{S_{t,max}}p_{t,j}*j \end{split} \tag{1}\]  
where \(SC_{t}^{*}\) and \(\mathbf{p}_{t}\) are the regularized spherical cost volume and the probability for one of the hypothetical spheres in stage \(t\), respectively.  
As shown in the light blue rectangular region in Figure 2, we utilize an upsampling layer (shown in Figure 3a) to increase the resolution of the regularized spherical cost volume. Then, the regression of the refined volume yields the basic sphere for sweeping, and an uncertainty net (shown in Figure 3b) is used to predict the uncertainty, thereby determining the sweeping interval and range for the next stage.  
**Loss Function.** At each stage, all our network results are used for supervised training. The loss function uses SmoothL1 Loss, which is determined as  
\[loss=\sum_{t=1}^{3}w_{t}\times SmoothL1 Loss\Big{(}s_{t}^{pred},s_{t}^{gt}\Big{)} \tag{2}\]  
\(s_{t}^{gt}\) and \(w_{t}\) are the ground truth (GT) sphere index and the weight corresponding to the loss in stage \(t\).
###### Abstract  
Sound speed reconstruction has been investigated for quantitative evaluation of tissue properties in breast examination. Full waveform inversion (FWI), a mainstream method for conventional sound speed reconstruction, is an iterative method that includes numerical simulation of wave propagation, resulting in high computational cost. In contrast, high-speed reconstruction of sound speed using a deep neural network (DNN) has been proposed in recent years. Although the generalization performance is highly dependent on the training data, how to generate data for sufficient generalization performance is still unclear. In this study, the quality and generalization performance of DNN-based sound speed reconstruction with a ring array transducer were evaluated on a natural image-derived dataset and a breast phantom dataset. The DNN trained on breast phantom data (BP-DNN) could not reconstruct the structures on natural image data with diverse structures. On the other hand, the DNN trained on natural image data (NI-DNN) successfully reconstructed the structures on both natural image and breast phantom test data. Furthermore, the NI-DNN successfully reconstructed tumour structures in the breast, while the BP-DNN overlooked them. From these results, it was demonstrated that natural image data enables DNNs to learn sound speed reconstruction with high generalization performance and high resolution.  
break cancer; deep learning; natural image; numerical simulation; ultrasound computed tomography 2024
## 3 Evaluation Results and Considerations  
### Performance of Double-Sided Bonding Waterproofing Sheet  
#### 3.1.1 Basic Property Test Results  
The basic evaluation results of a double-sided bonding waterproofing sheet are shown in Table 7 below. As a result of the evaluation, it is confirmed that the corresponding performance of the newly proposed waterproofing layer shows improved performance over quality standard values of nine evaluation items of KS F 4931-\"17\" \"Sheet Waterproofing Material for Concrete Bridge Deck.\" Most of the performance of the proposed double-sided bonding waterproofing sheet more than satisfies the quality standard of KS requirement. In Table 7, under the column 'test results,' arrows (\(\uparrow\)) mark the increase in the value in terms of percentage ratio from the minimum requirement outlined in the test standard. The tensile strength value increases by about 182%, elongation rate by about 357%, the chloride ion penetration resistance by 14%, and the high-temperature stability by 45%. Furthermore, there are no other defects found in other parameters including water permeability, impact resistance, fatigue resistance, crack resistance, etc.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Items** & & **Quality Standards** \\ \hline \multirow{4}{*}{Shear adhesive performance} & Shear adhesive strength (MPa) & \(-20\)\({}^{\circ}\)C & \textgreater{}0.80 \\ \cline{2-4}  & Shear adhesive strength (MPa) & \(20\)\({}^{\circ}\)C & \textgreater{}0.15 \\ \cline{2-4}  & Shear adhesive deformation (\%) & \(-20\)\({}^{\circ}\)C & \textgreater{}0.5 \\ \cline{2-4}  & Shear adhesive deformation (\%) & \(20\)\({}^{\circ}\)C & \textgreater{}1.0 \\ \hline \multirow{2}{*}{Tensile adhesive strength (MPa)} & \(-20\)\({}^{\circ}\)C & \textgreater{}1.2 \\ \cline{2-4}  & \(20\)\({}^{\circ}\)C & \textgreater{}0.6 \\ \hline \multicolumn{4}{l}{Tensile adhesive strength after 7 days of water immersion (\%)} & \(20\)\({}^{\circ}\)C & \multicolumn{2}{c}{70\% or more before water immersion} \\ \hline \hline \end{tabular} Note: According to the evaluation of the physical properties of the construction method, the upper pavement layer is made of asphalt concrete and concrete differently during the test piece production process, and the test is carried out.  
\end{table}
Table 6: Evaluation criteria and quality standards of waterproofing sheet.
# Use of Hydrogels to Regulate Orthodontic Tooth Movement in Animal Models: A Systematic Review
## 4 Discussion
### Biological Effects
#### 4.2.2 Acceleration  
Acceleration of OTM is one of the main objectives in recent years, since it not only determines the duration of treatment, but also reduces its possible adverse effects and is one of the most frequent requests from patients. Physiologically, the speed of tooth movement reflects the rate of bone turnover and regeneration [6].  
Several active principles have been used with the aim of accelerating OTM but only three studies have used a hydrogel as a vehicle. In this sense, one of the drugs evaluated was sclerosin, previously reported to be able to inhibit osteoblast proliferation, since the expression of the SOST gene (which encodes the synthesis of sclerostin) is limited to skeletal tissue, almost exclusively to the osteocyte cell lineage, which prevents the proliferation of osteoblasts, which, in theory, could favor OTM [38; 43]. This is consistent with the results obtained by [PERSON] et al., where local injection of sclerostin into the alveolar bone on the compression side increased tooth movement and osteoclastisensis in rats [21].  
On the other hand, [PERSON] et al. used a hydrogel loaded with PTH [23]. Parathyroid hormone (PTH) mobilizes calcium from bone by activating osteoclasts, leading to bone resorption [44]. Application of the PTH-loaded hydrogel promoted OTM acceleration up to 1.6-fold compared to the control [23], while [PERSON] et al., in turn, found that the RANKL formulation group had significantly (129.17%) higher tooth movement (\(p<0.05\)) compared to the no formulation group and 71.8% more than the placebo formulation [22]. The chemical nature of the active ingredient used to regulate OTM, the consequences at the systemic level and its possible adverse effects should be considered; however, at the moment, the results are promising and evidence that hydrogels are possibly a suitable alternative to deliver active ingredients for orthodontic purposes.
# In Situ Measurement of Alkali Metals in an MSW Incinerator Using a Spontaneous Emission Spectrum
## 4 Results and Analysis  
### MSW Flame Spectra  
Figure 3 shows three examples of the spontaneous emission spectra of the MSW flame in the incinerator from 500 nm to 900 nm. As shown in Figure 3a, some emission peaks or lines at wavelengths of 589, 765.9, 769.3, 779.4, and 793.8 nm were observed. According to the National Institute of Standards and Technology (NIST)'s Atomic Spectra Database, these emission peaks represent the atomic emission lines of Na (588.995 nm, 589.592 nm), K (766.490 nm, 769.896 nm), and Rb (780.027 nm, 794.760 nm), respectively. Since the spectral resolution of this spectrometer was low, only 0.8 nm, the two emission lines of Na at approximately 589 nm were not distinguished. Generally, vegetable and garden wastes form a significant part of household trash, so the composition of the MSW contains Na and K. However, the presence of Rb in the MSW is seldom reported in the literature. It may come from small electronic components in the MSW. As the absolute spectral intensity distribution of the MSW flames depicted in Figure 3b shows, it can be seen that the peak intensities of the Rb emission lines are too weak to be identified. Continuous emission is mainly due to thermal radiation from soot particles in the MSW flame.
## 3 Results
### Blood Perfusion and Animal Condition  
To determine whether dietary consumption of TTK (150 or 450 mg/kg/day) could stimulate vascular endothelial cells to blood circulation in the context of hind-limb ischemia. No abnormal weight gain, daily food intake and other findings in our animal model have been observed, and uterine weight were not changed to estimate estrogen activity of TTK under our animal condition (Table 1). However, uterine weight was higher in E\({}_{2}\) treatment group compared the other groups.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multicolumn{2}{c}{**Body Weight (g)**} & \multicolumn{1}{c}{**Body Weight**} & \multirow{2}{*}{**Daily Food**} & \multirow{2}{*}{**Uterine**} \\ \cline{2-2} \cline{5-6}  & **Baseline** & **After 28 Days** & **Gain (g)** & **Intake (g)** & **Weight (mg)** \\ \hline
**OH** & 19.80 \(\pm\) 0.49 & 23.06 \(\pm\) 1.22 & 3.27 \(\pm\) 1.36 & 3.72 \(\pm\) 0.41 & 17.48 \(\pm\) 3.61 \\
**OH + SV** & 20.11 \(\pm\) 0.88 & 23.00 \(\pm\) 1.68 & 2.89 \(\pm\) 1.99 & 3.91 \(\pm\) 0.96 & 16.34 \(\pm\) 3.31 \\
**OH + E2** & 19.98 \(\pm\) 0.73 & 22.51 \(\pm\) 0.82 & 2.53 \(\pm\) 0.63 & 3.84 \(\pm\) 0.62 & 265.10 \(\pm\) 176.02 a  \\
**TTK 150** & 20.48 \(\pm\) 0.63 & 23.36 \(\pm\) 1.69 & 2.88 \(\pm\) 0.88 & 3.39 \(\pm\) 0.40 & 14.79 \(\pm\) 3.09 \\
**TTK 450** & 20.41 \(\pm\) 1.26 & 24.19 \(\pm\) 1.63 & 3.78 \(\pm\) 1.86 & 3.73 \(\pm\) 0.52 & 15.83 \(\pm\) 2.62 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Body weight and daily food intake of hind-limb ischemic mice.  
Figure 3: Effect of TTK on in vitro tube formation in HUVECs. **(A)** Cells were treated with different concentration of TTK (10, 50 mg/mL), 50 ng/mL VEGF, or 1 nM 17ß-estradiol, 10 mM Simvastatin on matrigel. After 12 h of incubation, tube formation was observed using a microscope (magnification, 4\(\times\)) and photographed. **(B)** The tube formation was analyzed by measuring each branch interval value using Image J analyzer (angiogenesis ability). Data are expressed as the mean \(\pm\) SEM. of three experiments. a \(p<0.05\), b \(p<0.01\), c \(p<0.001\) vs. control, d \(p<0.01\), e \(p<0.001\) vs. Simvastatin.  
The flow rate of peripheral Blood was observed at the lesion of hind-limbs in all surgical groups after ischemic surgery (0 d) (Figure 4). At day 7 after surgery, the rate of blood perfusion in ischemic hind-limbs had significantly ameliorated in the E\({}_{2}\) and TTK 450 groups than vehicle group, however, there was no therapeutic effect in other experimental groups. At 14 d and 28 d (after ischemia surgery), blood perfusion rate was significantly improved in all treatment groups. Although no significant difference in blood flow between the vehicle and the TTK 150 group was found in 7 d following ischemic surgery, however, the high dose of TTK 450 significantly improved the signs of hind-limb ischemia (Table 2).