esa-sceva/satcom-chunk-collection huggingface dataset:
# Demonstration of Subretinal Injection Using Common-Path Swept Source OCT Guided Microinjector
## 3 Results
### Ex-Vivo Bovine Eye Injection Test  
Finally, we performed an ex-vivo bovine retina injection test in order to evaluate the effectiveness of the SMART injector system in a biological sample, as shown in Figure 7. To preserve the structure of the bovine retina, the experiment was conducted on the same day as death. In this test, we removed cornea, lens and vitreous humor and the needle tip had direct access to the bovine retina. This experimental setup is different from the actual surgical environment that a surgeon performs vitrectomy in so much as it occurs in air and the surgeon operates in fluid. In application, the needle passes through a trocar. The trocar provides both access to the interior of the eye and provides a stabilizing influence to the tool shaft. Despite these differences the interaction of the SMART with biological tissue provides useful information with regard to tool development and performance. During this test we injected small volumes of fluorescein into bovine retina and illuminated it with a blue light to excite the fluorescein and thereby increase its visibility; as a result, the injected volume is clearly shown in Figure 7a,c. We predetermined the injecting target depth to be 200 \(\upmu\)m, as this would constitute an intra-retinal injection in bovine tissue [27]. The intended injecting volume was 50 nL but it was not tightly controlled due to capillary effect. Figure 7 shows ten representative injection trials and corresponding distance tracing data. The RMSEs during the depth-locking are shown in the graph. The units of each value is microns. The total average of the RMSE is 9.38 \(\upmu\)m and the maximum RMSE is 10.95 \(\upmu\)m. Prior knowledge regarding the typical thickness of the human retina and also the spacing between the various well described layers [28,29], demonstrates the theoretical potential of SMART injector system to target individual retinal layers.  
Figure 7: The ex-vivo bovine eye injection experiment: (**a**,**c**) are snap shots displaying the multiple fluorescein injections. Fluorescein was excited by blue light for fluorescence emission, (**b**,**d**) are tracking data of the needle-tip. Each graph has root mean square error (RMSE) below it during depth locking.
## 5 Conclusions  
The results of this study reveal significant vegetation changes along the 12,674 km Triple Crown National Scenic Trail system due to shifting land covers, frequent wildfire events, and slow recovery after disturbances. The vegetation within the 20,556 km\({}^{2}\) conservation corridor transitioned over the past two decades, with an increase in grasslands and a decrease in woody vegetation along the PCT and CDT. Between 2001 and 2023, wildfires affected 1248 km\({}^{2}\) of the study area (9.5% within the AT, 39% within the CDT, and 51% within the PCT). As the frequency and severity of wildfires is projected to increase in the future [127], these vegetation transitions are likely to continue. In the analysis of the vegetation recovery, the 15 sample sites demonstrated an average recovery period of 10.8 years to the pre-disturbance normalized burn ratio (NBR) spectral reflectance values. The results indicate that all the trails exhibit a highly seasonal growth pattern, with the AT exhibiting the most extensive forest cover and highest productivity, followed by the PCT and CDT. The shifts in vegetation health, as indicated by the increased net primary productivity (NPP) and earlier snowmelt, suggest potential impacts of climate change. However, shifts in the start-of-season and end-of-season phenology could not be conclusively identified at the broad spatial scale of this study. To advance the science, future studies should investigate more localized land cover and phenological shifts within the Triple Crown, as vegetation responses vary across species and ecoregions. In addition, installation of additional PhenoCam sites would support more localized validation approaches compared to the analysis performed here. This study underscores the utility of remote sensing in conducting comprehensive, landscape-level analyses over large geographic areas The findings offer trail managers and citizen participants essential data to identify vulnerable vegetation along the trail system, which may require additional conservation efforts. By contributing to trail management and conservation initiatives, this work emphasizes the importance of ongoing monitoring of the vegetation dynamics within these protected corridors of the Triple Crown National Scenic Trail system. **Author Contributions:** All authors contributed to the conceptualization. [PERSON] led the methodology, writing, editing, and visualization. Formal analysis of forest loss, wildfire, and recovery by A.R.I. Phenological analysis, writing, and visualization by C.A.H. and D.F.R. Study area map and land cover trends by A.N.A. PhenoCam data acquisition and management by A.W.R. Supervision and project administration were conducted by A.R.I. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding.
## 2 Results and Discussion
### Characterization of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\)
#### 2.1.6 Nitrogen Content Analysis  
The Elementar Vario EL element analyzer was used to analyze the nitrogen content of the sample, and the results are shown in Table 1. In the synthesis process of C18 functionalized nano Fe\({}_{3}\)O\({}_{4}\) magnetic polymer materials, GMA is the only oxygen source, and N, N-dimethyloctadecylamine was the only nitrogen source. Therefore, the grafting rate of C18 functional groups can be calculated by detecting the oxygen and nitrogen content in the materials. It could be concluded from this Table that the content of O atoms in the non-functionalized nano-magnetic polymer composite was 9.6%, that the content of GMA in the material was 0.40 g/g, and that there was 2.546 mmol of the epoxy group in the 1.0 g magnetic polymer composite. After C18 functionalization, O content decreased to 8.5%, while N content was 1.5%. It can be calculated that the contents of GMA and N,  
Figure 6: FTIR analysis of PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) and C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\).  
N-dimethyloctadecylamine in the 1.0 g C18 functionalized magnetic polymer composite were 0.25 g/g and 0.30 g/g, respectively. In the 1.0 g magnetic polymer composite, the C18 functional group exists at 1.766 mmol and the epoxy group exists at 1.035 mmol. It can be concluded that the grafting rate of the C18 functional group was 58.6%.
## 2 Structure and Connectivity of the MC4L-IMU Device  
To accurately detect the motion state and directional changes of a target object, we designed a circular structured vision-based ranging system. The system consists of four KY-008 laser transmitters, which are evenly distributed along a circular orbit with a radius of 7.7 cm. A standard high-definition monocular camera is placed at the center of the orbit, ensuring that the laser projections form consistent geometric patterns on the target surface. The choice of a circular laser arrangement improves depth perception and range accuracy through triangulation and irradiated area estimation, as supported in prior visual-laser fusion systems [12; 13]. All sensors and components are connected to a Raspberry Pi 4 Model B (4 GB RAM), which serves as the control and data processing unit. The Raspberry Pi platform is widely used in embedded vision systems due to its low power consumption, integrated I/O interfaces, and sufficient computational capability for lightweight image processing tasks [14].  
The target object was positioned at an initial distance of 120 cm, and measurements were conducted every 5 cm until a maximum distance of 300 cm was reached. All images were captured at a resolution of 640 \(\times\) 480 pixels, a commonly adopted setting in monocular vision research for balancing image detail and computational cost [15].  
Furthermore, to reduce the impact of ambient light and ensure consistent illumination conditions, all experiments were conducted in a dark environment. Each distance point was measured three times to account for variability and improve the statistical robustness of the dataset. Figure 2 shows the structure of the MC4L-IMU device.  
We employed a Raspberry Pi 4B as the core processing unit, interfacing it with a KY-008 laser module, an MPU6050 IMU, and a USB camera to establish a comprehensive sensing system. The KY-008 laser module was connected via the GPIO interface, with four signal lines assigned to distinct GPIO pins for precise control over laser activation. The MPU6050 IMU sensor utilized an I2C communication protocol, with GPIO 2 (SDA) and GPIO 3 (SCL) facilitating motion tracking and orientation estimation. A USB camera, serving as the primary vision sensor, was linked through a standard USB port to enable real-time image acquisition and processing. Figure 3 shows Integration of different sensors for the MC4L-IMU device. To ensure system stability and reliable operation, appropriate power connections were configured, maintaining a 5 V power supply and proper grounding. A detailed pin mapping of sensor connections to Raspberry Pi is provided for clarity and ease of implementation.  
Figure 3: Integration of different sensors for the MC4L-IMU device.  
Figure 2: Structure of the MC4L-IMU device.
# Influence of Sea Level Anomaly on Underwater Gravity Gradient Measurements
## 2 Data and Methods  
This study uses daily gridded sea level data from 1993 to the present from global ocean satellite observations that were provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and the Copernicus Climate Change Service (C3S). The dataset provides gridded daily global estimates of sea level anomalies based on satellite altimetry with a spatial resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), including absolute dynamic terrain, sea level anomaly, absolute geostrophic velocity, and geostrophic velocity anomaly. Moreover, sea level anomalies were calculated relative to the 20-year average reference period (1993-2012). The dataset is based on a satellite constellation with a stable number of altimeters (Figure 1) to ensure long-term stability of the ocean observing system. After the steps of satellite acquisition, preprocessing, input data quality control, multi-task cross-calibration, along-orbit product generation, gridded product generation, and final quality control, estimates of sea level anomalies, absolute dynamic topography and corresponding geostrophic velocities were obtained [12; 13; 14].  
Gravity gradient forward modeling is based on [PERSON]'s law of gravity and utilizes known mass anomalies to calculate the gravity gradient. The methods for the gravity gradient forward modeling from the terrain mainly include right rectangular prism, polyhedron, direct numerical integration, and fast Fourier transform method. Comparison of the various methods showed that the right rectangular prism method affords results with the highest accuracy [2; 3; 16]; thus, this study uses the right rectangular prism method for calculations.  
Figure 1: Complete altimetry satellite constellation used in the C3S sea level product [15].
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 2 Research Status of Tunnel Lighting System  
Based on the above reasons, tunnel lighting has been extensively considered and studied worldwide. The hazards of traffic accidents and the lessons of secondary disasters related to tunnel lighting have repeatedly drawn the attention of experts in the field of  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **2012** & **2013** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** \\ \hline Total & 10,022 & 11,359 & 12,404 & 14,006 & 15,181 & 16,229 & 17,738 & 19,067 & 21,316 & 23,268 \\ \hline Long & 1944 & 2303 & 2623 & 3138 & 3520 & 3841 & 4315 & 4784 & 5541 & 6211 \\ \hline Extra-long & 441 & 562 & 626 & 744 & 815 & 902 & 1058 & 1175 & 1394 & 1599 \\ \hline \end{tabular}
\end{table}
Table 1: Progress of tunnel construction in China (2012–2021).  
transportation to the quality of tunnel lighting. The research into tunnel lighting systems mainly includes three aspects, the evolution of tunnel lights, the control modes of lighting sources and the overall architecture of lighting control system.
## 1 Introduction
### Findings and Contribution  
To the best of the author's knowledge, very little research literature is available to identify both the black and gray hole attacks in the dynamic environment of MANETs. Specifically, the utilization of CDS and IDS approaches are used for the detection of black hole and gray hole nodes.  
The contributions are as follows:  
* We have proposed a prominent technique for the detection of malicious attacks, such as black hole and gray hole attacks, through the intrusion detection system (IDS) nodes using the connected dominating set (CDS) technique. Our work is different from past research, wherein the approach used was only for black hole attacks [12; 13; 14] or the proposed scheme worked only for gray hole attacks [15; 16; 17].
* The proposed DDBG technique also detects the malicious nodes, particularly nodes with smart gray hole attacks, even in dense networks. Our approach is different from [18; 19; 20], where the proposed technique demonstrated an inadequate detection of the gray hole attacks in a dense network.
* Comprehensive experimental outcomes indicated that the proposed method is an effective and prominent approach for the detection of black and gray hole attacks.  
The remaining work of the paper is arranged into the following sections: Section 2 describes the related work, Section 3 presents the proposed methodology and algorithm, Section 4 describes the simulation scenario and results, and Section 5 summarizes the paper.
## 3 Breast Cancer Image Classification
### Evaluation  
The image comparisons were applied to the mediolateral oblique (MLO) mammogram views of both the left and right breast of patients that were diagnosed with cancer and patients that were diagnosed as normal, or free from cancer. The analysis was performed over test and training data sets, with cases that were roughly split between normal mammograms and mammograms with malignant spiculated lesions from the Digital Database for Screening Mammography [51]. The focus was on one type of breast cancer which creates spiculated lesions in the breasts. Spiculated lesions are defined as breast cancers with central areas that are usually irregular and with ill-defined borders. Their sizes vary from a few millimeters to several centimeters in diameter and they are very difficult cancers to detect [47].  
The training set had 39 non-cancerous cases and 37 cancerous cases, while the test set had 38 non-cancerous cases and 40 cancerous cases. The data is roughly spread across the density of the breasts and the subtlety of the cancer. The breast density and subtlety were specified by an expert radiologist. The subtlety of the cancer shows how difficult it is to determine that there is cancer. The training data set was used to determine optimal parameters the volumes _dA_. The inputs are the extracted AFUM features for each image in the screening mammogram set, as shown in Figure 3. The output is a classification as either cancerous or non-cancerous. We used exhaustive search because we could, and require only a single stage. These cases indicated that a difference in the clusters of one or more AFUM features indicated cancer in both the two and three cluster experiments.  
Figure 9: Comparison Data. The maxima in learning the two-cluster method with respect to one of the parameters, the y value parameters P is in (a) and the method is shown to generalize well from training to test data. The same information for the three-cluster method is shown in (b). The performance relative to the number of suspicious points used in the two-cluster technique is in (c). The performance of the three-cluster method on normals, or non-cancerous cases, is shown in (d).
# Multitarget Search Algorithm Using Swarm Robots in an Unknown 3D Mountain Environment
## 2 Ground Target Search Modeling in an Unknown Mountain Environment
The mountain surface is separated into obstacle points with spacing \(\Delta l\), which are static, and the position of each obstacle point can be specifically expressed as \(\mathbf{obs_{k}}\)=\([xs_{k},ys_{k},zs_{k}]\). In a 3D search task environment, each robot can locate itself through its own sensor position and speed information, can communicate through a communication device within the scope of communication with other robots, and can sense obstacle slope information in its detection scope. The robot pose and location information followed within the search environment is as Equation (2):  
\[\left\{ \begin{array}{l}\mathbf{R}_{i}(t)=\left[xu_{i|t},yu_{i|t},zu_{i|t}\right] \\ \mathbf{V}_{i}(t)=\left[xi_{i|t},yu_{i|t},zu_{i|t}\right]\\ xii_{i|t}=\frac{d(xu_{i|t})}{d(t)}=\|\mathbf{V}_{i}(\mathbf{t})\|\cos\theta\sin\phi\\ yii_{i|t}=\frac{d(yu_{i|t})}{d(t)}=\|\mathbf{V}_{i}(\mathbf{t})\|\cos\theta\cos\phi\\ zii_{i|t}=\frac{d(zu_{i|t})}{d(t)}=\|\mathbf{V}_{i}(\mathbf{t})\|\sin\theta\end{array}  \right.. \tag{2}\]where \(xu_{i|t},yu_{i|t}\) and \(zu_{i|t}\) are the coordinate positions of the robot at time \(t\) in the Cartesian global coordinate system \(xoyz\). \(\mathbf{V_{i}}(t)\) is the movement speed of the robot at time \(t\), \(\phi\) is the angle between the projection vector of \(V_{i}(t)\) in the \(xoy\) plane and the forward direction of the \(x\)-axis, and \(\theta\) is the angle between \(\mathbf{V_{i}}(t)\) and the forward direction of the \(z\)-axis. If the time change \(\Delta t\) is small enough, the relationship between the robot's position and speed in Equation (2) can be expressed as Equation (3):  
\[\mathbf{R_{i}}(t+\Delta t)=\mathbf{R_{i}}(t)+\mathbf{V_{i}}(t+\Delta t) \tag{3}\]  
To facilitate the planning of the trajectory of the robot, this study takes \(\Delta t\) as unit time, that is, \(\Delta t=1\), and the iterative relationship between the position and velocity of the group robot satisfies the following as Equation (4):  
\[\mathbf{R_{i}}(t+1)=\mathbf{R_{i}}(t)+\mathbf{V_{i}}(t+1) \tag{4}\]
## 3 Data Preparation
### Data Preprocessing
#### 3.3.2 Data Normalization  
Normalization is a widely used data preprocessing technique that aims to standardize the scale of data within a common range. It helps eliminate differences in features and bring the data to a unified scale. Several normalization methods are commonly employed, including Min-Max Scaling, Standardization, and Norm Normalization, among others. In this study, the data were normalized using Min-Max Scaling. By applying this scaling technique, the original data is transformed proportionally within the range of [0, 1] to remove any biases that might arise from the original data scale.
## 4 Data Processing
### Between-Class Comparison  
#### 4.4.1 Typical Compounds of Interest during Wine and Beer Studies  
With VOC profiling results, the conventional learning objective is to find the chemical component characteristics of different sample classes and relate them to their physicochemical properties. This cross-class analysis gives rise to many applications, including chemical fingerprinting, sample clustering analysis, and biomarker discovery. In the case of beverage studies, the above applications can be specified as differences in primary materials, sensory properties, microbial activity, spoilage, aging, etc. For example, monoterpenes are important contributors to the aroma of primary materials such as grapes for wine and hops for beer. Muscat wines have high levels of \(\alpha\)-terpineol and linalool [175]. Linalool, at the same time, is an indicator of using hops during beer making [176]. (\(Z\))-Rose oxide is important in Gewurztraminer wines [177]. Flavanoids have correlations with red wine texture [178]. Its concentration depends to a large extent on the contact time of the juice with the exo-carp and winemaking practices [179, 180]. Furan derivatives, such as 5-methylfurfural and furfural, are sourced from toasted oak. [181]. Premature aging flavors in dry white wines are caused by a chiral furanone, namely, sotolon [182]. The fruitiness of a wine depends on the content of esters and acetate, which changes as the wine matures [81]. Through secondary metabolism via the Ehrlich pathway, amino acids produce higher alcohols and their corresponding acetate esters [183]. Compounds containing one or more carbonyl groups may contribute off-flavor, for example, 2, 3-pentadione and diacetyl [184].  
As we know, a GC\(\times\)GC chromatogram can easily contain hundreds of peaks. Mining useful information manually from such \"big data\" can quickly become overwhelming for large-scale, untargeted research. Although chemometric strategies do not guarantee the selection of valuable chromatographic information, they can assist in screening out irrelevant information and thus improve the efficiency of manual exploration. A common chemometric cross-class analysis consists of two steps: chromatogram/peak alignment and statistical exploration [185].
## 4 A Focus on Relevant Applications of Apta-POF-SPR Sensors
### Antibiotics Detection  
Antibiotics are antibacterial agents used in the prevention and treatment of bacterial infections: their use can, on the one hand, save lives, but on the other hand, can increase bacterial resistance if overused. The presence of antibiotic residues in food and environments, for instance, can alter the microbiome, compromising the immune system [76].  
The detection of antibiotics in human fluids, environment, food, and beverages is therefore crucial for human health. Compared to commonly used techniques, such as mass chromatography or enzyme-linked immunosorbent assay (ELISA), biosensors are growing due to their fast response and easiness in the operational aspects. In this context, aptamer-based sensors are also increasingly used [77; 78; 79].  
Apta-sensors based on POFs represent an attractive kind of device. In fact, they are simple to realize and use. An Apta-POF-SPR sensor for antibiotic detection was reported by [PERSON] et al. [70]. A D-shaped POF SPR probe was coupled to an electrochemical sensor to detect ampicillin, an antibiotic widely used in the veterinary field. A thiolated aptamer was immobilized on the gold-coated POF, and the two detection systems were coupled (see Figure 3), allowing the antibiotic detection at the concentration of 100 uM in buffer solution.  
Figure 2: Comparison of antibody-, MIP-, or aptamer-based POF sensors during years (The number of items were obtained from Web of Knowledge website for searching specific keywords; aptamer or MIP or antibodies and plastic optical fiber).  
Figure 3 reports the scheme of the coupling between the two systems, highlighting the easy connection with the cloud and the possibility of achieving a quick and easy data exchange. Even if this platform was not tested on the low analyte concentration, the electro-opto coupling opens the way to overcome the limitations of the single technique with the possibility of increasing the system's sensitivity.  
This was the first reported example of an Apta-POF-SPR sensor for the detection of antibiotics, but surely it will not be the last. One of the main challenges remains antibiotic detection in real samples. A complex matrix such as milk, honey, urine, serum, or plasma, apart from a possible cross-reaction, can decrease or even suppress the label signal [78]. In this context, an optical SPR aptasensor that does not require labels offers the possibility to overcome this critical aspect.