esa-sceva/satcom-chunk-collection huggingface dataset:
# ICN-Based Enhanced Content Delivery for CDN
## 3 Proposed Design
### Design Overview  
An overview of our proposed design is illustrated in Figure 1. The major modification to CDN in our architecture is that we integrate an ICN service layer with routers supporting ICN paradigms, which mainly involves in-network caching and naming service. These routers constitute a large distributed caching entity of the ICN service layer. Moreover, Ingress Gateways (IGs) and Egress Gateways (EGs) are respectively deployed at the entry and exit points of the ICN service layer. These gateways function as both HTTP proxy and protocol conversion components. To be specific, IG can be viewed as an HTTP proxy, to which HTTP requests for content are redirected, and as a protocol conversion component which translates an HTTP request into an ICN chunk request so as to discover and retrieve content from the ICN service layer. EG receives HTTP content responses from the internet content provider (origin server) and publishes this content to the ICN service layer, which subsequently performs in-network caching in compliance with certain caching policies. A detailed process of the content request and delivery will be elaborated on in Section 3.3.  
A standalone NRS system is deployed to resolve IDs to NAs. When a router caches a chunk, it simultaneously registers to NRS the value of its physical device NA with the key of chunk ID. Afterwards, this chunk can be discovered and retrieved with this ID: first, an ID resolution request is submitted to the NRS, and NRS returns a resolution response that comprises a list of chunk replica NAs, then, a replica selection mechanism is adopted to  
Figure 1: An overview of content delivery network enhanced with ICN (IECDN).  
select a suitable chunk replica and triggers the download. In our context, the nearest replica selection mechanism [40] is adopted since it is commonly used in related work. Finally, the subsequent transmission is conducted through NA routing.  
Content is cached at the granularity of a file in a CDN, whereas ICN performs caching at the granularity of a chunk. For the purpose of this discussion, we use the terms \"content\" and \"file\" interchangeably to refer to data within a CDN network, and the term \"chunk\" within an ICN network. Further information regarding this issue is discussed in Section 3.4.
# Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration
## 2 Power Quality Issues of WPP Integration
### Voltage Issues of WPP Integration
#### 2.2.2 Voltage Swell  
Voltage swell is the increase in the nominal voltage lasting for more than 1 cycle and less than a few seconds. Voltage swell is less commonly occurring than the voltage sag and is usually associated with system fault conditions. A swell can occur due to the start or stop of heavy loads, fault on the system, which can result in a temporary voltage rise on the unfaulted phases. Further, the sudden interruption of current can generate a large voltage, and switching on large capacitor banks may also cause a swell as well. The consequence of the swell is damaging sensitive devices and disconnecting of the WTGs. In DFIG, the operation during a grid fault is complex because the stator is directly connected to the grid while the rotor is connected via a converter. Hence, most of the WTGs installed are automatically disconnected from the grid and reconnected when the fault is cleared to avoid such a problem. However, this action is not sufficient to meet the grid code requirements. So, the high voltage ride-through capability of WTGs with DFIG is implemented to ensure staying connected to the grid and stabilize the grid voltage in case of grid faults [21; 27].  
A case study conducted in Ref. [30] explained the voltage variation due to wind integration into the grid. This study has been implemented using four scenarios. The first scenario considers supplying electricity without wind integration while the second scenario includes 50% wind integration. The third scenario shows 100% wind energy integration while the fourth scenario demonstrates a grid with centralized 100% integration. The results of this study show that voltages are the same for all phases in the first scenario as it is a balanced system. However, the phase voltage rises vary with the increase of wind integration. This happened because the WTG was connected near the customer load and caused bi-directional power flows in the PCC.
## 4 Confirmation from Simulated and Practical Results  
This section confirms the theoretical and real value analysis of the previously discussed concepts. Here it is proved that the proposed circuit can vary the load peak or rms voltage with the control of the duty cycle. For this purpose, two values of the output voltage were selected. One value of the output voltage is less than the half value of the input voltage, while the other is greater than this level. The peak value of the input voltage was set to 170 V peak to obtain the 50 V and 100 V peak voltage by adjusting the value of the duty cycle control near about 0.3 and 0.6, respectively. The other value of the duty cycle control may be employed to obtain any desired value of the output voltage. With this voltage regulation capability of the output voltage, its non-inverting and inverting features were also tested for the application of voltage control and frequency control. Half frequency output voltage waveform also verifies the voltage regulating features and voltage non-inverted and inverted forms of input voltage. The test of the features mentioned above was separately validated with simulation results from MATLAB software and practical results obtained from hardware setup.
# Rhythm-Based Attention Analysis: A Comprehensive Model for Music Hierarchy
## 3 Proposed Methods
### Quantization Scheme for Rhythmic Attention
#### 3.1.4. Fractal Characteristics Analysis  
Mathematically, we can adopt the MF-DFA algorithm [41] to analyze the fractal dimension.  
Figure 3: Recursive transformation of the primitive function. (**a**) \(k=2\). (**b**) \(k=3\).  
For a time duration \(t\) and a time sequence \(\{x_{i}\}\) of length \(N\), where \(i=1,2,\ldots,N\), and \(x_{i}=I\big{(}\frac{i-1}{N}t,\frac{i}{N}t\big{)}\), the cumulative sum \(Y(j)\) is calculated as Equation (7):  
\[Y(j)=\sum_{i=1}^{j}(x_{i}-\hat{x}),j=1,2,\ldots,N, \tag{7}\]  
where \(\hat{x}=\frac{1}{N}\sum_{i=1}^{N}x_{i}\)  
Then, \(Y(j)\) is divided into \(M=\left\lfloor\frac{N}{s}\right\rfloor\) non-overlapping windows of equal size \(s\). Any remaining part of the series at the end that is shorter than \(s\) is disregarded. This division is carried out from both ends, resulting in \(2M\) segments.  
In each segment, local trends are removed by fitting the data with a polynomial. For each segment \(\
u=1,2,\ldots,M\), we compute the detrended time series by Equation (8):  
\[Y_{s}(\
u)=Y[(\
u-1)s+j]-p_{\
u}(j), \tag{8}\]  
where \(p_{\
u}(j)\) is the fitted polynomial in segment \(\
u\).  
The fluctuation function \(F_{\
u}(s)\) is then computed by Equation (9) for each segment:  
\[F_{\
u}^{2}(s)=\frac{1}{s}\sum_{k=1}^{s}[Y[(\
u-1)s+k]-p_{\
u}(k)]^{2}. \tag{9}\]  
To obtain the overall fluctuation function, the results are averaged over all segments using Equation (10):  
\[F_{q}(s)=\{\frac{1}{2M}\sum_{\
u=1}^{2M}[F_{\
u}^{2}(s)]^{\frac{q}{2}}\}^{ \frac{1}{q}}, \tag{10}\]  
where \(q\) is a real parameter that characterizes different orders of the moments.  
Finally, the scaling behavior of the fluctuation function is analyzed. If the time series exhibits multifractal properties, \(F_{q}(s)\) will follow a power-law scaling relation: \(F_{q}(s)\sim s^{h(q)}\). Here, \(h(q)\) is the generalized Hurst exponent. In the case of the monofractal time series, \(h(q)\) is independent of \(q\), whereas for the multifractal time series, \(h(q)\) varies with \(q\).  
Figure 4 shows the curve of \(h(q)\) for \(k\) is 2, 3, and 5 throughout. The values of \(h(q)\) decrease as \(q\) increases. The curve of \(h(q)\) is convex, which indicates that different segments of the time series have different scaling behaviors. This is typical of multifractal systems, where short-term fluctuations and long-term correlations vary across segments.  
In addition, when \(k\) is 2, 3, and 5, for \(q=2\), the Hurst exponent \(h(2)\approx 0.496\). The fractal dimension \(D_{f}\) can be approximated by Equation (11):  
\[D_{f}=2-h(2). \tag{11}\]  
So, the fractal dimension \(D_{f}\approx 2-0.496=1.504\) for our proposed quantization scheme for rhythmic attention. This value of the fractal dimension suggests a highly complex structure within the time series, lying between a one-dimensional line and a two-dimensional plane.
## 4 Environmental Impact and Cost Comparison  
As already stated, the no-rinse processes, unlike the rinse ones, do not require the washing downstream of the pre-treatment phase, but only the drying. It has been estimated that the no-rinse process uses approximately 40% less water; accordingly, 40% more water must be treated/purified in order to be disposed of when using the rinse-type process. This allows the elimination of the production of polluted wastewater and the disposal of sludge, carrying out the process with a lower environmental-impact, and reducing costs.  
\"True-Cr-free\" no-rinse conversion-coatings are not widely utilised in industries, because they are believed to exhibit poor performances and to be expensive compared to CrIII-based rinse-type treatments. This paper aims to demonstrate that some of no-rinse \"true Cr-free\" products possess performances similar to, if not better, than the rinse CrIII-based one, highlighting the use of \"true Cr-free\" no-rinse conversion coatings permitting the reduction of the process's environmental impact and diminishing the production costs.
# Chemicals from Brominated Flame Retardants: Analytical Methods, Occurrence, Transport and Risks
## 3 Distribution of BFRs in Different Matrices
### Biota
#### 3.2.1 Water  
[PERSON] et al. [59] found varying levels of TBBPA in surface seawater samples in the Yellow Sea and Bohai Sea, ranging from ND to 0.46 ug/L. [PERSON] et al. [61] reported that the TBBPA concentrations in water samples in Weihe River Basin, China were in the range of not detected N.D to 12.279 ng/L (mean value of 0.937 ng/L), [PERSON] et al. [62] recorded 18.5-82.6 ng/L in Baiyang Lake, China and reported ND-32.3 ng/L in surface water at Taihu Lake, China. PBDEs detected by [PERSON] et al. [64] in Bohai Sea, China ranged from 0.00226 to 0.00751 ng/L, by [PERSON] et al. in [82] in surface water in Taizhou, China ranged from nd to 71.77 ng/L, by [PERSON] et al. [83] in the South China Sea ranged from nd to 4.28 ng/L, and in Dongjiang River, China ranged from 0.723-3.796 ng/L [83]. According to [PERSON]. et al. [64] NBFRs ranged from 0.0107 to 0.0104 ng/L in Bohai Sea, China, while in Taizhou, China they ranged from nd to 3.34 ng/L according to [PERSON] et al. [82], and in the South China Sea they ranged from nd to 7.63 ng/L [65]. In the case of Decabromdiphenyl ethane (DBDPE) in Lian River and Beigang River, Guiyu, South China, the concentration ranged from nd to 35,000 ng/L. As detected by [PERSON] et al. [63] in Taizhou, East China the range was 9.5 (nd) to 120 ng/L. As detected by [PERSON] et al. [58] in Shihua Lake, Republic of Korea, the range was 7.28 (0.06-69.5) ng/L [69], and the range in Ulsan/Onsan Bays, Guiyu, South China was 3.29 (0.22-37.6) ng/L [69]. [PERSON] et al. [58] measured BTBPE concentrations in Lian River and Beigang River, Guiyu, South China as 830 (nd-36,800) ng/L, and 0.043 (nd-0.60) ng/L was found by [PERSON] et al. [58] in Taizhou, East China. Variability in concentrations of TBBPA, PBDEs, NBFRs, DBDPE, and BTBPE across different water bodies indicates localized sources of contamination. High levels in certain areas, like the Lian and Beigang Rivers, indicate severe pollution requiring targeted interventions.
## 1 Introduction
However, the obtained AuNRs were relatively short, having lower aspect ratio (2-4) and LSPR (ca. 800 nm) [20]. Although the synthesis of AuNRs has gained much attraction in synthesis of SERS-related nanomaterials, hydroquinone-based fabrication of AuNRs with a high aspect ratio and LSPR \(>\)850 nm as a SERS platform for rapid detection of fungicides has not been tapped yet. Thiophanate methyl is well known as a member of the benzimidazole group of fungicides. It is a broad-spectrum systemic fungicide that has been employed to control pathogens recently. However, according to the Office of Environmental Health Hazard Assessment (OEHHA), long exposure to residual thiophanate methyl may cause serious effects on human health [25; 26]. Therefore, it is essential to develop a sensitive method to detect residual thiophanate methyl in agriculturalproducts. Until now, various techniques have been developed to detect thiophanate methyl such as high-performance liquid chromatography (HPLC), fluorescence assay (FL), high-performance liquid chromatography-mass spectrometry/mass spectrometry (HPLC-MS/MS), electrochemical assay (EC), and gas chromatography/mass spectrometry (GC/MS) [27; 28; 29]. Although thiophanate methyl can be analyzed by those methods with high accuracy, it has many limitations such as time-consuming operation, complicated sample preparation, and so forth, leading to a low ability to be applied for portable analysis where high accuracy and quick response are required. Meanwhile, SERS is a method that has a short measurement time and simpler sample preparation process. Therefore, the analysis of residual thiophanate methyl by SERS has the potential to be an accurate portable analytical method. For all these reasons, in this article, AuNRs with a high aspect ratio and LSPR \(>\) 850 nm were synthesized via the reduction of HAuCl\({}_{4}\) by NaBH\({}_{4}\) to make gold nanoseeds from which AuNRs were grown with the aid of AgNO\({}_{3}\), HAuCl\({}_{4}\), CTAB, and HQ. In this seed-mediated method, NaBH\({}_{4}\) and CTAB acted as primary reducing and stabilizing agents during the nucleation stage and HQ and AgNO\({}_{3}\) as secondary reducing and aspect tuning agents, respectively. The as-synthesized AuNRs were used as a SERS-based sensing platform for rapid detection of thiophanate methyl (Figure 1).
# Towards an Automatic Recognition of Artifacts and Features in Plethysmographic Traces
## 3 Data Collection and Preparation  
### A Summary of the Capacitive Strain-Gauge Sensor and Experimental Activity  
The device considered in this study consists of a wearable, non-invasive plethysmographic sensor that is externally wrapped around the neck of the patient [3,4]. The device contains a highly stretchable central zone with a capacitive strain-gauge sensor [32]. The capacitive strain-gauge sensor is formed by a single-layer dielectric electro-active polymer (DEAP), PolyPowerfilm(r), sandwiched between two deformable electrodes [33]. It results in a sensing element in the form of a stretchable capacitor (ElastiSense Sensor Technology, formerly LEAP technology, Aabenraa, Denmark). The capacitance of the sensor increases when the device is stretched, allowing it to detect minimal deformations. The sensor adheres to the skin and records the perimeter variation in the examined anatomic part. The device, applied to a patient, is depicted in Figure 2.  
Integrated in the wearable device, an electronic unit collects data from the strain-gauge sensor and from three electrodes, providing a complementary and independent ECG trace. The plethysmographic and ECG signals are then transferred via a Bluetooth(r) connection to a PC for analysis and storage.  
A detailed description of the device used in this study can be found in ref. [4,32]. The characterization of the capacitive strain-gauge and ECG sensors are also discussed in ref. [4]. The device is derived from the one developed in the context of the Drain Brain project [10,32,34].
# Compact Aberration-Free Relay-Imaging Multi-Pass Layouts for High-Energy Laser Amplifiers
## 5 Astigmatism due to Round-Trips
Again, the ray-tracing results are in good agreement with the results from the simplified matrix model. Only for Type I systems, there seems to be a small deviation. This is most likely caused by a combination of a residual influence from the system's folding angle and inaccuracies due to deviations in the propagation lengths, which are not included in the matrix model. These originate from the different distances between the image planes and \(L_{1}\) and \(L_{2}\) for different round-trips. In Figure 9, the evolution of the astigmatic deformation over the round-trips is shown for sample systems with \(\
icefrac{{D}}{{f}}\)=0.3 with five and with 10 round-trips. It can be seen that due to the variation of \(\varphi\) over the individual passes, the main contribution to the wavefront deformation originates from the first and the last passes, while the intermediate passes add only a minor contribution. Furthermore, as there is virtually no change in the amount of deformation during the middle passes, the assumption that the chosen folding angle \(\theta\) is sufficiently small to be negligible is confirmed. Figure 7: Upper image: arrangement of passes on optic \(L_{1}\) for different numbers of round-trips. The numbers denote the sequence of hits on the optic \(L_{2}\). Center image: parameters for folding angles on \(L_{2}\) due to the round-trips in a Type II system with four round-trips. Lower image: parameters for folding angles on \(L_{1}\) due to the round-trips in a Type I system with four round-trips. In comparison to the astigmatism caused by the system's folding angle, the obtained astigmatic deformation due to the round-trips is rather small. Nevertheless, for compact systems using a short focal length and a high number of round-trips, a considerable amount of deformation accumulates. In general, Type II systems are less critical by a factor of about two when using the focal length of the optic \(L_{1}\) as a criterion.
## 5 Simulation Results and Performance Evaluation  
### Realistic Models and Parameters  
This section will give a comprehensive assessment on the performance of the proposed algorithm in realistic scenarios. Unlike Section 3 where ideal models are assumed, this section applies realistic models based on measurement. In the literature, the file size can follow either lognormal distributions [32; 33; 34; 35; 36] or power law distributions [37]. Here, we adapt the lognormal distribution because it is more frequently reported. The content popularity, which measures the aggregated interest of a particular content over multiple users, was reported to follow Zipf distribution [38] or power law distribution [39]. The widely-used Zipf distribution is adopted. Given a piece of content, the interest distribution among multiple users varies. It can be normal distribution [40], Levy alpha-stable distribution [41], Beta distribution [42], or U-shaped (or J-shaped) distribution [43; 44]. The normal distribution is adopted in our paper by default. For the convenience of readers, Table 1 summarizes the parameters used in this section.
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 4 Problem Conversion and Solution
### Optimization of RIS Phase Shift Matrix
#### 4.2.1. RIS Phase Shift Matrix for eMBB \(\Phi_{e}^{*}\)  
The problem formulation is given in \(P1.2.2\), and the slack variables are introduced to solve the problem, \(\kappa_{e}=\text{Re}(G_{RIS,e}\Phi_{e}H_{gNB,RIS}+D_{gNB,e})\varepsilon_{e}= \text{Im}(G_{RIS,e}\Phi_{e}H_{gNB,RIS}+D_{gNB,e})\).  
Then problem \(P1.2.2\) can be expressed as a feasibility check problem:  
\[P1.2.3:Find\Phi_{e} \tag{33}\]  
\[s.t.|\Phi_{n}|\leq 1,0\leq\theta_{n}\leq 2\pi,\forall n \tag{34}\]  
\[\kappa_{e}=\text{Re}(G_{RIS,e}\Phi_{e}H_{gNB,RIS}+D_{gNB,e}),\forall e \tag{35}\]  
\[\varepsilon_{e}=\text{Im}(G_{RIS,e}\Phi_{e}H_{gNB,RIS}+D_{gNB,e}),\forall e \tag{36}\]  
\[\frac{P_{eb}\big{(}\kappa_{e}^{2}+\varepsilon_{e}^{2}\big{)}}{\sigma^{2}\Gamma ^{\prime}}\geq S_{e,b},\forall e,b \tag{37}\]  
To solve the non-convexity of problem \(P1.2.3\), the successive convex approximation (SCA) technique is applied in each iteration. For a given local point \(\left(\kappa_{e,b}^{(r)}+\varepsilon_{e,b}^{(r)}\right)\), a lower bound on \(\left(\kappa_{e}^{2}+\varepsilon_{e}^{2}\right)\) can be found according to the first-order Taylor expansion [22] and denoted by \(\psi_{e}\):\(\kappa_{e}^{2}+\varepsilon_{e}^{2}\geq\left(\kappa_{e}^{(r)}\right)^{2}+\left( \varepsilon_{e}^{(r)}\right)^{2}+2\kappa_{e}^{(r)}\left(\kappa_{e}-\kappa_{e }^{(r)}\right)+2\varepsilon_{e}^{(r)}\left(\varepsilon_{e}-\varepsilon_{e}^{ (r)}\right)=\psi_{e}\).  
Thus, the problem \(P1.2.3\) can be further formulated as:  
\[P1.2.4:Find\Phi_{e} \tag{38}\]  
\[s.t.|\Phi_{n}|\leq 1,0\leq\theta_{n}\leq 2\pi,\forall n \tag{39}\]  
\[\varepsilon_{e}=\text{Im}(G_{RIS,e}\Phi_{e}H_{gNB,RIS}+D_{gNB,e}),\forall e \tag{40}\]  
\[\kappa_{e}=\text{Re}(G_{RIS,e}\Phi_{e}H_{gNB,RIS}+D_{gNB,e}),\forall e \tag{41}\]  
\[\frac{P_{eb}\psi_{e}}{\sigma^{2}\Gamma^{\prime}}\geq S_{e,b},\forall e,b \tag{42}\]  
At this point, problem \(P1.2.4\) is a convex optimization problem that can be solved efficiently using CVX.
## 2 Background and Literature Review
### Routing in SDN  
In SDN, routing occurs in switches based on flow tables, which are modified by using the logically centralized control plane [33]. The Shortest Path First (SPF) routing algorithm has been used in early SDN [34]. However, this method does not employ network resources in the most effective way. Rule-based (heuristic) algorithms have been used for routing, but these algorithms' computation complexity has been found to be high, so machine learning can be used to replace such algorithms to reduce the computational cost [35]. A routing protocol for SDN routing in wireless multihop networks with a higher network lifetime was proposed in [36]. The authors in [37] employed a Long Short-Term Memory (LSTM) neural network to predict and learn traffic characteristics in real time and generate forwarding rules to replace heuristic algorithms. Reinforcement Learning (RL) for routing optimization in SDN has shown better Quality of Service (QoS) parameters than the traditional Open Shortest Path First (OSPF) algorithm, according to research conducted in [38]. In these algorithms, the controller serves as the agent, while the network, traffic, and routing serve as the environment, state, and action, respectively. Adaptive routing, which is aware of QoS protocols, is present in multilayer hierarchical SDNs that employ RL and a QoS aware reward function for packet forwarding [39]. Traffic prediction is an important function in routing optimization. Offline traffic load prediction using neural networks has been used to obtain online routing decisions in software-defined mobile metro-core networks [40]. Some use load features (bandwidth utilization ratio, packet loss rate, transmission latency, and transmission hops) to calculate integrated load for different paths using neural networks and choose the one with the least load [41].
## 5 Experiments and Analysis
### Student Learning Analysis Lsing Classification
#### 5.5.1 Random Forest Importance (RFI)  
The RFI findings indicate that the XGBoost algorithm exhibited the highest performance, with an accuracy of 78.00%, precision of 79.00%, recall of 78.00%, and F-measure of 78.00%. The DT model demonstrated favorable performance, achieving an accuracy of 77.00%, precision of 78.00%, recall of 77.00%, and an F-measure of 77.00%. The ensemble model and NN exhibited a marginally reduced level of efficacy, with an accuracy rate of 73.30% and 72.00%, correspondingly. The results are briefly presented below in Figure 6.  
The findings indicate that using RFI for feature selection and implementing XGB and DT models are a viable approach for accurately predicting the target class within the context of student learning analytics classification problems. This study's results can assist educators in identifying students who may benefit from supplementary support and interventions to enhance their academic achievements.