esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis of Scattering Models and Channel Characteristics
### Poisson Distribution Expression  
Considering the ultraviolet NLOS scattering communication systems tailored for weak signal reception, the signals detected are characterized by faint discrete photodetectors due to significant path losses [20]. Due to the low probability \(P\) of photon arrivals within a single sampling period and the large number of sampling instances, each photon arriving at the receiver occurs as an independent random event. This process describes the number of photon arrivals within a unit time in UV communication, which aligns with the theory of discrete Poisson distributions. Therefore, the received photons follow a discrete Poisson distribution model. For the 4 FSK frequency modulation scheme, the probability of receiving photons within a sampling period of the analog-to-digital converter (ADC) can be represented by the Poisson distribution formula:  
\[P_{s}(K_{t}=n)=\frac{\lambda_{s}{}^{n}}{n!}e^{-\lambda_{s}} \tag{5}\]  
\[P_{b}(K_{t}=n)=\frac{\lambda_{b}{}^{n}}{n!}e^{-\lambda_{b}} \tag{6}\]  
where \(K_{t}\) represents the number of photons received during the sampling period at time \(t\), \(\lambda_{s}\) and \(\lambda_{b}\) denote the average number of photoelectrons detectable during the sampling period at the receiver for signal light and background noise, respectively. \(P_{s}\) denotes the probability of detecting \(n\) photons for signal light at time \(t\), while \(P_{b}\) represents the probability of detecting \(n\) photons for background noise at time \(t\). Under 4 FSK modulation, \(\lambda_{s}\) and \(\lambda_{b}\) satisfy the following relationship:  
\[\lambda_{s}=\frac{\eta P_{t}F(f_{c},t)}{L\eta\
u} \tag{7}\]\[\lambda_{b}=\frac{J}{r} \tag{8}\]  
where \(\eta\) represents the overall quantum efficiency of the receiver's filter and detector; \(F(f_{c},t)\) represents a sequence of probability density functions for different frequencies \(f_{c}\), varying with time \(t\); \(P_{t}\) denotes the optical power emitted by the transmitter; \(L\) represents the path loss caused by channel transmission; \(h\) and \(\
u\), respectively, denote the Planck constant and the frequency of the optical signal; \(r\) represents the system sampling frequency. \(J\) indicates the number of noise photons in the environment within a 1 s time frame. These two formulas are used to simulate the distribution of photons received from data and noise.
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
## 4 Measurement Setup
### Demonstrator Board  
To test the chip, a printed circuit board (PCB) was fabricated and the packaged chip was mounted on the PCB. The picture inset in Figure 10 shows the manufactured PCB. The board is 84 mm by 95 mm. For alternative test paths and for circuit debugging purposes, more SMAs were included on the PCB. Two possible pathways are made for current control: direct current supply and a potentiometer connected to the voltage supply. These controls are used for the oscillator of the BPSK receiver, the oscillator of the IR-UWB transmitter, and the pulse delay. The IR-UWB transmitter output SMA connector was placed very close to the chip as it is the highest frequency on the PCB.
# Influence of Source Shape on Semi-Airborne Transient Electromagnetic Surveys
## 4 Discussion
If the influence of the source shape is disregarded and an ideal linear source is directly employed for optimization-based inversion of all data, shallow false anomalies will inevitably appear in small offset regions, while deep true anomalies in large offset regions will be suppressed. Therefore, information about the source shape and spatial distribution must be incorporated into the inversion process [29].
## 2 Research Status of Key Technology for Environmental Perception of Intelligent Excavators
### Research Status of Segmentation and Recognition Technology of 3D Operation Scenes
[105] from MIT applied graph convolutional neural network to 3D point cloud processing based on PointNet, and proposed DGCNN, a dynamic graph volume point cloud processing algorithm, which effectively solved the problem of poor perception of local spatial features of PointNet, and greatly improved its point cloud classification and segmentation ability and robustness. To improve the efficiency of semantic segmentation of three-dimensional point clouds in large scenes, [PERSON] et al. [106] proposed RandLA-Net, which selected the most efficient random sampling by comparing existing sampling methods. They proposed a local feature sampler to reduce the information lost by random sampling. The network realizes efficient segmentation and identification of objects such as cars and trees in the cloud data of outdoor scenic spots. [PERSON] et al. [107] introduced a multi-modal feature fusion network (MFFNet) for 3D point cloud semantic segmentation. Unlike the previous approach of learning directly from colored point clouds (XYZRGB), MFFNet converts point clouds into 2D RGB images and frequency image representations for efficient multimodal feature fusion. [PERSON] et al. [108] proposed a novel end-to-end point-by-point LiDAR and image multimodal fusion network (PMNet) based on deep learning, which is used to fuse aerial image features for 3D segmentation of aerial point clouds. Due to the low resolution of the point cloud model and the lack of spatial location information of the image model, a single model cannot accurately calculate ore fragmentation. To solve this problem, [PERSON] et al. [109] proposed an ore fragmentation calculation method (ORFCM) based on the multi-modal fusion of point cloud and image. ORFCM takes full advantage of multi-modal data, including fine-grained object segmentation of images and spatial location information of point clouds. [PERSON] et al. [110] proposed a multi-level framework for three-dimensional point cloud ground segmentation of outdoor scenes based on shape analysis. The method explores the local shape and multilevel structure of the outdoor scene and constructs a probabilistic ground model to improve the accuracy and adaptability of the ground segmentation. [PERSON] et al. [111] proposed a semantic segmentation network for outdoor panoramic images based on distorted convolution. In this network, the distortion convolution module (DCM) and depth feature aggregation network (DFAN) are introduced into the semantic segmentation of panoramic images, which improves the segmentation accuracy. Table 5 summarizes the methods proposed in the literature [77; 78; 79; 80; 85; 86; 87; 88; 89; 90; 100; 101; 102; 103; 104; 105; 106; 109; 112; 113; 114; 115]. All of the above deep learning methods rely on a large set of labeled data. However, the process of data set annotation is time-consuming and labor-intensive, and the degree of automation is low. Therefore, the development of deep learning algorithms is limited.
## 4 Applications of PCEM  
The PCEM imaging system can be applied to monitor kinetic changes in the spatial distribution of dielectric permittivity for surface-adsorbed materials. This section describes PCEM applications with several examples, such as label-free live cell imaging, nanoparticle and protein-protein binding detection, and intensity enhancement of fluorescent tags embedded within live cells.
## 3 Extracting the Frequency Domain Features of Printed Images
### Image Multiscale and Multiorientation Texture Features Based on Gabor Filters
From the illustration, it is evident that larger-scale filters offer superior noise reduction capabilities but may also lead to a loss of finer details. The filters are grouped into \(l\) categories based on the number of scales (\(l=1,2,3,4,5\)). Additionally, each scale has four orientations (\(\theta=0^{\circ},45^{\circ},90^{\circ},135^{\circ}\)). Therefore, when computing the similarity of texture features, the weighted average of the correlation coefficients for the four orientations is calculated as follows:  
\[CC_{l}(E_{o},\ E_{s})=\frac{1}{4}\underset{\theta}{\sum}\frac{\sum_{i=1}^{N}\left( E_{s}^{\theta}(i)-\overline{E_{s}^{\theta}}\right)\cdot\left(E_{o}^{\theta}(i)- \overline{E_{o}^{\theta}}\right)}{\sqrt{\sum_{i=1}^{N}\left(E_{s}^{\theta}(i)- \overline{E_{s}^{\theta}}\right)^{2}}\cdot\sum_{i=1}^{N}\left(E_{o}^{\theta}( i)-\overline{E_{o}^{\theta}}\right)^{2}}, \tag{26}\]  
where \(E_{o}\) and \(E_{o}\) denote the response energy of the \(l-th\) scale and \(\theta-degree\) Gabor filter for the pre-print digital image and the scanned image of the printed product, respectively. \(\overline{E_{o}^{\theta}}\) and \(\overline{E_{s}^{\theta}}\) represent the mean energies corresponding to the \(\theta-degree\).
# Fake Base Station Detection and Link Routing Defense +
###### Abstract
Our scheme achieves zero false positives when the detection uses the optimal thresholds for both the time duration and the number of repeated requests. We also implemented the link routing part of the scheme to verify that the user equipment successfully redirects its connectivity to the legitimate base station, and we measured the link routing time duration to measure the overall threat impact after implementing our scheme. We show that our scheme, which consists of passive and active defenses, reduces the availability threat impact from an indefinitely long time to a few seconds. The rest of this paper is organized as follows. Section 2 discusses the related works that focus on attacks and defenses against fake base stations exploiting radio and wireless communications. We provide relevant background information on the overall 5G architecture with underlying protocols in Section 3. We describe our threat model with a clear distinction between a fake base station and a faulty base station in Section 4. In Section 5, we explain our detection and link routing scheme, while Section 6 describes our scheme incorporation in 5G networking. Section 7 provides a performance analysis of our scheme's experimental results for detection and link routing. We discuss future directions in Section 8 and conclude our paper in Section 9.
## 5 Discussion  
The advantage of the proposed method is to obtain the reasonable weightings of tomography observations quickly, but the feasibility remains to be further validated for different regions and weather conditions. With the further development of the GNSS meteorology, the proposed method can also be used for determining the weightings of the different data derived from more multi-GNSS observations. In addition, the weightings of PWV data from Moderate-resolution imaging spectroradome (MODIS), Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and Interferometric Synthetic Aperture Radar (InSAR) used for the troposphere tomography  
Figure 10: The change of final weightings for different input information over the period of Doy 303 to 317, 2015, while the weight of GPS observation is regarded as one.  
Figure 9: Wet refractivity profiles derived from two schemes and radiosonde (left), as well as the RMS and relative error (RE) of their differences (right) at various altitudes over the period of Doy 303 to 317, 2015.  
can also be determined based on the method proposed in this manuscript. As for the positioning aspect, [49] proposed five different weighting schemes for the GPS + GLONASS + Galileo + BeiDou combination in real-time PPP, and the result shows, when compared to the GPS-only solution, that the weighted strategy can reduce the formal errors and shorten the convergence time for horizontal and vertical components, respectively. Therefore, the proposed method in this manuscript may also have the potential for determining the accurate weightings of multi-GNSS observations during the PPP processing, but the final result remains to be further verified.
###### Abstract  
Cercospora leaf spot disease is a serious problem for lettuce cultivation worldwide. _Cercospora lactucae-sativa_, the causative agent of leaf spot disease on lettuce, was treated with non-thermal atmospheric pressure gliding arc (GA), tesla coil (TC) and dielectric barrier discharge (DBD) plasmas for the in vitro fungal inactivation of both mycelial growth and conidial germination. The fungus was exposed to the three plasmas individually for 5, 10, 15 and 20 min. The results showed that DBD plasma inactivated fungal growth during all exposure periods, and the highest inhibitory effect was caused by exposure to DBD plasma for 20 min, at 93.33% inhibition. The germination of fungal conidia was completely inactivated after exposure to all three non-thermal plasmas for 5 min, as observed 4 and 24 h after incubation. The pathogenesis of _C. lactucae-sativa_ on lettuce after plasma treatments for 5 min was examined by spraying an inoculation of the treated conidia on lettuce. The results showed that all three plasmas reduced the disease incidence and severity compared to the non-treated control. Therefore, the non-thermal atmospheric pressure GA, TC and DBD plasmas have antifungal potential for the inactivation of _C. lactucae-sativa_, making them an interesting novel technology for plant disease control.  
non-thermal plasma; antifungal activity; fungal inhibition; plant disease
# Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN
## 4 Performance Evaluation  
We implemented the EECMR protocol and compared it to LEACH [5], DBR [7], and EERBLC [9] in terms of network lifetime and residual energy. DBR, LEACH, and EERBLC were selected for performance comparison since the three protocols consider the clustering and depth of the routing protocol. Network performance was compared using the MATLAB simulation tool. The simulation parameters were set up as in [10; 12].
## 1 Introduction
### Related Works
Existing studies often focus on individual components or technologies without providing a holistic view of integrating 6G features into MCNs, including space-air-ground-sea-underwater layers. There is also a lack of studies describing both new applications and emerging optimization paradigms enabled by the 6G-enabled MCNs. This paper aims to bridge this gap by systematically examining how 6G technologies and applications, such as AI-driven networks, ultra-low latency communications, and advanced spectrum management, can be tailored to enhance MCNs, addressing current limitations and paving the way for future optimization and developments.
# Design of an Open Remote Electrocardiogram (ECG) Service
## 4 A Prototype for a Remote ECG Service
### The Server  
The server for our experiment is a container (or _dyno_) in the Heroku cloud. For the sake of simplicity, the prototype envisions one single container hosting several ECG services, instead of a hub deploying short-lived micro-services upon request.  
The server is implemented using the _Python/Flask_ micro-framework and the gunicorn WSGI HTTP server. It provides four families of endpoints:  
* / which returns a introductory page,
* /in/<id> used by patient's UA WebSocket,
* /<id> used by doctor's browser to open the ECG visualization page,
* /out/<id> used as endpoint for doctor's WebSocket,  
where <id> stands for the unique identifier of the sensor's device.  
The server waits for a patient's request at an /in/<id> endpoint. When the request arrives, the server upgrades the session to a WebSocket. Then, it records patient's identifier in a dictionary and prepares to receive a request with the corresponding <id> from doctor's side. Until the occurrence of such event, all ECG samples from patient's side are discarded. If doctor's request arrives first, the server returns a \"Retry later\" reply.  
When the server receives doctor's request on a /<id> path matching patient's identifier, it returns to the doctor's browser a response containing the JavaScript code that opens the WebSocket on /out/<id> and displays the ECG. After that point, the server implements a buffer for patient's data frames.  
Since each patient UA has a different identifier, a single server may host several ECG services at the same time, each of which is received by only one doctor UA.  
The security mechanisms announced in Section 3 are not fully implemented: namely, the server is persistent and communication uses plain HTTP. However, the design is ready for HTTPS security.  
We maximized share-ability using a free Heroku plan; this entailed some limitations (e.g., absence of HTTPS support) but was more consistent with our goal.
# Defect Tailoring in HfO2/Si Films upon Post-Deposition Annealing and Ultraviolet Irradiation
###### Abstract  
In the present work, a study of the structural defects in HfO2 thin films deposited by dip-coating on p-type silicon substrates treated under different conditions, such as air-annealing, ultraviolet irradiation, and simultaneous annealing-UV irradiation, is presented. HfO2 thin films were analyzed by grazing incidence X-ray diffraction, Raman spectroscopy, optical fluorescence, atomic force microscopy, and UV-Vis diffuse reflectance. Films treated at 200 \({}^{\circ}\)C and 350 \({}^{\circ}\)C present peaks corresponding to monoclinic HfO2. After UV treatment, the films became amorphous. The combination of annealing at 350 \({}^{\circ}\)C with UV treatment does not lead to crystalline peaks, suggesting that UV treatment causes extensive structural damage. Fluorescence spectroscopy and UV-Vis spectroscopy suggest that films present oxygen vacancies as their main structural defects. A reduction in oxygen vacancies after the second thermal treatment was observed, but in contrast, after UV irradiation, fluorescence spectroscopy indicated that more defects are created within the mobility gap, irrespective of the simultaneous annealing at 350 \({}^{\circ}\)C. An electronic band diagram was proposed assigning the observed fluorescence bands and optical transitions, which, in turn, explain the electrical properties of the films. The results suggest that the electronic structure of HfO2 films can be tailored with a careful choice of thermal annealing conditions along with the controlled creation of defects using UV irradiation, which could open the way to multiple applications of the materials either in microelectronics, optoelectronics, as well as in photocatalytic/electrocatalytic applications such as photodegradation and hydrogen generation.  
h +
Footnote †: journal: Applied Sciences  
_Article_
## 5 Real Security Implementations for VPNs and VLANs
### Security Issues and Countermeasures in VLANs
#### 5.2.2 Integration of IDSs and IPSs with VPNs  
There are several works in the literature tackling the issue of strengthening the security level in VPNs with IDSs and IPSs. For example, in [52] the authors considered the DoS attack based on the TCP SYN field, which is able to start TCP connections on HTTP serversin a very short time and periodically, using fake IP addresses. Firewalls, of course, are able to block the attack, but they become the victims. It is necessary therefore to create an IDS with a timely detection, consequently fixing the problem. After attack detection, the attack can be successfully blocked by creating a dedicated Access Control List (ACL), able to block the attack. The automatic blocking of DoS and similar attacks is feasible by integrating the VPN with an IPS. In [23], the authors face the issue of implementing an IPS in wireless networks (generally the solution is a Wireless IPS--WIPS): their idea is called WTLS-Based VPN (WBVPN), by which a single path is built between wireless device and their destination, exploiting the session-resuming feature of WTLS. In this way, the IPS can analyze the traffic and prevent unauthorized operations. The authors also considered a real case, showing the good performance of the proposed scheme. The article in [53] focuses on the additional efforts which could be made to overcome security issues in a network via VPN, proposing a new framework. It argues about the maximization of the synchronization of the security services, while reducing the overhead traffic.
## 5 Experimental Environment
### Experimental Results and Discussion
#### 5.2.3 Security Analysis
* **Throughput vs. Other Variables:** The throughput has a positive correlation with the send rate (\(0.46\)) and a negative correlation with the latency (\(-0.51\)), workers (\(-0.32\)), and failure rate (\(\%\)) (\(-0.32\)). Higher throughput is associated with a higher send rate, a lower latency and failure rate, and fewer workers. * **Failed Transactions vs. Other Variables:** Failed transactions have a strong positive correlation with the total transactions (\(0.89\)), indicating that as the number of transactions increases, the number of failed transactions also increases. * **Failure Rate (\(\%\)) vs. Other Variables:** The failure rate (\(\%\)) has a strong positive correlation with failed transactions (\(0.99\)) and total transactions (\(0.88\)), indicating that it closely tracks the number of failed transactions and total transactions.
## 4 Case Study
### MCS-PSO Optimization Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Controller**} & \multicolumn{5}{c}{**T\({}_{0}\)**} \\ \cline{2-7}  & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{2}{*}{\(K_{P1,K_{I1}}\)} & \(-0.1662\), & \(-0.1603\), & \(-0.1990\), & \(-0.2000\), & \(-0.1818\), & \(-0.1742\) \\ \cline{2-7}  & \(-0.2221\) & \(-0.2172\) & \(-0.2234\) & \(-0.2123\) & \(-0.2081\) & \(-0.2240\) \\ \hline \multirow{2}{*}{\(K_{P2,K_{I2}}\)} & \(-0.0729\), & \(-0.0852\), & \(-0.0601\), & \(-0.0700\), & \(-0.0771\), & \(-0.0764\) \\ \cline{2-7}  & \(-0.4805\) & \(-0.4940\) & \(-0.4840\) & \(-0.4891\) & \(-0.5255\) & \(-0.4665\) \\ \hline \multirow{2}{*}{\(K_{P3,K_{I3}}\)} & \(-0.1664\), & \(-0.1520\), & \(-0.1705\), & \(-0.1880\), & \(-0.1728\), & \(-0.1492\) \\ \cline{2-7}  & \(-0.1167\) & \(-0.1217\) & \(-0.1216\) & \(-0.1224\) & \(-0.1195\) & \(-0.1253\) \\ \hline \multirow{2}{*}{\(f(T_{O})\)} & \(27.6133\) & \(27.5518\) & \(27.5888\) & \(27.5918\) & \(27.6526\) & \(28.2008\) \\ \cline{2-7}  & \(-0.0729\) & \(-0.0852\), & \(-0.0601\), & \(-0.0700\), & \(-0.0771\), & \(-0.0764\) \\ \cline{2-7}  & \(-0.4805\) & \(-0.4940\) & \(-0.4840\) & \(-0.4891\) & \(-0.5255\) & \(-0.4665\) \\ \hline \multirow{2}{*}{\(K_{P3,K_{I3}}\)} & \(-0.1664\), & \(-0.1520\), & \(-0.1705\), & \(-0.1880\), & \(-0.1728\), & \(-0.1492\) \\ \cline{2-7}  & \(-0.1167\) & \(-0.1217\) & \(-0.1216\) & \(-0.1224\) & \(-0.1195\) & \(-0.1253\) \\ \hline \multirow{2}{*}{\(f(T_{O})\)} & \(27.6133\) & \(27.5518\) & \(27.5888\) & \(27.5918\) & \(27.6526\) & \(28.2008\) \\ \cline{2-7}  & \(-0.0729\) & \(-0.0852\), & \(-0.0601\), & \(-0.0700\), & \(-0.0771\), & \(-0.0764\) \\ \cline{2-7}  & \(-0.4805\) & \(-0.4940\) & \(-0.4840\) & \(-0.4891\) & \(-0.5255\) & \(-0.4665\) \\ \hline \multirow{2}{*}{\(K_{P3,K_{I3}}\)} & \(-0.1664\), & \(-0.1520\), & \(-0.1705\), & \(-0.1880\), & \(-0.1728\), & \(-0.1492\) \\ \cline{2-7}  & \(-0.1167\) & \(-0.1217\) & \(-0.1216\) & \(-0.1224\) & \(-0.1195\) & \(-0.1253\) \\ \hline \multirow{2}{*}{\(f(T_{O})\)} & \(27.6133\) & \(27.5518\) & \(27.5888\) & \(27.5918\) & \(27.6526\) & \(28.2008\) \\ \cline{2-7}  & \(-0.0729\) & \(-0.0852\), & \(-0.0601\), & \(-0.0700\), & \(-0.0771\), & \(-0.0764\) \\ \hline \multirow{2}{*}{\(K_{P2,K_{I2}}\)} & \(-0.4805\) & \(-0.4940\) & \(-0.4840\) & \(-0.4891\) & \(-0.5255\) & \(-0.4665\) \\ \cline{2-7}  & \(-0.1664\) & \(-0.1520\), & \(-0.1705\), & \(-0.1880\), & \(-0.1728\), & \(-0.1492\) \\ \hline \multirow{2}{*}{\(K_{P3,K_{I3}}\)} & \(-0.1667\) & \(-0.1217\) & \(-0.1216\) & \(-0.1224\) & \(-0.1195\) & \(-0.1253\) \\ \hline \multirow{2}{*}{\(f(T_{O})\)} & \(27.6133\) & \(27.5518\) & \(27.5888\) & \(27.5918\) & \(27.6526\) & \(28.2008\) \\ \cline{2-7}  & \(-0.1705\) & \(32.4716\) & \(32.5921\) & \(32.3587\) & \(32.4138\) & \(32.7917\) & \(33.0520\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The optimal parameters of PI controller.