esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Normalized Approach for the Throughput Prediction
What has just been shown is of considerable scientific interest as it implies that, in the case of a single-product, single-routing production line that is balanced, the bottleneck rate \(r_{b}\) acts as a scale factor for the throughput even in the general variability case. Hence, once the mean and the coefficient of variation of the processing times in the production line are known, the absolute throughput of the line at the steady state can be estimated by passing through the normalized one. For the purposes of discussion, we can proceed with the construction of the model for predicting the throughput of a single-product line with single routing, without considering the average processing time as an input to the network and instead setting the normalized throughput as the network output. Thus, once trained, this neural network model will be able to predict the throughput of a wide variety of cases, resulting in a more generic tool. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Alpha Value**} \\  & \(\alpha=0.5\) & \multicolumn{2}{c}{\(\alpha=1\)} & \multicolumn{2}{c}{\(\alpha=1.5\)} & \multicolumn{2}{c}{\(\alpha=2\)} & \multicolumn{2}{c}{\(\alpha=2.5\)} & \multicolumn{2}{c}{\(\alpha=3\)} \\ \hline
**WIP** & \(E(TH/r_{b})\) & \(\Delta\%\) & \(E(TH/r_{b})\) & \(\Delta\%\) & \(E(TH/r_{b})\) & \(\Delta\%\) & \(E(TH/r_{b})\) & \(\Delta\%\) & \(E(TH/r_{b})\) & \(\Delta\%\) \\ \hline
1 & 19.99 & 0.57\  
Figure 4: The registered throughput and normalized throughput when varying alpha value (i.e., different color) with processing time fixed at 10 min. Figure 8: The registered throughput and normalized throughput when varying the machines’ processing time value (_i.e._, different color) with alpha value fixed at 1.5.
###### Abstract
This mechanism captures global feature information, effectively reducing the semantic gap between the encoder and decoder. The main contributions of this work can be divided into the following sections:  
1. We introduce a new segmentation network called MSLUnet, specifically designed for lesion segmentation in medical images. MSLUnet efficiently extracts multi-scale and global contextual information while requiring fewer parameters and less computational effort, thereby enhancing the accuracy of lesion segmentation;
2. We present the Multi-scale Feature Extraction (MSE) block, which captures multi-scale feature information at a granular level. This block utilizes a symmetric structure with multiple basic blocks to comprehensively extract features at different scales;
3. We propose a convolutional decoder module that combines depth-separable convolution with an inverse bottleneck structure to optimize the extraction of global contextual information and minimize the parameter count. The module is further enhanced with normalization, residual concatenation, and activation functions to improve its performance in medical image segmentation;
4. Experimental evaluations demonstrate that our MSLUnet, with only 2.18 million parameters--just 38% of the parameters of the traditional Unet--achieves superior segmentation results compared to other models across various public medical image datasets. The remainder of this paper is structured as follows: in Section 2, some research advances related to the work of this paper are presented. In Section 3, we elaborate on the specific architecture of MSLUnet. Specific experimental results are provided in Section 4. Section 5 discusses the limitations of the methodology used in this study and future research directions. Finally, Section 6 summarizes the paper.
###### Abstract
(2010) proposed a cumulative tower lifting cable strut installation technology based on the understanding that the process of constructing a cable dome is essentially a process of assembling components with known original length according to a certain topological relationship. By arranging a lifting tower at the construction site to lift the central tension ring and drive the spine cable to rise together, most components can be installed near the ground as much as possible, so as to reduce the internal force and traction of components in the construction process. Taking a 100 m cable dome as an example, the whole construction process is analyzed using the dynamic relaxation method to verify the feasibility of the method [31]. Based on cumulative tower lifting cable strut installation technology, [PERSON] et al. (2011) ultimately canceled the erection of the central tower and proposed a non-bracket construction method suitable for the cable dome, which uses the compression ring of the structure as the lifting support of the cable strut system to integrally pull and lift it. Taking the cable dome roof of the Wuxi New Area Science and Technology Exchange Center (Wuxi, China) as an example, the whole construction process was simulated using the nonlinear dynamic finite element method, verifying the advantages of this method, such as good tension efficiency and high cable network stability in the whole process [32]. For the cable truss, [PERSON] et al. (2014) proposed an inclined non-bracket tow-lifting construction technology with fixed jacks, which utilizes jacks connected to the anchor node around the cable truss to obliquely pull and lift the assembled cable strut system from low altitude to high altitude. Taking the tension ring truss of Yueqing Stadium as an example, an analysis of the whole process of self-mass initial state, lifting traction and tension, and disturbance stability was carried out. The results show that this method has the advantages of less high-altitude operation, lower cost and shorter construction period for erecting brackets, simple construction equipment, and high construction efficiency [33; 34]. Therefore, considering the advantages of less aerial work, no need to erect brackets, simple and economical construction equipment, flexible arrangement of construction period, and a safe and reliable construction process, this paper introduces the idea of non-bracket construction for annular cable supported grid structures, optimized according to the characteristics of semi-rigid hybrid structures. A non-bracket construction method suitable for cable supported grid structures is proposed. Taking the annular cable supported grid structure of the roof of the Education City Stadium in Qatar (Ar Raysyan, Qatar), a simulation of the whole construction process is carried out using the nonlinear dynamic finite element method to verify the feasibility and the key technologies of this method are introduced in detail.
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 3 Test Procedures  
### TVAC Overview  
The test was carried out in the TVAC located at the Experimental Test Laboratory of the Department of Aerospace Science and Technology of Politecnico di Milano. Figure 4 offers an overview of the facility overview: the chamber is a steel cylinder with a diameter of 1 m, 1.2 m long, 5 mm thick, for an effective volume of 0.9 m\({}^{3}\). Inside, an aluminum cold plate--800 mm \(\times\) 630 mm \(\times\) 15 mm size--is located in the bottom part, as shown in Figure 4b. The plate can be heated up to 200 \({}^{\circ}\)C due to the six embedded resistors, powered at 350 W each. The resistors are controlled with a Geran 1600 PID controller that can keep the plate temperature within \(\pm 1\,^{\circ}\)C from the set point whenever the controller feedback thermocouple is placed on the plate. The base plate is also connected to the fluid cooling system through a serpentine attached underneath, as shown in Figure 4c. The cooling system is composed of two independent single-stage refrigeration systems, that can lower the temperature of the plate to \(-75\,^{\circ}\)C. For more information on the cooling and heating systems of the chamber, the reader is invited to read [30, 31, 32, 33].  
The cooling system is controlled by a second Gefran controller that operates in relay mode, i.e., it keeps switching on and off the cooling system in order to reach the set point temperature. This peculiar control policy is more subject to inaccuracies: when cooled, the plate temperature can undershoot the set point up to \(-10\,^{\circ}\)C. This behaviour is well known and documented [30], and it has to be compensated during the TVAC operations.  
Above the plate there is a shroud (Figure 4b) connected to the cooling system, which can be operated independently with regard to the base plate (Figure 4d). In addition, six infrared lamps of 1 kW each are equally distributed on the upper part of the shroud as shown in Figure 4b. As explained in Section 3.2, the shroud cooling systems and the lamps are not activated during the test.  
A high vacuum inside the chamber is achieved by a dual stage pumping system [30]: the first stage brings the pressure down to \(1\times 10^{-3}\) mbar; passed this threshold, the second stage (i.e., a turbomolecular pump) is activated and the pressure can be dropped to \(1\times 10^{-6}\) mbar which satisfies the ECSS maximum pressure requirement for thermal vacuum test of \(1\times 10^{-5}\) mbar [34].
## 4 Experiments
### Dataset  
We performed experiments on a real large-scale location-based social network (LBSN) dataset, UMN/Sarwat Foursquare [3; 5]. This dataset contains 2,153,471 users, 1,143,092 venues, 27,098,480 social connections, 1,021,970 check-ins, and 2,809,581 ratings that the users have assigned to the venues. All this information was extracted from the Foursquare application through the public API. The information of all users has been anonymized according to users' geolocations; ID is used to represent each user and his geospatial location. The same process has been conducted for the venues' information. The data consist of five files--user.dat, venues.dat, socialgraph.dat, checkins.dat, and ratings.dat. The content details and the usage of the files are described in Table 2.  
Before running the algorithm, reverse geocoding was performed on both users.dat and venues.dat in order to find the natives. Reverse geocoding is a process of reversing the coding of a certain location (latitude and longitude) to a readable address or place name. This process permits the identification of a nearby street address, place, or area subdivision, such as a neighbourhood, state, or country. After implementing the reverse geocoding process, users who originate from the same venue or hometown will be classified as the natives of that location. Users who are linked by the socialgraph.dat will be classified as friends. For this case study, we chose users from Malaysia, Singapore, and Indonesia.
## 4 Analysis of Research Variables
### DL iBLER  
The term _Block Error Rate_ (BLER) can be defined as the ratio of transmitted blocks with error and the total number of transferred blocks and is expressed as a percentage. Detection of errors in the transport blocks on the receiver side is performed using the _Cyclic Redundancy Check_ (_CRC_) technique. In measuring network performance, the _Initial Block Error Rate_ (iBLER) metric is often used, which shows the ratio of the number of blocks with initial transmission errors and the total number of initially transmitted transport blocks [42]. If certain types of modulation are observed in the transport channel, then the expression for iBLER can be written in the following form:  
\[\text{DL iBLER}= \frac{\text{QPSK.ErrTB.Ibler}+16\text{QAM.Err.TB.Ibler}+64 \text{QAM.ErrTB.Ibler}}{\text{QPSK.TB}+16\text{QAM.TB}+64\text{QAM.TB}} \tag{3}\]  
where the meanings of the marks of the variables appearing in the model are defined in Table 4. The three variables shown are, in addition to iBLER, also taken as independent variables of the predictive model.
## 2 Materials and Methods
### Timeline and Data Sources  
Keeping in view RTM use and recent technological advancements for crop yield prediction, a short timeline from 2015 to June 2023 was selected, as radiative transfer use in agriculture is a recent area of interest from researchers. This timeframe effectively highlights recent advancements and current status, as described in the objectives of the article.  
Five electronic databases, namely ScienceDirect, Scopus, Nature, IEEE Xplore and MDPI, were selected as primary data sources to find relevant articles for comprehensive coverage in this review. Moreover, Google Scholar was also used as an external source of studies. Search findings from Google Scholar were merely duplications of primary data sources. As such, after the removal of duplicate articles, only unique results from the primary sources mentioned above were considered in this review.
# Intelligent Dynamic Power Control with Cell Range Expansion for Small-Cells in 5G HetNets
## 1 Introduction
That is, in the proposed DPC scheme, each MUE first collects the received signal strength indicator (RSSI) measurements from SBSs and sends them to the serving MBS. Then, the MBS finds MUEs with strong interference from neighboring SBSs based on a given target threshold of CRE and offloads a fraction of MUEs from MBSs to SBSs. In addition, SBSs divide their SUEs and CUEs into two groups, i.e., inner and outer groups, to assign different subchannels and dynamically allocate the appropriate transmission power to increase the performance of both SUEs and CUEs. Through simulation results, it is shown that the proposed DPC scheme outperforms others in terms of the capacity and outage probability of SUEs and CUEs. In other words, the proposed DPC scheme increases the mean SUE capacity by up to 119% with the same performance of the mean MUE capacity compared to other DPC schemes with CRE. The rest of this paper is organized as follows. Section 2 introduces the system model of 5G HetNets and assumptions adopted in this paper. Section 3 describes the proposed DPC with CRE while Section 4 evaluates the simulation results with various parameters. Finally, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **Duplex Mode** & **Almost Blank Subframe (ABS)** & **Split Subchannels** & **Power Control** & **Power Control Devices** & **Target Devices** \\ \hline
[27] & TDD & \(\surd\) & \(\surd\) & Dynamic & MBS & CUE \\
[28] & TDD & \(\surd\) & n/a & Static & MBS & CUE \\
[29] & TDD & \(\surd\) & n/a & Dynamic & MBS, SBS & CUE \\
[30] & TDD & \(\surd\) & n/a & Dynamic & MBS & CUE \\
[31] & TDD & \(\surd\) & n/a & Dynamic & SBS & CUE \\
[32] & FDD & n/a & n/a & Static & SBS & CUE \\
[33] & FDD & n/a & \(\surd\) & Dynamic & SBS & CUE \\
[34] & FDD & n/a & \(\surd\) & Dynamic & MBS & CUE \\
[35] & FDD & n/a & \(\surd\) & Dynamic & MBS & CUE \\
[36] & FDD & n/a & \(\surd\) & Dynamic & SBS & CUE \\ Proposed & FDD & n/a & \(\surd\) & Dynamic & SBS & CUE, SUE \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related work. Section 5 contains the conclusion with future research directions. Table 2 summarizes the main symbols and notations in this paper.
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 4 Results and Discussion
### Results for Only Voice Streams
#### 4.1.2 Results during the TCP ACK Attack  
During this attack, Asterisk was flooded with TCP ACK packets [58]. Figure 8 represents the number of different TCP packets. As can be seen from the graphs, the number of TCP SYN and the TCPSYNACK packets is almost zero. There were only a few of these packets and of TCP FIN packets because Asterisk was successfully accessed through a browser and the session was terminated. A huge number of TCP RST packets was noticed. These were generated by the Asterisk web server to terminate the problematic session that was created by the TCP ACK attack. Termination of the session (attack) was impossible.  
Figure 8: Number of different TCP packets sent during the TCP ACK attack.  
Figure 7: Instantaneous values of the jitter for the voice stream between VM_4 and Asterisk (**a**) and VM_3 and Asterisk (**b**) during the TCP SYN attack.  
Figure 9 presents the summarized results for voice traffic that was exchanged in both directions between VM_4 and VM_3 during the attack. Figure 9a shows the summarized results for the voice stream that was exchanged between VM_4 and Asterisk, and Figure 9b shows the summarized results for the voice stream that was exchanged between VM_3 and Asterisk. There was a decrease in the maximum jitter value and the average jitter value for both streams. Compared to the results from when Asterisk was not being attacked, the maximum jitter values were still higher. The average jitter values were almost identical to those in Figure 4. In spite of the occurrence of the TCP ACK attack, the values were within the norm. Again, there was no packet losses.  
Figure 10a shows how the jitter changed for the voice stream between VM_4 and Asterisk in both directions. Figure 10b presents the variation in jitter values for the voice stream between VM_3 and Asterisk in both directions. As can be seen from the two graphs, the jitter values were lower than those obtained during the TCP SYN attack.
# Data Reduction in the String Space for Efficient kNN Classification Through Space Partitioning
## 1 Introduction
A relevant algorithm in this topic is Reduction through Homogeneous Clusters (RHC) by [PERSON] and [PERSON] [17], which reduces the initial set of data by obtaining same-class clusters of prototypes for then generating a new single prototype as the median value of the ones in the cluster. As most PG techniques, RHC was designed for statistical representations, being thus unsuitable for its application to structural data. In this work, we aim at further studying the possibilities of PG as a DR strategy in structural representations due to their aforementioned relevance in the PR field. More precisely, we present an adaptation of the state-of-the-art RHC method by [PERSON] and [PERSON] [17] to the string space. As aforementioned, this algorithm replaces same-class subsets of prototypes by new elements generated by estimating their median value. Thus, the main issue to tackle is the actual retrieval of the median value of a group of strings, which in our case we resort to the set median as the calculus of the exact median string constitutes an NP-hard problem [18]. Additionally, in order to compare the performance of RHC strategy in both statistical and structural spaces, we make use of the Dissimilarity Space (DS) technique [19] to map the initial strings representation onto a feature-based codification so that additional conclusions can be gathered. The rest of the work is structured as follows: Section 2 introduces the general background of the work; Section 3 presents the adaptation of the RHC algorithm to the string space; Section 4 explains the evaluation methodology proposed; Section 5 shows and discusses the results obtained; finally, Section 6 concludes the work and proposed future work to be addressed.
# The Influence of Laser Linewidth on the Brillouin Shift Frequency Accuracy of BOTDR
## 3 Experiment Setup  
### Experiment Setup for Measuring Laser Linewidth  
To measure the linewidth of the utilized lasers, a linewidth measurement setup based on the delayed self-heterodyne interferometer (DSHI) was configured as shown in Figure 6.  
The laser output was split into two beams by a 1 \(\times\) 2 coupler. The upper beam was delayed by a section of single-mode fiber. A polarization controller was used for reducing the polarization mismatch. The lower beam passes through an acoustic optical modulator (AOM) to be frequency shifted by 200 MHz. Finally, the two beams beat in a photodetector through a 3-dB coupler. The electronic signal after PD was acquired and recorded by an electrical spectrum analyzer (ESA). Five lasers with different linewidths were utilized in the experiments. Table 1 gives their nominal specifications.  
\begin{table}
\begin{tabular}{c c c} \hline Model & DFB-DSM-1550-20-PM & Manufacturer \\ \hline Wavelength & 1550.12 nm & \\ \hline Nominal linewidth & 1.1 MHz & **OPELAK** \\ \hline Max power & 20 mw & \\ \hline Model & KG-DFB-15-M-10-S-FP & Manufacturer \\ \hline Wavelength & 1550.12 nm & \\ \hline Nominal linewidth & 100 kHz & **ONQUER** \\ \hline Max power & 20 mw & \\ \hline Model & COSF-SC-1550-M & Manufacturer \\ \hline Wavelength & 1550.12 nm & \\ \hline Nominal linewidth & 10 kHz & **connect** \\ \hline Max power & 10 mw & \\ \hline Model & SDAS-NLW-PL & Manufacturer \\ \hline Wavelength & 1550.12 nm & \\ \hline Nominal linewidth & 3 kHz & \\ \hline Max power & 17 mw & \\ \hline Model & Koheras BasiK E15 & Manufacturer \\ \hline Wavelength & 1550.12 nm & \\ \hline Nominal linewidth & \textless{}100 Hz & **NLKT** \\ \hline Max power & 40 mw & \\ \hline \end{tabular}
\end{table}
Table 1: Nominal specifications of the five lasers used in the experiments.  
Figure 6: Delayed self-heterodyne interferometer to measure the laser linewidth.
## 5 Proposed Algorithm
### Markov Decision Process Formulation  
Generally, RL problems can be represented as instances of MDP [47], in which the actions taken in the present state determine the future state. Therefore, the process of optimizing the center position, major radius, minor radius, and rotation angle of the ellipsoidal trajectories of UAV-MEC servers to minimize the total transmission latency and total transmission energy consumption of MDs can be formulated as an MDP with tuple \(<S,A,R>\). At each time step t, the agent observes a state \(s\in S\), selects an action \(a\in A\)transitions to the future state \(s^{\prime}\), and obtains a reward \(r\in R\). The design of state, action, and reward are presented as follows.  
State: Consists of positional parameters of each UAV's ellipsoidal trajectory:  
\[S=\{C_{x,j},C_{y,j},R_{x,j},R_{y,j},\theta_{j},\forall j\in N\}, \tag{32}\]  
where \(C_{x,j},C_{y,j},R_{x,j},R_{y,j}\), and \(\theta_{j}\) denote the x-coordinate center, the y-coordinate center, the x-coordinate radius or major radius, the y-coordinate radius or minor radius, and the rotation angle of the ellipsoidal trajectory of UAV-MEC server \(j\), respectively.  
Action: An action involves one of the following: moving the center position horizontally or vertically in a positive or negative direction, incrementing or decrementing the major or minor radius, changing the rotation angle to either clockwise or anticlockwise, or keeping the current state unchanged.  
Reward: An effective reward design acts as a map, directing the learning agent in the direction of the desired result. It gives the agent important feedback on the effectiveness of its actions. Thus, to effectively solve the center position, size, and shape of the ellipsoidal trajectories of the UAV-MEC servers, we have designed our reward function with the help of our objective function, which is defined as  
\[r=\begin{cases}\frac{1}{\eta_{1}\Phi},&\text{if objective decreases}\\ &\text{in current state},\\ \frac{1}{\eta_{2}\Phi},&\text{otherwise},\end{cases} \tag{33}\]  
where \(\eta_{1},\eta_{2}\) are constants. The values of \(\eta_{1},\eta_{2}\) are both within the range \((0,1)\) and \(\eta_{2}>\eta_{1}\), and \(\Phi\) represents the system objective in Equation (22).
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Approximate Nonlinear Method  
The approximate nonlinear method (see Table 2) was used to fit the Gompertz and Logistic curve models separately.