esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Experiments
### Comprehensive Evaluation of the Algorithms
#### 6.4.4 Algorithmic Stability  
As mentioned earlier, the SAEA, AEA, DPCEA, and EA algorithms are stochastic in their nature; and, therefore, the objective function value at termination, obtained by the algorithms may vary from one algorithmic replication to another for a given problem instance. A significant variation in the objective function values from one replication to another for a given algorithm is not desirable, as such algorithm cannot be considered to be a reliable decision support tool (e.g., the berth schedules will significantly vary in terms of the total weighted vessel turnaround time and the total weighted vessel late departures). The scope of numerical experiments includes evaluation of the stability for all the developed solution algorithms. Specifically, the algorithmic stability was assessed based on variability in the objective function values at termination over ten replications, which were performed for each large size problem instance. This study adopted coefficient of variation as an indicator of the objective function variability. The objective function coefficient of variation over ten replications was estimated for each one of the developed solution algorithms and all large size problem instances, and results are reported in Figure 13.  
It can be observed that the objective function coefficient of variation did not exceed 1.98% over all large size problem instances for the presented solution algorithms, which highlights stability of the algorithms. However, lower objective function coefficient of variation values was typically recorded for the SAEA algorithm. On the other hand, higher objective function coefficient of variation values was generally observed for the EA algorithm.  
Figure 12: Evolution of the average mutation probability values for problem instances 49–60.
## 4 Geotechnical Information  
The set of parameters to be determined in the inversion process requires additional geotechnical information (number of layers, thicknesses, Vp, Vs, and densities). For a large area, such as the Middle-Chelif Basin, where lateral variations in the facies are quite frequent [2; 9; 32], a wide coverage of geotechnical data is recommended. In this study, geological and geophysical data were used and compiled for a well-guided inversion process with reliable results.  
The synclinal aspect of the study area made it easier to determine the number of sedimentary layers since the oldest layers outcrop in succession on the edges of the Middle-Chelif Quaternary Plain, and dip under the alluviums [36]. The modified geological map, along with the lithological cross-sections (Figure 2), were good enough for fixing the number of layers.  
A total of 16 geotechnical boreholes were also used, whose depths varied from 20 to 250 m [6; 7; 18]. These boreholes allowed us to fix the thicknesses of the Quaternary and Pliocene layers in some areas. In order to complete the lack of data and to obtain information on deeper layers, a total number of 13 electrical profiles were added. Nine of them were made by the General Company of Geophysics between El-Attaf and Ain-Defla [7]. The profiles are oriented in a N-S direction, with an average length of 5 km and a maximum depth of investigation of 1000 m. The remaining electrical profiles were carried out by the ANRH (Agence National de Ressource Hydraulique) in the El-Attaf region. The average length was 800 m, with an investigation depth of 150 m.  
The velocity and density values for the different sedimentary rocks are provided in Talaganov [17], obtained by using the seismic refraction method in the cities of Chlef, Beni-Rached, and El-Attaf. For the seismic bedrock, a Vs value ranging between 1800 and 2600 m/s was assigned. These values were obtained by the inversion of the ambient vibration data for the cities of Chlef [20] and Oued-Fodda [22]. There are two different formations composing the bedrock, the Cretaceous flysches, clays, and marks in the Middle-Chelif Plain, and the older Jurassic limestones on its southern borders. For the limestones, a Vs value of 2800 m/s and a Vp value of 4800 m/s were assigned using local earthquake tomographic inversion results [53]. The younger marls and clays of the late Cretaceous constitute the bedrock of the sedimentary deposits in the region of Oued-Fodda since it outcrops about 2 km southwest of the city. The Vs values obtained in Issaadi [22] are, therefore, assigned to the Cretaceous marls (Upper Senonian).  
A lithological and geotechnical model for the Middle-Chelif Basin was built from the compilation of the whole of the gathered data (Table 1).
# Design and Applications of GLANCE: GLanceable Alarm Notification for a User Centered Experience
## 3 Results of the 1 st Macro-Phase: The GLANCE Project
### Second Iteration: Dealing with Multiple Alarms  
The second iteration was aimed at the achievement of research objective RO2 (Definition of an abstract model for notification/overview of multiple active faults in TLC networks) and problem objective PO2 (Proof-of-concept ability for notification/overview of multiple active alarms).
# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 3 Materials and Methods
### The Proposed Detection Model, SUD-YOLO
#### 3.2.1 The Proposed Semi-Supervised Learning Method for Training SUD-YOLO
To address this issue, we propose a multiscale pseudo-label bounding box enhancement module, as shown in Figure 4.  
In general semi-supervised object detection algorithms, a teacher model is responsible for generating pseudo-labels on unlabeled images, which are then treated as ground truth and incorporated into the semi-supervised training process. During this process, the quality of the generated pseudo-labels plays a crucial role in determining the success of the entire semi-supervised training. For instance, if the generated pseudo-labels are very close to the ground truth (GT), the semi-supervised training process can essentially be treated as supervised training, leading to highly effective results. Conversely, if the generated pseudo-labels are incorrect, it is akin to training on a mislabeled dataset in a supervised manner, which will inevitably result in poor performance. A key parameter that controls the quality of pseudo-label generation is the confidence threshold when generating predictions for pseudo-labels. High-confidence bounding boxes indicate that the model is highly confident that the detected box likely contains a correct object, while low-confidence bounding boxes suggest that the box is more likely to be incorrect--it may either not contain any object or fail to properly classify the object it contains. While a high confidence threshold ensures that the generated bounding boxes are generally correct, it can also lead to some correct objects being overlooked, as they might not achieve a high enough confidence score due to insufficient feature learning by the model. In this study, for the training images, we first apply flipping and scale transformation, followed by inference prediction. Once the model's prediction results are obtained, they are resized back to their original dimensions, and the results are then aggregated for further selection. In contrast to the approach of lowering the confidence threshold to increase the number of pseudo-labels, which may introduce low-quality pseudo-labels, the proposed method not only ensures that the generated pseudo-labels are more accurate but also facilitates the acquisition of more meaningful pseudo-label information. This allows the network to become more confident during training and amitigates the risk of incorrect learning due to the presence of low-quality pseudo-labels.
## 4 Design of the Equalizers
### Least Square Method for Channel Estimation
For example, in Figure 9, after a certain number of iterations imposed by the turbo decoder, the sequence estimated \(\hat{\mathsf{b}}_{\mathsf{k}}\) at its output is encoded and interleaved, and then BPSK (binary phase-shift keying modulation) modulated and brought to the channel estimator. Then, the estimator channel based on the received sequence \(y_{\mathrm{p}}\) and the transmitted estimated sequence \(\hat{\mathsf{x}}_{\mathrm{p}}\) recalculates the value of \(\hat{\mathsf{h}}_{\mathrm{n}}\) using the LS estimation algorithm, according to  
\[\hat{\mathsf{h}}_{\mathrm{LS}}=\left(\hat{\mathsf{X}}^{\mathrm{T}}\hat{ \mathsf{X}}\right)^{-1}\hat{\mathsf{X}}^{\mathrm{T}}y, \tag{25}\]  
where \(\hat{\mathsf{X}}\) is the Toeplitz matrix corresponding to the sequence \(\hat{\mathsf{x}}[\mathrm{p}]\), which enters the LS estimator, and \(\hat{\mathsf{X}}^{\mathrm{T}}\) represents the transposition of the matrix \(\hat{\mathsf{X}}\). If it is assumed that  
\[\hat{\mathsf{x}}[\mathrm{p}]=[\hat{\mathsf{x}}[1],\;\hat{\mathsf{x}}[2], \ldots,\;\hat{\mathsf{x}}[\mathrm{p}]], \tag{26}\]  
and the received signal is  
\[y[\mathrm{p}]=[y[1],y[2],\ldots,y[\mathrm{p}]], \tag{27}\]  
and the estimated value of \(\mathrm{h}\) is  
\[\hat{\mathsf{h}}_{\mathrm{LS}}=\left[\hat{\mathsf{h}}[1],\;\hat{\mathsf{h}}[2 ],\ldots,\;\hat{\mathsf{h}}[\mathrm{n}]\right]^{\mathrm{T}}, \tag{28}\]  
then, the matrix \(\hat{\mathsf{X}}\) looks like this  
\[\hat{\mathsf{X}}= \begin{bmatrix}\hat{\mathsf{x}}[1]&0&\ldots&0\\ \hat{\mathsf{x}}[2]&\hat{\mathsf{x}}[1]&\ldots&0\\ \vdots&\vdots&\vdots&\vdots\\ \hat{\mathsf{x}}[\mathrm{p}]&\hat{\mathsf{x}}[\mathrm{p}-1]&\ldots&\hat{ \mathsf{x}}[\mathrm{p}-\mathrm{n}+1]\end{bmatrix} , \tag{29}\]
## 3 Results and Discussion  
### Model Validation  
#### 3.1.1 The Normal Case  
In this section, the accuracy of calculation results of air-gap magnetic field without eccentricity was examined. The spatiotemporal distributions of the radial flux density and tangential flux density of the air-gap magnetic field obtained by analytical method and FEA are shown in Figure 4, where the initial spatial angle of the temporal distribution calculation is 3.7\({}^{\circ}\) in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Numerical Values** & **Parameters** & **Numerical Values** \\ \hline Rated power \(P_{n}\) & 160 kW & Axial length of the iron core \(L_{r}\) & 130 mm \\ Rated speed \(n_{0}\) & 5100 rpm & Number of wire layers per groove \(N_{t}\) & 4 \\ Number of pole-pairs \(p\) & 4 & Number of parallel branches \(N_{p}\) & 2 \\ Number of slots \(N_{s}\) & 48 & Magnet thickness \(h_{\text{M}}\) & 6 mm \\ Radius of the slot bottom \(R_{sb}\) & 93.6 mm & Width of the ornament magnet \(L_{M}\) & 17 mm \\ Radius of the slot top \(R_{st}\) & 80.6 mm & Permeability of the vacuum \(\mu_{0}\) & \(4\pi\times 10^{-7}\) H/m \\ Groove width \(h_{sa}\) & 0.05 rad & Relative permeability of the permanent magnet \(\mu_{r}\) & 1.059 \\ Rabbet width \(h_{ba}\) & 0.011 rad & Width of the bridge \(\bar{u}_{b}\) & 0.035 rad \\ Inner radius of the stator \(R_{S}\) & 79.8 mm & End thickness of the bridge \(h_{b}\) & 2.1 mm \\ External radius of the rotor \(R_{r}\) & 79.1 mm & Interpolar thickness of the bridge \(h_{i}\) & 2.8 mm \\ Basic frequency \(f_{0}\) & 340 Hz & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main parameters of IPMSM.  
Figure 4: The spatial and temporal distributions of the normal air-gap flux density. (**a**) The spatial distribution of radial flux density; (**b**) The spatial distribution of tangential flux density; (**c**) The temporal distribution of radial flux density; (**d**) The temporal distribution of tangential flux density.  
According to Equation (29), the _RMSPEs_ of the spatial radial component and the spatial tangential component are 0.9% and 0.89%, respectively, while the _RMSPEs_ of the temporal radial component and the temporal tangential component are 1.01% and 1.1%, respectively. This shows that the results calculated by the proposed analytical model are consistent with the results of FEA, which demonstrates the accuracy of the proposed analytical model.
# The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning
## 2 Data Review
### Training Dataset Construction  
(1) Data Preprocessing  
For learning from the large amount of the original data collected by TBM during excavation, it was necessary to remove the invalid data for the training model through preprocessing. The preprocessing of excavation parameters was as follows. An abnormal sampling of the sensor leads to the missing value data, deleted directly. Routine maintenance and cutter change during excavation will result in non-working state data, which can be judged by zero value of the products of thrust, penetration, torque, and rotational speed, and deleted directly. Every normal cycle of TBM excavation went through three stages: start-up, stabilization, and shut-down, as shown in Figure 1. Only the data in the stabilization stage are suitable for training the model. So, the origin data were divided according to the tunneling cycle, and the data at start-up and shutdown state were judged according to the fixed time of start-up and shutdown stages and deleted directly. For abnormal data caused by external factors, the 3\(\sigma\) method was used to judge and delete the abnormal values. The paper only considers the attitude control under the situation of the designed tunnel axis of a straight line; the excavation data under this situation were selected as the training data. In this paper, the origin excavation data from Xinjiang Yiner Water Supply Phase II Project were used. There were 1,157,693 sets of original excavation data, and only 151,447 sets of valid data were obtained after preprocessing. (2) Data Matching and Combination  
The training dataset for GEC predictive model should be built from the preprocessed data. Based on physical mechanism analysis, 40 excavation parameters were selected according to the survey questionnaires from the construction engineers. Then 40 excavation parameters were evaluated according to the correlation with GEC using the Decision Tree method. Finally, 20 excavation parameters composed of the control parameters and the response state parameters were selected from 228 excavation parameters according to the correlation with GEC as the input of the GEC predictive model, of which the names and abbreviations are shown in Table 1. The GEC data in the engineering data used in this paper included GEC 2, 3a, 3b, 4, and 5. In order to have enough data for model training of GEC 3, 3a, and 3b were treated as the same category, finally obtaining four GECs. The recorded excavation parameters and GEC data were all indexed by time, so they could be matched by time, forming the training dataset of the GEC prediction model.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 4 Waveform Selection
### Candidate Waveforms
#### 4.1.4 SC-FDMA  
SC-FDMA is the multiple access form of SC-FDP. In this waveform the subcarriers are also allocated with a specific pattern to the users. Figure 6 shows the block diagram of SC-FDMA massive MIMO system in both uplink and downlink. In this case if we also assume \(N\) is the number of subcarriers allocated to a user and we have total of \(K\) users, where \(\bar{K}\) is the number of users that share the same subcarriers, then the total number of subcarriers will be \(\bar{N}=\frac{NK}{K}\). We should note that this is valid if we have no guard subcarriers in the system.
## 1 Introduction
### Contributions of Our Study  
In view of the current research status, several studies have pointed out the technical and economic analysis of various PV tracking technologies. However, most of the studies only focus on the PV system as an independent power system, such as [49; 50; 51; 52; 53; 54; 55]. In addition, few researchers put forward its application in a hybrid or integrated system. At present, there is no research report on a hybrid or integrated energy system (PV-FC). The significance and contribution of this study are summarized as follows:  
1. Based on the authors' understanding, the influence of PV tracking technology on the technical and economic performance of grid connected hybrid energy system (PV-FC) is studied for the first time. The research of a hybrid energy system (PV-FC) is more meaningful in a real application.
2. In our study, many technical and economic factors, such as PV power generation, excess energy, renewable energy penetration, GHG emissions, power purchased from the grid, power sold to the grid, lifetime project cost and LCOE and so on, are considered. Unlike other studies in [14; 61; 62; 63], decision variables considered in their model are less so that the feasibility in other applications is poor.
3. As described in the literature review [27; 28; 29; 30; 31; 32; 33; 34], hydrogen energy system equipment still faces challenges, such as efficiency and cost. Therefore, in our study, it is considered to assist PV and the grid to meet critical load demand, which makes the hybrid system more economical.
## 6 Evaluation of Performance Indicators  
After the application of the proposed method, its performance in predicting the expected improvement in the bearing capacity of soil can be evaluated using three indices: (1) the prediction accuracy (PA) for a specific soil category (j), (2)the overall prediction accuracy (OPA) of the project as a whole, and (3) the prediction performance index (PPI). The PA and OPA indices assess the accuracy of the proposed method in predicting the post-implementation improvement in the bearing capacity of soil at the soil category level as well as at the project level. On the other hand, PPI measures the deviation of the prediction accuracy of the proposed method between planned and actual predictions at the project level. PA, OPA, and PPI can be calculated using Equations (15)-(17), respectively.  
\[PA=\frac{NP[\;SIR\;(j)]}{N(j)} \tag{15}\]  
\[OPA=\frac{\sum_{j=1}^{j=n}\bigl{(}N\;(j)*NP[SIR\;(j)]\bigr{.}}{\sum_{j=1}^{j=n} N\;(j)} \tag{16}\]  
where,  
\(N(j)\) is the total number of data points in soil category \(j\).  
\(NP[SIR(j)]\) represents the number of predictions that fall within the soil improvement range (\(SIR_{j}\)) of Category (j) after the implementation of DC.  
\[\text{PPI}=\frac{OPA}{EFF_{\text{DC}}}\;\left\{\begin{array}{l}\text{if PPI }>\;1\;\text{that\;means\;the\;DC\;over\;-\;performed\;in\;this\;project}\\ \text{if PPI}=1\;\text{that\;means\;the\;DC\;well\;-\;performed\;in\;this\;project}\\ \text{if PPI}<\;1\;\text{that\;means\;the\;DC\;under\;-\;performed\;in\;this\;project} \end{array}\right. \tag{17}\]
# On Edge Pruning of Communication Networks under an Age-of-Information Framework
## 4 Problem Reduction
### Parallel Links Reduction  
Here, we simply show that a two-terminal graph that consists of a simple pair of source-destination nodes connected through \(n\) links (i.e., consists solely of the operation \(C_{p}\) performed on a set of connected pair of nodes).  
In this case, a message waiting at the source node will be transmitted to the destination node instantly once the first one of the \(n\) links gets activated. That is it, the distribution of the wait time, in this case, will follow the distribution \(\min\{X_{1},X_{2},\ldots,X_{n}\}\), where \(X_{i}\) is the exponential distribution of the wait time for link \(i\) characterized with an average \(\lambda_{i}\) wait time.  
Now we prove the simplest case, that \(\min\{X_{1},X_{2},\ldots,X_{n}\}\) follows the exponential distribution too, yielding a total distribution of \(X_{p}\) with a reciprocal of average value \(\lambda_{p}=\lambda_{1}+\cdots+\lambda_{n}\). First, we know that the cumulative distribution function (CDF) of \(X_{i}\) is \(F_{X_{i}}(x)=1-e^{\lambda_{i}x}\). Since the CDF of \(\min\{X_{1},X_{2},\ldots,X_{n}\}\) generally equals \(1-\Pi_{i=1}^{n}(1-F_{X_{i}}(x))\), which yields to \(1-\Pi_{i=1}^{n}(e^{\lambda_{i}x})=1-e^{\frac{\pi^{n}-1}{\lambda_{i}x}}=1-e^{ \lambda_{p}x}\) where \(\lambda_{p}=\lambda_{1}+\cdots+\lambda_{n}\). Hence, the result distribution is exponential with average \(1/\lambda_{p}\). Hence, a simple pair of nodes connected through multiple links in parallel can be reduced to a single pair of source and destination nodes with an average of \(1/\lambda_{p}\) and exponential distribution \(X_{p}\). Figure 3 shows the reduction.  
For the general case when you have a distribution that represents any mixture of independent exponentially distributed links that would result from the SP reduction operations, we use the general phase-type distribution with applying the reduction shown in Equations (16) and (17).
# A Feedback Control Method to Maintain the Amplitude of the RF Signal Applied to Ion Traps
## 3 FPGA-Based Feedback Control
### FPGA-Based Feedback Controller
#### 3.3.3 Design of the Controllers
To reduce the overshoot and maintain the settling time at less than 1 s, with the least steady-state error, we determined the pole and zero. When the pole and zero of the lag compensator are 533 and 293, respectively, no overshoot occurs, and the setting time is 10.2 ms while showing the lowest steady-state error.
## 4 Conclusions and Future Outlook  
There has been substantial progress in the development of multifunctional fluorescent-magnetic nanomaterials and their biomedical applications over last 10 years. The development of new synthetic approaches involving silica and various polymer coatings enabled us to address issues related to paramagnetic quenching of fluorophore by magnetic components. The new fluorescent-magnetic nanocomposites have been successfully used for biological imaging, hyperthermia and drug delivery applications. However, one of the major challenges and roadblocks for in vivo applications of multifunctional fluorescent-magnetic nanocomposites is their potential toxicity. Despite very extensive studies of toxicity of many nanomaterials, the results are still inconclusive, and further research on the toxicity and of biocompatibility of fluorescent-magnetic nanocomposites is necessary. Moreover, fluorescent-magnetic nanomaterials are quite attractive objects in terms of their in vitro and in vivo toxicity studies; due to the dual modality, they can be easily monitored by fluorescence microscopy in vitro and MRI in vivo, enabling identification and visualization of their bio-distribution. In addition, this research will be necessary for further development of potential drug delivery systems based on fluorescent-magnetic nanostructures.  
Fluorescent-magnetic nanocomposites are also very promising candidates for multimodal theranostic materials which could serve as all-in-one contrast agents, photosensizers for photodynamic therapy and hyperthermia agents at the same time. This important goal can be realised, for example, by combining magnetic nanoparticles and organic dyes which are traditionally used in photodynamic therapy (e.g., porphyrin or phthalocyanine derivatives). In addition fluorescent-magnetic nanocomposites can be easily functionalized with specific biomarkers (e.g., antibodies, receptors, etc.) that would facilitate their targeted delivery to specific sites (e.g., tumors) in the body. This would be of particular importance, as one of the major drawbacks of any chemotherapy is the difficulty in delivering the drug to the biological site of interest. In addition, it is essential to notice that in this case, all processes could be monitored by MRI and fluorescence microscopy techniques, allowing the full control of the medical procedures. However, this area of research is still in the very early stage of development and requires intensive future studies.  
Overall, we believe that further development of new magnetic-fluorescent nanomaterials will be very important for advances in modern biology and medicine.  
This work was supported by the EU-ITN network Mag(net)icFun (PITN-GA-2012-290248) and Irish Research Council (IRC) postgraduate scheme.  
All authors wrote and cross-checked the manuscript together.  
The authors declare no conflict of interest.