esa-sceva/satcom-chunk-collection huggingface dataset:
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
###### Abstract  
The millimeter-wave (mmWave) band, which can provide data rates of multi-gigabits per second, could play a major role in achieving the throughput goals of 5G networks. However, the high-bandwidth mmWave signal is susceptible to blockage by various obstacles, which results in very large and frequent degradation in the quality of the received signals. TCP, the most representative transport layer protocol, suffers from significant performance degradation due to the very dynamic channel conditions of the mmWave signal. Therefore, in this paper, we propose a congestion control algorithm that guarantees sufficient throughput in 5G mmWave networks and that does not significantly worsen TCP fairness. The proposed algorithm, which is a modification of Scalable TCP (S-TCP) that is designed for high-speed networks, provides a more stable performance than the existing TCP congestion control algorithm in mmWave networks through simple modifications. In various simulation experiments that considered the actual mobile user environment, the proposed mmWave Scalable TCP (mmS-TCP) algorithm demonstrated throughput up to 2.4 times higher than CUBIC TCP in single flow evaluation, and the inter-protocol fairness index when competing with CUBIC flow significantly improved from 0.819 of S-TCP to 0.9733. Moreover, the mmS-TCP algorithm reduced the number of duplicated ACKs by 1/4 compared with S-TCP, and it improved the average total throughput and intra-protocol fairness simultaneously.  
active queue management; congestion control algorithm; TCP; 5G mmWave 2022223609. [[https://doi.org/10.3390/s22103609](https://doi.org/10.3390/s22103609)]([https://doi.org/10.3390/s22103609](https://doi.org/10.3390/s22103609))  
15 April 2022  
Accepted: 6 May 2022  
Submitted: 10 May 2022
## 1 Introduction
### Motivations and Contributions
The DNN-UC will be implemented in two phases where during the training phase, the DNN-UC model is trained with datasets whereas, during the testing phase, the performance of the DNN-UC scheme is validated and benchmarked with other existing UC schemes. In short, the contributions of this paper are summarized as follows:  
1. A new NOMA-based DNN architecture is developed and the DNN model is adapted to various NOMA environments with asymmetrical channel fading to carry out UC operation. A novel algorithm for the DNN-UC scheme is developed for the BS to optimally cluster the users before transmitting the superposed signals to different clusters. 2. The proposed DNN-UC model is comprehensively trained using a back-propagation learning algorithm with datasets containing cluster formation in a large variety of NOMA scenarios. Then, the trained model is validated by examining the mean squared error (MSE) in the testing phase. 3. An efficient power allocation scheme is derived based on the ultimate coalition formation to further enhance the throughput attainment subject to the SIC constraints. 4. Optimization of the hyper-parameters during the learning process of the DNN model is performed extensively to minimize the MSE and maximize the sum throughput to achieve a symmetrical balance between the MSE and throughput. The impact of different activation functions on the performance of the DNN-UC is also analyzed. 5. The performance of the DNN-UC scheme in terms of throughput maximization is investigated for different NOMA environments to demonstrate the capability of the proposed scheme of adapting to various scenarios.
## 4 Results and Discussion
### Analysis of Spatial Location Accuracy of Sugarcane Nodes  
In Figure 7, the three-dimensional spatial coordinates of each sugarcane node were calculated by Formula (3). Now the spatial location coordinate and its accuracy are shown in Table 4.  
According to Table 4, the maximum and minimum errors of the \(Z\) coordinate of the sugarcane node spatial location were 2.86 mm and 1.13 mm, and the average error was 1.88 mm. It should be noted that the position of the XOY plane will have a slight deviation with the movement, which is reflected in the slight coordinate difference in the \(X\)- and \(Y\)-axis before and after moving.
## 1 Introduction
This study aims to explore the feasibility and effectiveness of Sentinel-1 images and InSAR technique in landslide monitoring and analysis in the TGRA as a case example from an underdeveloped landslide-prone region.
## 3 Results
### Temporal Variation of Captures  
The temporal variations of captures can be observed in Figure 4. As can be observed, the first capture peak was reached at the beginning of April and corresponds to the peak of primary AH workers. It should be noted that the peak of captures of secondary workers had not yet been reached by the end of August when the trial was halted. As can also be observed, the added trend lines for the captures in the different combinations of trap and attractant are very coincident, indicating the lack of significant differences found in captures, in the different combinations of trap and attractant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fortnight** & **Trap/Bait** & **2** & **3** & **4** & **F Test** \\  & & & & **_p-Value_ \\ \hline
1 February & 1.83 & 1.70 & 1.80 & 1.30 & \textgreater{}0.05 \\
2 February & 4.67 & 4.77 & 4.80 & 4.20 & \textgreater{}0.05 \\
1 March & 8.73 & 7.97 & 8.40 & 7.67 & \textgreater{}0.05 \\
2 March & 18.67 & 17.93 & 17.87 & 16.90 & \textgreater{}0.05 \\
1 April & 23.40 & 22.60 & 23.20 & 22.20 & \textgreater{}0.05 \\
2 April & 14.27 \({}^{\text{a}}\) & 16.77 \({}^{\text{b}}\) & 14.47 \({}^{\text{a}}\) & 16.17 \({}^{\text{b}}\) & \textgreater{}0.05 \\
1 May & 7.23 \({}^{\text{a,b}}\) & 6.73 & 7.83 & 7.10 & \textgreater{}0.05 \\
2 May & 3.23 \({}^{\text{a,b}}\) & 3.03 \({}^{\text{a}}\) & 4.30 \({}^{\text{c}}\) & 3.90 \({}^{\text{b,c}}\) & \textgreater{}0.05 \\
1 June & 2.07 & 1.97 & 2.40 & 1.80 & \textgreater{}0.05 \\
2 June & 7.13 & 6.63 & 7.97 & 7.37 & \textgreater{}0.05 \\
1 July & 14.13 & 15.33 & 14.60 & 14.83 & \textgreater{}0.05 \\
2 July & 32.17 & 31.27 & 29.97 & 30.33 & \textgreater{}0.05 \\
1 August & 75.67 & 75.03 & 72.97 & 71.23 & \textgreater{}0.05 \\
2 August & 114.43 & 112.20 & 112.87 & 109.93 & \textgreater{}0.05 \\ \hline \hline \end{tabular} Note. – Commercial trap, commercial attractant; 2–Commercial trap, artisanal attractant; 3—Artisanal trap, commercial attractant; 4—Artisanal trap, artisanal trap, artisanal tar, artisanal trap, artisanal art attractant. In the fortnights where significant differences were found (\(p<0.5\)), different letters in superscript are indicative of significant differences between captures in the different combinations of trap/attractant.  
\end{table}
Table 1: Results of the ANOVA test of the quantities of Asian hornet captures in the different combinations of trap and attractant (bait), and in the different temporal intervals considered in the study.
## 3 LCA as a Tool in Digestate Use  
### LCA as a Tool to Measure Environmental Footprint  
Life Cycle Assessment (LCA) is a widely recognized and standardized methodology for assessing the environmental impacts associated with all life cycle stages of a product, process, or service. From raw material extraction to final disposal, LCA allows a comprehensive view of material and energy flows, emissions, and waste. This methodology is particularly valuable because it provides a scientific basis for identifying environmental improvement opportunities, making informed decisions, and comparing options transparently. The use of LCA is supported by international bodies and regulatory frameworks, such as the International Organisation for Standardization Standards [86; 87] and the Product Environmental Footprint (PEF) methodology promoted by the European Union. These guidelines ensure consistency and comparability of results, reinforcing their global acceptance as a key tool in environmental impact assessment.  
LCA studies typically follow four main steps: (1) goal and scope, which defines the system boundaries and establishes the functional unit for the assessment; (2) inventory analysis, whereby inputs and outputs are measured and documented; (3) impact assessment, which transforms inventory data into potential environmental impacts; and (4) interpretation, whereby the results are analyzed to support informed decision-making. This systematic approach encourages transparency and easier comparisons among various products or systems.
## 3 Results and Discussion
### Analysis of Spectral Reflectance  
Figure 4 shows the average reflectance spectra \(\pm\) standard deviation of fresh tea leaves of different varieties, where Figure 4a presents Vis/short-wave NIR spectra of samples collected by FieldSpec HandHeld 2 spectrometer, and Figure 4b presents the reflectance curves of long-wave NIR spectra of samples collected by SW2520 spectrometer. An obvious strong reflectance peak near 550 nm is shown, corresponding to low absorption of green light by green plant leaves; a deep absorption peak is observed near 480 nm and 675 nm, which is attributed to strong chlorophyll absorption within the green leaves; a large variation in spectral reflectance can be seen in the range of 700-775 nm, but a small variation is seen between samples [34; 36]; the absorption peaks in the range of 960-980 nm correspond to the second overtone of the vibrations of the free O-H group in phenols [37]. A gentle absorption peak near 1200 nm is assigned to C-H group vibrations; an absorption peak at 1465 nm is mainly associated with the vibration of phenolic O-H group [13]; the reflection peak appears at 1650 nm occurs in response to the C-H group vibration [38]. It demonstrated that both Vis/short-wave and long-wave NIR spectral ranges contain valid spectral information of tea polyphenol content. Therefore, it is necessary to splice the two waveband regions to characterize the content of tea polyphenols.  
Figure 3: Histogram of tea polyphenol content in fresh tea leaves of two varieties. Note: Data are presented as mean \(\pm\) standard deviation. The different letters (a and b) in the figures show significant differences in the tea polyphenol content of different varieties using Duncan test at \(p\)\(<\) 0.05 confidence level.
# A Harmonized Information Security Taxonomy for Cyber Physical Systems
## 2 Background
### Standards' Lifcycles
#### 2.2.1 ISO27000: Information Security Management Systems  
The ISO27000 suite covers an extensive range of topics, and while there is no single or definitive security lifecycle, there is an application flow, shown in Figure 1. This resembles the economics-based Deming cycle to ensure continuous quality improvement [12].  
Though widely applied, it does not provide a specific focus on CPS implementation. For CPSs, availability and integrity--rather than confidentiality--are the primary concerns. For IT information security, on the other hand, confidentiality has the highest priority. A similar iterative process is found in other standards.
## 3 Results  
### Mean Alignment 300 ms before Preferred Hand Move during Reach Out  
Group effects existed in the transverse plane (rotation) of head and trunk movement (head: F = 4.816, df = 1, \(p\) = 0.009; trunk: F = 3.080, df = 1, \(p\) = 0.046) and in the frontal plane of pelvis movement (F = 5.995, df = 1, \(p\) = 0.003). Children with CP apparently aligned their head with greater degrees of rotation in advance than did TD children (Table 1), indicating that children with CP prepared their body orientation with consideration of the reaching direction. For the task effect, it was found that children side-bent their head laterally before anterior and lateral reaches, and changed head position back toward neutral before initiating medial reach (F = 9.555, df = 2, \(p\) = 0.001). Children with CP reacted differently than TD children in different task directions in terms of the transverse plane of head and trunk alignments by apparently adjusting their head rotation alignment more laterally or medially before reaching in accordance with the task directions compared with TD children, especially when reaching medially (post hoc multiple comparisons with LSD procedure: CP mean: \(-7.21^{\circ}\) vs. TD mean: \(-0.07^{\circ}\), \(p\) = 0.027). For the interaction effect of trunk rotation (F = 4.092, df = 2, \(p\) = 0.029), children with CP positioned their trunk with greater degrees of rotation to the lateral side than TD children before anterior and lateral reaches. Children with CP aligned the trunk with certain degrees of rotation toward the medial side before medial reach, whereas TD children more likely kept the trunk in a neutral position before reaching. For pelvis movement, children with CP tended to position their pelvis tilt more to the medial side before reaching than TD children (significant group effect). No task or interaction effect of the pelvis was found. A group effect of the hips was found in hip alignment 300 ms before reaching. Children with CP aligned their hip in a more flexed and adducted position than did TD children (flexion: F = 13.732 and 11.752, respectively, df = 1, \(p\) < 0.001; adduction/abduction: F = 8.416 and 3.045, respectively, df = 1, \(p\) < 0.001 and \(p\) = 0.047, respectively). An interaction effect of the medial hip was found in adduction/abduction movement (F = 4.267, df = 2, \(p\) = 0.025). Before anterior and lateral reaches, children with CP aligned the medial hip in an adduction position (pointing toward the reach direction), but TD children did this in abduction position (away from the reach direction). At medial reach, children with CP adjusted the hip in a rather neutral position, but TD children used the hip abduction position. The group effect of ankle alignment 300 ms before the reach was significant, except for the alignment of ankle dorsiflexion and plantarflexion (F = 0.985 and F = 0.220, respectively,
## 2 Background
### Basic Theory
#### 2.2.2 Shrinkage Prediction Theory  
Models built using training samples have poor predictive power when predicting test samples. The reason may be that the test samples have different inclusion criteria, different monitoring strategies, or different result criteria. There may be over-fitting problems. After a certain limit is exceeded, the more variables are included in the model, the better the model fits, but the worse the ability to predict future data. In response to the above problems, a shrinkage theory [4] is proposed to improve prediction by shrinkage factors. Details could be found in [4].
## 1 Introduction
### Paper Organization  
The remainder of this paper is structured as follows: Section 2 provides an overview of related work. Section 3 outlines the preliminaries and assumptions underlying the proposed method. Section 4 details the FCM-OR protocol and its methodology. Section 5 presents simulation results and performance evaluations. Sections 6 and 7 discuss key findings and future research directions, respectively. Finally, Section 8 concludes the paper with insights and contributions.
# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 2 Methods
### Mask Decoding Module  
After feature extraction, traditional methods for detecting audio copy-move forgery often rely on manual thresholds, heuristic thresholds, or rules to determine whether two segments are similar. However, the Mask Decoding Module takes a different approach. It performs upsampling and convolution operations on the deep feature map obtained from the Correlation Detection Module, gradually transforming the 128 \(\times\) 8 \(\times\) 16 deep feature map to its original dimensions of 1 \(\times\) 128 \(\times\) 256 in terms of length and width. Finally, the similarity region mask is generated, visually locating the forged segments. Since the duration of copy-move forgery directly influences the width of similar regions in the Mel-spectrogram, and each feature point in the deep feature map integrates the featuresof a region in the original image, the Mask Decoding Module processes the deep feature map using multi-sized convolutional kernels, such as 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5, to better adapt to variations in the duration of copy-move forgery. The network architecture of the Mask Decoding Module is illustrated in Figure 6.  
Firstly, the width and height of the feature map are doubled by upsampling. Then, the convolution operation is performed using multi-sized kernels of different sizes. The results are concatenated along the channel dimension. After four layers of upsampling and multi-sized convolution, the feature map is transformed to 6 \(\times\) 128 \(\times\) 256. Subsequently, a 3 \(\times\) 3 convolution with an output channel of 1 is applied, resulting in a final output size of 1 \(\times\) 128 \(\times\) 256. The values range from [0, 1], which are mapped to [0, 255] to obtain the similarity region mask. Finally, the cross-entropy loss is computed between the similarity region mask and the label (a binary image of size 1 \(\times\) 128 \(\times\) 256, where forged region pixels are 255 and normal region pixels are 0, normalized to [0, 1] during the calculation). The loss function is defined as Equation (6).  
\[H(P,Q)=-\sum_{i=1}^{n}P(x_{i})logQ(x_{i}) \tag{6}\]  
In Equation (6), \(n\) represents the total number of features, which is 1 \(\times\) 128 \(\times\) 256, where \(P(x_{i})\) represents the value of the label and \(Q(x_{i})\) represents the predicted value of the localization model.
## 2 Theory
### Traditional Cam-Shift Algorithm  
A Cam-Shift algorithm is a target tracking algorithm that uses a color histogram as the target mode. It includes three parts: calculating color projection (back projection), Mean-Shift iteration, and Cam-Shift tracking.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Related** & \multirow{2}{*}{**Configuration**} & \multicolumn{3}{c}{**Number of Components Used**} & \multicolumn{3}{c}{**Filter Functions Resitized**} & \multicolumn{3}{c}{**Grouped**} & \multicolumn{3}{c}{**Inbuilt**} & \multicolumn{3}{c}{**Independent**} & **Need for Inverting or** \\
**Works/Year** & & **Active** & **Passive** & **VM** & **CM** &  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline
**Related** & **Configuration** & \multicolumn{3}{c}{**Number of Components Used**} & \multicolumn{3}{c}{**Filter Functions Realized**} & \multicolumn{3}{c}{**Grounded**} & \multicolumn{3}{c}{**Input**} & \multicolumn{3}{c}{**Input**} & \multicolumn{3}{c}{**Input**} \\
**Works/Year** & \multicolumn{3}{c}{**Active**} & \multicolumn{1}{c}{**Passive**} & **VM** & **CM** & **TAM** & **TIM** & **Tim** & **Elements** & **Tunability** & **Control of \(\omega_{0}\)** & **Double Input** \\ \hline
[31]/2017 & SIMO & DXCC+ 1, & R = 4, C = 2 & - & all five & – & all five & no & no & yes & no \\
[31]/2017 & SIMO & MOC+ 1, & R = 4, C = 2 & - & all five & – & all five & no & no & yes & no \\
[32]/2017 & SIMO & MTA+ 1, & C = 2, & – & LP, BP: HP & LP, BP: HP & – & yes & yes & yes & no \\
[33]/2017 & SIMO & DXCC+ 1, & R = 2, C = 2 & all five & all five & – & all five & no & yes & no & yes \\
[33]/2017 & MSDO & DXCC+ 1, & R = 2, C = 2 & all five & all five & – & all five & no & yes & no & yes \\
[33]/2018 & SIMO & DXCC+ 2 & R = 4, C = 2 & all five & all five & all five & no & no & yes & no \\
[33]/2018 & SIMO & DXCC+ 2 & R = 5, C = 2 & all five & all five & all five & – & no & yes & no \\
[33]/2018 & SIMO & DXCC+ 2 & C = 3 & 12, BP: HP & LP, BP: HP & – & – & no & yes & no & no \\
[33]/2019 & SIMO & DX+ 3 & R = 3, C = 3 & LP, BP: HP & LP, BP: HP & LP, BP: HP & no & no & no & no \\
[33]/2019 & SIMO & \(\leq\) 1, & R = 3, C = 3 & LP, BP: HP & HP, BS, AP & LP, BP: HP & LP, BP: HP & no & no & no & no \\
[33]/2019 & MSO & DXCC+ 1 & R = 2, C = 2 & all five & all five & – & no & yes & yes & yes & yes \\  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Related Works/Year**} & \multirow{2}{*}{**Configuration**} & \multicolumn{3}{c}{**Number of Components Used**} & \multicolumn{3}{c}{**Filler Functions Realized**} & \multicolumn{3}{c}{**Geounded**} & \multirow{2}{*}{**Inbuilt**} & \multirow{2}{*}{**Independent**} & \multirow{2}{*}{**Coord of \(w_{o}\)**} & \multirow{2}{*}{**Double Input Signals**} \\ \cline{3-3} \cline{5-10}  & & & **Active** & & & & & **Passive** & **VM** & **CM  
The paper is organized as follows, Section 2 describes the VDBA. The proposed mixed-mode universal filter is proposed in Section 3. The non-ideal gain effect, sensitivity performance, and parasitic impedance effect are investigated in Sections 4 and 5, respectively. The simulation results are given in Section 6, while the practical circuit implementation and the experimental results are presented in Section 7. Finally, the paper is concluded in Section 8.
## 6 Conclusions  
The following conclusions were obtained through a field test study on the vibration of a 30-ton heavy truck passing over a speed bump and analyzing the vibration and propagation laws caused by different travel speeds at different positions.  
1. The change law of vibration velocity response with the distance of vibration source is similar under different travel speeds. With the increase of vibration source distance, the vibration response decays exponentially, and the vibration velocity increases with the increase of travel speed, showing a power function relationship overall.
2. The superior frequency band of the vibration response caused by passing the speed bump is 0-40 Hz, and the primary frequency of the truck vibration is 20 Hz. The vibration response of each frequency band decreases with the increase of the vibration source distance. The decay rate of the high-frequency vibration is significantly higher than that of the low-frequency vibration.
3. Combined with the experimental monitoring data, the general functional relationship of vibration intensity with travel speed and vibration source distance is established. The empirical parameters of the impact of travel speed on the vibration of adjacent underground cultural relics are obtained. This research can predict the vibration of underground cultural relics, which has specific significance for protecting underground cultural relics.
4. Indeed, regarding the effect on historic structures, road traffic vibration surpasses subway vibration and proves to be more challenging to manage. Integrating micro-vibration control techniques in historic buildings should be accompanied by strategies like minimizing road traffic and expanding the buffer zone between buildings and roads.
## 3 Guard Interval Analyses  
We describe how to build the IIR system based on the channel with loops in Section 2. In this section, we derive the properties of the dedicated guard interval in a general multi-order IIR channel, which is different from the classical CP obtained by replicating the samples at the tail end of the signal. Additionally, we address two special models for 1 st-order and delay IIR channels.
# Evaluation of Early-Age Compressive Strength in Winter Prefabrication: A Comparative Study
## 3 Experimental Methods
### Compressive Strength Test  
Firstly, compressive strengths for Scenario 1 at 20 \({}^{\circ}\)C were evaluated to understand different formulations and cement effects in strict adherence to the guidelines specified in the NF EN 12390-3 standard. Subsequently, for the other two scenarios, the assessment of concrete compressive strength was conducted at a concrete age of 24 h. The decision to test at this specific time point was motivated by the need to evaluate early demolding characteristics in unfavorable conditions. In prefabrication factories, the challenge often lies in achieving early-age strength to facilitate rapid demolding, as opposed to the final strength, which typically presents fewer concerns.  
The evaluation process involved the use of cylindrical samples measuring 11 \(\times\) 22 cm, a dimension specified by the standard for assessing compressive strength. To ensure robust and representative results, three cylindrical samples were tested for each concrete mixture, and the average compressive strength was calculated.