esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic SDN Controller Load Balancing
## 5 DCC Two Phase Algorithm
### Phase 1: Initial Clustering
#### 5.1.1 Initial Clustering with the Distance Constraint
procedure:
1:\(S\gets C\)
2:\(S\gets S-M\)
3:for\(i=1:K\)do
4:\(CL_{i}\gets M_{i}\)
5:endfor
6:Candidates\(\leftarrow\)\(CL\)
7:while\(S\
eq\emptyset\)do
8:\(C_{next}\leftarrow\) The next controller in \(S\)
9:\(CL_{near}\leftarrow\) Find the nearest master from Candidates list
10:\(CL_{near}\gets CL_{next}\cup CL_{near}\)
11:\(S\gets S-C_{next}\)
12:if\(\mid\)\(CL_{near}\)\(\mid=M/K\)then
13:\(Candidates\gets Candidates-CL_{near}\)
14:endif
15:endwhile
16:for all\(CL_{i}\in CL\)do
17:\(maxDistanceCL_{i}\leftarrow\) max distance between two controllers in \(CL_{i}\)
18:endfor
19:\(maxDistance\leftarrow\) maximum of all \(maxDistanceCL_{i}\)
20:return\(CL\), \(maxDistance\)
```  
**Algorithm 2** Find Masters by 2-Approximation Greedy k-Center Solution  
Regarding the time complexity, Lines 1-6 take \(O(K)\) time. For each controller Line 8-12 checks the distance of a controller from all candidates, which takes \(O(M*K)\) time. In lines 16-18 for each cluster the heuristic checks the maximum distance between any two controllers in the cluster. There are \(M/K^{2}\) different distances for all clusters, thus taking \(O(M^{2})\) time. Line 19 takes \(O(K)\) time (\(K<M\)). The initial process with Heuristic 1 entails an \(O(M^{2})\) time complexity. Heuristic 1 is based on the distances between the controllers. When the controllers' position is fixed, the distances do not change. Consequently, Heuristic 1 can be calculated only one time (i.e., before the first cycle) and the results are used for the remaining cycles.
## 3 System Model and Parameterization
### Operation Cycle of SCN  
Under a dynamically varying environment, one or more edges can fail partially or fully. For a resilient system, there should be immediate substitution in the service. The requirement of immediate substitution can be achieved by recovering the failed or partially failed edges. Thus, the overall operation of the SCNs can be analysed in the following three states: normal state, partially failed state, and failed state.
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 4 Performance Evaluation
### Efficiency Evaluation of PPDC
The corresponding results are presented in Figure 11. In Figure 11, the encryption and decryption step which is the most important part of achieving users' privacy-preserving in PPDC, is the most costly compared with other steps of hashing and coding. Due to the bitwise XOR operation of encryption and decryption, such result is reasonable. As the size of dataset increases, the increase of encryption and decryption time will slow down, since we employ the knack on FM sketches in PPDC. Besides, the increasing curves of all steps in Figure 11 tend to be lower than the linear increase, which is in accordance with the result in Figure 9. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**A User**} & \multicolumn{3}{c}{**The Aggregator**} \\ \cline{2-6}  & **Comm. Cost** & **Comp. Complexity** & **Comm. Cost** & **Comp. Complexity** & **Round Complexity** \\ \hline Baseline & \(N\lceil\log_{2}n\rceil\) & \(O(N\lceil\log_{2}n\rceil)\) & \(nN\lceil\log_{2}n\rceil\) & \(O(nN\lceil\log_{2}n\rceil)\) & \(1\) \\ PPDC & \(q\lceil\log_{2}N\rceil\) & \(O(q\lceil\log_{2}N\rceil)\) & \(nq\lceil\log_{2}N\rceil\) & \(O(nq\lceil\log_{2}N\rceil)\) & \(d\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Communication Cost and Computation Complexity. Figure 9: Computing time of the unencrypted process and that of PPDC.
# Emotion Recognition in Usability Testing: A Framework for Improving Web Application UI Design
## 2 Materials and Methods
### Architecture of a Web Application Testing Framework Prototype with Integrated Emotion Recognition  
In the context of this research, the architecture of the proposed framework prototype has been designed as depicted in Figure 2 to incorporate web application testing integrated with emotion recognition capabilities.  
A Docker image (Docker, Inc., Palo Alto, CA, USA--Docker Desktop v.4.25.2; Docker version 24.0.6) was built which is being used to ensure portability and easy deployment of the emotion recognition application. The image is stored in the AWS ECR repository (Amazon Web Services, Inc., Seattle, WA, USA), allowing for easy management and configuration of the application in the cloud. The application is integrated into a Lambda function, which acts as the main component of the system and is activated during API requests from the web page being tested. When the Lambda function receives a request, it uses an emotion recognition algorithm to identify the emotions based on the data provided (Figure 3).  
Figure 2: Architecture of the proposed framework prototype for web application testing.  
After recognizing the emotion, the function sends a response back to the web page via the API. Lambda function performance can be monitored and analyzed using the AWS CloudWatch platform, which provides detailed logs and metrics about function performance. This information helps to identify problems, optimize performance, and ensure prototype efficiency. Data collected during the user session is sent to the Hotjar (Hotjar Ltd., St Julian's, Malta) and Google Analytics (Google LLC, Mountain View, CA, USA) platforms, providing a more detailed picture of user behavior and interaction with the website. This data allows for a more detailed analysis of user behavior and identification of potential problem areas.  
The integrated architecture provides a comprehensive test application process that effectively monitors user behavior, identifies problems, and improves web applications based on data and emotion recognition (Figure 4).  
This ensures a reliable usability testing process and optimizes system efficiency and effectiveness using modern cloud computing technologies.  
Figure 3: Emotion recognition algorithm activity diagram.
## 6 On Control Messages Protection
### A Spoofing-Based DoS Attack  
In this section, we show that FLOOD protocol is subjected to the spoofing-based DoS attack. Figure 6 shows an example of how an attacker can perform a spoofing-based DoS attack in a network composed of three nodes. Whenever the attacker receives a HELLO message, it can add a _new fake node_ and broadcast the message to the other vehicles.  
In this way, the protocol never completes. In order to avoid this kind of attack, each node could be deployed with the list of all the nodes identifiers belonging to the system. Thus, if the attacker tries to add a new fake node to the network, each node rejects it, because the fake node is not contained in that  
Figure 6: A DoS attack.  
list. However, due to the underwater conditions and to the high distances among nodes, it may happen that a node \(u_{1}\) is not able to directly contact nodes \(u_{2}\) and \(u_{3}\) belonging to the system. The attacker could exploit this situation in order to use \(u_{2}\) and \(u_{3}\) identifier to perform a DoS attack.
## Appendix B  
To compute the absorption saturation effect, first we need formulas for the absorption efficiency \(Q_{a}\) of the chloroplasts. The original model assumed that the chloroplasts were spherical and that their absorption efficiency can be modeled using the anomalous diffraction theory which is applicable since there is almost no difference in the real part of the index of refraction for the chloroplasts and the surrounding cell medium.  
For spherical chloroplasts, the absorption efficiency is given by:  
\[Q_{a-sph}(z)=2\biggl{[}\frac{1}{2}+\frac{e^{-z}}{z}+\frac{(e^{-z}-1)}{z^{2}} \biggr{]}\;, \tag{11}\]  
\[z=a_{o}^{*}(\lambda)u\;,\]  
\[u=\rho_{cp}d_{cp}\;,\]  
\(a_{o}^{*}(\lambda)\) is the specific mass absorption coefficient of chlorophyll-a at low concentration in units of m\({}^{2}\cdot\)gr\({}^{-1}\). \(\rho_{cp}\) is the chlorophyll-a mass density inside the chloroplast in gr\(\cdot\)m\({}^{-3}\) and \(d_{cp}\) is the diameter of the chloroplast in meters.  
Note that in the limit of small \(z\) we have:  
\[Q_{a-sph}(z)=\frac{2z}{3}\;, \tag{12}\]  
Since in the limit of small concentrations the chloroplast absorption will be unsaturated and equal to the limiting absorption, we can write the absorption saturation gain function for a spherical chloroplast as:  
\[G_{a-sph}(z)=\biggl{(}\frac{3}{2\,z}\biggr{)}Q_{a-sph}(z)\;, \tag{13}\]  
The saturated absorption spectrum for spherical chloroplasts can, therefore, be computed as:  
\[a_{o}^{*}(\lambda)=\biggl{(}\frac{3}{2\,z}\biggr{)}Q_{a-sph}(z)a_{o}^{*}( \lambda)\;, \tag{14}\]  
It is interesting to estimate what the effect of chloroplast shape maybe in the estimate of this packaging effect. Chloroplasts are often disk shaped and we will use the form for the absorption efficiency for randomly oriented disks.  
\[Q_{a-disk}(z_{d})=2-E_{3}(z_{d})\;, \tag{15}\]  
With,  
\[z_{d}=a_{o}^{*}(\lambda)\rho_{cp}\tau_{cp}=a_{o}^{*}(\lambda)\;u_{cp}, \tag{16}\]  
\(\tau_{cp}\) is the thickness of the disk. We also define \(f\) as the ratio of the thickness \(\tau_{cp}\) of the disk to its diameter \(d_{cp}\). \(E_{3}(z)\) is the exponential integral function of order 3 which is defined as:  
\[E_{n}(z)=\int_{1}^{\infty}\frac{e^{-z\;t}}{t^{n}}dt\;,\]In the limit of small \(z\) we have:  
\[Q_{a-dsk}(z_{d})=2\,z_{d}\, \tag{19}\]  
The gain function for randomly oriented disks becomes:  
\[G_{a-dsk}(z_{d})=\left(\frac{1}{2\,z_{d}}\right)Q_{a-dsk}(z_{d})\, \tag{20}\]  
Finally the saturated absorption spectrum for disks can be computed as:  
\[a_{v}^{*}(\lambda)=\left(\frac{1}{2\,z_{d}}\right)Q_{a-dsk}(z_{d})a_{o}^{*}( \lambda)\, \tag{21}\]  
The exact formulas given above can be approximated to within a 10% relative error by the following simple exponential forms.  
\[a_{v}^{*}(\lambda) =\left(\frac{3}{2\,z}\right)\Bigl{(}1-e^{-2z/3}\Bigr{)}a_{o}^{*}( \lambda)\, \tag{22}\] \[a_{v}^{*}(\lambda) =\left(\frac{1}{2\,z_{d}}\right)\Bigl{(}1-e^{-2z_{d}}\Bigr{)}a_{o}^ {*}(\lambda)\, \tag{23}\]
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 1 Introduction
### Entry Points for Attacks to 5G Networks
#### 1.1.3 The Backhaul and Core Network  
The third entry point includes the backhaul and CN [22; 23]. The backhaul, which is responsible for data transfer between the RAN and the CN, presents a potential access point for attackers to intercept control and data traffic. Integration of diverse access technologies like femtocells and non-Third Generation Partnership Project (3 GPP) WiFi [2] introduces new vulnerabilities, particularly in 4G/5G, with interfaces like X2/Xn (see Figure 1) and diameter signaling protocols amplifying signaling overload and transitioning to Internet Protocol (IP). The EPC CN faces severe threats due to its flat IP-based architecture and direct connections from Base Stations (BSs) to the ALL-IP network [22]. Weaknesses in the Evolved Packet System (EPS)-AKA scheme, handover procedure, and Machine Type Communication (MTC) security architecture exacerbate risks [22; 24]. Additionally, vulnerabilities in the General Packet Radio Service (GPRS) Tunnelling Protocol (GTP) protocol used in EPC Non-Access Stratum (NAS) expose networks to abnormal packet threats and traffic analysis [25]. Furthermore, virtualization and software-defined networks in the CN introduce emerging threats by dispersing user and control-plane traffic across network elements and non-trusted networks [7].
## 3 Frequency-matched Resonators  
Analogous to [PERSON] et al.'s [PERSON] et al. (2019) axial rod design, the frequency-matched resonator was designed to vibrate at 50 kHz in a flexural mode under fixed-free boundary conditions. The fixed boundary condition was expected to apply the desired clamping effect described in [PERSON] et al. [PERSON] et al. (2019). The refined design of these frequency-matched resonators consists of a 7.9 \(\times\) 7.9 mm square rod that is 23.4 mm long. Figure 2a shows the 2 DFFT of a baseline measurement which was taken prior to bonding the resonators to the plate waveguide. The peak at a wavenumber of \(-\)58.1 rad/m and a frequency of 53.9 kHz shown in Figure 2a indicates that the S0 mode is being excited.  
The Frequency-matched Resonators were positioned on the waveguide as shown in Figure 1 and adhered to the plate waveguide. The A-scans described in section 2 were measured and used to calculate the 2 DFFT shown in Figure 2b.  
When comparing Figure 2a to Figure 2b, the frequency-matched resonators do not reduce the transmitted S0 signal. This is believed to be due to the resonators vibrating differently than expected. That is, unlike the axial vibration described in [PERSON] et al., the flexural vibration of the frequency-matched resonator is likely to cause unwanted out-of-plane displacement.  
The frequency-matched resonators are an attempt at designing resonators based on resonant frequency, but as shown, it was not an effective approach for this application. In the next section, we propose a clamping resonator that was designed to effectively clamp the S0 wave's in-plane motion without introducing unwanted out-of-plane displacements. That is, we focused on how the resonator clamps the surface of the waveguide.  
Figure 1: Schematic (not to scale) of experimental setup: (**a**) top-view of the transducer positions with respect to the resonator array, and (**b**) a zoomed in view of the resonator array that shows how the resonators are positioned with respect the wave-propagation direction. The squares are the contact areas between the resonators and the plate waveguide.  
Figure 2: The 2 DFFT of the transmission measurements for (**a**) the Baseline, (**b**) the Frequency-matched Resonator and (**c**) the Clamping Resonator. The diagonal lines are the dispersion curves for the forward and backward propagating S0 Lamb wave modes. The vertical and horizontal lines show where the frequency- and wavenumber-spectrum slices shown in Figure 6 are obtained. The peak values are noted in each figure.
## 2 Materials and Methods
### Data Preprocessing
#### 2.3.1 Data Cleaning  
The presence of redundant and anomalous records in the dataset can affect the learning of a model and give unrealistic accuracy due to overfitting [13], so such instances need to be removed in the preprocessing phase. For example, we have dropped out'saddr' and 'daddr' features, which represent source and destination IP addresses. In our work, these features have been excluded because they mostly represent private IP addresses of packets, and packets usually have different IP addresses in such realistic datasets, so they have no value in training. Moreover, these two columns contain erroneous values. The'sport' and 'dport' columns have been dropped because they contain the same values and have no value in class distinction. Two more features--'stime' and 'ltime'--have not been included as they represent start packet time and end packet time, even though these represent important information. However, the reason for their elimination is that the same information has been provided by 'Srate' and 'Drate'. Another feature, 'daddr', contains erroneous values, such as memory errors, so it was eliminated. We noticed that the dataset has many missing values. Missing values in data can make analysis difficult and can lead to decreased accuracy [14]. We have replaced all the missing values with zeros.
# YOLO-OHFD: A YOLO-Based Oriented Hair Follicle Detection Method for Robotic Hair Transplantation
###### Abstract
Figure 1 illustrates the superiority of OBB labeling over HBB labeling for hair follicles. While oriented detection methods, such as R2 CNN [20], RRPN [21], SCRDet [22], Rol-Transformer [23], and Gliding Vertex [24], among others [25; 26; 27; 28; 29], have proven effective in applications like remote sensing and text detection, their direct application to oriented hair follicle detection is limited due to the increased classification difficulty of densely packed and overlapping follicles. As shown in Figure 2, the research question of this study focuses on addressing the challenges in manual follicle analysis, which include inefficiency due to time-consuming processes, subjectivity arising from operator-dependent variability, susceptibility to errors in dense follicle classification, and the high costs associated with the need for trained personnel and delayed procedures. Figure 1: Comparison of hair follicles labeled with different detection boxes. **(a)** HBB-labeled hair follicles. **(b)** OBB-labeled hair follicles. To overcome the limitations of existing approaches, this study introduces a YOLO-based oriented hair follicle detection method named YOLO-OHFD. The proposed method incorporates oriented object detection to address the challenges of detecting hair follicles with large aspect ratios and arbitrary orientations. It employs ECA-Res2 Block to enhance feature focus on hair follicles, improving classification accuracy. Additionally, the Feature Alignment Module (FAM) and decoupled heads are utilized to enhance OBB localization precision. An OBB-based dataset is constructed specifically for hair follicle detection and classification, providing a robust foundation for robot-assisted hair transplantation tasks. The main contributions of this paper are summarized as follows:  
* An oriented object detection method is introduced for the hair follicle detection task, enabling precise localization of hair follicles and reducing background interference in robot-assisted hair transplantation. * A YOLO-OHFD framework is proposed, integrating ECA-Res2 Block, FAM, and decoupled heads to enhance the network's accuracy in detecting and classifying hair follicles. * An OBB-based oriented hair follicle detection dataset is developed, offering a benchmark for evaluating methods designed for hair follicle detection and classification in dermoscopic images. The rest of this paper is organized as follows. Firstly, Section 2 provides a detailed description of the proposed method's framework and its specific details. Section 3 presents the rotating frame-based scalp hair follicle dataset, followed by the experimental results and discussion. Finally, the conclusions are presented in Section 4.
# An Assessment of Environmental RF Noise Due to IoT Deployment
## 2 Literature Review
They combined the USRP N210 device with LabView as an SDR system and post-processing in MATLAB; however, to produce a cost-effective RNSS, this study used GNU Radio software version 3.8.1.0 [22] as part of the SDR tool. The integration of USRP and GNU Radio has shown reliability. Additionally, GNU is a free software that contributes to achieving low-cost development. This literature review found limited resources from previous studies on ambient RF noise or environmental RF interference in South Africa. [PERSON] [23] experimented with RFI measurements from the Square Kilometer Array (SKA) environment in South Africa; however, their campaign surveyed only remote and reserved locations per the SKA guidelines. In contrast, this research had focused primarily on populated agglomerations, which would possibly present different characteristics than the SKA's uninhabited places. The South African Radio League (SARL) is another organization concerned with monitoring the level of ambient radio noise. As reported by [PERSON] [24], SARL concluded that the noise floor in the radio spectrum was rising as the number of devices emitting radio energy increased; however, it was challenging to support this claim without quantitative data. This paper, therefore, makes an effort to address SARL's concerns.
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
## 2 Orbit Determination Method
### Construction of Multi-Source Observation Equations
\begin{array}{l}\rho_{\text{STAi}}=H_{\text{GG}}(x_{\text{GNSSI}},x_{STAi}, x_{0i},t_{i})+\xi_{STAi},\xi_{STAi}\in(0,P_{STAi}^{-1})\\ \rho_{LEOI}=H_{\text{LC}}(x_{\text{GNSSI}},x_{LEOIi},x_{0i},t_{i})+\xi_{LEOIi}, \xi_{LEOIi}\in(0,P_{LEOIi}^{-1})\\ \rho_{ISLi}=H_{ISL}(x_{\text{GNSSI}},x_{0i},t_{i})+\xi_{ISL},\xi_{ISLi}\in(0,P_{ ISLi}^{-1})\\ \end{array} \right. \tag{1}\]  
where \(\rho_{\text{STAi}}\), \(\rho_{LEOI}\) and \(\rho_{ISLi}\), respectively, represent the satellite-to-ground, satellite-based and inter-satellite ranging data at reference time \(t_{i}\); \(\xi_{STAi}\), \(\xi_{LEOIi}\) and \(\xi_{ISLi}\) are, respectively, the observation noises of the corresponding observation v; \(P_{STAi}\), \(P_{LEOIi}\) and \(P_{ISLi}\) are corresponding weight matrixes; \(t_{i}\) is the normalized calculation time; \(x_{\text{GNSSI}}\) is the orbit state parameters of the navigation satellites; \(x_{STAi}\) is the state parameters of the ground monitoring stations; \(x_{LEOIi}\) is the orbit state parameters of the LEO satellites; \(x_{0i}\) is the state parameters related to the observation data, such as the carrier phase ambiguity, clock difference parameters or inter-satellite link device delay; \(H_{GG}\) is the partial derivative matrix of the ranging data \(\rho_{\text{STAi}}\) and the estimated parameters \((x_{\text{GNSSI}},x_{STAi},x_{0i})\) at the time \(t_{i}\); \(H_{\text{LC}}\) is the partial derivative matrix of the ranging data \(\rho_{LEOIi}\) and the estimated parameters \((x_{\text{GNSSI}},x_{LEOIi},x_{0i})\) at the time \(t_{i}\); \(H_{ISL}\) is the partial derivative matrix of the ranging data \(\rho_{ISLi}\) and the estimated parameters \((x_{\text{GNSSI}},x_{0i})\) at the time \(t_{i}\).
## 5 Conclusions  
Detailed inspection of the mucosa in [PERSON]'s esophagus is often performed using Narrow Band Imaging with a magnified view, to improve visualization of mucosal and vascular patterns. However, most clinical classification systems have suboptimal diagnostic accuracy and inter-observer agreement. Computer-aided detection is able to overcome limitations in the varying observations of different endoscopists and help improve diagnostic accuracy.  
This work has presented the benefit of endoscopy-driven pretraining for the classification of NBI-zoom videos. The accuracy of the ensemble based on pseudo-labeled bootstrapping with 5 models, increases by 10% when a domain-specific endoscopy data set (GastroNet) is used to initialize the weights compared to random initialization. Additionally, this training approach outperforms ImageNet pretraining by approximately 2% for the classification of NBI-zoom videos in addition to more stable convergence as indicated clearly by the lower standard deviation. Since the performance of the proposed model is non-inferior, and even slightly better, compared to the broadly used conventional ImageNet pretraining, we anticipate that it serves as a better source for pretraining other models related to endoscopic image analysis. Future studies should confirm this hypothesis.  
Conceptualization, J.v.d.P., F.v.d.S., and M.S.; methodology, J.v.d.P. and F.v.d.S.; software, J.v.d.P.; validation, J.v.d.P., M.S. and J.d.G.; formal analysis, J.v.d.P.; investigation, J.v.d.P., M.S., and J.d.G.; resources, W.C., E.S., J.B., F.B.-S. and P.H.N.d.W.; data curation, M.S., J.d.G., W.C., E.S., F.B.-S., and J.B.; writing--original draft preparation, J.v.d.P.; writing--review and editing, F.v.d.S. and P.H.N.d.W.; visualization, J.v.d.P.; supervision, F.v.d.S., P.H.N.d.W., and J.B.; project administration, F.v.d.P., J.B. and P.H.d.W.; funding acquisition, J.B and P.H.d.W. All authors have read and agreed to the published version of the manuscript.  
This research is part of The Argos Project and was funded by the Dutch Cancer Society and the Netherlands Organization for Scientific Research (NVO), as part of their joint strategic research program 'Technology for Oncology'. Grant number 15191 ARGOS.  
We gratefully acknowledge the support of NVIDIA Corporation by the donation of the Titan Xp GPU used for this research.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BE & Barrett's Esophagus \\ CAD & Computer-Aided detection \\ EAC & Esophageal AdenoCarcinoma \\ NDE & Non-Dysplastic Barrett's Esophagus \\ \end{tabular}