esa-sceva/satcom-chunk-collection huggingface dataset:
# A Programmable ROADM System for SDM/WDM Networks
## 4 Simulation Results
### Traffic and Bandwidth Blocking Probability Results in NSF Topology  
The proposed SDM/WDM ROADM system is evaluated in the National Science Foundation (NSF) network. In the NSF network, 7-core MCFs are used for the simulation. As shown in Figure 5a, bypass links are set up based on numbers of neighbor nodes, to ensure direct connections are available between two neighbor nodes of the current node.  
Random traffic is used for the simulation. Heuristic routing wavelength/core assignment (RWCA) algorithm is used to evaluate the blocking probability.  
Figure 5 shows the NSF topology and the results of blocking probability in NSF topology network by comparing between fully-flexible setup and the proposed setup with bypass configurations. In both traffic and bandwidth blocking probability as shown in Figure 5b,c, the proposed solution shows similar performance as the fully flexible setup.  
The simulation results in Figures 4 and 5 suggest the proposed SDM/WDM ROADM system has an acceptable performance by comparing with the fully flexible topology in terms of traffic and bandwidth blocking probabilities. In addition, the proposed system reduces the total cost by roughly calculating the required quantity of WSSs and the total port number.  
Figure 4: Traffic blocking probabilities and bandwidth blocking probabilities. The traffic request ratio between Neighbor pair-nodes:one pair-nodes:two-hop pair-nodes is 1:1:1, 3:2:1, 6:3:2, respectively shown in (**a**,**d**), (**b**,**e**), (**c**,**f**) named traffic request 1, 2, 3. Three bypass connection topologies are corresponding with Figure 3 Topology 1, 2, 3.  
Figure 5: (**a**) NSF topology. (**b**) Traffic and (**c**) Bandwidth blocking probabilities in NSF topology.
## 6 Results: Application of the Sensitivity Analysis on the \"Traffic Jam Chauffeur\"
#### 6.1.2 Sensitivity Distribution Function on Logical Situations  
The distribution of the sensitivity of the AV to logical situations, as presented in Section 5.1, is obtained as follows:  
\(q\left(Y_{j}\right)=\frac{S_{j}}{W}\) and \(\sum_{Y_{j}}q\left(Y_{j}\right)=\sum_{Y_{j}}\frac{S_{j}}{W}=1\), with \(\mathrm{W}=\sum_{Y_{j}}S_{j}\) the normalization factor, \(Y_{j}\) the logical situation j and \(S_{j}\) its sensitivity.  
Considering the eight logical situations and their sensitivity, we obtain  
\[\mathrm{W}=\sum_{j=1}^{8}S_{j}=0+0.4+1.6+1.6+1.4+1.4+2.6+2.6=11.6\]  
Thus, for each of the above logical situations, the sensitivity distribution is:  
- Logical situation Y1: \(q\left(Y_{1}\right)=\frac{0}{11.6}=0\)  
- Logical situation Y2: \(q\left(Y_{2}\right)=\frac{0.4}{11.6}=3.45\times 10^{-2}\)  
- Logical situations Y5 and Y6: \(q\left(Y_{5}\right)=q\left(Y_{6}\right)=\frac{1.4}{11.6}=12.07\times 10^{-2}\)  
- Logical situations Y3 and Y4: \(q\left(Y_{3}\right)=q\left(Y_{4}\right)=\frac{1.6}{11.6}=13.79\times 10^{-2}\)  
- Logical situations Y7 and Y8: \(q\left(Y_{7}\right)=q\left(Y_{8}\right)=\frac{1.6}{11.6}=22.41\times 10^{-2}\)  
Obviously, the value obtained for \(q\left(Y_{7}\right)\) and \(q\left(Y_{8}\right)\) is greater because the sensitivity of the AV to Y7 or Y8 is stronger. Note also that the total sum of probability for the eight situations is equal to 1 by definition.  
Therefore, we have five classes, which in terms of priority can be considered as follows:  
- Priority 1: Y8 = Y7  
- Priority 2: Y4 = Y3  
- Priority 3: Y6 = Y5 (rather close to priority 2)  
- Priority 4: Y2  
- Priority 5: Y1
## 3 Results
### Cooling Efficiency  
In Figure 5, the variation of the cooling efficiency with respect to air velocity, pad thickness, dry-bulp temperature and relative humidity of the inlet air for each pad analysed is shown. The \(CL\) pad shows the highest cooling efficiency, followed by the \(CS\) pads. The \(PS\) pads presented the lowest one. An inversely proportional correlation between the cooling efficiency and inlet air velocity for \(CL\) and \(PS_{1}\) pads is shown in the Figure 5a, meanwhile a direct proportional correlation is presented for the other ones. The effect of the inlet air velocity over the cooling efficiency for the \(BC\) material was negligible. In the range of the inlet air velocity analysed, \(CL\) and \(CS_{1}\) pads presented the biggest change in the cooling efficiency, up to 5.8 and 7.0%, respectively. The change was below 1.5% for the other pads. Figure 5b shown that the effect of the pad thickness over the cooling efficiency was stronger, with up to 17.4% of change for the \(CL\) pad. Figure 5c,d show the effect of the inlet air thermal conditions. All the pads show a similar behaviour, the cooling efficiency increase for high dry-bulb temperatures and low relative humidity of inlet air. The cooling efficiency was more susceptible to the changes in the relative humidity of the inlet air (\(\phi_{i}\)) in comparison with the changes of the dry-bulb temperature of the inlet air (\(T_{db_{i}}\)). For the conditions evaluated, the cooling efficiency increased up to 1.2% (\(CL\) pad) when \(T_{db_{i}}\) increase, and fall up to 5.0% (\(CL\) pad) when \(\phi_{i}\) augment.  
No statistically significant evidence was found (\(p\)-value = 0.5114) to consider that there are differences between the medians of the \(PS_{1}\) and \(PS_{2}\) pads for the cooling efficiency and pad thickness relationship.  
Figure 4: Comparison of the convective mass transfer coefficient (\(h_{M}\)) for different pad materials and their dependency of the operative and constructive variables as air velocity (\(u\)), pad thickness (\(L\)), dry-bulb temperature of inlet air (\(T_{db_{i}}\)) and relative humidity (\(\phi_{i}\)). (**a**) \(h_{M}\) as a function of \(u\). (**b**) \(h_{M}\) as a function of \(L\). (**c**) \(h_{M}\) as a function of \(T_{db_{i}}\). (**d**) \(h_{M}\) as a function of \(\phi_{i}\). For each plot the other parameters remained constants: \(u\) = 1.5 m s\({}^{-1}\), \(L\) = 0.1 m, \(T_{db_{i}}\) = 30 \({}^{\circ}\)C and \(\phi_{i}\) = 60%. \(CL\) pad: black solid line, \(CS_{1}\) pad: blue solid line, \(CS_{2}\) pad: blue dashed line, \(BC\) pad: red solid line, \(PS_{1}\) pad: magenta solid line, \(PS_{2}\) pad: magenta dashed line.
# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
## 1 Introduction
### Main Contribution  
Combining the above content, this paper focuses on the SAG-IoT network architecture, considering the multi-objective optimization problem of energy efficiency and spectrum efficiency of the system. Analyzing the uplink, UAVs collect data from IoT devices on the ground and transmit them to the satellite. The main contributions of this paper are as follows:  
* This paper formulates a multi-objective optimization (MOO) problem problem for SAG-IoT network, which maximizes the energy efficiency and spectrum efficiency of the system by optimizing the power of IoT devices, sub-channel selection, UAV power and UAV position deployment.
* We take into account non-line-of-sight (NLOS) channels between UAVs and IoT devices, which deviates from the common practice in most articles that only consider line-of-sight channels. This approach is more aligned with real-world scenarios, where obstacles such as buildings and trees can obstruct the direct path between the transmitter and receiver in wireless communication, although it makes UAV position deployment sub-problem more complex. We solve this sub-problem by introducing relaxation variables and SCA algorithm.
* With the help of the \(\epsilon\)-constraint method, we transform the multi-objective problem into a single-objective optimization (SOO) problem. Then we decompose the complex single-objective optimization problem into three sub-problems. The matching algorithm is used to solve the sub-channel selection sub-problem, Dinkelbach algorithm, SCA algorithm, and Lagrange dual algorithm are used to solve the power optimization sub-problem, and the UAV position deployment sub-problem is solved by introducing relaxation variables and SCA algorithm. Finally, the solution of the original problem is obtained by solving the three sub-problems alternately.
* Simulation results show the proposed algorithm converges to at least one sub-optimal solution, and the influence of each variable on the result is simulated.  
The rest of the paper is organized as follows: Section 2 introduces the system model and formulates the problem of maximizing the EE and SE of the system. Section 3 presents the solution to the proposed problem and gives a detailed derivation process. Section 4 conducts numerical simulation, gives the simulation results, and analyzes them. Section 5 summarizes this paper, and gives the shortcomings and future research directions.
# A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters' Diacritics and Image Layers
## 3 The Proposed Approach
### The Metrics of Evaluating Steganography
#### 4.2.1 Imperceptibility  
Steganography is a method used to hide information in a way that is difficult to detect or predict. The success of steganography is usually measured by its imperceptibility, which refers to the ability of an attacker to locate or predict the hidden information [51]. In the case of concealing text, achieving imperceptibility requires the use of cover texts that are very large. However, using text alone does not always guarantee imperceptibility, especially when concealing large amounts of data. Most methods for handling Arabic texts rely on the principle of modifying elements within the text. If these modifications are made in certain locations, the text's reader may not detect them or assume them to be inadvertent errors. Nonetheless, if the errors are repeated in a specific or excessive manner within the text, the reader may become suspicious that something is being hidden. To ensure imperceptibility in this situation, an additional cover, such as an image, may be used to conceal the entire text.  
In terms of imperceptibility, the scenario referred to as the \"Hamza scenario\", where the entire text comprises movements, may be more effective than the scenario of hiding and revealing diacritics. This is because in the Arabic language, it is more likely that an expert would notice an incorrect diacritic than detect a hidden diacritic, especially when it concerns a Hamza.  
When considering situations where the cover text entirely consists of diacritics and the amount of secret data is limited, it is prudent to select the diacritic scenario as the preferred option. This preference arises from the difficulty in detecting any modifications made to diacritics embedded within fully formed text, particularly when the changes occur within the interior of words. Incorporating diacritics into such scenarios is therefore likely to enhance the robustness and effectiveness of the text steganography technique, leading to an improved quality of secret data transmission.
## 3 Automatic Language Identification Using Speech Rhythm Features
### Usability of Rhythm Metrics in Language Identification  
Several studies on linguistics reported some tendencies of rhythm metrics for certain languages [11; 12]. In general, English showed the lowest value for %V. \(\Delta\)V provided little differences between languages, whereas significant differences in \(\Delta\)C were observed, indicating an adverse tendency to %V. [PERSON] et al. [12] investigated nPVI and rPVI for 18 languages. According to the study, the nPVI value of English was significantly different from values of French, Spanish, Singapore, and Mandarin, while rPVI resulted in hardly any difference. In [14], four languages (English, Spanish, Dutch, and French) were investigated with regard to seven different metrics (%V, \(\Delta\)V, \(\Delta\)C, Varco-V, Varco-C, nPVI-V, and rPVI-C).  
Some studies have investigated rhythmic characteristics for Asian languages. The Korean language was examined by [29], in which the characteristics of Korean were found to be similar to those of Japanese; both belong to mora-timed languages--that is, they are located between syllable-timed languages and stress-timed languages. In the results of rhythm metrics evaluation, Korean showed characteristics of intermediate languages of syllable-timed languages and stress-timed languages, except for \(\Delta\)V and nPVI-V [30]. Chinese showed low \(\Delta\)C and rPVI-C compared to English and Japanese [11; 12]. However, its nPVI value was higher than that of Japanese and its %V value was even higher than that of English.  
According to the studies, the rhythm metrics are meaningful to represent language-specific prosodic properties such as accentual lengthening, word-initial lengthening, and phase-final lengthening [14]. Although some metrics such as \(\Delta\)V and nPVI-V did not show a consistent tendency, rhythm metrics provided evident properties for discriminating different languages [11; 31]. Thus, it imparts a strong expectation that rhythm metrics provide sufficient language information as language-discriminativefeatures in LID tasks. In particular, the features are simply estimated and require lower computation time and hardware capacity compared to the conventional feature extraction approaches such as i-vector and DNN-BN, thus supporting a device-driven LID module.
## 2 Results
### Analysis of Volatile Constituents  
In total, 128 volatile compounds were separated and identified based on comparison of the mass spectra with the National Institute of Standards and Technology (NIST, Gaithersburg, MD, USA) atomic spectra database, Wiley Registry of Mass Spectral Data, and the related literature (Table 1) [20]. The total yield of the volatile compounds was indicated by the highest amount in the root (95.66%), followed by stem (84.05%), leaf (74.91%), and flower (65.33%). We identified 18 monoterpenes, 34 sesquiterpenes, and 80 other volatile compounds from _V. fauriei_. Monoterpenes, namely borneol (**11**, 22.96%), bornyl acetate (**13**, 19.92%), and camphene (**1**, 3.33%) were found in high amounts in the roots. Interestingly, \(\beta\)-terpinene (**3**), myrcene (**4**), \(p\)-cymene (**5**), and borneol (**11**) were detected only in the root. The leaf extract was characterized by a high content of _endo_-borneol (**10**, 11.48%) and \(\gamma\)-terpinene (**7**, 3.23%). Sesquiterpenes accumulated primarily in the stem (21.55%) and roots (20.66%). The stem contained the highest amount of the sesquiterpene caveophyllene oxide (**34**, 17.45%). Seven sesquiterpenes (**20**-**22**, **24**-**26**, and **50**) were root-specific, while four others (**33**, **37**, **39**, and **51**) were flower-specificIsovalerenic acid (**53**, 3.33%), which produces the strong smell of _Valeriana_ species, was found only in the root of _V. fauriei_. Figure 3: Transcript levels of MEP biosynthetic genes in different organs from _V. fauriei (Vf)_. Genes encode the following enzymes: (**a**) _V/DXS_, 1-deoxy-d-xylulose 5-phosphate synthase; (**b**) _V/DXR_, 1-deoxy-d-xylulose 5-phosphate reductoisomerase; (**c**) _V/MCT_, 2-C-methyl-D-erythritol 4-phosphate cytidyylyltransferase; (**d**) _V/CMK_, 4-(cytidine 5-diphosphate)-2-C-methyl-D-erythritol kinase; (**e**) _V/MDS_, 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; (**f**) _V/HDS_, (_E_)-4-hydroxy-3-methylbut-2-enyl diphosphate synthase; (**g**) _V/HDR_, (_E_)-4-hydroxy-3-methylbut 2-enyl diphosphate reductase; and (**h**) _V/GDS_, geranyl diphosphate synthase. Figure 2: Transcript levels of MVA biosynthetic genes in different organs of _V. fauriei (Vf)_. Genes encode the following enzymes: (**a**) _V/AACT_, acetoacetyl-CoA thiolase; (**b**) _V/HMICS_, 3-hydroxy-3-methylglutaryl-CoA synthase; (**c**) _V/HMRG_, 3-hydroxy-3-methylglutaryl-CoA reductase; (**d**) _V/MK_, mevalonate kinase; (**e**) _V/PMK_, phosphomevalonate kinase; (**f**) _V/MVD_, mevalonate diphosphate decarboxylase; (**g**) _V/FDS_, famesyl diphosphate synthase; and (**h**) _V/IDI_, isopentenyl diphosphate isomerase.
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 2 Speech Enhancement
### Speech Estimation Algorithms  
Many speech enhancement algorithms follow the aforementioned scheme. For instance, the well-known approaches, Wiener filter, SS [4], STSA [5], and minimum mean-square error log-spectral amplitude estimator (LSA) and its modified versions [6; 24; 26].  
The main difference among these speech estimators lies in the filter gain function definition. For example, the Wiener filter is an MMSE estimator that minimizes the expected value of the squared error between the clean speech and the enhanced speech \(E\{|x(n)-\hat{x}(n)|^{2}\}\). Therefore, in the following, we express the gain function of the Wiener filter directly as [5]  
\[G_{MMSE}(k,l)=\frac{\xi_{k,l}}{1+\xi_{k,l}}, \tag{2}\]  
where \(\xi_{k,l}\) is the a priori SNR computed for each \(k\) bin frequency and each time segment \(l\).  
From this statement, we can define other speech estimation algorithms in terms of the \(G_{MMSE}\). For instance, in the SS algorithm the gain function is defined as the square root of the maximum likelihood estimator of each spectral component variance [25]. In terms of \(G_{MMSE}\), this can be defined as  
\[G_{SS}(k,l)=\sqrt[6]{G_{MMSE}} \tag{3}\]  
with \(\beta=2\). However, several modifications of this algorithm have been studied in terms of changing the value of \(\beta\)[1].  
For the LSA family, the gain function also depends on the \(G_{MMSE}\)[6]  
\[G_{LSA}(k,l)=G_{MMSE}\exp(\frac{1}{2}\int_{v(k,l)}^{\infty}\frac{e^{-l}}{t}dt) \tag{4}\]  
where  
\[v(k,l)=\frac{|Y(k,l)|^{2}}{\lambda_{d}(k,l)}G_{MMSE} \tag{5}\]  
with \(\lambda_{d}(k,l)=E[|D(k,l)^{2}|]\) the variance of the kth spectral component of the noise for frame \(l\). Thus, different modifications of the LSA estimator also express the gain function in terms of the \(G_{MMSE}\). For instance, the gain function of the optimally modified version of LSA is defined as  
\[G_{OMLSA}(k,l)=G_{LSA}^{p(k,l)}G_{min}^{1-p(k,l)} \tag{6}\]  
where \(G_{min}\) is a gain lowest boundary threshold, and \(p(k,l)\) is the speech presence probability. This optimally modified gain function outperforms previous alternatives of the spectral-domain speech estimator [24].  
As we can see, \(G_{MMSE}\) is a common element of main importance in the definition of the gain function of classical speech estimator algorithms. However, its dependence on the a priori SNR (\(\xi_{k}\)) makes it sensitive to errors, because it is not directly accessible from the observed spectral power \(|Y(k,l)|^{2}\) and has to be estimated for each segment \(l\).
# Integrated Digital-Twin-Based Decision Support System for
## 7 Discussions
### Risks and Challenges in Implementing the DT System  
While the proposed DT-based system introduces significant advancements in RMB logistics management, several risks and challenges must be considered to ensure its successful implementation. One key risk involves data inaccuracies and incomplete information, which could lead to incorrect allocation decisions if sensor readings, module conditions, or location data are misclassified or outdated. Ensuring data integrity and reliability requires the implementation of automated validation algorithms, anomaly detection mechanisms, and regular database updates [67].  
Technological failures and system integration issues also pose significant risks. IoT sensors, GIS tracking, and cloud-based data synchronization may experience hardware malfunctions, network disruptions, or latency issues, which could compromise real-time decision making. To mitigate these risks, the system should incorporate edge computing solutions to allow localized data processing, ensuring that critical operations can continue even in the absence of stable connectivity [68]. Furthermore, ensuring interoperability between BIM, IoT, and GIS platforms through standardized data exchange protocols can enhance system resilience.  
Environmental and logistical uncertainties also impact RMB module allocation. Unpredictable weather conditions, sudden project delays, and traffic congestion can disrupt module transportation schedules, increasing operational costs and leading to inefficiencies. To address these uncertainties, integrating real-time risk assessment and adaptive route optimization capabilities within the DT framework is necessary. By leveraging geospatial analytics and historical transport data, the system can dynamically adjust logistics plans based on changing conditions, ensuring timely and cost-effective module deployment [69].
## 1 Introduction
Meanwhile, techniques proposed for detecting DDoS attacks with varying attack rates have low accuracy and a high false positive rate when dealing with attacks targeting several victims. Therefore, this work offers a novel technique capable of detecting DDoS attacks with a high detection rate and a low false positive rate, regardless of attack traffic rate or victim number. Many existing detection approaches are limited due to the reliance on a static threshold to detect DDoS attacks with varying attack traffic rates. Unfortunately, some entropy-based techniques [23; 24] that depend on static thresholds cannot detect DDoS attacks with a high detection rate and low false positive rate, particularly when involving low-rate DDoS attacks on multiple victims [25; 26; 27]. Additionally, most existing detection approaches depend on a single incoming packet header feature to detect DDoS attacks on controllers launched by a single host on a single or on multiple victims. As a result, the detection rate is low and the false positive rate is high [28]. This paper contributes to the body of knowledge by (i) adapting a dynamic threshold that caters to varying DDoS attack traffic rates, resulting in reduced false positive rates and increased detection rates, and (ii) proposing a rule-based detection mechanism to detect DDoS attacks against the controller. Furthermore, the proposed approach is compared to the existing approaches using the proposed approach environment for different DDoS attack rates (low or high), sources of the attacks (single or multiple), and the number of victim hosts (single or multiple). The paper is organized as follows: Section 2 presents the related work and the motivation behind this research, followed by Section 3, which presents the proposed DDoS attack detection approach. Section 4 describes the experimental result in terms of dataset generation, discusses the proposed performance evaluation, and compares the proposedapproach with existing detection approaches. Finally, Section 5 summarizes this paper and recommends several directions for future research.
# Power Control Method for Energy Efficient Buffer-Aided Relay Systems
###### Abstract  
In this paper, a power control method is proposed for a buffer-aided relay node (RN) to enhance the energy efficiency of the RN system. By virtue of a buffer, the RN can reserve the data at the buffer when the the channel gain between an RN and a destination node (DN) is weaker than that between SN and RN. The RN then opportunistically forward the reserved data in the buffer according to channel condition between the RN and the DN. By exploiting the buffer, RN reduces transmit power when it reduces the transmit data rate and reserve the data in the buffer. Therefore, without any total throughput reduction, the power consumption of RN can be reduced, resulting in the energy efficiency (EE) improvement of the RN system. Furthermore, for the power control, we devise a simple power control method based on a two-dimensional surface fitting model of an optimal transmit power of RN. The proposed RN power control method is readily and locally implementable at the RN, and it can significantly improve EE of the RN compared to the fixed power control method and the spectral efficiency based method as verified by the rigorous numerical results.  
**Keywords:** UAV; relay; cooperative communications; buffer; power control; energy efficiency +
Footnote †: journal:  
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