esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
When starting with many locations, identifying which ones should serve as hubs requires evaluating the importance of each location--often measured through centrality values. Centrality encapsulates a location's significance within a network, especially regarding its spatial positioning and interconnectivity.  
In terms of mathematical representation, a network can be defined as \(G=(V,E)\), where \(V\) is the set of nodes (or vertices), and \(E\) is the set of edges. In this context, each node represents a _location_, while edges represent _connections or relationships_ between nodes--such as the existence of a direct road between two locations. The adjacency between nodes \(u\) and \(v\) is indicated by \(a_{uv}\), where  
\[a_{uv}=\begin{cases}1,&\text{if there is an edge between node $u$ and node $v$,}\\ 0,&\text{otherwise.}\end{cases} \tag{1}\]  
The total number of nodes in the network is denoted by \(N\). The graph \(G\) can also be represented using the _adjacency matrix_\(A\) of dimension \(N\times N\), where each row and column corresponds to a node in the network. The entry at the row corresponding to node \(u\) and the column corresponding to node \(v\), denoted \(a_{uv}\), indicates whether an edge exists between nodes \(u\) and \(v\).  
To assess the significance of each node within a network, a variety of quantitative measures have been developed. Some of these measures are classic centrality metrics,which focus on characteristics such as connectivity, accessibility, or intermediary roles. Others, however, incorporate additional structural or contextual factors, like clustering behavior or influence diffusion. These methods are particularly valuable in contexts such as transportation, communication, or information networks, where understanding both local and global node influence is crucial for decisions regarding hub selection or network optimization. The following sections introduce several commonly used metrics for evaluating node importance, ranging from traditional centrality measures to more recent hybrid and enhanced models.
## 1 Introduction
Nonetheless, these recent results from whole-organ and whole-animal studies do not invalidate the prior studies in reduced systems, which show apparent neuromodulation by focused US in the absence of a functioning auditory system, like in _Caenorhabditis elegans_, tissue culture, retina [31], and brain slices (reviewed in reference [9]). Peripheral neuromodulation targets the PNS to preclude off-target CNS effects and thus is even more selective than the CNS neuromodulation. PNS neuromodulation is particularly appealing to treat chronic pain, as the pain circuitry in the CNS is complex and widespread. State-of-the-art peripheral neuromodulatory strategies to treat chronic pain include spinal cord stimulation that targets peripheral nerve entry to the spinal cord [8; 32], peripheral nerve field stimulation that targets a region of tissue [33; 34], direct peripheral nerve stimulation [35], and the recent dorsal root ganglion (DRG) stimulation of sensory neural somata [36; 37]. US neuromodulation of the PNS is far less advanced compared to CNS neuromodulation (see reference [38] for a review), and its mechanisms of action remain unclear. In this paper, we comprehensively review the methods and outcomes of studies on US effects in altering neural activities in the peripheral nerve axons, endings, and DRG. The seemly contradictory effects of US on peripheral nerves are discussed in the context of the different study designs and methods. In addition, we summarize the existing theories that account for the effects of US neuromodulation on peripheral nerves and DRG.
## 2 Materials and Methods
### Evaluation of Antimicrobial Activity  
The antimicrobial activity of the polymeric films was assessed by direct contact against _E. coli_ HB101 and _S. aureus_ ATCC6538 using an adapted version of ISO 22196 [12]. Films were sterilized by UV exposure for 30 min and transferred to sterile non-treated polystyrene (PS) flat bottom 24 well plates. The surface of the films was inoculated with 50 \(\upmu\)L of bacterial cell suspension (1 \(\times\) 10\({}^{6}\) CFUs/mL) and incubated for 120 min at 37 \({}^{\circ}\)C. After incubation, 950 \(\upmu\)L of sterile 0.87% NaCl solution was added and carefully agitated by gently pipetting up and down 10 times, followed by plating 100 \(\upmu\)L of serial diluted cell suspensions on LB agar (10 g tryptone, 5 g yeast extract, 10 g NaCl, 15 g agar, per liter). Cell suspensions inoculated in empty wells (without protein films) were used as negative control for antibacterial activity. Antimicrobial assays were performed in triplicate at different days with three replicates each. Results were expressed as lethality percentage (% kill) calculated by the equation:  
\[\%\,kill\ =\ \frac{CFUs\ (control)-CFUs\ (sample)}{CFUs\ (control)}\times 100 \tag{1}\]
# Laser Beam Positioning by Using a Broken-Down Optical Vortex Marker
## 2 Numerical Simulations  
The mathematical expression describing an optical field with a optical vortex inside can be written as [18]  
\[E(r,\theta,z)=E_{0}\exp\left(-r^{2}/\omega^{2}(z)\right)\exp(im\theta) \tag{1}\]  
where \(E_{0}\) is a characteristic amplitude,\(\omega(z)\) characterizes the beam size, \(k\) is the wave number, m is the topological charge, and \((r,\ \theta)\) are polar coordinates. Figure 1 shows the intensity and phase maps in the initial state in the case when \(m=3\). A dark area and a bright ring can be observed in the intensity picture, and three phase ramps converging to a common point can be observed in the phase map. As mentioned in the Introduction, when the beam propagates, a multi-charge vortex splits into the characteristic constellation of separated, unit-charged vortices. The separated optical vortices with the same TC can be described by the following expression [18]:  
\[E(r,\theta,z)=E_{0}\exp\left(-r^{2}/\omega^{2}(z)\right)\exp(-ikz)\prod_{j=1} ^{M}A_{j}\left(r_{j},z\right)\exp\left(im_{j}\theta_{j}\right), \tag{2}\]  
where \(A(r,z)\) describes the amplitude profile of the vortex core, \(m\) is a signed integer called topological charge, M is the number of singly charged vortices, and \(\left(r_{j},\theta_{j}\right)\) are the polar coordinates measured with respect to the center of the \(j\)th vortex; for example, if \(M=3\), then \(m_{1}=m_{2}=m_{3}=1\). Figures 1 and 2 show an exemplary generation of a vortex beam with an optical vortex with a topological charge of \(m=3\). Figures 1a and 2a show the beam intensity, and Figures 1b and 2b show the phase field.  
Figure 1: Vortex beam with a topological charge of \(m=3\): (**a**) intensity, (**b**) phase profile.  
Figure 2 presents the appearance of the intensity and phase of the beam with a TC of \(m=3\) that decays into three vortices with a charge of \(m=1\) each. There are three separated phase faults on the phase maps. The beginnings of the phase faults indicate the location of the vortex. The geometry of the vortex constellation is a stable structure and behaves like a rigid body; i.e., the distances between the vortices do not change, unless the weather conditions are drastic.  
In this work, methods that can be used to locate the vortex points are presented. With designated points, the center of gravity of the beam can be determined, which in turn allows the entire optical beam to be positioned.
# Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection
## 4 Experimental Evaluation
### Implementation and Automation of the Experiments
At the end of each experiment, the pcap capture file is analyzed by running Wireshark from Python, and employing the statistical capabilities of the network sniffer in order to calculate the features. Using Wireshark, we collect network data during a customized period of time, and perform an analysis at the end of that period. For the training phase, our approach was to split the entire training pcap file into subfiles, with the duration of the sampling period, using the editcap utility available with Wireshark.
## 3 Application Domain
### Connecting Driving Data with the I/O Interface of the ANM System  
As mentioned above, the length of each driving clip is 100 time series points, which is approximately 50 s of driving time. Each time series point has six values, including eye movement x- and y-axis data (represented by eye-x and eye-y), left and proper finger pressure on the steering wheel (represented by hand-left and hand-right), the average foot pressure of the brake pedal (expressed as foot-brake), and the average pressure of the accelerator pedal (expressed as foot-gas). This study assumes that during each driving clip, the behavior in the first 25 s will affect the behavior in the next 25 s. The method of this  
Figure 12: A schematic diagram of the driving environment setting for this study.  
study is to use the movements, finger pressure, and accelerator foot pressure (including accelerator and brake) in the first 25 s as the input data of the system, and then the eye movements, finger pressure, and accelerator foot pressure in the next 25 s as the system's output data. For each data set, the smaller the difference between the outputs generated by the ANM system and the expected output, the better its learning performance is. Figure 13 gives an example of timing data 25 s before and 25 s after a specific time. In other words, we hope to convert the input data of Figure 13a into the waveform of Figure 13b through the ANM system.  
All information-processing neurons of the ANM system are divided into six groups, corresponding to the above six categories of data (eye-x, eye-y, hand-left, hand-right, foot-brake, foot-gas) (Figure 14). The firing behavior of each group of information-processing neurons represents the data conversion of specific output data. In the current implementation, we use the time difference between two adjacent firing neurons of the same group to describe the degree of data conversion. This study assumes that the relationship between the time difference and the degree of conversion is similar to a sigmoid-like waveform (Equation (1)). For a particular group of outputs, the waveforms generated by all of the same group of neurons that produce firing behavior will be superimposed in series to form a specific output waveform. Loss is the absolute difference between the waveform generated by the ANM system and the expected waveform. The smaller the loss value, the better the fitness of the system.  
\[\text{Degree of transformation}=(\frac{1}{1+e(-2\times\Delta t)}-0.5)\times 2 \times 90 \tag{1}\]  
\[\text{Loss}=\sum_{i}\left|\sum_{j=1}^{50}\left(E_{ij}-A_{ij}\right)\right| \tag{2}\]  
where \(E_{ij}\) and \(A_{ij}\) represent the expected trajectory and the trajectory generated by the ANM system, respectively; \(i\) = eye-x, eye-y, hand-left, hand-right, foot-brake, and foot-gas.  
Figure 13: (**a**) An example of timing data 25 s before a specific time (**b**) An example of timing data 25 s after a particular time.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation
### Research on Vulnerability Mitigation  
Based on vulnerability discovery and detection, mitigation is also a research issue of concern to the industry. According to public literature of research, the main research hot-spots are automated patch generation and access control. The former research aims to fix vulnerabilities, while later research can limit malicious behavior.
## 2 Results
### Functional Analysis of UrGES In Vivo  
For further functional analysis of the UrGES protein in vivo, we performed _Agrobacteriumium_-mediated transient overexpression of _UrGES_ driven by the CaMV 35S promoter in _N. benthamiana_. Polymerase chain reaction (PCR) analysis confirmed successful transgene expression, with _UrGES_ transcripts being abundantly present in overexpression leaf sectors but undetectable in empty vector-transformed control leaves. Subsequent gas chromatography-mass spectrometry (GC-MS) analysis of _N. benthamiana_ leaves revealed a notable increase in geraniol production in _UrGES_-overexpressing plants compared to controls (Figure 3c), consistent with the in vitro experimental results. These findings demonstrate that UrGES functions as a monofunctional enzyme responsible for transforming GPP into geraniol in plants.  
Figure 3: Detection of catalytic activity of UrGES protein in vitro and in vivo. (**a**) Sodium dodecyl sulfate–polyacrylamideamide gel electrophoresis (SDS-PAGE) of recombinant pET32a-UrGES protein. Lane 1 represents protein marker 10–180 kDa. Lane 2 indicates purified isopropyl-\(\beta\)-D-thiogalactoside (IPTG)-induced UrGES. The target recombinant protein UrGES is shown by the red box. (**b**) High-performance liquid chromatography (HPLC) analysis of UrGES catalytic activity. The blue line represents the geraniol standard; the red line, the product of incubating heat-inactivated recombinant UrGES protein with geranyl pyrophosphate (GPP); and the black line, the product of incubating recombinant UrGES protein with GPP. (**c**) Gas chromatography–mass spectrometry (GC-MS) detection of geraniol production in _Nicotiana benthamiana_ leaves with _UrGES_ transient overexpression. The black trace represents the geraniol standard, the red trace shows the extract from leaves transformed with the empty vector (pBI121) control, and the blue trace displays products from _UrGES_-expressing leaves.
## 2 Communication Framework Description
### Overall Communication Mechanism  
The proposed protocol for WBANs focuses on low-latency and efficient communication, implemented on an ESP32-centered Printed Circuit Board (PCB). The ESP-NOW-based protocol diverges from traditional star-like systems by adopting a token-based, multi-hop approach, which distributes the computational load and communication traffic across all devices in the network. This design notably enhances efficiency and reduces energy consumption. Furthermore, the aspect of transmitting information as a kind of 'wired' channel over the skin by using capacitive properties is included in the overall concept. Figure 1 illustrates the comprehensive architecture of the proposed low-latency and energy-efficient WBAN protocol. The heart of all sensor nodes is based on an ESP32-S3-Controller implemented on a PCB developed as a sensor carrier. The system employs an ESP-NOW-based protocol that departs from traditional star configurations, effectively distributing computational and communication loads, mimicking a 'train' going from sensor node to sensor node accumulating data, which refers to the used description of the 'data train'. The figure highlights wireless communication paths (blue arrows) originating from the master unit, traversing through each sensor node, aggregating sensor information, and looping back to the master unit. Green arrows indicate the transmission of 'full packages' when the maximum packet length of 250 bytes is reached within a cycle. The red dotted lines represent the innovative HBC 'wired' channel that utilizes the body's capacitive properties for a secure, tap-proof data exchange. Finally, the master unit is depicted as a gateway to cloud services and smart hospital systems, with orange arrows denoting external communication pathways.
## 5 Results and Discussion
### Comparison of R-CNN and YOLO Results  
Based on the performance metrics presented in Sections 5.1 and 5.2, we can draw several conclusions about the relative strengths and weaknesses of R-CNN and YOLO for the task of pothole detection. The results demonstrate that YOLO models outperform R-CNN models in terms of accuracy and speed of inference. Figure 5 shows the detection accuracy of the best-performing YOLO and R-CNN architectures. As can be seen, the models from the R-CNN group achieved higher results (up to 30% mAP@.5) on the night data subset, which presents worse possible conditions. In conclusion, the overall accuracy represented by Mean_mAP@.5 shows that R-CNN models are competitive regarding the detection task under adverse visual conditions.  
A visual comparison of models' accuracy versus the number of parameters and model size is shown in Figure 6. YOLOv7 is the most efficient model, with a Mean_mAP@.5 of 55.6%. The best-performing R-CNN model, Sparse R-CNN, achieved a Mean_mAP@.5 of 51.4%. YOLOv7 has a smaller model size of 35.5 MB and requires only 36.9 million parameters, which is approximately a 91.5% and 52.6% reduction compared to the Sparse R-CNN model, which has a size of 415.4 MB and 77.8 million parameters. This makes YOLOv7 highly effective for real-time object detection in applications such as pothole detection.  
When comparing YOLOv7 to R-CNN models in terms of detection speed, the percentage improvement is substantial. The YOLOv7 model achieved an inference time of 78.3 ms, which is approximately a 46.1% reduction compared to the Sparse R-CNN model, with an inference time of 146.4 ms. Additionally, with each new version of the YOLO model, performance continues to improve.  
Interestingly, the YOLOv5m model has a higher Mean_mAP@.5 (53.0%) than all the R-CNN models, while also having a smaller number of parameters and model size than most R-CNN as well as some YOLO models. These results demonstrate the efficiency and effectiveness of the YOLO architecture in the pothole detection task, particularly with its latest versions of YOLOv5, YOLOv6, and YOLOv7.  
Despite R-CNN-based models having significantly more parameters, model size, and requiring longer inference time, they can extract useful features that contribute to the improvement in pothole detection, especially under the worst visibility at night.  
The findings obtained throughout this work have practical implications for pothole detection applications, as YOLOv7 can accurately detect potholes in real time with a low computational cost, making it suitable for deployment on resource-constrained devices.  
Figure 5: The accuracy of selected YOLO and R-CNN architectures in terms of pothole detection under diverse visual conditions.  
Figure 6: Comparison of the prediction accuracy versus the number of parameters and storage requirements.  
This can potentially lead to more efficient road maintenance and improved road safety. It is important to note that the performance of any object detection algorithm is highly dependent on the quality and size of the dataset used for training, as well as the specific implementation details.
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 2 PPP Mathematical Model and Satellite Selection Algorithms
### Optimal DOP Algorithm  
This conventional algorithm is a brute-force algorithm that (a) computes GDOPs or PDOPs of all possible solutions by combining all the \(n\) satellites selected from all the visible satellites (\(N\)) and (b) selects a set of satellites with the minimum GDOP or PDOP. Here, the GDOP, which is used for formal errors (theoretical impact of the observational geometry), is adopted to select satellites in this paper. Assuming only GPS satellites are observed, GDOP can be described simply as follows.  
\[\mathbf{Q}=\left[\begin{array}{cccc}x_{1}&y_{1}&z_{1}&-1\\ \vdots&\vdots&\vdots&\vdots\\ x_{n}&y_{n}&z_{n}&-1\end{array}\right], \tag{4}\]  
\[\mathbf{A}=\left[\mathbf{Q}^{T}\mathbf{Q}\right]^{-1}=\left[\begin{array}{cccc}\text{a }_{11}&\text{a}_{12}&\text{a}_{13}&\text{a}_{14}\\ \text{a}_{21}&\text{a}_{22}&\text{a}_{23}&\text{a}_{24}\\ \text{a}_{31}&\text{a}_{32}&\text{a}_{33}&\text{a}_{34}\\ \text{a}_{41}&\text{a}_{42}&\text{a}_{43}&\text{a}_{44}\end{array}\right], \tag{5}\]  
\[\text{GDOP}=\sqrt{\text{trace}(\text{A})}=\sqrt{\text{a}_{11}+\text{a}_{22}+ \text{a}_{33}+\text{a}_{44}}. \tag{6}\]  
However, the huge amount of computation of the optimal DOP algorithm is also prominent when the number of the selected satellites and systems increases. This method guarantees the optimal solution, but it requires large computation time. The computation time is mostly spent on matrix multiplication and inverse operation for all possible combinations [18]. To show this situation in detail, the number of all possible combinations based on daily observations (day of year: 244, in 2017) at the JFNG station is shown in Table 1. It can be seen that if 12 satellites are selected in GPS/BDS/GLONASS PPP, 5,200,300 combinations should be computed and compared, which takes up a lot of computation time.
## 3 Biosynthesis and Function of Glandular Trichome-Produced Compounds
### Hormonal Regulation of Induced Defenses in Trichomes  
In the literature, often two forms of plant defense are discriminated. The first are the constitutive defenses, _i.e._, those defenses that are always present (such as trichomes), and the second are the induced defenses, which are activated or increased upon attack by herbivores or pathogens (such as some parts of the trichome metabolism). Typically, wounding and/or herbivore infestation activates the octadecanoid pathway, resulting in increasing levels of jasmonic acid (JA) which triggers the expression of defense genes, such as protease inhibitors (PIs), as well as the accumulation of secondary metabolites, like terpenoids [49]. Besides regulating herbivore-induced defense responses, JA is also linked with trichome formation, since JA biosynthesis and reception mutants in the cultivated tomato were shown to have less glandular trichomes [23, 50] while, in addition, herbivore feeding as well asJA treatment can give rise to increased trichome densities on newly formed leaves [51, 52, 53]. Furthermore, terpene emission can be induced in tomato glandular trichomes by spraying plants with JA [54] and protease inhibitors were shown to be induced in glandular trichomes when trichomes were ruptured by walking insects [50]. Apart from terpenoids [54] and defensive proteins [55], also acyl sugars [55] and alkaloids [56] can be induced in glandular trichomes by spraying plants with MeJA. Thus, JA is essential for induction of defenses in glandular trichomes. Downstream from hormonal regulation, production of many trichome metabolites is also under tight transcriptional control, thereby allowing for temporally regulated emission of, for example, plant volatiles [57, 58].
## 2 Materials and Methods
### Assessment of Segmentation Accuracy
#### 2.5.1 Sampling of Reference Parcels  
In this step, a sample of 100 parcels was selected as ground truth from the set of cadastral parcels provided by AE. First, it was necessary to ensure that the centroid of each reference parcel fell within the boundaries of the land-cover class considered during the segmentation process (Croplands, Grasslands, Forage Crops, and Pastures) (Figure 8). Secondly, reference parcels were selected by means of photointerpretation by superimposing the cadastral dataset over the 20 cm-orthophoto provided by AGEA, which was displayed as a false-color composite to enhance vegetation features. This step ensured the selection of only those parcels that were effectively extant in 2023 and that were delineated by well-recognizable radiometric borders (Figure 9a). Despite the time-consuming nature of this stage, it ensured the quality of the reference parcels used as ground truth to test the accuracy of the segmentation. It is important to note that the sampling was conducted to maintain the initial proportion of parcels belonging to each cover type; thus, the sampling included forty-eight parcels belonging to the class Cropland; twenty-seven belonging to the class Grasslands; fourteen belonging to the class Pastures; and belonging eleven to the class Forage Crops.