esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
As discussed in [11; 12; 13; 14; 15], network QoS impacts the QoE of VoD significantly. Hence, it is important to measure QoS parameters accurately. Network measurement techniques are technically mature and discussed in detail in [6; 7; 25], but their accuracy is still the subject of ongoing research. Network measurement techniques were examined for statistical accuracy in [7]. The authors of [7] reported that the measurements of packet delay results in low accuracy when measuring a congested network using packet probes. The authors of [25] attempted to measure the packet loss using probes and reported that increasing the probe frequency compromises the accuracy of the measurement. A similar challenge was presented in [26] which reported that increasing the sampling rate causes probes to interfere with application traffic thus making the measurements prone to error. [PERSON] [27] has also reported bounds on the accuracy and precision of sampling packet loss, and has tried to quantify the error in packet loss using statistical techniques. The size of the probes, sampling frequency of the probes and the load on the network can also cause inaccurate measurements as presented in [28]. Since QoE can be presented as a mathematical function of one or more QoS parameters, it is possible to work out the sampling error propagated to QoE, if the sampling error in the measurements of QoS parameters is quantified. The detailed study of analytical propagation of error in a function, ref. [8], concludes that if the independent variables have errors in their measurements, then these measurement errors propagate to the dependent variable(s). In all branches of science and engineering, it is considered to be vital to quantify this uncertainty when quantifying the magnitude of the dependent variables.
_Article_  
**Extraction of Irregularly Shaped Coal Mining Area Induced Ground Subsidence Prediction Based on Probability Integral Method**  
**[PERSON] 1,2**, [PERSON] 1,2**, [PERSON] 1,2**, [PERSON] 3,*\({}^{\text{\textregistered}}\),**\({}^{\text{\textregistered}}\)**, [PERSON] 1,2 and [PERSON] 1,2**  
1 Lunan Geo-Engineering Exploration Institute of Shandong Province, Jining 272100, China; [EMAIL_ADDRESS] (X.T.); [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS] (H.B.); [EMAIL_ADDRESS] (M.V.); [EMAIL_ADDRESS] (G.L.)
2 Technology Innovation Center of Restoration and Reclamation in Mining-induced Subsidence Land, Ministry of Natural Resources, Jining 272100, China  
3 School of Geodesy and Geomatics, Wuhan University, Wuhan 430000, China  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +86-159-6472-2133  
**Abstract:** Underground coal mining-induced ground subsidence (or major ground vertical settlement) is a major concern to the mining industry, government and people affected. Based on the probability integral method, this paper presents a new ground subsidence prediction method for predicting irregularly shaped coal mining area extraction-induced ground subsidence. Firstly, the Delaunay triangulation method is used to divide the irregularly shaped mining area into a series of triangular extraction elements. Then, the extraction elements within the calculation area are selected. Finally, the Monte Carlo method is used to calculate extraction element-induced ground subsidence. The proposed method was tested by two experimental data sets: the simulation data set and direct leveling-based subsidence observations. The simulation results show that the prediction error of the proposed method is proportional to mesh size and inversely proportional to the amount of generated random points within the auxiliary domain. In addition, when the mesh size is smaller than 0.5 times the minimum deviation of the inflection point of the mining area, and the amount of random points within an auxiliary domain is greater than 800 times the area of the extraction element, the difference between the proposed method-based subsidence predictions and the traditional probability integral method-based subsidence predictions is marginal. The measurement results show that the root-mean-square error of the proposed method-based subsidence predictions is smaller than 3 cm, the average of absolute deviations of the proposed method-based subsidence predictions is 2.49 cm, and the maximum absolute deviation is 4.05 cm, which is equal to 0.75% of the maximum direct leveling-based subsidence observation.  
**Keywords:** irregularly shaped coal mining area; subsidence prediction; probability integral method; Delaunay triangulation method; Monte Carlo method
# A Frequency-Reconfigurable Dual-Band RF Crossover Based on Coupled Lines and Open Stubs
## 5 Conclusions  
In this paper, a planar crossover based on CLs and open-ended stubs has been presented. It introduced a crossover design that combined two desired aspects that had not been previously recorded. These two aspects are the dual-band response and the continued tunability of the two frequency bands. For the presented crossover, even-odd analysis, optimization of circuit parameters, simulation, fabrication, and measurements were conducted. As to the fabrication process, RO4003C (\(\epsilon_{r}=3.55\)) with a height of 1.524 mm was used for manufacturing the crossover using printed circuit board (PCB) technology. Eventually, a crossover capable of sweeping the two operating frequencies from 2.06 GHz to 2.40 GHz and from 5.44 GHz to 5.84 GHz was realized. This was accomplished through the employment of four CLs and two open-ended stubs connected through two SMV1405 varactor diodes. The change in the applied biasing voltage from 0 V to 30 V led to an alteration of the varactor diodes' capacitance from 2.67 pF to 0.63 pF. Consequently, tuning ranges of 15.3% and 7.1% for the low- and high-frequency bands, respectively, were attained. Also, the patterned ground technique was employed in the design to reduce the coupling effect. As a result, good performance in terms of insertion loss, return loss, isolation, rejection between the two frequency bands, frequency ratio, and size were demonstrated. In addition, the results obtained from both the simulation and measurements showed good agreement. Also, by changing the design parameters, the proposed crossover can operate at different frequencies to satisfy variant applications. As to the fabricated crossover, it can be used for WiMAX (2.3 GHz and 5.8 GHz) and IEEE 802.11 WLAN (2.4 GHz and 5.725 to 5.82 GHz) applications. Also, further work can be performed to enhance the presented design, such as using folded lines to reduce the crossover size, selecting a dielectric substrate with better specifications to reduce losses, and employing MEMS instead of varactor diodes. Conceptualization, A.J.A. ; methodology, D.H.A. ; formal analysis, D.H.A. ; investigation, D.H.A. ; resources, A.J.A. ; data curation, D.H.A. ; writing--original draft preparation, D.H.A. ; writing--review and editing, A.J.A. ; supervision, A.J.A. ; project administration, A.J.A. All authors have read and agreed to the published version of the manuscript. The research received no external funding. Data Availability Statement:Data is contained within the article. Conflicts of Interest:The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 5 Fault Detection on Dublin-Belfast Route
### Data Scaling  
The TRV data shows several possible track defects in a zone between 2 to 8 km from Conolly station in Dublin. The measured bogie vertical accelerations from this 6 km of the Dublin-Belfast railway line is the dataset considered for fault detection. Figure 11a shows the 60 acceleration signals measured at the bogie. There are different energy levels in each signal, which makes it hard to infer the condition of the track. Figure 11b shows the PF5 of the Hilbert amplitudes of the signals that were calculated while using Equation (5). Although it constitutes an improvement, high variations remain between passes, e.g., between 6000 to 7000 m.  
Figure 11: Sixty passes of data: (**a**) Acceleration signals, and (**b**) PF5 of Hilbert amplitudes.  
Figure 10: The fault detection algorithm.  
Figure 12 shows the forward speeds for all passes and show a great deal of variation. As proposed in Section 4.1, data are removed for any 1 km length in which the minimum forward speed of the train is less than 50 km/h. The 1 km segments for which data has been removed are shown in bold red in the figure. It can be seen that there are many passes between 5000 to 7000 m that need to be removed from the data set. This could explain the high amplitude variations in this zone in Figure 11b.  
Figure 13a shows the cleaned version of Figure 11b, where segments of passes with speed below the limit have been removed. This figure clearly shows greater consistency in the amplitudes as compared to Figure 11b, particularly in the peaks. This is further improved with the energy scaling of Equation (5). The scaled amplitudes are plotted in Figure 13b and they can be seen to be more consistent than those of Figure 13a. Table 2 gives the average standard deviations of the amplitudes in Figure 13a,b for different ranges. It shows that the average standard deviation of the data at all ranges has decreased after rescaling, which is a sign of greater consistency.  
Figure 12: The train forward speeds for all 60 passes (red indicates 1 km segments for which data has been removed).  
Figure 13: The PF5 (**a**) after cleaning to remove segments below the speed limit, (**b**) after rescaling the cleaned signals.  
Figures 11 and 13 show how the raw acceleration signals are processed to create a robust and reliable representation of the signal energy along the track. The mean value of the amplitudes in Figure 13b can be treated as a condition indictor for the track that can be used for long term monitoring. A current high energy level can be considered as a possible defect at the track. Therefore, monitoring the track condition while using the proposed indicator and keeping the energy level of the indicator minimum might be good practice for railway track maintenance.
# Grading Algorithm for Orah Sorting Line Based on Improved ShuffleNet V2
## 2 Materials and Methods
### Improved ShuffleNet_wogan Model
#### 2.5.2 Efficient Channel Attention Module  
The structure of the ECA module [32] is shown in Figure 12. The workflow is as follows:  
First, the input feature map is as follows:  
\[X\in R^{B\times C\times H\times W} \tag{2}\]  
where \(B\) represents the batch size, \(C\) denotes the number of channels, and \(H\) and \(W\) are the spatial resolution (height and width) of the feature map.  
Figure 11: ShuffleNet_wogan model architecture.  
Figure 12: ECA module architecture.  
Next, global average pooling is performed on the spatial dimensions of each channel to compute the global response of the channel:  
\[z_{c}=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}X_{c,i,j} \tag{3}\]  
The result is as follows:  
\[z\in R^{B\times C} \tag{4}\]  
where \(z_{c}\) represents the global average value of channel \(c\), indicating the global response strength of that channel. \(X_{\text{c,i,j}}\) is the value of channel \(c\) at position (_i,j_) in the input feature map.  
Then, a one-dimensional convolution (Conv1\(D\)) with a dynamically determined kernel size is applied to \(z\), completing cross-channel threshold interaction and capturing local relationships between channels:  
\[w_{c}=Conv1D_{k}(z). \tag{5}\]  
Here, \(w_{c}\) denotes the weight of channel \(c\), Conv1D represents the one-dimensional convolution operation, and \(k\) is the kernel size, which is adaptively determined using the following equation:  
\[k=\Phi(C)=\left|\frac{log_{2}(C)}{\gamma}+\frac{b}{\gamma}\right|_{odd}. \tag{6}\]  
In this paper, the parameters \(\gamma\) and \(b\) are set to 2 and 1, respectively. \(C\) represents the number of channels, and \(\left|\left.t\right|\right._{\text{odd}}\) denotes the nearest odd number to \(t\).  
The channel weights are then mapped to the range [0, 1] using the sigmoid activation function and applied to weight the original feature map, resulting in the weighted feature map:  
\[Y_{c,i,j}=\sigma(w_{c})\cdot X_{c,i,j} \tag{7}\]  
The output is as follows:  
\[Y\in R^{B\times C\times H\times W} \tag{8}\]  
where \(\sigma\) represents the sigmoid activation function, and \(Y\) is the newly generated feature map.
## 2 Related Work  
### Multimodal Sentiment Analysis  
Multimodal fusion networks provide a distinct advantage in various applications, showcasing their superiority over unimodal approaches. They excel in tasks such as sentiment analysis [6; 10; 13], action recognition [14; 15], and semantic segmentation [16; 17; 18], benefiting from the integration of multiple modalities to achieve improved performance and accuracy. Multimodal fusion is a core problem for multimodal sentiment analysis. According to the different stages in the algorithm model, existing methods can be categorized into early fusion (feature-level fusion) and late fusion (decision-level fusion) [19]. Early fusion methods are the fusion on the feature level by simple concatenation. [PERSON] et al. [8] proposed Early Fusion LSTM (EF-LSTM), which connects feature vectors from different modalities as input and captures the long-range dependencies with LSTM. [PERSON] et al. [9] proposed a memory fusion network, which stores the interaction information between modalities through gated memory units. To effectively address the complexities of multi-modal relationships, [PERSON] et al. [10] introduced a Tensor Fusion Network (TFN), which utilizes tensor fusion to model the intermodal dynamics, while the intramodal dynamics are captured by three modal embedding subnetworks. Furthermore, [PERSON] et al. [20] introduced Low-Rank Multimodal Fusion (LMF), leveraging modality-specific low-rank factors to improve efficiency in the fusion process. [PERSON] et al. [21] presented an advanced framework that utilizes an attention-based model to dynamically adapt word embeddings based on auxiliary nonverbal features such as video and audio features. [PERSON] et al. [22] devised the Interaction Canonical Correlation Network (ICCN), which uses the outer product of feature pairs and canonical correlation to establish associations between language-based video and language-based audio.  
Our work is fundamentally different from these available works. We do not use contextual information and neither focus on complex tensor fusion mechanisms. Instead, we emphasize the importance of joint representation and multitask learning.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 10 AI Support for 5G Operations and Services  
5G systems have brought us the long-waited promise of support for Enhanced Mobile Broadband (eMBB) services in addition to the introduction of machine-type devices, interconnected through the Internet of Things for massive Machine Type Communications (mMTC), as well as novel services that require Ultra-Reliable Low-Latency Communication (URLLC). The emergence of this range of innovative mobile applications and online services pose unprecedented challenges to the fifth generation of mobile networks, which are thus required to introduce new solutions and mechanisms to optimise the use of limited resources, e.g., finite radio spectrum. Moreover, compared to existing mobile communication techniques, 5G has more varied applications, thus its corresponding system design is more complicated. The resurgence of AI techniques offers an alternative option that is possibly superior to traditional ideas and performance of system management.  
In addition to the new requirements of increased network capacity, reliability and latency, future networks are still expected to meet requirements of energy efficiency and heterogeneity of user terminals and Quality of Service/Experience (QoS/QoE) demands. In a concept paper [111] the authors outlined the AI challenges which need to be overcome to enable the perception of zero delay networks. Typical and potential research directions related to the promising contributions that can be achieved through AI were identified, evaluated, and investigated in [112] that combed through several promising research directions in AI for 5G technologies based on an understanding of the key technologies in 5G. The authors of [113] surveyed how 5G network management, with an end-to-end perspective of the network, can significantly benefit from AI/ML solutions. The authors reviewed and provided the basic concepts and taxonomy for SON, network management and ML; and also analyzed the state of the art in the literature, standardization, and in the market.  
Based on these recent surveys taxonomies, we group the use cases of AI technologies in the 5G ecosystem as follows.  
* Service management: operations support systems, resource provisioning, fault localization, failure root cause analysis, business support systems, security;
* Network and cloud resource management: flexible function deployment, NFV orchestration, network slicing, green operation;* Radio management: air interface coordination, site collaboration, user mobility.
## 1 Introduction  
Phenol compounds such as nitrophenols, bisphenols and alkylphenols are classified as potential endocrine disruptor chemicals and often detected in the environment [1]. These compounds are widely used as intermediates in industrial processes, namely in the production of explosives, pharmaceuticals, pesticides, resins, among others, and released into the environment in their original form or as by-products through waste or industrial discharges [2; 3; 4]. Due to the adverse effects on public health and the frequency at which they are detected in the environment, these compounds are listed as priority pollutants by the US and European environmental protection agencies [1]. Bisphenol-A (BPA), in particular, is one of the most produced compounds worldwide [5; 6]. The major concern in the control of BPA is its use in the manufacture of materials that come into direct contact with foods, such as water bottles, cans, containers for food, among many others, which are the major sources of human exposure [7; 8; 9].  
The most widely used methodologies for the determination of trace levels of phenol compounds is solid phase extraction followed by gas chromatography (GC) after derivatization [3; 10]. Alternatively, solid phase microextraction (SPME) and stir bar sorptive extraction (SBSE) with _in-situ_ derivatization followed by GC coupled to mass spectrometry (GC-MS) analysis, or LC-MS without the derivatization step had also been proposed to monitor the class of compounds [4; 11; 12; 13; 14; 15]. Recently, our group has introduced a novel static microextraction technique, bar adsorptive microextraction (BA\(\mu\)E), that uses nanostructured materials, and have demonstrated it to be a remarkable alternative for trace analysis of medium-polarity to polar compounds in aqueous media [16; 17]. This new analytical approach operates under the floating sampling technology and has demonstrated great advantages compared to other sorption-based methods (e.g., SBSE). Furthermore, BA\(\mu\)E allows one to tune the most convenient sorbent phase (e.g., activated carbons (ACs), polymers, _etc._) for each particular target compound and has shown high effectiveness in many types of applications [18; 19; 20; 21].  
The present contribution aimed to apply BA\(\mu\)E followed by high performance liquid chromatography with diode array detection (HPLC-DAD) without derivatization for the determination of phenol compounds, including 3-nitropenol (3 NOP), 4-nitropenol (4 NOP), BPA, 4-\(n\)-octylphenol (OP) and 4-\(n\)-nonylphenol (NP) in water matrices. The optimization, validation, application and comparison with others sorbent-based techniques are also addressed.
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 2 Design of Proposed PIDD\({}^{\bullet}\) Controller  
### PID Controller  
The PID controllers in feedback control systems manage a system's behaviour using proportional, integral, and derivative terms. The operation of these terms is explained as follows:  
* Proportional Control: This term refers to correcting action proportionate to the present error.
* Integral Control: This term is a correction based on accumulating errors over time through low-frequency compensation.
* Derivative Control: This term applies a correction based on the error's rate of change through high-frequency compensation.  
The PID control action in the Laplace domain is given as follows:  
\[C(s)=\frac{U(s)}{E(s)}=\frac{U(s)}{R(s)-Y(s)}=K_{p}+\frac{K_{i}}{s}+K_{d}s. \tag{1}\]  
In the above equation, \(K_{p}\), \(K_{i}\), and \(K_{d}\) are the proportional, integral, and derivative constants. The block diagram representation of the controller is shown in Figure 1. The figure and Equation (1) show that the PID controller combines the proportional, integral, and derivative control elements to provide a composite control signal that compromises error reduction, overshoot, and oscillation mitigation. The PID controller's settings, such as the gains for each control term, can be tuned to obtain the desired performance for a particular system.  
PID controllers can encounter problems like destabilising the system and poor performance if not tuned correctly, particularly with the integral and derivative terms. Integral windup can arise due to the integral action, causing the control action to saturate, leading to low-frequency oscillations and instability. A derivative kick can occur when the system introduces a step change to the controller, resulting in a significant spike in the derivative term as it acts upon the rate of error change [40]. It is crucial to tune the derivative term carefully as it improves the system's phase and gain margin, which can either enhance or reduce the system's stability. As a result, 80% of PID controllers used in industries have been turned off due to issues with tuning the derivative term [41].