esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 A Case Study
### Results and Validation
For instance, the exact method was not capable of solving the problem instance with 1,000,000 vehicles since it ran out of memory. Given the need for V2G systems to manage a large number of vehicles efficiently, it becomes clear that AI-driven methods should be prioritized. Additionally, the development and resolution of a more complex model is planned for future work; this case study, along with the algorithm and results obtained, aims to show the importance of applying intelligent algorithms in V2G systems. All the results in this section were obtained using a Chinese made personal computer (HP Omen 15-en1004 ns) with 16 GB of RAM and an AMD Ryzen 5800H processor. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Vehicles** & **Exact Method** & **Heuristic Approach** \\ \hline
1000 & 5.225 & 0.001 \\
10,000 & 56.052 & 0.014 \\
100,000 & 629.509 & 0.289 \\
1,000,000 & – & 4.487 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time in seconds each method needs to solve the problem. Figure 6: Dispersion of the solutions for both scenarios.
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 4 Method
### Channels
#### 4.2.3 Real World  
A prerecorded channel is used to test the performance of the approach using real-world channel impairments [18, 19]. A massive multiple-input and multiple-output (mMIMO) antenna array (64 elements) was installed on a building's roof approximately 20 m above the ground. A trolley carrying the receive antenna was pushed along the ground to imitate a pedestrian. A 2.180 GHz carrier frequency was used with 50 sub-bands over a bandwidth of 10 MHz for each antenna, and measurement impairments were eliminated by post-processing. To support our experiments using the IEEE 802.11p standard, we linearly interpolate the channel in the time and frequency domains to simulate the receive antenna moving between 60 km/h and 100 km/h. The prerecorded real-world channel is used to provide insights into how performant our approach is on non-simulated channels. As the intention is to use this approach to improve communication over real-world channels, it is important to include other possible impediments not captured by more simplistic channel models. For our experiments, we consider a pair from the MIMO channel recordings for our channel, as we are simulating a single-input and single-output (SISO) environment.
## 3 Verification  
### Case 1: Verification of Rock Brittleness Under Different Confining Pressures Using Proposed Index  
The triaxial compression test is an essential approach to investigate the mechanical behaviour of rocks. Over the years, numerous triaxial compression tests have been conducted [27; 28; 29], revealing that rock brittleness is significantly affected by confining pressure. The deformation behaviour of rocks changes from brittleness to ductility with the increment of confining pressure. We utilized triaxial compression test data of coal obtained by [PERSON] et al. [30] to verify Equation (15). Table 1 shows the calculated parameters (peak strain, peak strength, Poisson's ratio and elastic modulus) for various circumferential pressures (0, 6, 12, 18, 24 and 30 MPa). Note that the selection of Poisson's ratio refers to [PERSON], [PERSON] and [PERSON] [26]. Based on Table 1, Figure 2 displays the brittleness index of coal obtained by Equation (15). As the confining pressure rises from a zero-stress level to 30 MPa, the brittleness index declines from 4.75 to 2.60, which indicates that the defined brittleness index validly identifies the influence of confining pressure on brittleness characteristics. The results acquired from our proposed index are consistent with an energy-based indicator proposed by [PERSON] et al. [30].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Confining Pressure (MPa)** & **Peak Strength (MPa)** & **Peak Strain** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** \\ \hline
0 & 23.99 & 0.0075 & 3220 & 0.26 \\
6 & 34.33 & 0.0125 & 3310 & 0.26 \\
12 & 43.74 & 0.0152 & 3770 & 0.26 \\
18 & 64.92 & 0.021 & 3910 & 0.26 \\
24 & 68.44 & 0.023 & 4350 & 0.26 \\
30 & 80.03 & 0.029 & 4510 & 0.26 \\ \hline \end{tabular} Note that Poisson’s ratio was selected, in accordance with [PERSON] et al. [26].  
\end{table}
Table 1: Mechanical parameters of coal in Case 1.
###### Abstract  
In multi-institutional emergency room settings, the early identification of high-risk patients is crucial for effective severity management. This necessitates the development of advanced models capable of accurately predicting patient severity based on initial conditions. However, collecting and analyzing large-scale data for high-performance predictive models is challenging due to privacy and data security concerns in integrating data from multiple emergency rooms. To address this, our work applies federated learning (FL) techniques, maintaining privacy without centralizing data. Medical data, which are often non-independent and identically distributed (non-IID), pose challenges for existing FL, where random client selection can impact overall FL performance. Therefore, we introduce a new client selection mechanism based on local model evaluation (LMECS), enhancing performance and practicality. This approach shows that the proposed FL model can achieve comparable performance to centralized models and maintain data privacy. The execution time was reduced by up to 27% compared to the existing FL algorithm. In addition, compared to the average performance of local models without FL, our LMECS improved the AUC by 2% and achieved up to 23% performance improvement compared to the existing FL algorithm. This work presents the potential for effective patient severity management in multi-institutional emergency rooms using FL without data movement, offering an innovative approach that satisfies both medical data privacy and efficient utilization.  
federated learning; centralized learning; patient severity analysis; local model evaluation; client selection +
Footnote †: journal: Medical Medical Evaluation  
10.3390/app14166876
## 3 Dataset Collection
### Dataset comprising Video Sequences  
Unlike the aforementioned dataset, which is an annotation of a single image, the second dataset is an annotation of a video sequence. Thus, in addition to annotating the bounding boxes and categories of the objects, this dataset also requires an ID number to be assigned to each target, with the same target having a constant ID in the sequence. This ID number is used to determine whether the same target has been successfully associated in different frames. Moreover, the dataset is used for the collection of KMNs and contains four KMN types, namely, aircraft _in-block_ and _off-block_ and the _docking_ and _undocking_ of mobile aircraft landing stairs.  
Original videos of this dataset were captured by surveillance cameras of two remote stands (No. 734 and No. 939) at Beijing Capital International Airport shown in Figure 4. The field of view of the camera at No. 734 is much larger than that of the camera at No. 939. The dataset contains 42 individual video sequences. Table 2 presents the distribution of video sequences. The frame index corresponding to the time of occurrence of the KMN is manually recorded as the ground truth to evaluate the proposed framework. The frame rate of the original video is 30 frames per second (fps). Owing to the relatively low speed of the aircraft and the motion of the various working vehicles in the surveillance video, not only does lowering the frame rate not affect the performance of the proposed framework but also it reduces memory consumption and improves operational efficiency to a certain extent. To this end, the original surveillance video is sampled at 5 fps, and each sequence of the dataset contains an average of 300 frames with a resolution of 1920 \(\times\) 1080 pixels.  
Figure 3: Sample number of different categories and the location and scale distributions of the sample in the dataset. (**a**) Sample numbers of different categories. (**b**) Location and scale distributions of the samples in the dataset. For the location distribution (left), \(x_{c}\) and \(y_{c}\) are the horizontal and vertical coordinates of the center point of the bounding box of each object, respectively. \(W\) and \(H\) are the width and height of the image, respectively. For the scale distribution (right), \(w\) and \(h\) are the width and height of the bounding box of the object, respectively.
###### Abstract
Thus, performance modeling of systems involving such types of UEs requires time-dependent models that are generally much more difficult to solve as compared to static models. As a result, the performance of RedCap UEs has been conventionally evaluated using time-consuming system-level simulations. In this paper, we fill the above-mentioned void. The aim of this paper is to mathematically characterize time-dependent UE performance metrics in mmWave/sub-THz systems. To this end, by explicitly accounting for mmWave/sub-THz propagation, directional antenna radiation patterns, blockage, and mobility, we will utilize the tools of the stochastic geometry and random walks theory to build a general time-dependent performance evaluation framework. As metrics of interest, we will utilize spectral efficiency and signal-to-noise ratio (SNR) at a certain time instant t and derive them as an explicit function of the state of the UE at some initial time instant \(t_{0}\). In our numerical results, we will investigate the dependence of these metrics on the system parameters, including the number of antenna elements and mobility characteristics of a user. The main contributions of our study are:  
* A mathematical model allowing for the capture of performance metrics, including signal-to-noise (SNR), spectral efficiency, and rate as an explicit function of time for 5G/6G mmWave/sub-THz systems with directional antennas, and micro- and macro-mobility impairments;
* A qualitative analysis of time-dependent metrics, showing that macro-mobility effects are more pronounced at the large timescales specifying the overall trends in the considered metrics, while micro-mobility affects them at fine-grained timescales manifesting itself in local fluctuations;
* Observations that the difference between spectral efficiencies in a system with perfectly synchronized UE and BS antennas and the one with completely desynchronized antennas is rather small, amounting to 10% at most, implying that the communications can be performed without perfect synchronization between antennas at a slightly degraded quality. The rest of the paper is organized as follows. In Section 2, we will discuss the related work. The system modeling utilized in this study is formalized in Section 3. We will solve this model for time-dependent spectral efficiency and SNR metrics in Section 4. Numerical results are illustrated in Section 5. Finally, conclusions are drawn in Section 6.
## 2 Reliability Model of the Components in the WS
### Structure of the IEWS  
The structure of the IEWS is shown in Figure 1, where the desalination plant turns salt water into fresh water by consuming electrical energy. In addition, the water pumps also consume electrical energy to keep the WS operating properly. Therefore, the WS is tightly coupled with the power system through the desalination plant and the pumps.  
The regulation of the desalination process can improve the operational flexibility of the IEWS. In the peak period of renewable energy output or the valley period of power demand, the desalination plant's fresh water production can be increased, and the water will be transported to the reservoir for storage. On the contrary, the fresh water generation of the desalination plant can be reduced as well as the pumping power of the water pumps. The stored water in the reservoirs of the desalination plant and tanks of the distribution pipeline network can be released to maintain the balance between supply and demand in the WS.  
Figure 1: Typical structure diagram of the IEWS.
# A Site-Specific Response Analysis: A Case Study in Hanoi, Vietnam
## 3 Site Response Analysis Methodology
### Input Ground Motions  
To ensure an appropriate representation of ground motion intensities for the districts in Hanoi, earthquake motions representing the seismicity of the region were selected. Due to the lack of earthquake records available in Hanoi, ground motions recorded in other regions with a range of magnitude similar to the ancient earthquakes in Vietnam were selected. The ground motions recorded from earthquakes having a variety of rupture mechanisms were selected to account for the effect of the fault mechanism. The recorded ground motions were selected from the NGA-west2 database ([[https://ngawest2.berkeley.edu/](https://ngawest2.berkeley.edu/)]([https://ngawest2.berkeley.edu/](https://ngawest2.berkeley.edu/))). The ground motions with the following features were selected:  
* A range of moment magnitudes (M\({}_{\rm w}\)) 5.5-7.0.
* Rupture distance (R\({}_{\rm rup}\)) 0.92-85.17 km.
* V\({}_{\rm S30}\) of 760-1500 m/s.  
A total of 15 motions with peak ground acceleration (PGA) ranging from 0.04 g to 0.25 g were selected from six earthquake events, as shown in Table 5. The ground motions were scaled to the  
Figure 5: Selected soil profiles: (**a**) SPT-N value, (**b**) shear wave velocity.  
representative shaking intensity levels of each analyzed district, in which PGA levels at Cau Giay, Hoang Mai, and Hai Ba Trung Districts are scaled to 0.1032 g, 0.1001 g, and 0.0959 g, respectively. Furthermore, the mean response spectrum of the input ground motions matched closely to the target spectrum of site class A of the Vietnamese design code [14], as shown in Figure 6.
# Intelligibility and Listening Effort of Spanish Oesophageal Speech +
## 3 Experiment 1: Preliminary Word Error Rate and Listening Effort Measurements
### Analysis and Results
#### 3.2.2 Self-Reported Listening Effort  
Mean LE ratings are stated in Table 1. Figure 2 shows the speakerwise LE ratings. As expected, it is higher for OS compared to HS. However, when listening to OS, the LE is significantly lower for familiar listeners than for not familiar listeners. Indeed, ANOVA analysis shows that familiarity with OS has an effect on LE (F(1,55) = 20.22, \(p<0.001\), \(\eta^{2}=0.269\) ) and Speaker-type has a strong effect on LE (F(1,55) = 315.00, \(p<0.001\), \(\eta^{2}=0.808\)).  
The audio device used by the listener had no effect on LE (F(3,1256) = 0.705, \(p=0.549\)).
# Estimating Interference with a Two-Dimensional Viterbi Algorithm for Bit-Patterned Media Recording
## 3 Proposed Model
### Detection Scheme  
\(s_{v}[j,k]\) and \(s_{h}[j,k]\) are detected based on the 1D VA algorithm. After detection, the output of the 1D Viterbi detection is \(\overline{a}[j,k]\), which is similar to the original signal, \(a[j,k]\). Thus, the signal, \(\overline{a}[j,k]\), can pass through the interference target to regenerate \(\overline{TS}[j,k]\) or \(\overline{TT}[j,k]\), which are the ISI or \(\Pi\Pi\), respectively, with a higher accuracy compared to \(IS[j,k]\) or \(ITl[j,k]\).  
\[\begin{array}{rcl}\overline{TS}[j,k]&=&t\overline{a}[j-1,k-1]+t\overline{a} [j-1,k+1]\\ &+&r\overline{a}[j,k-1]+r\overline{a}[j,k+1]\\ &+&t\overline{a}[j+1,k-1]+t\overline{a}[j+1,k+1].\end{array} \tag{36}\]  
\[\begin{array}{rcl}\overline{TT}[j,k]&=&t\overline{a}[j-1,k-1]+p\overline{a} [j-1,k]+t\overline{a}[j-1,k+1]\\ &+&t\overline{a}[j+1,k-1]+p\overline{a}[j+1,k]+t\overline{a}[j+1,k+1].\end{array} \tag{37}\]  
Then, either \(\overline{TS}[j,k]\) or \(\overline{TT}[j,k]\) is again subtracted from \(\underline{z}[j,k]\) to create a signal with almost no ISI or \(\Pi\Pi\), which has only 1D interference (\(\overline{s_{v}}[j,k]\) or \(\overline{s_{h}}[j,k]\)). Finally, either \(\overline{s_{v}}[j,k]\) or \(\overline{s_{h}}[j,k]\) is detected by 1D VA to restore the original signal, \(\underline{a}[j,k]\).
## 5 Results and Discussion
### Optimization Results of Stress Distribution Based on BLSGAOM
Drawing inspiration from the principles of natural selection and genetic mechanics prevalent in biological evolution, the GA emerges as a heuristic global optimization approach renowned for its exemplary global search capabilities. The predictive model derived earlier was amalgamated with the three error terms, serving as the basis to forecast outcomes based on the input parameters. Thus, the predictions that were generated constituted the fitness function for the GA, with the overarching objective being the identification of the input parameters that optimize (minimize in this context) the aggregate of the maximum stress and the stress distribution STD. In this study, the configuration of the GA parameters was elucidated as follows: The solution vector length was set at three, directly corresponding to the three error terms. The defined search space bounds ranged from a lower boundary of [0 mm, 0 mm, 0\({}^{\circ}\)] to an upper boundary of [0.1 mm, 0.04 mm, 10/60\({}^{\circ}\)]. The output functions were integrated to record the optimal fitness values upon the conclusion of every generation. To bolster  
Figure 11: Variation of objective function model and minimum objective value during Bayesian optimization. (**a**) Objective function model. (**b**) Minimum objective value vs. number of function evaluations. Figure 12: Predicted value vs. actual value. population diversity and sidestep the potential challenges of the local optima, migrations were programmed at intervals of every 20 generations, with each migration facilitating a randomized transfer of 30% of the population. When the GA was executed, the resulting optimal solution was pinpointed at [0.0641 mm, 0.0058 mm, 0.0584\({}^{\circ}\)]. This solution corresponded to a peak fitness (predictive error) value of 0.4691. A graphical representation depicting the progression of the optimal fitness value across generational transitions can be found in Figure 13.
## 3 Results and Discussion
### Experimental Results  
In the previous subsection, the ray tracing simulation provided a solid understanding of the optical phenomena in the channel and the coupling coefficient \(r_{ext}\) distribution. Here, a series of flowmetry measurements were performed for validation. Both simulated and measured OFI signal spectra are compared and investigated to analyze the influence of several important factors on the OFI signal spectra features: The particle size, the particle concentration, the reflectivity of the channel rear interface and the channel dimension (Figures 5-8).  
Figure 5: Optical feedback interferometry (OFI) signal frequency spectra obtained with PS particles of 552 nm diameter (red), 4.89 μm (blue): (a) simulated spectra; (b) measured spectra.  
Figure 6: OFI signal frequency spectra obtained in 552 nm PS particle solution at concentration of 0.625% _w/v_ (green), 0.125% _w/v_ (black), 0.25% _w/v_ (red), 0.5% _w/v_ (blue): (a) simulated spectra; (b) measured spectra.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 2 Experimental Setup and Methodology
### Direct LSSVM Localisation Method
#### 2.6.2 Post-Processing and Outlier Removal  
The ray-tracing setup has BS-BS distance of 300 m so a coverage radius for each BS of 150 m is considered for determining outliers. The BS deployment seeks to approximate envisaged fifth generation (5G) system deployments, where a dense deployment of small cells is expected. The process of determining and excluding outliers involves calculating the distance \(d_{i}\) between the known BS position and the estimated MS location, as follows:  
\[d_{i}=\sqrt{[\left(BSx_{i}-\ \widehat{MSx_{i}}\right)^{2}+\ \left(BSy_{i}-\ \widehat{MSy_{i}}\right)^{2}]}, \tag{4}\]  
where \(BSx_{i}\) is the \(i^{th}\) BS's x-coordinate and \(\widehat{MSx_{i}}\) is the estimated \(i^{th}\) MS x-coordinate. The other symbols' meaning follow, for the y-coordinate. It also follows that for multiple MS positions using the same BS, \(BSx_{i}\) and \(BSy_{i}\) are constant, for all \(i\). A BS receives multiple rays from an MS and each ray is used to estimate the MS position. For a single MS position, some rays will estimate the position better than others, so those rays that result in the BS-MS distance greater than 150 m are discarded. Empirical tests show that more regressor errors start increasing for MS positions beyond 100 m from the BS. Outlier removal criteria may be tightened to any distance but that will create more coverage black spots, hence we settled on 150 m. On average, the total number of data points that were excluded because of this criterion were around 10%. Figure 5, below, shows the effect of excluding those rays that are resulting in outlying MS positions.
## 4 Future Research Directions
### Open Research Direction for DSM
#### 4.1.2 Security and Privacy  
The security and privacy of the consumers is the key aspect of an efficient DSM scheme. In DSM, there is a sharing of energy consumption information among the consumers. While designing a DSM scheme, secure information sharing among consumers needs to be considered. It is important to discover efficient novel DSM schemes where the consumers' privacy is considered. Moreover, robust and reliable communication needs to be developed for secure information sharing. Therefore, consumers' DSM schemes with security and privacy consideration can be a future research direction.
## 3 Results and Discussions
### Propagation of Uncertainty (Benchmark and Comparison Testing)  
Fifteen days with similar criteria are selected as a benchmark, for comparison purposes. As shown in Figure 14, the data for the days selected differ from the benchmark by \(-0.924\sim+1.141\%\) which is a permissible error. This shows that the method used to calculate energy savings is stable and could be a reference for future studies.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Antimony Sulfide Films  
The Sb2S3 films were deposited on glass slides [88]. The XRD and EDX analyses showed the orthorhombic phase and non-stoichiometric films, respectively. The Raman spectroscopy revealed two major peaks (250 and 300 cm\({}^{-1}\)) in all samples. Photoluminescence spectra indicated broad bands (430-480 nm) with very strong blue emission peaks (460 nm) in all films. Antimony (III) trioxide (Sb2O3) and sodium thiosulfate were used to produce thin films on the glass substrates [89]. It was observed that film thickness increased up to 0.78 um (14 deposition cycles), then films began to peel from the substrates. The band gap, resistivity value, and activation energy were 1.8 eV, 10\({}^{7}\)\(\Omega\)-cm, and 0.14 eV, respectively.