esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A proteomic method combining two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and matrix-assisted laser desorption/ionization-time-of-flight/time-of-flight mass spectrometry (MALDI-TOF/TOF) was used to compare the protein expression profiles of phosphine-resistant (PH\({}_{3}\)-R) and -susceptible (PH\({}_{3}\)-S) strains of _Stiophilus oryzae_. Thirty-nine differentially expressed protein spots were identified between the PH\({}_{3}\)-R and PH\({}_{3}\)-S strains; 20 protein spots were upregulated, and 19 protein spots were downregulated in the PH\({}_{3}\)-R strain compared with their expression in the PH\({}_{3}\)-S strain. In particular, cytochrome oxidase subunit I showed 15-fold higher expression in the PH\({}_{3}\)-R strain than in the PH\({}_{3}\)-S strain. Additionally, citrate synthase 2, delta-1-pyrroline-5-carboxylate dehydrogenase, and triose-phosphate isomerase were highly expressed in the PH\({}_{3}\)-R strain. In summary, our study has improved understanding of the molecular mechanisms of phosphine resistance in the rice weevil.  
2D-PAGE; phosphine resistance; _Stiophilus oryzae_ (Coleoptera: Curculionidae).
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 6 Applications of Machine Learning in FANET
### Routing  
RL provides the nodes with the capability to learn from their environment in order to autonomously and adaptively select the most suited next-hop node in packet routing. This has motivated researchers to develop routing techniques based on Q-learning. The role of RL for routing in FANETs was thoroughly investigated in [141] by assessing over 60 routing protocols that were proposed since the 1990s. It recommended RL as the best solution with reasonable overhead in terms of control packets, memory, and computation. In [142], the authors introduced a routing technique dubbed as QGeo which utilizes Q-learning. This methodology aims to reduce the routing overhead while maintaining a higher packet success delivery in comparison to other methods that do not rely on Q-learning. Moreover, the authors in [143] proposed a modified version of QGeo, where they enabled the system to learn the reward function of the Q-learning function in real-time through the inverse RL mechanism. The authors demonstrated an enhancement to the shortcomings of QGeo through real-time learning.  
Another significant Q-learning-based routing technique that deploys the multi-objective optimization routing protocol, dubbed as QMR was introduced in [144]. QMR enables the adaptive learning of the network to its dynamic environment. In [145], the authors proposed a routing scheme called Q-FANET which uses a specialized version of the Q-Learning algorithm designed to cut down on delivery times in highly mobile networks. The authors also demonstrated enhanced performance in terms of packet success ratio, delay, and jitter when compared to the other RL-assisted routing techniques.  
There are other algorithms that incorporate RL with other tools that have also been explored for FANETs. The authors in [146] proposed a routing protocol using fuzzy logic and Q-learning to enable routing path selection based on reducing the number of total hops and minimizing the delivery time. In this case, Q-learning is used to support the output obtained from fuzzy logic through a point system for evaluating potential routes. In addition, the authors in [15] relied on the use of Markov decision processes to propose an adaptive hybrid communication protocol based on RL.  
While the current mechanisms have been shown to provide a major enhancement in the network routing performance, the complexity of the system becomes very high with more inputs. The primary reason for this is the centralized nature of the proposed techniques, where all the data is communicated back to the server, which is responsible for the training, rewarding, and action-taking. Distributed learning could provide merit in large networks, which can enable the different nodes to adjust their parameters in real-time [147,148].
## 2 Proposed Lip-Reading Model
### Video Preprocessing  
We preprocess the sequential images of lips in order to balance training speed and recognition results, including keyframes extraction and lip location segmentation [17].  
Generally, the data from video capture is about 25 frames per second. Since there is a difference in the length of each utterance and any word actually pronounced has a series of redundant information about the movement of the lips, it is difficult for the model to extract the image features and discover the hidden relationship of the sequences. Therefore, we try to remove redundant information from all the original images, extract keyframes and segment lips in the following four steps as experimental datasets:  
* The time of the utterance is divided into 10 equally interval portions, and a random frame of each portion is selected as a keyframe, thus each word obtains a sequence image frame of equal length.
* We use OpenCV library to load images and convert them into a three-dimensional matrix [18]. Then, we use the facial landmark detection of Dlib toolkit [19]. It takes the face images as input, and the returned face structure consists of different landmarks for each specific face attribute. We choose to locate the seven key points of the mouth, labeled as: 49; 51; 53; 55; 57; 58; 59.
* We segment the mouth images and remove the redundant information, then calculate the center position of the mouth based on the coordinate points of the image boundary, denoted as \((x_{0},y_{0})\). The width and height of the lip image are represented by \(w\) and \(h\), respectively, \(L_{1}\) and \(L_{2}\) represent  
Figure 1: The architecture of our proposed convolutional neural network (CNN) with attention-based long short-term memory (LSTM).  
the left and right, upper and lower dividing lines surrounding the mouth, respectively. According to the following formula to calculate the bounding box of the mouth: \[L_{1}\ =\ x_{0}\pm\frac{w}{2},\] (1) \[L_{2}\ =\ y_{0}\pm\frac{h}{2},\] (2)
* After the mouth segmentation step, the original dataset will be processed into \(224\times 224\) pixels which take lips as a standard. This method has the characteristics of strong robustness, high computational efficiency and consistency of eigenvectors. The processes of pretreatment are as shown in Figure 2.
## 5 Conclusions  
This paper has dealt with automatic registration of remotely sensed imagery. This procedure appears often in the processing of multi-temporal and/or multisensorial image analysis, such as multisource data fusion, multitemporal change detection, structure recognition and tracking, _etc_.  
In this work, we have explored the critical elements for an automated image registration system using NOAA-AVHRR/3, SeaStar-SeaWiFS, AQUA-MODIS and ERS/2-ATSR imagery. These elements include reference image coastline extraction, sensed image gradient energy map estimation and feature correspondence.  
For this last process, we have developed a feature correspondence technique by optimization of the reference image contours on the sensed image contour matching techniques, based on a general affine transformation, which models directly the corrections in the image domain without an explicit identification of the distortion sources. Experimental results using optical and infrared images from different dates and sensors have verified the robustness and accuracy of the proposed algorithm, regardless the initial orbital prediction model, demonstrating that is capable of registering satellite images within one pixel. Furthermore, a new technique has been proposed to assess the accuracy of georeferencing approaches, which makes use of all reliable information in the image.  
In summary, the technique of automated image registration developed in this work is potentially powerful in terms of its registration accuracy, the degree of automation, and its significant value in an operational context. The approach is also robust, since it overcomes the difficulties of control-point correspondence caused by the problem of feature inconsistency and, consequently, improves the analysis and data interpretation accuracy and facilitates its update to future remote sensing sensors or other applications. We have, as well, demonstrated the excellent performance of our methodology when compared with area-based techniques, improving the registration accuracy by half a pixel.  
Especially, the favourable results from this study have spawned a follow-up project that consists in the tracking of mesoscale structures using multitemporal and multisensorial data [6,25]. This project estimates the motion of coastal upwelling in image sequences with different region matching and differential algorithms. The accuracy of the motion field has been computed using synthetic and real sequences, as well as in-situ measurements. To that respect it is important to emphasize the need of a precise registration methodology to achieve satisfactory results. In particular, this automated registration procedure has been used with great success in our work. Figure 7(a) shows the segmented upwelling for a multitemporal sequence while Figure 7(b) includes the contours for a multisensorial sequence along with the recovered motion field.
# Condition Rating Prediction Using an Interactive Deterioration Model Development Package
## 3 Dmdp
### Prediction of Condition Ratings  
The DMDP provides an option to investigate the previous inspection data, as illustrated in Figure 1. Supposing that the year \(r\) is used for the selection of the explanatory variables, the inspection data prior to the \(r\)th year are employed to develop the deterioration models. For stochastic deterioration models, the remaining inspection data can be used to estimate the prediction error. The normalized average error for a future year, denoted by \(e_{r}\), is estimated as follows:  
\[e_{r}=\frac{\sum_{1}^{m}\left|1-\rho\right|}{m} \tag{5}\]  
In Equation (5), \(\rho\) is the ratio between the predicted and inspected condition ratings. The prediction of condition rating is determined either from the dot product of the transition probability matrix and state vector when the Markov chain is utilized, or from the curve-fitting of sojourn times when the Weibull distribution is used. The normalized average error, \(\rho\), denotes the closeness of the prediction to the inspection results, and its ideal value is equal to unity.  
Figure 2: Normalized consistency comparison for (**a**) deck, (**b**) superstructure, and (**c**) substructure elements.
## 2 Problem Setting
Hence, the unit manufacturing cost was the unit cost \(Mc\) of producing one new product entirely from the direct transformation of raw materials minus the savings generated by the remanufacturing process. Therefore, the unit manufacturing cost for each period was \(Mc-\delta\cdot\tau(t)\)[18]. Also, the manufacturer bore the collection cost of used products. In addition, the collection cost of used products for each period \(t\) was considered in this work to be \((B\cdot\tau(t)^{2})/2\), where \(B\) is a scaling parameter [18; 20; 33]. Nevertheless, this work considered new products entirely manufactured from transforming raw materials and new products containing remanufactured subcomponents identical. In addition, a client chose to purchase a product from the pharmacy or directly from the online platform and had it delivered to their address. However, in case of dissatisfaction or failure, the client returned the products already purchased in both channels to the AS of the pharmacy (P). In this case, the pharmacy's AS bore a unit processing cost _Uas_ foreach returned product. In exchange, the online selling platform paid a commission \(C_{o}\) to the pharmacy for every sold product in return for the provided services by the AS of the pharmacy. In this paper, the commission \(C_{o}\) between both channels for provided services was determined by the manufacturer. For every period, the ratios of unsatisfied clients from the pharmacy's demand and the online demand were \(R_{p}\) and \(R_{o}\), respectively. The following figure illustrates the dual-channel supply chain examined in this work. As presented in Figure 1, this work treated the decentralized configuration of the supply chain where both distribution channels were considered external channels to the manufacturer. In fact, this paper formulated the pricing and remanufacturing decisions following the Stackelberg game theory where the manufacturer was the leader and both distribution channels were the followers. Therefore, for each period \(t\), the manufacturer determined the wholesale price \(W_{p}(t)\) for the pharmacy, the online wholesale price \(W_{o}(t)\) and the remanufacturing rate \(\tau(t)\) that optimized its own profit \(\pi_{M}\). After receiving the wholesale prices, both distribution channels determined their proposed prices \(P_{p}(t)\) and \(P_{o}(t)\) for the selling of the product during the same period that maximized the profit of the pharmacy \(\pi_{P}\) and the profit of the online platform \(\pi_{O}\).
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 3 Results and Interpretation
### GNSS Buoy Positional Components  
The buoy's positional components during tidal cycles A and B are shown in Figures 5 and 6, with East-West (E-W), North-South (N-S) and Up-Down (U-D) components. The poorest (Q2) solutions occurred more frequently during each flood tide: at maximum tidal current and on the approach towards high water. In addition, it is likely that the poor solutions at the start and end of both tidal cycles A and B were caused by the buoy being buffeted by small waves before becoming free-floating, i.e., with the central vertical bar catching the sand as the buoy moved with the small breaking waves.  
The poorest Q2 solutions occurred during each flood tide and when the buoy was within the surf zone, and being affected by spilling waves. The buoy tracks (Figures 3 and 4) suggest that the mooring line was under tension during the peak tidal flows on the flood tide, which may indicate the presence of relatively strong alongshore currents. When coincident with spilling waves this would briefly allow water to cover the antenna, resulting in loss of satellite lock; this type of effect can be seen clearly within the sea-level data.  
Some Q1 solutions were obviously erroneous with large jumps (e.g., Figure 5, U-D-00:45 29 August). This was likely due to the processing software not recovering from poor and limited satellite data, and the subsequent offset feeding into the following positional solutions; therefore, Q1 solutions are not always an indication of good sea-level data.
## 2 Related Work  
Research to ensure communication reliability between devices in various network environments has been actively conducted. The most widely used method evaluates the reliability of a specific node by observing its packet transmission behavior and can assess the energy usage, mobility, and network phase change by capturing the characteristics of each network. This section describes the existing schemes used in mobile ad-hoc networks (MANETs) and wireless sensor networks (WSNs) and introduces the trust evaluation method adopted in our proposed technique.
# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 5 Simulation Results
### Evaluation Model
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Category** & **Faster-RCNN/\%** & **YOLOv4/\%** & **YOLOv5/\%** & **RSyoloX[31]/\%** & **FCOS** [52]/\%** & **DA\({}^{2}\)FNet[50]/\%** & **Ours/\%** \\ \hline Aircraft & 90.2 & 88.8 & 92.6 & - & 91.0 & 95.7 & 97.4 \\ Oil tank & 88.3 & 89.6 & 94.0 & - & 97.6 & 97.7 & 98.6 \\ Playground & 72.1 & 76.4 & 81.2 & - & 86.1 & 90.6 & 85.6 \\ Overpass & 80.9 & 91.5 & 94.9 & - & 99.7 & 95.1 & 99.5 \\ Mean AP & 82.9 & 86.6 & 90.7 & 93.1 & 93.6 & 94.8 & 95.3 \\ \hline \end{tabular}
\end{table} 
Table 11: Comparison of the KCFS-YOLOv5 with other advanced algorithms on the RSOD dataset. Figure 13: There are some detection results of the KCFS-YOLOv5 on the NWPU VHR-10 dataset. (**a**) is the airplane, (**b**) is the vehicle, (**c**) is the ship, (**d**) is the tennis court, (**e**) is the baseball diamond, (**f**) is the bridge, (**g**) is the storage tank, (**h**) is the ground track field, and (**i**) is the basketball court. In the end, we used some advanced remote sensing image detection algorithms to test these three datasets, and we put the simulation datasets in Table 12.  
Figure 14: This is the detection result of the KCFS-YOLOv5 on the RSOD dataset. Where, (**a**) is the aircraft, (**b**) is the oiltank, (**c**) is the overpass, (**d**) is the playground. Figure 15: This is the detection result of the KCFS-YOLOv5 on the UCAS-AOD-CAR dataset. (**a–d**) are the vehicle. Compared with other advanced object detection algorithms, our KCFS-YOLOv5 object detection algorithm achieves the best detection results on all three datasets which demonstrates excellent detection accuracy.
## 2 Materials and Methods
### Assessment  
Prior to data collection, a light sponge was used to remove any surface layers of dead skin, then each participant's skin was cleansed with alcohol wipes prior to attachment of the EMG sensors, and lastly each sensor was affixed with strong double-sided tape (provided by Delsys). Eight wireless Delsys surface electromyography sensors (sEMG) (Trigno, Delsys, Boston, MA, USA) were placed on each muscle: the right and left rectus femoris (RF), right and left tibialis anterior (TA), right and left bicep femoris (BF), and right and left medial gastrocnemius (MG) (Figure 1). The sEMG data were collected using the Delsys Trigno System at a sampling frequency of 2000 Hz; the sEMG data was recorded, simultaneously with motion capture data, via Vicon Nexus 2.0 software.  
Figure 1: Locations of sEMG sensors: (**a**) Anatomical muscular system describing the location of each sEMG sensor placed on all participants’ (1) right and (2) left rectus femoris (RF), (3) right and (4) left tibialis anterior (TA), (5) left and (6) right bicep femoris (BF), and (7) left and (8) right medial gastrocnemius (MG) [19]; (**b**) example of participant performing forward walking task while donning EMG sensors.  
Each participant completed a pre-assessment (at 0 weeks) and post-assessment (at 6 weeks) once the training was completed. Each participant performed forward and backward walking trials (FW and BW, respectively), in addition to forward and backward tandem walking trials (FT-W and BT-W, respectively), for a distance of 3 m, two trials per condition, per participant. Tandem walking is described as walking with one foot directly in front of the other, or heel to toe. For all walking trials, there was overground, unassisted walking and the participants were instructed to walk at their own, self-selected moderate pace.
# Maritime Over the Horizon Sensor Integration:
## 2 Operational Environment and HFSWR Resolution Capabilities  
The environment where the whole system is deployed is the Gulf of Guinea. From our point of view, this environment is one of the most challenging in the world for the task we are targeting. A detailed environment description may be found in [21], while a full description of the HFSWR that is used may be found in [22].  
It is important to point out the resolution and coverage of HFSWRs which are used:  
* The range resolution is 1.5 km,
* The angle resolution varies from 0.3\({}^{\circ}\) in the center of the coverage area (the line that is practically perpendicular to the receive array) up to 2.5\({}^{\circ}\) at the edges of the coverage area (the areas which are practically located at \(\pm\)60\({}^{\circ}\) from the line perpendicular to the receive array).
* The nominal range is 80 nautical miles (approx. 150 km) for the Bonn express class of vessel [23] during night-time and sea states [24] up to 3. For larger vessels and during the day-time, the range can extend even beyond 125 nautical miles (approx. 230 km). The angle coverage is set to 120 degrees, regardless of the vessel's size or the time of day.
* The network coverage area is currently the western part of the Gulf of Guinea, and it is shown in Figure 1.  
Based on the aforementioned HFSWR resolution capabilities, it becomes clear that the positions of newly formed targets (detected vessels) may differ significantly. For example, let's say that a vessel is detected for the first time by both radars approx. 200 km offshore at an azimuth angle of 40\({}^{\circ}\). Since the angle resolution at 40\({}^{\circ}\) is about 1.3\({}^{\circ}\), the arc length at that distance is nearly 5 km. Since the range resolution is 1.5 km, it means that the resolution cell covers an area of about 7.5 km\({}^{2}\). The same is valid for the second radar as well. Since the resolution cells of the two radars are mutually overlapping, the reported positions may range from 7.5 km\({}^{2}\) up to nearly 15 km\({}^{2}\). This makes the fusion of such vastly separated targets quite a challenging task. It is also important to note that vessel positions for long and stable tracks (vessels which are tracked by a certain radar for a few hours) are quite precise and do not differ more than 500 m from the actual vessel position (the one reported by AIS).  
In the end, in order to verify the vessel's position and thus demonstrate the proposed algorithm accuracy, the data obtained via the following sensors will be used:  
* Land AIS data--provided by six coastal sites equipped with AIS receivers and
* Satellite AIS data--provided from the SAIS provider Orbcomm [25].  
Please note that the vessel's positions are represented with geographical longitude and latitude.