esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## Appendix A Per-Job Energy Consumption Model  
We denote by \(E_{j}\) the total energy consumed by job \(j\):  
\[E_{j}=\int_{0}^{T_{j}}R_{\text{GPU}}(t)dt,\]  
where \(T_{j}\) is the job's makespan and \(R_{\text{GPU}}\) the instantaneous device power.  
Building on the findings of [PERSON] [221], dynamic power consumption scales approximately with the cube of the SM frequency--a relationship that has also been observed in GPUs [222].  
\[P_{\text{dyn}}=\kappa\cdot f^{3},\]  
where \(\kappa\) is a device-specific coefficient calibrated once via NVML (Nvidia management library) power sampling in the P0 state (typical values: \(\kappa=1.2\)\(\sim\)1.4 W/Hz\({}^{3}\) for an A100 40 GB at 250 W TDP (thermal design power), the maximum heat a processor is designed to dissipate).  
[PERSON] et al. [222] also fit DNN inference latency to  
\[t(f)=a\cdot f^{-b}+c, \tag{1}\]  
with \(b\approx 1\) for compute-bound kernels and \(b<1\) when memory bandwidth dominates. Combining (1) with the cubic power law gives  
\[E_{j}(f)=\kappa\cdot f^{3}\Big{(}a\cdot f^{-b}+c\Big{)}=\kappa\cdot a\cdot f^{ 3-b}+\kappa\cdot c\cdot f^{3}. \tag{2}\]  
For autoregressive LLM workloads, [PERSON] et al. [223] show that _iteration-level_ DVFS guided by a polynomial tail-latency predictor can cut energy by 22.8\(\sim\)45.5% on A100/A30 GPUs while meeting an 8.4\(\sim\)10 p99 latency SLO. Their scheme is compatible with Equation (2): the scheduler simply selects the minimum frequency \(f\) that satisfies the predicted latency bound.  
A cubic-frequency power model, calibrated empirically and coupled with the latency fit of Equation (1), offers sufficient fidelity for cluster-level optimization while remaining analytically tractable.
## 2 Materials and Methods
### Sorbents Characterization  
In Table 2 are presented the methods and apparatus used to characterization of sorbents.  
Pore size and surface area were determined both for activated charcoal, and mineral clay by N\({}_{2}\) adsorption techniques. Brunauer-Emmett-Teller (BET) equation was used to calculate the BET surface area from the isotherms [16]. Using the value of amount of adsorbed nitrogen at P/Po 0.95, the total pore volume was calculated. Dubinin-Radushkevich equation was used to determine the micropore volume [17]. The value of the amount of N\({}_{2}\) adsorbed between relative pressures P/Po 0.40-0.95 was used to calculate mesopore volume. The molar volume of liquid nitrogen was considered of 35.0 cm\({}^{3}\)/mol [18]. The pore size distribution is ascertained by non-local density functional theory (NLDFT) [19].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. Crt.**} & \multirow{2}{*}{**Method Used**} & \multicolumn{2}{c}{**Characteristics**} & \multirow{2}{*}{**Apparatus Used**} \\  & & & **Obtained** & \\ \hline \multirow{3}{*}{_1_} & Brunauer–Emmett–Teller (BET) equation & Pore size and surface area & Micromeritics TriStar II 3020 \\ \cline{2-5}  & \multirow{2}{*}{X-ray fluorescence of natural Na-montmorillonite} & Chemical composition of natural Na-montmorillonite & \multirow{2}{*}{JASCO FP-6500 Analyzer} \\ \cline{1-1} \cline{5-5}  & \multirow{2}{*}{X-ray diffraction (XRD) analysis} & X-ray diffraction (XRD) pattern of natural Na-montmorillonite. & diffractometer with Ni filtered Cu K\(\alpha\) radiation (\(\lambda\) = 1.5406 Å) \\ \hline \multirow{3}{*}{_4_} & Fourier transform infrared spectroscopy (FT-IR) & IR Spectrum of mineral clay and activated charcoal samples & Perkin-Elmer Spectrum 100 spectrometer with ATR, wave number range 4000–400 cm\({}^{-1}\) \\ \cline{1-1} \cline{5-5}  & Scanning electron microscopy (SEM) & SEM images for activated charcoal and mineral clay & HITACHI S2600N \\ \hline \hline \end{tabular}
\end{table}
Table 2: Methods to characterization of sorbents.  
In a previous study we have determined pore size distribution, pore volume and surface area of natural Na-montmorillonite [7].
# System Design and Experimental Study of a Four-Roll Bending Machine
## 5 Experimental Validation  
### Experimental Validation of Machine Error  
The machine error experiment aims to determine the magnitude of equipment error [21] in the four-roll bending machine, evaluating its geometric accuracy, motion stability, and forming error range. The experimental apparatus is shown in Figure 18a. To prevent excessively large roll bending radii from affecting the evaluation of machine error, this study conducted roll bending tests with different lateral roller displacements (10 mm, 15 mm, and 20 mm). This experiment employed a three-group repeated testing scheme: each group conducted three repeated rolling tests on 6063 aluminum alloy profiles (specifications: 20 mm \(\times\) 20 mm \(\times\) 1500 mm) under identical process parameters, yielding a total of 9 valid datasets as presented in Table 9. Analysis of the experimental data reveals that the equipment error remains within 1%, satisfying the rolling requirements. The rolled aluminum profiles from the experiment are displayed in Figure 18b. The roll bending radius is calculated using formula (23).  
\[r=\frac{\left(a^{2}+b^{2}\right)}{2b}. \tag{23}\]  
where: \(a\) is half of the chord length, and \(b\) is the sagitta (height of the arc).
## 3 Results and Discussion
### Relation between Chloride Ion Diffusion Coefficient and Compressive Strength  
Figure 10a shows the chloride ion diffusion coefficient of each concrete mix by age. C24_O3_E5_O2 exhibited lower chloride ion diffusion than C35_O10, as with the tendency of the chloride penetration depth. A slightly different result from the chloride penetration depth is that large differences in chloride ion diffusion coefficient were observed between C24_O3_E5_O2 and C35_O10 even at early ages. Large differences were also continuously observed in the long term. This appears to be due to the difference in ion penetration mechanism.  
Figure 10b shows the relation between compressive strength and chloride ion diffusion coefficient. Under the same compressive strength development, C24_O3_E5_O2 continuously exhibited 1-\(1.2\times 10^{-1}\) m\({}^{2}\)/s lower chloride ion diffusion coefficients than C35_O10 due to the influence of the surface watertightness and chemical reactions with penetrating chloride ions. This can be explained by the internal watertightness improvement and chloride ion immobilization caused by the action of GGBS [39; 40; 41].
# Economic Feasibility of Underground Pumped Storage Hydropower Plants Providing Ancillary Services
## 2 Methodology  
A three-step scheme of the methodology proposed to study the economic feasibility and profitability of UPSH plants is shown in Figure 1.  
Figure 1: Methodology scheme to analyze the economic feasibility of underground pumped storage hydropower (UPSH) plants.  
The model considers the electricity prices in the day-ahead and ancillary services markets hour by hour for a time period of three years (2016-2018). In step 1, the maximum and minimum prices are analyzed for the energy generated and consumed in the Iberian electricity system, considering DTCs between 4 and 10 h. The number of annual generation cycles and the amount of energy generated are obtained for all scenarios. In step 2, the investment costs of a UPSH plant are estimated for the case of using the existing infrastructure and the case of excavating new tunnels or caverns as a lower reservoir. Amortization costs are calculated considering a typical operation period of 35 years. Finally, in step 3, the operating margin, which is defined as the income from selling energy minus the costs for purchasing energy and the operation costs (O&M, start-up costs, hydraulic cannon and grid access tariffs), is estimated for the three hydropower stations (HPSs) considered in this study.  
The earnings before interest and taxes (EBIT) and the cash flows have been calculated to analyze the economic feasibility and the profitability. The NPV, i.e., the difference between the present value of cash inflows and the present value of cash outflows over a period of time; IRR, i.e., a discount rate that makes the NPV of all cash flows from a project equal to zero; and PB, i.e., the time in which the initial outlay of an investment is expected to be recovered, have been calculated in all scenarios.
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 4 UAV's Behavior in the Approaching Area  
Consider a UAV, \(UAV_{B}\), and a UAV ahead of \(UAV_{B}\), which is \(UAV_{A}\). In this section, we discuss how a \(UAV_{B}\) should control its speed while travelling in the approaching area such that it can avoid a collision with a \(UAV_{A}\) and such that it can enter the intersection at the scheduled time. This depends on the locations of both \(UAV_{B}\) and \(UAV_{B}\) in the approaching area. The different scenarios to be considered based on their locations and how \(UAV_{B}\) should behave in each scenario are listed below. Table 1 lists the symbols used in this section.  
(1) \(UAV_{B}\) is in the reservation zone  
When \(UAV_{B}\) is in the reservation zone, it may or may not have been scheduled yet to enter the intersection because the latest time it can be scheduled is when it reaches the queueing zone. Hence, when \(UAV_{B}\) is in the queueing zone, it just needs to avoid collision with a UAV ahead, if there is any. In case there is no UAV ahead, then \(UAV_{B}\) maintains its speed.  
Listed below are different scenarios on how to avoid collision with \(UAV_{A}\) depending on its location.  
(a) \(UAV_{A}\) is in the reservation zone or queueing zone  
In this case, \(UAV_{B}\) must avoid collision with \(UAV_{A}\). How to avoid collision with a UAV ahead will be discussed in Section 4.1.  
(b) \(UAV_{A}\) is in the acceleration zone  
Since \(UAV_{B}\) is still in the reservation zone, it has enough distance from \(UAV_{A}\). Hence, \(UAV_{B}\) maintains its speed until it reaches the queueing zone.  
(2) \(UAV_{B}\) is in the queueing zone  
(a) \(UAV_{A}\) is in the queueing zone  
Since \(UAV_{A}\) is still in the queueing zone, \(UAV_{B}\) must avoid collision with \(UAV_{A}\), which will be discussed in Section 4.1.  
(b) \(UAV_{A}\) is in the acceleration zone  
\(UAV_{B}\) is in the queueing zone, which means it was already scheduled. There is no UAV ahead in the queueing zone, hence it must adjust its speed such that it can enter the intersection as scheduled. How to enter the intersection as scheduled will be discussed in Section 4.2.  
(3) \(UAV_{B}\) is in acceleration zone  
In the acceleration zone, \(UAV_{B}\) will accelerate at the rate of \(r_{max}\) until its speed reaches \(s_{max}\). Afterwards, it must maintain its speed.
## 2 Materials and Methods
### Statistical Analyses  
Chi-square rank (\(\alpha\) = 1%) tests were performed to establish the similarity of demographic data between the WP and REM users. Differences were considered significant at the \(p\) < 0.01 level after Bonferroni corrections. Bonferroni correction was used here as a method to counteract the multiple comparisons problem. Incidences are presented as numbers and percentages of patients. Kruskal-Wallis statistical tests were used to evaluate the differences between the Avg.bg between the racial/ethnic groups at T\({}_{1}\) and at T\({}_{12}\) as follows: between the WP/REM users and between the racial/ethnic groups: WP, Latino, Black, and Asian; as well as between WP/REM users among the BMI groups. Furthermore, paired Wilcoxon statistical rank tests were used to evaluate the changes in the Avg.bg within each group over the year (from T\({}_{1}\) to T\({}_{12}\)). In addition, Kruskal-Wallis statistical tests were used to evaluate the differences between glucose variabilities by the average standard deviation (STD) within the racial/ethnic groups WP/REM at T\({}_{1}\) and at T\({}_{12}\), representing how much glucose levels fluctuate over time from a given average. Paired T-test was performed in order to evaluate the significance of reduction in average standard deviation from T\({}_{1}\) to T\({}_{12}\) for both groups.  
Three models of the general linear model (GLM) were conducted to evaluate the effect of the independent variables Bgm and the F.m on the dependent variable Avg.bg. The first model (Model 1) was executed on all users, with Bgm as the predictor, with dichotomic ethnicity as a possible moderator. The second and the third models (Model 2 and Model 3, respectively) were performed on the WP and the REM groups separately with F.m as the predictor to test if consistency of measurement for a year predicts blood glucose levels reduction in each group of WP and REM. The typical estimation method was performed with the least squares approximation method. The statistical analysis was conducted with R studio version 4.1.2.  
The Independent variables that were included in the GLM models are as follows:  
Model 1:  
\[\hat{y}=\beta_{0}+\beta_{1}X_{1}+\beta_{2}I(X_{2}=1)+\beta_{1}X_{1}\cdot\beta_ {2}I(X_{2}=1)\]  
\[y=\textit{Blood Glucose Average level}\]  
\[\beta_{0}=\textit{Intercept}\]  
\[\beta_{1}=\textit{Bgm}\]  
\[\beta_{2}=\textit{Dichotomic Ethnicity}\]  
_Dichtomic ethnicity_ (_WP vs. REM_)  
Model 2 and Model 3:  
\[\hat{y}=\beta_{0}+\beta_{1}X_{1}\]  
\[\beta_{0}=\textit{Intercept}\]  
\[\beta_{1}=\textit{f.m.}\]
# Numerical Assessment of Side-Wind Effects on a Bus in Urban Conditions
## 3 Results
### Drag Coefficients  
While the 20 km/h cruising speed scenario is nominal, other cases were also modelled to study the drag changes. As was previously mentioned, the drag coefficient depends on velocity in the denominator, which is the cruising velocity of the vehicle, not the sum of the velocities corrected by the yaw angle. In Table 5, the drag coefficients with standard deviations are listed for different side-wind scenarios.  
It can be seen that a notable jump occurred between symmetrical and 1 m/s side wind in all three speeds. At first, this is credited to the stagnation pressure increase at the low speeds; then, a relatively proportional increase followed at 20 km/h, while at lower cruising speeds, when side wind increased, \(C_{D}\) slightly changed.
## 3 Cyclic Direct Simple Shear Tests  
### Test Conditions and Sample Preparation  
The cyclic simple shear device used in this study is a type that was developed by the Norwegian Geotechnical Institute (NGI) as shown in Figure 3. It consists of a shear box for retaining the sample, two high-speed precision micro stepper motors for controlling the loading mechanisms for horizontal and vertical loads, sensors for measuring load and displacement and two embedded controllers for test control and data acquisition. A constant volume of the specimen is obtained by maintaining the specimen height constant, and the excess pore pressure generated under shearing is equal to the change in the value of the vertical confining pressure [15]. To prevent displacement in the lateral direction, the specimen was enclosed in a wire-reinforced membrane instead of a stack of Teflon-coated rings. [PERSON] et al. [16] compared the response of specimens confined with a wire-reinforced membrane with that of specimens confined within Teflon-coated rings by using a simple shear apparatus made by Geocomp Corporation. The responses were similar in the shear stage. In this study, a wire-reinforced membrane with an initial diameter of 63.5 mm and an initial height of 30 mm was used for preparing the specimen. The wire-reinforced membrane was purchased from Seiken Inc., Japan, and had a lateral pressure capacity of 600 kPa.  
The specimen was prepared by using a static compaction method. A static compaction method that applied less energy to the lower layer of the specimen was used to achieve a uniform density in the height direction [17]. The target relative densities after consolidation were approximately 40%  
Figure 3: Cyclic simple shear device used in this study.  
Figure 2: Grain size distribution curve of sand obtained from Nakdong River.  
and 80% for the loose and dense sand, respectively. For loose sand (D\({}_{\rm r}\) = 40%), the same amount of sand was used every time and compacted lightly to achieve a final height of 30 mm. For dense sand (D\({}_{\rm r}\) = 80%), the specimen was separated into five layers; the same amount of sand was used for each layer, and the sand was compacted to achieve a height of 6 mm. The lower layers were compacted to a height slightly greater than 6 mm by trial and error, and the height of the specimen was made to exceed 30 mm by approximately 1 mm for obtaining the target density after consolidation. After sample preparation, a series of CDSS tests was performed at initial vertical effective stresses (\(\sigma_{\rm v0}\)\({}^{\prime}\)) of 50, 100, and 200 kPa. Sine waves with four frequencies (\(f\)), namely, 0.05, 0.1, 0.5, and 1 Hz, were applied to the specimens. Appropriate cyclic stress ratios were chosen for each case.
# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 2 Related Work  
In general, machine learning approaches [14] designed for speaker change point detection contains two stages: (i) audio feature extraction, and (ii) change point detection stage. Many approaches with these stages have been studied for speaker change point detection in recent decades, and examined different audio features with different detection method.  
In the first stage, a number of audio features have been applied for SCD [15; 16; 17; 18]. The paper [15] describes the evaluation of different feature extraction techniques and processing methods in the context of speaker diarization. Their experimental results showed that perceptual linear predictive (PLP) features, particularly when excluding energy components, often outperform MFCC.  
The author presented [17] an approach to speech classification, and unsupervised speaker segmentation using linear spectral pair (LSP) divergence analysis. In first step, they classify the audio into four categories: speech, music, environment sound and silence.  
The zero-crossing rate, short-time energy, spectrum flux were used for characterizing different audio signals. If classify it a speech segment, the process will be involved to detection of speaker transitions. The method includes incremental speaker modeling and adaptive threshold setting, enabling effective unsupervised segmentation. Experimental results demonstrate the algorithm's effectiveness, achieving an overall recall of 89.89% and a precision of 83.66%. Recent development of features for SCD is to extract speaker-specific information, like i-vector [19; 20] and feature vectors [21] from neural networks. d-vector [22] was successfully applied to SCD as feature and yielding a excellent result. The latest trends is to use end-to-end manner [23; 24], to extract deep speaker embedding as features.  
For the second stage of speaker change detection, the approaches can be divided into three groups: (i) metric-based and (ii) energy or model-based approach, (iii) hybrid method. Metric-based methods for SCD involve measuring the differences between consecutive audio frames as they move through the signal. Distance between these frames are computed and if the value is larger than a predefined threshold, a change point is detected. Frequently used distance measures include the Bayesian information criterion [5; 25], the generalized likelihood ratio [7], the Kullback-Leibler divergence [6]. Model-based approaches are usually supervised, train on a labeled audio dataset for detecting speaker changes. Early methods include the GMM [26], and hidden Markov models (HMM) [27]. Latter, deep learning models like DNN [28], CNN [29] and bi-direction LSTMs [30] were applied.
## 3 Proposed Approach
### Reinforcement Learning (RL) Driven Energy Savings  
The Energy-Aware Framework proposes SC On-Off switching operation by using RL algorithm where MC senses the environment and takes an action which is rewarded or penalised depending on the conditional state of action being taken. RL has been chosen to support SC On-Off switching operation due to its suitability of making decisions out of a wide-range of options. MC interacts with the network environment and obtains SCs traffic information and user association criterion to make decisions. Hence, RL copes with the dynamic environment via adaptability through learning and then deciding the required actions to maintain QoS. We have adopted \(Q\)-learning algorithm (QL) [3; 34; 35] to solve the constraints. QL is one of the most well-known RL algorithms that has a proven capability of working in dynamic environments [9; 11]. QL is an off-policy method that follows different policies to determine next possible action state in conjunction with action-value table update. The six main components in QL are: (i) agent; (ii) environment; (iii) action; (iv) state; (v) reward/penalty; and (vi) action-value table. An agent takes actions by interacting with a given environment in order to maximise the reward or minimise the penalty. After each action that the agent takes, the resulting state and reward/penalty are evaluated with the following rule [3; 13; 35].  
\[Q(s_{t},a_{t}):=Q(s_{t},a_{t})+\phi\big{[}\gamma_{t+1}+\lambda_{r}\min_{a}(Q( s_{t+1},a))-Q(s_{t},a_{t})\big{]}, \tag{14}\]  
where \(s_{t}\) and \(s_{t+1}\) are the current and next states, \(\phi\) is a discount factor, \(\gamma_{t+1}\) is the expected penalty for the next step, \(a_{t}\) is the action taken after MC learned from the environment, \(a\) is the set of all possible actions and \(\lambda_{r}\) is the rate of learning. Due to QL algorithm's model-free learning [3; 34; 35] that adapts to behaviour of the environment varying conditions such as dynamic traffic loads, it bears very low computational overhead compared to the Exhaustive Search (ES) and Greedy techniques [25; 26; 36]. In our study, we considered a simple HetNet live model which has one MC and nine SCs that have been distributed geographically on one of the busiest streets in Central London. With this consideration, the state space is small enough to apply a simple look up table (\(Q\) table), which is updated for every state action pair. In designing the SC switching mechanism, our goal is to find the best switching strategy with low \(ECR\) coupled with CO\({}_{2}\) emissions that is dependant on the selection of the best set of SCs to switch off, out of all possible set of SCs.
# Semantic Features Based N-Best Rescoring Methods for Automatic Speech Recognition
## 4 N-Best Rescoring with Coordination Scores
### Sentence Coordination Scores
#### 4.1.4 Word-Discourse Probability Score Using Word Embedding  
As explained in the second section, the probability of a word given a sentence is proportional to the exponent of the inner product of the word embedding of the word and the discourse vector, which can be simplified as the average of word embeddings of all the words in the sentence. The probability of the \(t\)-th word in sentence \(s\) is calculated as follows:  
\[p(w_{t}|s)=\frac{exp(v_{t}^{T}c_{s})}{\sum_{k=1}^{V}exp(w_{k}^{T}c_{s})}= softmax(W^{T}c_{s})_{w_{t}}, \tag{16}\]  
where \(w_{t}\) is the word embedding of the \(t\)-th word in sentence \(s\), \(V\) is the size of vocabulary. \(c_{s}\) is the discourse vector introduced in the previous section. As all words in the same sentence share the same discourse vector, we firstly compute \(c_{s}\) when we begin to process the sentence. Therefore, the computation of word-discourse probability score is more time-consuming than the word-pair probability score. Similar to the topic similarity score using LDA, the sentence word-discourse probability score is the sum of the logarithms of word-discourse probabilities for all the words in the sentence.