esa-sceva/satcom-chunk-collection huggingface dataset:
# Car-Sense: Vehicle Occupant Legacy Hazard Detection Method Based on DFWS
## 3 Preliminaries  
### CSI Signals from ESP32  
CSI (Channel State Information) is a metric that describes signal propagation in a multipath environment and contains information about the changing channel environment in wireless signal propagation. The ESP32 Wi-Fi driver supports multiple 802.11 b/g/n protocols and can work in workstation mode or ap mode. When packets are transmitted from TX to RX, the channel frequency response contained in the CSI in ESP32 can be calculated. Each CFR register has two bytes of signed characters, the first part is the imaginary part, and the second part is the real part. Moreover, three types of CFR can be obtained from the CSI information, which are legacy long training field (LLTF), high throughput LTF (HT-LTF), and spacetime block code HT-LTF (STBC-HT-LTF) [12]. The ESP32 supports Wi-Fi bandwidth of either HT20 or HT40, but not both, in the default case of HT40. There are 128 subcarriers with complete signal gain, but the number of effective subcarrier bars is only 114, and the amplitude of each subcarrier is not the same. This is demonstrated by [13].  
\[H(f)=[H(f_{1}),H(f_{2}),\cdots,H(f_{n})],n\in[1,N] \tag{1}\]  
where \(H(f_{n})\) is the CSI on the nth subcarrier, and \(N\) is the number of valid subcarriers 114. The 114 CSI subcarrier variation curves caused by human activity within the ESP32 radio signal range are referred to in Figure 2.  
In the presence of influencing object activities within the Wi-Fi signal coverage, the Channel Impulse Response (CIR) [14] on different paths in the wireless channel will be forced to change accordingly. CIR is an extension of CSI in the time domain, and assuming linear time invariance, the channel impact response can be expressed as:  
\[h(\omega)=\sum_{i=1}^{N}a_{i}e^{-\beta i}\sigma(\omega-\omega_{i}) \tag{2}\]  
where \(a_{i}\) is the amplitude decay of the ith path, \(\theta_{i}\) is the phase shift of the \(i\)th path, \(\omega_{i}\) is the time delay of the \(i\)th path, \(N\) is the total number of propagation paths, and \(\sigma(\omega)\) is the Dirichlet pulse function.  
In the IEEE 802.11 n protocol, CSI can be extracted from all carrier bands of each channel in the Wi-Fi signal using OFDM techniques. Therefore, under the IEEE 802.11 n wireless protocol, CSI can be modeled as [15].  
\[\overset{\rightarrow}{Y}=H\overset{\rightarrow}{X}+Noise \tag{3}\]  
where \(\overset{\rightarrow}{Y}\) denotes the received signal, \(\overset{\rightarrow}{X}\) denotes the transmitter signal, \(Noise\) is the ambient noise. \(H\) is the channel matrix, which can be estimated as CSI. When a human body moves within the Wi-Fi signal, it affects the CSI, causing the CSI amplitude to change accordingly, and different numbers of humans in different locations will cause different CSI amplitude changes. In addition, CSI can capture the wireless characteristics of the nearby environment and is highly resistant to environmental noise.  
Figure 2: Human Activity CSI from ESP32.
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 5 Results
### Scenario III: LTE with a Millimeter-Wave Backhaul Link
The capacity results show that using the simultaneous resource allocation could lead to a feasible solution, serving up to 54 (21.6%), 188 (72.4%) and 178 (68.5%) users for a 3.5 GHz, 3.5 GHz with CA and 60 GHz network configurations, respectively. Finally, we presented in Table 4 a resume of the principal results of the network performance for the three scenarios with three types of drones for 260 users and the optimal flight height. Here it can be seen how Scenario II could support more users (188 users) and slightly outperformed the scenario of  
Figure 7: Performance of 60 GHz band and comparison with 3.5 GHz scenarios. (**a**) Coverage using the 60 GHz band. (**b**) Backhaul resource block (RB) efficiency. (**c**) Resource block used. (**d**) Provisioned users. the 60 GHz band (178 users). Contrarily, in scenario III, each UABS could support more users (10 users in III and eight in II). As expected, the usage of better drones (type 2 and type 3) reduced the number of drones required in all three scenarios by a factor of four, being the hybrid-type was slightly (11%) better than type 2. The power consumption was also stable among the kinds of drones and quite different among the scenarios, concluding that power consumption was mainly related to the technology and network requirements rather than drone flight usage. It can be seen that for the scenarios I and II, the power consumption per UABS was nearly 34 and 36 watts respectively, meanwhile, for scenario III, it was just 25 watts. Moreover, the total capacity was consequent with the number of users served and scenario II was the one that outperformed with nearly 173.1 Mbps over 163.8 Mbps and 47.6 Mbps for scenarios III and I respectively. Similarly, to the more users per UABS, scenario III could support more backhaul bitrate per UABS with 8.8 Mbps. Next, due to the differences in the MCS for both frequencies, the 60 GHz band had less spectrum efficiency. Hence, it consumed much more resource blocks compared with the 3.5 GHz band. In can be seen that scenario I and II were saturated in 100 and 500 RB, but for scenario III, it consumed almost 1620 RB still far from its 45000 RB limit. In this case, the scenario I had more RB capacity with 476.5 kbps/RB followed by scenario II and III with 346.2 kbps/RB and 101.2 kbps/RB respectively. Lastly, the RB efficiency increments as the number of RB per UABS were used. This is due to the optimization algorithm that organized the UABS capacity in a better way leading to an average of 87.1%, 96.2% and 98.5% of efficiency for scenarios I, II and III respectively.
# Real-Time Location-Positioning Technologies for Managing Cart Operations at a Distribution Facility
## 4 Case Study: Tracking of Carts in a Large and High-Traffic RFID-Enabled Air Mail Centre
### Actual Implementation at a Mail Facility
The positioning accuracy of the system was within 1 m.  
Figure 4: The display board (with the components of an RFID communication module and antenna module) installed on each of the carts. Figure 3: Several positioning readers installed in the facility. This system can help the staff members of the facility manage the cart operations more efficiently. When an AMC staff requires moving the cart to the ULD area, he or she can immediately locate the carts in real time (as demonstrated in Figure 7) and extract information about their loaded mail bags. Staff members can further reduce their searching time by sending a request to turn on the signal lights of the selected flights via the RFID infrastructure using the mobile application. This practice can save their time and effort in searching for the right carts. As a result, the risk of having delay in the delivery of mail bags to flights is mitigated, while throughput and efficiency are enhanced. Figure 5: A tagged cart (with a display board showing the destination of the mail bags and a signal light) for circulating mail bags within the facility. Figure 6: The above example illustrates the idea of using the antenna arrays and the angles of signal arrivals for positioning computation. Information about the transaction such as the time stamp, RFID tag ID and the location is also stored into the AMC information system.
## 1 Introduction
This is likely due to the fact that in order to monitor fire damage in WUI, it is necessary to have images at high frequency (in order to capture the fire) and at a relatively high spatial resolution because of the intermixed nature of this area. However, there is a well-known trade-off between the spatial and temporal resolutions of the images that satellites can currently provide. Recently, the use of nano-satellites constellations (i.e., using a large number of small compact satellites at the same time) was suggested to overcome the spatio-temporal limitation of satellite images [30]. An example of such a company that uses the advantage of a large constellation of satellite systems is Planet Labs (www.planet.com). Planet has been providing image collection in 4 bands at a high spatial resolution of 3 m on a daily basis from their so-called \"Dove\" satellites since the early 2016. A fire that was ignited in 24 th November 2016 and lasted for two days in the Mediterranean city of Haifa (Northern Israel) caused great damage to houses, infrastructure, and trees; the resulting economic loss is yet to be estimated. We used information acquired from Planet Doves to derive high spatial resolution maps of burn severity, as well as pre-fire stand density and woody biomass maps in the WUI burnt area of Haifa in order to evaluate economic losses to the urban forest caused by the fire. Three vegetation indices were used to derive maps of green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), and green chromatic coordinate (GCC). We examined the reliability of our Planet-derived maps by comparing them with similar maps derived from the coarser spatial resolution (10 m) European Sentinel-2A satellite, which is considered to be more reliable in terms of signal-to-noise and radiometric characteristics [30]. Maps were then used with an ecosystem services valuation model (_i-Tree_), based on the annual value of a single tree, to provide a spatially-distributed map of economic losses due to fire-damaged trees in the WUI area.
# PCB Network Analysis for Circuit Partitioning
## 5 Experimental Results
### Comparison Results  
We compare our method with the MaxFlow algorithm [10]. The MaxFlow algorithm is a popular method for graph cut. We report the comparison results in Table 2. It can be seen that our proposed algorithm can achieve 82.8-96.8% on partition accuracy, 80.2-93.0% on Dice, 72.9-89.5% on IOU, 80.6-94.2% on SE, and 79.8-93.9% on precision, while MaxFlow only has 11.4-39.2% on partition accuracy, 15.2-68.5% on Dice, 10.4-59.4% on IOU, 19.4-55.8% on sensitivity, and 12.5-63.6% on precision. This is because the MaxFlow method only considers the geometric attributes, while our method considers not only the geometric connection but also the circuit network attributes, and discovers the potential patterns to help partition algorithm, so it outperforms the MaxFlow algorithm significantly through all netlist datasets. Especially for N2, N3, N4, N5, and N6, netlists that have more than 100 subsets, the partition results from our method are also as good as those netlists that have fewer than 100 subsets, such as ANDZ, N1, N7, N8, N9, and N10. That means our proposed method is universal for the datasets and can be applied to different netlists.
# Pallet Recognition with Multi-Task Learning for Automated Guided Vehicles
## 1 Introduction
A few researchers have recently adapted a framework that combines machine learning and deep learning for use in pallet detection for a forklift. [PERSON] et al. proposed a pallet detection methodology using a haar-like feature detection and Adaboost algorithms [6]. They achieved 95% accuracy by using Adaboost for pallet detection after selecting candidates using haar-like detection. [PERSON] et al. proposed a pallet detection model using single shot multi-box detector (SSD) algorithms [7]. To recognize pallets in various warehouse situations, an experiment was conducted with 4,620 images, similar to real situations collected from open sources. The experimental results achieved 92.7% accuracy. However, this approach was limited by its inability to detect the forklift pocket. [PERSON] et al. suggested a methodology of simultaneously detecting pallets and pallet pockets with RGB images [8]. They compared several object detection algorithms such as SSD, faster region-based convolutional neural networks (Faster R-CNN), and you only look once version four (YOLOv4). Experiments were conducted on images with multiple pallets and pallet pockets. Overall, the faster R-CNN performed best. Object detection and segmentation using deep neural networks showed high accuracy in finding the pallets in images. However, accurate determination of proper pallet placement continued to be a problem. In this study, we propose a method using a visionary sensor to guide a forklift. This method is used in a precision recognition situation before the AGV lifts the pallet. When the AGV arrives in front of the pallet, the visionary sensor takes an image. This image is then used to calculate and predict the angles and distances of AGV movement for the AGV to proceed to lift the pallet. The main contributions of this study can be summarized as follows:  
(1) To the best of our knowledge, this research is the first attempt to build a deep learning model to predict and guide the movement of an AGV with pallet images. Each image permits measurements of the angle and distance the AGV should move. (2) We propose a multi-task learning framework for a deep learning model that predicts the angle and distance simultaneously. It shares the rest of the network except for classifiers, and takes fewer parameters and less time to learn. The two labels (angle and distance) are related to each other, enabling better feature learning when learning at the same time. (3) To demonstrate the usefulness and applicability of the proposed method, we used real-world image data to compare the proposed methods within a single-task framework and multi-task framework in terms of classification accuracy. In addition, we used data with the lights turned on and with flipped images for efficient data collection to further improve the detecting accuracy. The remainder of this paper is organized as follows. Section 2 describes the details of the proposed method. Section 3 presents the experimental results. Finally, Section 4 contains our concluding remarks.
## 2 Results and Discussion
### Volatile Compounds in CRMP
#### 2.2.1 Ketones  
For many cheeses, methyl ketones are by far the most abundant compounds. The two major methyl ketones are heptan-2-one and nonan-2-one, considering the quantity of octanoic and decanoic acids present in milk fat [22]. In our research, all ketones detected were methyl ketones from C3 to C11 including acetoin, acetophenone and 3,5-octalien-2-one, in addition to 5 alkan-2-ones and two diketones.  
The cheese made with _S. thermophilus_ SP1.1 showed lower contents of heptan-2-one and nonan-2-one than the other two cheeses (\(p<0.05\)). The results suggested that _S. thermophilus_ TM11 and SP1.1 had different synthesis and degradation abilities for heptan-2-one and nonan-2-one. Two metabolism pathways of methyl ketones have been certified: the degradation of the corresponding fatty acid and the successive \(\beta\)-oxidation of long-chain fatty acid [23], both of which can be performed by the enzymes from bacteria.  
The presence of acetone was observed in CRMP, and a decreased level was found in the cheese made with _S. thermophilus_ SP 1.1 compared to other cheees (\(p<0.05\)). We also identified relatively high levels of acetophenone and 3,5-octalien-2-one, especially in the cheese with _S. thermophilus_ TM11 (AR \(>1\)).  
The production of diacetyl and acetoin, obtained from pyruvate, is mainly due to the activity of lactic acid bacteria, especially _Lactococcus lactis_ ssp. _lactis_ biovar _diacetyl lactis_. Diacetyl seems to be produced in lesser amounts than acetoin and can be reduced to acetoin by the action of a diacetyl reductase in some bacteria [24,25]. In our study, however, higher contents of 2,3-butanedione than acetoin were displayed both in the cheeses made with _S. thermophilus_ TM11 and SP1.1 (Table 2). The result might be due to the differences of some reductase in _S. thermophilus_ TM11 and SP1.1, such as the reductases of diacetyl or acetoin. Moreover, this result might be attributed to the heat history of the milk powder used. [PERSON] _et al._[4] identified diacetyl as a heat-generated aroma compound in Ezine cheeses and did not find acetoin.
## 4 Virtual Inertia Control of the VSC-HVDC
### Initialization of \(H_{\text{DC}}\) Based on Frequency Nadir  
With (9), the equivalent inertia of the VSC-HVDC is given by,  
\[H_{\text{DC}}=\frac{\frac{NC_{\text{DC}}U_{\text{DC0}}^{2}}{2S_{\text{VSC}}} \left[\left(\frac{\Delta U_{\text{DC}}}{U_{\text{DC0}}}+1\right)^{2}-1\right] }{2\Delta f} \tag{10}\]  
As stated in Section 2.2, the SGs with governor delay cannot provide fast support after disturbance, thus yielding the frequency nadir. According to \(H_{\text{DC}}\) and \(\Delta f\), the virtual inertia control adjusts the reference of the DC voltage, thus the frequency nadir is a constraint to set \(H_{\text{DC}}\). In the following, based on estimation to frequency nadir, \(H_{\text{DC}}\) is determined to provide inertia to restore system frequency without violating the constraint of the DC voltage.  
Since \(f\) is close to 1 even at the early stage of the regulation, a derivative of system frequency is simplified as,  
\[\frac{df}{dt}=\frac{P_{\text{acc}}}{2\sum H_{\text{G}}f}\approx\frac{P_{\text {acc}}}{2\sum H_{\text{G}}} \tag{11}\]  
The accelerating power at the start of the load disturbance is denoted as \(P_{\text{acc0}}\), and approximates to 0 at the frequency nadir. Without losing generality, the nadir is reached after \(l\) time intervals in the DPF analysis. The maximum \(\Delta f\), i.e., \(\Delta f_{\text{max}}\) will be solved by combining (12) and (13).  
\[\Delta f_{\text{max}}=\frac{(P_{\text{acc0}}+0)/2}{2\sum H_{\text{G}}}\Delta tl \tag{12}\]\[\frac{\Delta f_{\rm max}}{l}{\sum}K_{\rm G,M}\left(l-\sum_{m=1}^{l}e^{-\Delta tm/T} \right)+\Delta f_{\rm max}{\sum}K_{\rm L}=P_{\rm acc0} \tag{13}\]  
Since a large deviation of the DC voltage, e.g., more than \(\pm 5\%\), is not acceptable [28], with the frequency drop, \(\Delta U_{\rm DC}\) is set to \(-5\%\) of \(U_{\rm DC0}\) to determine \(H_{\rm DC}\) to fully utilize the inertia of the VSC-HVDC. Thus, the \(\Delta U_{\rm DC}\) and \(\Delta f_{\rm max}\) are obtained separately.
# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 2 Related Work
### Visual-Copy Detection  
Visual-Copy Detection refers to the process of identifying duplicates or copies within image and video content. This task is crucial for detecting unauthorized use or infringement of digital content and is divided based on the input format into 'Image-Copy Detection' and 'Video-Copy Detection'. These areas play a key role in protecting the originality and copyright of visual content. Image-copy detection is a research field that focuses on detecting the replication or manipulation of original images and identifying subtle differences between the original and altered images or finding illegally manipulated copies. The study in [23] proposes an image copy-move forgery-detection method based on fused features and density clustering. This method offers a novel approach to more precisely identifying replicated or manipulated parts of an image, integrating features of the duplicated image to accurately locate the forged area. The proposed method has proven effective in detecting unauthorized image replication and manipulation, emphasizing the utility of CNNs in this domain. In [24], a Self-Supervised Contrastive Descent (SSCD) model based on self-supervised contrastive training was proposed. This method altered the architecture and training objectives, incorporated pooling operators in the instance-matching domain, and applied contrast learning to augmentation for combining images for image-copy-detection tasks. Video-copy detection focuses on identifying and matching original video content and its various forms of alterations, which can include cropping parts of the video, adding logos, color changes, etc. This field primarily aims to prevent copyright infringement and detect illegally uploaded content. Video-copy detection typically involves stages of feature extraction, video matching, and validation. In [25], significant improvements were made in the processing speed and accuracy for video-copy detection and video-retrieval tasks by extracting crucial information from video data and selecting relevant information for optimal search results. The 2 SVS [26] model leverages self-supervised learning to train the model in proxy tasks and transition to target tasks after fine tuning. A single universal model trained using the method proposed in this research achieved the state-of-the-art in video-copy-detection tasks. In this study, we compare and analyze image- and video-copy-detection performance before and after de-identification, based on the research in [24; 25; 26]. This comparison demonstrates the effectiveness of the proposed de-identification method in image- and video-copy-detection tasks and how it differs from the existing methodologies.
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
###### Abstract  
Vehicular ad hoc networks consist of self-organizing nodes using multi-hop wireless links for communication without any infrastructure support. Traditionally, ad hoc routing protocols use the minimum hop count for their routing metric since a smaller number of transmissions is typically equivalent to a higher throughput, lower delay, and minimal power consumption. However, with the muti-rate capability of emerging radio interfaces, e.g., 802.11 ax/be standards, the min-hop metric no longer results in high throughput. For instance, if the higher data rate links are selected for the route, it could result in a higher throughput even if the route takes more hop counts. In this paper, we propose a high throughput routing scheme, called MAV, which makes two key contributions. MARY searches for high throughput paths using an on-demand route searching algorithm so that the routing overhead is smaller compared to other multi-rate-aware routing schemes. MARY also searches for multiple paths to maintain both min-hop and high-throughput paths to select the adequate path depending on the data packet size. We conduct simulations to demonstrate that MARY outperforms not only min-hop path metrics but also previously proposed high-throughput metrics.  
vehicular networks; ad hoc networks; routing 2023  
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