esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Autonomous Forklifts: Economic Impact and Ethical Considerations  
While an in-depth examination of empirical validation, economic impact and ethical considerations of autonomous forklifts is beyond the scope of this research, their importance warrants a brief overview. Autonomous forklifts leverage advanced robotics, AI and sensor technologies to revolutionize material-handling processes. Rigorous testing in real-world conditions has demonstrated their reliability, efficiency and safety, as well as their adaptability to diverse warehouse layouts and seamless integration with existing logistics systems.  
From an economic perspective, these technologies offer substantial benefits, including reduced labor costs, enhanced operational efficiency and minimized inventory damage, significantly improving profitability and competitiveness. However, ethical considerations, such as potential job displacement, equitable distribution of economic benefits and ensuring safe human-robot interactions, remain critical. Addressing these challenges is vital to achieving the sustainable and socially responsible integration of autonomous forklifts into supply chain operations.
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 5 Experiment and Analysis
### Comparative Experiments and Analysis  
In order to fully prove the superiority of our proposed LSI model-based metadata retrieval method, the following comparative experiments and analysis were carried out. Each type of comparative experiment contained six groups, and each group of experiments was carried out under the same data volumes and the same query parameters as the LSI model-based metadata retrieval experiments, using 20 average response time measurements [45].
## 2 Materials and Methods
### The Framework of Proposed Fusion Method
#### 2.3.4 Image Quality Metrics  
In cellular imaging, cellular information is more important in the fused image. Therefore, background information should be ignored when evaluating the quality of the fused image. To remove the background, a method based on the Otsu algorithm is used. Otsu [16] is an image segmentation method based on the principle of inter-class variance maximization, which is shown as:  
\[T_{otsu}=\text{arg}max[\sigma(T)],0\leq T\leq L-1 \tag{13}\]  
where \(T\) is the gray level and \(\sigma(T)\) denotes the inter-class variance of \(T\). Equation (13) shows that the \(T_{otsu}\) maximizes the inter-class variance.  
Due to there are not just one cell in the image, a single Otsu segmentation cannot accurately obtain all cell boundaries. To solve this problem, the improved Otsu algorithm was proposed. To get the ROIs of different cell parts, the morphology image is segmented with the Otsu algorithm repeatedly, and the parts below the threshold are discarded until a relatively consistent region is obtained. Then, dilate the part to include the cell boundary. The detail steps are described as follows:  
* Initialize \(T_{stop}\),\(T_{last}=0\), \(T^{*}=0\);
* Repeat \(T^{*}=T_{last}\) \(h=\text{histogram}\left(I_{morp}\geq T^{*}\right)\) /* get the histogram of the part \(I_{morp}\) greater than the threshold*/ \(T^{*}=\text{Otsu}(h)\) /* use otsu algorithm to get the new threshold */ Until \(|T^{*}-T_{last}|\leq T_{stop}\)
* ROI = \(I_{morp}\geq T_{last}\)
* ROI=dilate (ROI).  
Figure 3a shows the multiple peaks in the image histogram, and Figure 3b (green) shows the remaining background areas after only once Otsu segmentation. But, with continuously segmenting the remaining regions, the background part is discarded, and the ROI converges to the cell regions.  
Fusion steps:  
All the fusion steps are shown in Figure 2.  
* Step 1: Decompose the morphology and phase images using 3 level NSST to obtain their low-frequency sub-bands {\(L_{morp}\), \(L_{phase}\)} and a series of high-frequency sub-bands {\(H_{morp}^{K,J}\), \(H_{phase}^{K,J}\)} at each \(K\)-scale and \(J\)-direction.
* Step 2: Use CLAHE for the source images, and use Equations (8) and (9) to calculate \(S_{morp}\) and \(S_{phase}\). Get the weight map \(W\) using Equation (10), and fuse the low-frequency sub-bands using Equation (11) to obtain \(L_{fusion}\). The weight coefficient in our experiment is set as \(\alpha=2.0\) and the size of the region to calculate the variance is \(5\times 5\).  
* Step 3: Equation (12) is utilized to deal with the high-frequency sub-bands.
* Step 4: Perform the inverse NSST of the low-frequency and the high-frequency sub-bands to obtain the fused image.
* Step 5: Segment the image ROI and evaluate the results.
## 2 Materials and Methods
### Apparatus and Analytical Procedures  
HPLC-MS. Liquid chromatography analyses were performed on an Agilent 1260 Infinity II Chromatograph (Agilent, CA, USA) coupled with an Agilent InfinityLab MSD XT mass spectrometer (Agilent, CA, USA). This setup featured an electrospray ionization (ESI) source operated with an analyzer. The ESI system employed a 4.5 kV (positive polarity) spray voltage and a heated capillary temperature of 200 \({}^{\circ}\)C. The sheath gas and the auxiliary gas (nitrogen) flow rate were set to 0.75 and 1.2 L/min, respectively. The analytical parameters were based on the article from the same authors [26] with some modifications. Mass chromatograms were obtained in both scan mode (50-600 \(m/z\)) and single-ion monitoring mode using the most abundant ion that corresponded to either pseudo molecular ion: [M + H]+ or [M-H\({}_{2}\)O + H]+. [M + H]: 435 \(m/z\) for FN and 411 \(m/z\) for FM. [M-H2O + H]: 357 \(m/z\) for MP, 391 \(m/z\) for BC, 413 \(m/z\) for BD, 457 \(m/z\) for BM 17-V, 503 \(m/z\) for BCDP, and 373 \(m/z\) for DM. An XSELECT CSH C18 column (150 mm \(\times\) 2.1 mm) was used for the chromatographic analyses. The mobile phases included A (0.1% formic acid in acetonitrile), B (0.1% formic acid in water) with a linear gradient from A-B 25:75 (\(V/V\)) to A-B 95:5 (\(V/V\)) over 30 min, and a flow rate of 0.3 mL/min. A 3 min re-equilibrium time was considered between runs, and the injection volume was 10 \(\upmu\)L.  
ELISA. A commercial corticosteroid group kit was used to detect steroids. The 96-well plate was pre-coated with anti-flumenthosene antibody. The kit was kept in the fridge below 5 \({}^{\circ}\)C until use and the entire procedure was conducted at room temperature. A total of 20 \(\upmu\)L of sample solutions were added to the microwells. The conjugate enzyme was diluted 1:180 in a PBS-based buffer. A total of 180 \(\upmu\)L of the diluted conjugate was added to the sample solutions in each well. The plate was incubated for 45 min at room temperature. The liquid was removed from the wells and each well was washed three times with 300 \(\upmu\)L of washing solution diluted 1:10 in distilled water. A total of 150 \(\upmu\)L of substrate were added to each well, and the plate was incubated for 30 min. A total of 50 \(\upmu\)L of stop solution were added and the absorbance was measured at 650 nm using a spectrophotometer. For each analysis, a blank sample (corresponding to the maximum signal obtainable in the absence of the target analyte) and the signal related to the absorbance of the colorimetric substrate were measured.
## 2 Materials and Methods
### Image Processing
#### 2.2.1 Features Detection and Matching  
Agisoft Metashape starts the image alignment process by executing the scale invariant feature transform (SIFT) algorithm [12; 13; 14]. The SIFT algorithm detects the features of each image and stores them as keypoints in a database. As the algorithm processes a new image, it recognizes its features and compares them to those already stored in the database using their descriptors. This results in finding common features that are considered as matching points (i.e., tie points) among the overlapping images. The limit for the number of points to be detected per image was set to 100,000 points for all data sets. Out of these detected features, only the matched ones in two or more images were reconstructed as 3D points. The limit of the matching points was set to 50,000, and the alignment accuracy was set to \"high\" for all data sets.
## Materials and Methods
### Volatile Compound Collection and Analysis  
The dynamic headspace volatiles from eggplant, cucumber and cotton were collected and analyzed by a gas chromatography-mass spectrometry system (GC-MS). A solid-phase microextraction (SPME) fiber that was attached with polydimethylsiloxane-divinylbenzene (PDMS-DVB, 65 mm; Supelco, Bellefonte, PA, USA) was used to collect plant volatiles [20]. Devices were installed as described above but not connected to the Y-tube, and finally, the SPME fiber absorbed the odors from the upper exits of volatile source bottles. Prior to volatile collection, each plant was sealed for 45 min in volatile source bottles to increase the concentration of volatiles. The volatile collection had five repetitions for each odor source using a new plant sample.  
The SPME fiber was inserted in a gas chromatograph (GC) injection port at 250 \({}^{\circ}\)C for 30 min for activation. After 30 min, the SPME fiber was inserted into the volatile source bottles with plants to absorb dynamic headspace volatiles. At the same time, a filter paper (5 \(\times\) 2 mm) with 25 \(\upmu\)L hexane solution of heptyl acetate at 10 ng/\(\upmu\)L was added as an internal standard. The collection of volatiles was maintained for 45 min according to preliminary results. After 45 min, the SPME needle was carefully taken out and immediately inserted into the GC-MS system, and desorption was carried out for 5 min. The GC-MS analysis of headspace extracts was carried out on a GC (TRACE 1310, Thermo Fisher Scientific, Waltham, MA, USA) connected with an MS (ISQ Single Quadrupole MS, Thermo Fisher Scientific) fitted with a DB-17 MS UI capillary column (30 m, 0.25 mm i.d., 0.25 \(\upmu\)m film, Agilent). GC conditions were consistent with those in [20]. The initial GC oven temperature was set to 40 \({}^{\circ}\)C for 2 min, ramped up at 8 \({}^{\circ}\)C min\({}^{-1}\) to 280 \({}^{\circ}\)C and held for 5 min. Helium flow was carried out at a rate of 1.0 mL/min, with a transfer line at 280 \({}^{\circ}\)C, ion source temperature of 280 \({}^{\circ}\)C, electron impact ionization energy of 70 eV, emission current of 25 \(\upmu\)A and a scan range from 45 to 500 amu.  
Individual volatile compounds were tentatively identified by comparing the fragment ions to the NIST 2011 (National Institute of Standards and Technology, Washington, DC, USA) mass spectral library with the Xcalibur program (Ver.2.1, Thermo Electron Corporation, San Jose, CA, USA). Part of the result depended on a retention index, which was calculated from a homologous series of n-alkane standards (C7-C40, Sigma, Louis, MO, USA) and then compared to those of components from the literature.
# Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas
## 7 Conclusions  
This paper presents a tightly-coupled GNSS/vision integration approach. Vehicle motion was estimated by using the visual odometry technique implemented based on feature points detected in successive images acquired by a camera system mounted on the top of the car. An NLOS effect mitigation algorithm tailored for harsh environments and based on LOS satellite selection was also introduced. To improve the LOS satellite selection method, an appropriate image segmentation method using ASF filtering and the flood-fill approach was used to classify image contents into sky and non-sky. Finally, the selected LOS satellites, as well as the vision measurements were integrated in a tightly-coupled manner, taking into account the scale factor since a monocular vision system was used and these types of systems are able to determine the relative translation up to a scale factor only. Results show improvements of 82% and 20% compared to loose coupling of GNSS and visual odometry and to a GNSS-only Kalman filter navigation system, respectively, suggesting that both LOS satellites' selection and the visual odometry were beneficial. However, some portions of the travelled path were challenging, and the reasons why the algorithm could be influenced were discussed. One of the solutions to stops under bridges where our vision system setup could face challenges (as most of the systems using a camera in a dark environment) could be adding a sensor that is able to provide relative position such as INS or absolute position such as a 3D Building Model (3 DBM). In future work, we will use a 3 DBM to improve the proposed method. Moreover, instead of employing the commonly-used Kalman filtering technique, the Multiple Model Particle Filter (MMPF), which has proven to better characterize the position errors and estimate the multipath effects [66; 67], will be investigated.  
The first author would like to thank [PERSON] for his guidance and suggestions during the early development of this work. The data used in this paper can be found in [68].  
[PERSON] conceived of and designed the algorithms and performed the related experiments. [PERSON] and [PERSON] analysed the data and wrote the paper.  
The authors declare no conflict of interest.
## 3 Proposed Method
### DFT Spreading  
The complex input data \(x_{m,n}\) after passing through the FFT block are expressed as follows:  
\[X_{m,n}=C_{n,m}+jD_{n,m} \tag{7}\]  
The real part \(C_{n,m}\) and imaginary part \(D_{n,m}\) are split and multiplied by phase rotation factors, which are set such that the DFT spreading achieves the minimum PAPR value. In the proposed method, we selected the following phase factor pattern [12]:  
\[\alpha_{n,m}=j^{n+2m} \tag{8}\]  
\[\beta_{n,m}=(-1)^{m}j(-j)^{n} \tag{9}\]  
Using the phase factors (8) and (9), we can satisfy a condition referred to as the identical time-shifted pattern (ITSM), which has better PAPR performance than the conventional DFT spreading method. The DFT-spread FBMC/OQAM signal equation is as follows [12]:  
\[s(t)=\sum_{n=0}^{N-1}\sum_{m=0}^{M-1}(-1)^{m}\bigg{\{}C_{n,m}h(t-mT)+jD_{n,m}h \bigg{(}t-mT-\frac{T}{2}\bigg{)}\bigg{\}}e^{ip^{2m}\frac{m}{T}(t+\frac{T}{2})} \tag{10}\]  
Although ITSM-conditioned DFT spreading reduces the PAPR, it is only a marginal reduction compared to that of the DFT spreading in OFDM systems. In [12], the authors proposed a method that generates four different versions of the \(m^{th}\) data blocks that are identical, but with different phase shifts; further, the data block that produces the least PAPR was selected. In the proposed method, we combine a modified PTS method with ITSM-conditioned DFT spreading to achieve better PAPR reduction.
# Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems
## 3 Software-Defined Network (SDN) for Transactive Energy in SMGs
These security protocols are not limited to specific network architectures and can be used in SDN architectures. Furthermore, these security protocols are static by nature. Data encryption is one of many methods to provide network security. [PERSON] et al. [34] provide a practical example of how SDN can support real-time reconfiguration of the control plane to isolate compromised devices during cyber-attacks. Their design monitors the network with the expected traffic patterns. When their system detects a traffic pattern that differs from the expected model, their algorithm removes the flow entry of the affected element. [PERSON] et al. [35] confirm that the dynamic programmability in SDN guarantees timely detection and rapid response to the impact of malicious attacks on the Smart Microgrid System. Their design follows a three-step approach:  
* Step 1: Detect and isolate the compromised element. * Step 2: Eliminate traffic from the malicious source closer to the source, and
* Step 3: Ensure connectivity of sensors to the network. Encryption on the SDN network's control and data path improves the SDN solution's security for TESMS.
# Methodology for Evaluating the Generalization of ResNet
## 5 Experimental Analysis
### Robustness Testing
#### 5.4.2 Bounding Box Replacement Testing  
To ease the manual bounding box annotation efforts, we employed a saliency detection-based approach to generate pseudo-labels, thereby eliminating the need for manual editing. Specifically, we utilized the MCCL algorithm [29] to produce a saliency map, which was subsequently binarized to create a mask for IoU (Intersection over Union) calculations. To calculate the IoU in this case, we denoted the non-zero portion of the saliency map as \(A_{G}\) in Equation (4). Figure 6 illustrates the method's effectiveness through a comparison of the original image, labeled frames, and the corresponding saliency map.  
Table 7 presents the correlation between generalization gap and IoU derived from the manually labeled bounding boxes and automatically generated saliency maps for the ResNet18 models. The findings indicate that a robust correlation persisted between generalization gap and IoU, albeit slightly diminished when using saliency maps compared to manual bounding boxes. This attenuation is attributed to the inherent limitations of saliency maps in precisely delineating the spatial extent of the subject category. As illustrated in Figure 6, while columns a and b exhibited accurate saliency mapping, the precision deteriorated across columns c, d, and e, culminating in column e's failure to identify the objects within the image.  
From the results of this experiment, it can be seen that the use of pseudo-labels (saliency maps) to calculate the ratio of task-relevant features is still valid. This alleviates the dependence on manually labeled bounding boxes.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## ZigBee (IEEE 802.15.4)  
ZigBee is an open wireless mesh network standard based on the IEEE 802.15.4 standard. It is a short range, low data rate, and energy efficient technology. ZigBee operates on four different frequency bands, 868 MHz (20 kbps per channel), 915 MHz (40 kbps per channel), and 2.4 GHz (250 kbps per channel) [85; 86]. ZigBee has mesh capabilities and a coverage range from 10 to 100 m [54]. Mesh networks are decentralized, where each node are self-manageable, and can re-route, and connect with new nodes when needed. This makes ZigBee well suited for use in HAN applications such as remote monitoring, home automation, consumer electronics and smart meter readings [87; 88]. A ZigBee mesh network is constructed of three different types of nodes; Coordinator, Router, and End-Device. ZigBee uses AES-128 access control to manage a high level of security. Because of the low transmission power level, this technology is vulnerable to multipath distortion, noise and interference [86]. ZigBee operating on the 2.4 GHz band is also affected by interference from technologies such as Wi-Fi, USB, Bluetooth, and microwave ovens as these operate on the same unlicensed frequency band [89; 90; 91].
## 3 System Design
### Deployment Details and Implementation Environment
For auxiliary communication between the HTTP service and nodes, the system employs the HTTP protocol to carry certain management functions, including IP list maintenance, task scheduling, and link status queries. Concurrently, WebSocket long connections are utilized for real-time dissemination of link topology changes and abnormal switching signals, ensuring physical isolation and logical synchronization between the data and control planes. Regarding node management and secure deployment, the system assigns an independent listening port and a local buffer directory to each client. All critical pro cesses, such as registration, chain reconstruction, fault recovery, and fallback switching, are meticulously logged to facilitate problem tracing and engineering debugging. For deployment, it is recommended that each venue node operate on Windows 10/11 or a mainstream Linux distribution environment, with Python 3.12 and FFmpeg 7.1.1 installed. Network security groups should be configured to permit necessary UDP/SRT ports. In actual operation, the main venue server is typically deployed on a publicly accessible node, while sub-venue clients can be deployed on various local or public network nodes. Both IPv4 and IPv6 network environments are supported, ensuring compatibility with a wide range of practical application scenarios. To further enhance security and operational efficiency, the system supports multiple levels of permission assignment and key configuration. All SRT sessions can enable AES-128/AES-256 encryption, allowing users to set session keys according to their security requirements. Log directories, cache files, and recording outputs can be configured with persistent paths to meet data backup and compliance needs. Looking ahead, the system is also designed to integrate with cloud platform automation deployment solutions. This will facilitate the rapid engineering rollout of large-scale, multi-venue chain broadcasting in diverse application scenarios such as education, conferences, and sporting events.
## 5 History of MEMS AE Sensors, 1998-2020
[PERSON] et al. [50] designed the first flexible MEMS AE sensor by depositing PZT nanofibers on a composite membrane using the electrospinning method. The sensor exhibited anisotropic sensitivity, which could be advantageously used to understand the direction of the AE signal. While signal-to-noise is a concern, being a flexible geometry increases its use in conformal surfaces and weight-restricted applications. [PERSON] and [PERSON] [51] designed a low-frequency (~78.9 kHz resonance) MEMS AE sensor for sound waves by depositing a 3.0 \(\upmu\)m-thick ZnO layer on a square silicon diaphragm. They recently studied the optimized fabrication and annealing temperature [52]. [PERSON] et al. [39] designed multifrequency MEMS sensors by depositing a 0.5 \(\upmu\)m-thick AlN piezoelectric layer on silicon and utilizing PiezoMUMPs provided by MEMSCAP. A low-frequency sensor tuned to 35 kHz was designed by utilizing the entire silicon substrate as a mass, which reduced the total size of the sensor to less than 1 mm. The sensors exhibited a very high quality factor, which could be advantageous for source localization in dispersive media by detecting a single frequency and reducing the effect of low-frequency friction emissions by behaving as a mechanical filter. They showed that amplitude-to-size ratios of an R15 sensor (resonant frequency near 150 kHz, manufactured by MISTRAS Group Inc., Princeton Junction, NJ, USA) and piezoelectric MEMS AE tuned near 200 kHz are 1.24 mV/mm\({}^{3}\) and 99.59 mV/mm\({}^{3}\), respectively. Table 4 summarizes the piezoelectric MEMS AE sensors using silicon as a microstructure and their specifications. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Design Geometry** & \begin{tabular}{c} **Frequency** \\ **Range** \\ \end{tabular} & \begin{tabular}{c} **AE** \\ **Demonstration** \\ \end{tabular} & \begin{tabular}{c} **Other** \\ \end{tabular} & \begin{tabular}{c} **Reference** \\ \end{tabular} \\ \hline Square plate with & & & \\ L-shaped spring elements at middle & 100–500 kHz & & \begin{tabular}{c} Crack initiation \\ and growth at \\ weld metal \\ \end{tabular} & Packaging and shielding need improvements. & [25] \\ \hline Square plate with & & & \\ L-shaped spring elements at edges & 185 kHz & PLB stimulation & In-plane sensing studied. & [47] \\ \hline Thick metal layer & 68 kHz and 149 kHz & Steel ball impact, laser ultrasound & SNR ratio similar to conventional piezoelectric AE sensors & [26] \\ \hline Circular geometry & \
\begin{tabular}{c} Resonant in the range of \\ 80–2000 kHz \\ \end{tabular} & PLB stimulation &
\begin{tabular}{c} Studied the geometric variables of \\ capacitive MEMS and \\ multifrequency aspects \\ \end{tabular} & [24] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Capacitive MEMS AE sensors and their specifications.