esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset
## 1 Introduction
Firstly, we present an annotated dataset, the Game Lie Dataset (GLD), based on frontal facial recordings of 19 participants who try their best to fool their opponents in the _liar_ card game. Secondly, we depart from the dominating trend of lie detection based on micro-expressions and investigate whether a lie can be detected by analyzing solely the facial patterns contained on single images as input to cutting-edge machine learning [13; 14; 15] and deep learning [16; 17; 18; 19] facial analysis algorithms. Using our collected dataset and several automatic lie detection models, we perform lie detection experiments under three different settings: (1) generalization test to evaluate the performance on unseen subjects; (2) person-specific test to evaluate the possibility to learn how a given participant would lie; and (3) cross-application test to evaluate how the models generalize to a different acquisition setup. Thus, the overall contributions of this work can be summarized as follows:  
1. We present the GLD dataset, a novel dataset which contains colored facial data as well as ground truth (lie/true) annotations, captured during a competitive card game in which participants are rewarded for their ability to lie convincingly. 2. We also present quantitative comparisons results of several machine learning (ML) and deep learning (DL) models tested on the newly captured dataset. 3. We provide several experiments that outline the current limitations of facial-based lie detection when dealing with several different lie tasks. The combination of our novel lie-detection dataset with the respective evaluations of current ML and DL methods are expected to benefit research in automatic lie detection systems, and can also be relevant for several targeted real-life tasks, where the understanding of generalized (in daily settings) lie intentions is important, such as the following:1. Health practitioners/psychiatrists for general counseling: understanding whether people lie or not is important to improve their conditions, e.g., drug addictions. 2. Educator: to know whether students might be lying or not during a test or experiment. 3. Credit in the finance sector: to know if the prospective client is lying about their background and the past. The rest of this paper is organized as follows: in Section 2, we provide an overview of the related work, both regarding previous lie-detection methods and current lie detection task datasets. In Section 3, we explain the characteristics of our collected dataset alongside the recording pipeline. In Section 4, we describe several ML- and DL-based techniques used to evaluate our dataset along with the associated evaluation metrics. In Section 5, we present our experimental results divided into generalized, person-specific and cross-task lie detection settings. Finally, in Section 6, we provide our conclusions.
# The Influence of Co-Firing Coal with Biomass Syngas on the Thermodynamic Parameters of a Boiler
###### Abstract  
Biomass syngas can be considered as a supplementary fuel to partially substitute coal, which is beneficial to CO\({}_{2}\) emission reduction. For the case study, the influences of co-firing typical biomass syngas (gasification from palm, straw, and wood) with coal on the thermodynamic parameters of a 300 MW tangentially fired boiler are evaluated through a thermal calculation based on the principles of mass conservation, heat conservation, and heat transfer. The effects of boiler loads, biomass syngas species, and consumption rates are discussed. The results show that the introduction of biomass syngas weakens the radiative characteristics of the flame and reduces the furnace exit flue-gas temperature. As \(3\times 10^{4}\) m\({}^{3}\) h\({}^{-1}\) of wood syngas is introduced, the decrement of thermal efficiency reaches 0.4%, while that of the coal consumption rate is 5.1%. The retrofitting of the boiler was not necessary and the corrosion of the low-temperature heating surface did not appear. The CO\({}_{2}\) annual emission reduction could achieve 0.001 to 0.095 million tons for palm syngas, 0.005 to 0.069 million tons for straw syngas, and 0.013 to 0.107 million tons for wood syngas with increasing biomass syngas consumption rates under the full load. Moreover, the main thermodynamic parameters changed more significantly under the low loads.  
coal-fired boiler; biomass syngas; co-firing; thermal calculation; thermodynamic parameter; peak regulation +
Footnote †: journal: Applied
## 3 Experiments
### Subjects  
This study was conducted in collaboration with Spinal Cord Unit, Santa Corona Hospital of Pietra Ligure (Savona, Italy). This study was approved by the Institutional Review Board (code CE DIBRIS protocol-N. 2022/52 approved on 22/09/2022) and it conformed to the ethical standards of the 1964 Declaration of Helsinki. Each subject provided written informed consent to participate in the study and to publish individual data. The study involved 12 unimpaired subjects (age: \(26.9\pm 2.6\) y.o., 3 females) with the only exclusion criterion being the presence of sensorimotor, neurological, or psychiatric impairments. All participants were naive to the control algorithm to avoid biases from prior knowledge. To evaluate the potential application of the control algorithm in assistive devices, a pilot test was conducted with two individuals with spinal cord injuries, providing feedback from potential end-users. The SCI subjects were classified based on injury level (cervical, thoracic, lumbar, sacral) and completeness (complete or incomplete). The American Spinal Injury Association (ASIA) offers a standardized international classification system [24], which grades the severity of the injury from A (complete) to E (normal). The first participant (SCI 1) was a 35-years-old tetralegic male (C7 lesion, ASIA B), and the second (SCI 2) was a 34-years-old paraplegic male (L1 lesion, ASIA C). Both subjects followed the same experimental protocol as the unimpaired participants. The choice of including the tetralegic subject is motivated by the potential of this control system to exploit the minimal residual upper-body motions for operating the external device, which can significantly assist in performing ADLs. The paraplegic subject also serves as a valuable participant in testing the new control algorithm, as individuals with paraplegia often face challenges related to fatigue when performing continuous activities requiring extensive movements. Using this control system can help reduce the effort and fatigue associated with such tasks.
## 3 Perception Sensors for the UGV Detection System
### Laser-Based Sensors  
The most popular remote sensing technology that detects objects and their distances, is LiDAR (light detection and ranging).  
The LiDAR sensor typically rotates to scan its field of view, emitting laser pulses in all directions. The optical signal from the laser can be modulated in various ways to carry information about the environment. By transmitting a modulated optical signal, the LiDAR system can gather information about the distance, reflectivity, and other properties of objects and surfaces in the environment [20].  
The system then uses the time-of-flight measurement to determine the distance to each object in the field of view [21]. The LiDAR system can build a 3D map of the environment in real time, combining the distance information from multiple laser pulses as shown in Figure 5.  
Figure 5: Operational principles of LiDAR.  
LiDAR systems are not affected by lighting conditions or weather and can operate effectively in a variety of conditions, allowing for object detection at a long range and providing early warnings to a path-planning system accordingly. However, there are also some limitations to the use of LiDAR in unmanned vehicles, including cost and size.  
The TAERO platform is equipped with two different types LiDAR: Ouster OS0-32 and Velodyne -VLP 16 (Figure 6). Both sensors are very popular and widely used in autonomous vehicles.  
Laser scanners offer compact size, high-resolution imaging, and a long range, which make them ideal for providing detailed and accurate data about the environment, even in challenging conditions, such as low light or adverse weather. Sensor data are made available via Ethernet in real time in the form of UDP packets.  
Both the Velodyne VLP-16 [22] and Ouster OS01-32 [23] LiDAR are high-performance 3D LiDAR sensors that provide real-time, high-resolution 3D imaging of the environment, which is crucial for autonomous applications (Table 2). The choice between them for off-road use depends on the specific requirements of the application.
## 5 Influence of SDIEC on Optimization Results
### Performance Comparison Between Preferred Impellers and Baseline Impeller  
The performance comparison between the preferred impellers F1 and S2 and the baseline impeller is shown in Figure 17. When the flow rate is greater than \(0.8Q_{\text{des}}\), the pump efficiency of the optimized impellers F1 and S2 is higher than that of the baseline model. However, compared with impeller F1, impeller S2 has a higher pump efficiency at all flow rates, especially at low flow conditions. The pump efficiencies of impeller S2 at \(1.2Q_{\text{des}}\), \(1.0Q_{\text{des}}\), and \(0.8Q_{\text{des}}\) are increased by \(0.76\%\), \(1.24\%\), and \(1.21\%\), respectively over impeller F1.  
The pump head of the optimized impellers F1 and S2 presented an interesting change compared to the baseline impeller. The pump head of the optimized impellers F1 and S2 under the design condition are the same as the baseline impeller. However, under low flow conditions, the pump head of the baseline impeller is the highest, followed by the impeller F1, and the lower the flow rate, the greater the head difference. Under high flow conditions, the pump head shows an opposite trend to low flow conditions.  
Figure 16: Comparison of the circulation distribution of impellers F1 and S2: (**a**) blade loading distribution; (**b**) spanwise distribution of impeller exit circulation (SDIEC) distribution  
Figure 17: Comparison of external characteristics.  
To clarify the optimization mechanism, it is necessary to compare and analyze the internal flow field of the model. The comparison of pressure contours between the baseline impeller and the preferred impellers F1 and S2 at 1.2Q\({}_{\text{des}}\) and 0.8Q\({}_{\text{des}}\) are shown in Figure 18. After carefully considering the influence of the wall on the flow field, these data were extracted at \(r=0.9\), 0.5, and 0.1 respectively. As shown in Figure 18a, at 1.2Q\({}_{\text{des}}\), an obvious low-pressure region appeared on the leading edge near the shroud of the baseline impeller, which resulted in a large-scale backflow. After optimization, the low-pressure region was greatly weakened and the backflow was completely suppressed. Besides, the pressure distribution at the inlet of impeller S2 was more uniform than that of impeller F1. Figure 18b also shows the same phenomenon, but it should be noted that the low-pressure region appears on the blade suction surface and that there is no backflow in the low-pressure region.
## 5 Dataset Generation
The FSM transitions are simulated cycle-by-cycle to compute the corresponding delivery and return change outputs based on the system's logic and state evolution. Each TC is formatted by concatenating the input sequence, delivery output, and return change output into a 12-bit vector and is subsequently added to the dataset. ```
1:functionGenerateDataset
2: Initialise FSM configuration // e.g., vending machine logic
3: Define input encoding: // 0 = water, 1 = fizzy drink
4: 1 st bit: product selection // 0 = %0.50, 1 = %1,? = don't care
5: Next 3 bits: payment sequence // 0 = %0.50, 1 = %1,? = don't care
6: Define output encoding:
7: 4 bits: delivery signal per cycle
8: 4 bits: return change signal per cycle
9: Initialise empty dataset list \(D\)
10:for all valid product and payment scenarios do
11: Generate input sequence \(I\) // 4 bits
12: Simulate FSM state transitions for each cycle
13: Compute outputs:
14: Product delivery signal based on when payment completes
15: Return change signal if overpayment occurs
16: Format test case: \(TC=I\cdot O_{delivery}\cdot O_{change}\)
17: Add \(TC\) to dataset \(D\)
18:endfor
19: Initialise empty evaluation list \(V\)
20:for all \(TC\) in \(D\)do
21: Split \(TC\) (12 bits) into four 3-bit vectors:
22:for\(i=1\) to 4 do
23:\(v_{i}\leftarrow(input[i],delivery[i],change[i])\)
24: Add \(v_{i}\) to \(V\)
25:endfor
26:endfor
27:return \(V\) // 3-bit vectors for GE fitness evaluation
28:endfunction
```  
**Algorithm 1** Generate Training Dataset for FSM Evolution. In the distribution step tailored for the system's evaluation, each 12-bit TC is further split into four smaller vectors, each consisting of three bits. Each 3-bit vector comprises a single input bit and two associated output bits, corresponding to the delivery and return change signals for that particular cycle. These distributed vectors are then collectively used for fitness evaluation during the evolutionary process. The correct resolution of all vectors is essential for an individual to achieve the maximum fitness score. This structured approach ensures that all possible TCs relevant to the FSM are covered comprehensively, facilitating effective learning and circuit evolution within the GE framework. While the dataset size is deliberately constrained to ensure complete coverage with minimal redundancy, the test complexity arises from the sequencing of state-specific inputs and strict timing constraints enforced by the FSM structure. Future work may explore extensional models, such as those proposed by [PERSON] et al. [62], to formally assess and adapt test complexity in broader, adaptive scenarios.
## 3 Performance Validation

Table 3: Accuracy analysis of DLSODC-GWM technique with recent methods. Figure 13: Accuracy analysis of DLSODC-GWM technique with recent algorithms. However, the MLH-CNN approach has resulted in near-optimal \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 91%, 91%, and 91%, respectively. The presented DLSODC-GWM methodology has accomplished superior performance with \(prec_{n}\), \(reca_{l}\), and \(F_{score}\) of 95.23%, 94.29%, and 94.73%, respectively. From the aforementioned results and discussion, it can be stated that the DLSODC-GWM technique has accomplished enhanced waste object classification performance compared to existing techniques.
## 6 Outlook on Developments of the Conceptual Model for Spent Fuel-Water Interaction
In external radiolysis, much of the energy loss occurs at the higher energies and lower LET, so radicals can escape the track and interact with oxidizing species. By comparison, in situ radiolysis results in a substantial portion of the alpha particles being attenuated in energy before reaching the surface. The shift in average LET to lower values reduces the escape yields of radical species. Thus, radiolysis studies in bulk water can only provide limited information on the processes occurring at the spent fuel surface. It can be argued that even the concept of radiolytic yield does not apply to interfacial water radiolysis. Radiolytic yields (G values) obtained from traditional radiation chemistry experiments cannot be applied in the model of spent fuel dissolution because they apply to the homogeneous phase of water radiolysis, whereas, in this case, the yields of radiolytic species formed during the physical and physical-chemistry stage are of relevance. In summary, the spent fuel-water interface may play an active role in the radiolysis processes, and the primary yields of radiolytic products may be very different from the G values obtained from traditional radiolysis studies in homogeneous systems. Hence care should be used when applying such G values to model spent fuel dissolution as a function of the radiation dose. To improve the understanding of the interaction between the spent fuel surface and water, the effect of the intrinsic ionizing radiation on the UO\({}_{2}\) crystalline matrix, the confined water volumes present at the spent fuel surface, and the layers of water immediately adjacent to the spent fuel interface should be further investigated. The formation of both oxidizing and reducing species at the surface of UO\({}_{2}\) and at the UO\({}_{2}\)-water interface leads to a large diversity of reaction pathways to be taken into account for better understanding of reactivity of spent fuel in reducing conditions. The following are examples for potential future studies that could shed further light on the processes occurring at the spent nuclear fuel-water interface:  
* Comparison of the effects of alpha particles produced in situ (for example, by using neutron capture on soluble compounds containing B-10) to those of external alpha sources;
* Further exploration of the field of radiolysis in confined water volumes and of water at the surface of metal oxides, in particular, fluorite-type solids;
* Investigation of whether excess H\({}_{2}\) can be produced at the spent nuclear fuel-water interface;
* Modelling using Monte Carlo based techniques exploring physico-chemical processes at the spent fuel-water interface.
###### Abstract  
Increasing the data transfer rate is an urgent task in cellular, high-frequency (HF) and special communication systems. The most common way to increase the data rate is to expand the bandwidth of the transmitted signal, which is often achieved through the use of multitone systems. One such system is the filter bank multicarrier (FBMC). In addition, speed improvements are achieved using multi-input-multi-output (MIMO) systems. In this study, we developed an algorithm for equalizing FBMC signals with offset-QAM modulation (OQAM) with self-interference compensation due to its correlation properties in a MIMO channel with memory. An analytical derivation of the proposed algorithm and an analysis of the computational complexity are given. According to the results of simulation modeling and a comparative analysis of performance in terms of the bit error rate and error vector magnitude with solutions with similar computational complexity, a similar level of performance was shown compared to a more complex parallel multistage algorithm, and a better performance was demonstrated compared to a one-tap algorithm.  
FBMC-OQAM; HF; equalization; MIMO; self-interference Article
## 3 Results and Discussion
### Adsorption Isotherms  
The phosphate isotherm parameters are summarized in Table 3, whereas the arsenate isotherm parameters are summarized in Table 3 (see also Figures S7-S15 in Supplementary Materials for the Asi and Pi isotherms).  
Overall, Al-TMC has the highest adsorption capacity for both Pi and Asi. This can be explained by the higher charge density at the Al center and the lower PZC of Al-TMC. The n-value of the Sips model for both Al-TMC and Cu-TMC approaches unity and indicates multiple binding sites for Fe-TMC (\(n\)-value of ca. 0.3) while mostly homogeneous binding for the other composites is indicated.  
Figure 3: SEM-EDX analysis of Cu-TMC, (**A**) electron image without mapping, (**B**) computational mapping, (**C–F**) elemental distribution for O, Cu, C, and N.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**adsorbent** & **Al-TMC** & **Fe-TMC** & **Cu-TMC** \\ \hline Qmax (mg/g) & 93 & 77 & 17 \\ K & 0.90 & 0.28 & 0.36 \\ n & 0.82 & 0.28 & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best-fit Sips isotherm parameters (cf. Equation (2)) for Pi adsorption at pH 6.3 at 23 \({}^{\circ}\)C.  
The isotherm data for As\({}_{\text{i}}\) reveals a deviation of the heterogeneity factor (n) away from unity (\(n\
eq 1\)) for all materials that reflect heterogeneous binding sites for the biocomposites. In summary, Table 4 lists the monolayer equilibrium adsorption capacity (Q\({}_{\text{m}}\); mmol/g) of both As\({}_{\text{i}}\) and P\({}_{\text{i}}\) for direct comparison, while Table 5 lists the adsorption capacity in mmol/g.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Adsorbent** & **Al-TMC** & **Fe-TMC** & **Cu-TMC** \\ \hline Q\({}_{\text{max}}\) (mg/g) & 80 & 66 & 31 \\ K & 14.7 & 0.57 & \(2.58\times 10^{-5}\) \\ n & 0.18 & 0.38 & 2.72 \\ \hline \end{tabular}
\end{table}
Table 4: Best-fit Sips isotherm parameters (cf. Equation (2)) for As\({}_{\text{i}}\) adsorption at pH 6.7 at 23 \({}^{\circ}\)C.  
Figure 4: SEM-EDX analysis of Fe-TMC, (**A**) electron Image without mapping, (**B**) compositional mapping, (**C–F**) elemental distribution for Fe, O, C, and N of spot 1, and (**G**) electron Image without mapping, (**H**) compositional mapping, (**I–L**) elemental distribution for Fe, O, C, and N of spot 2.  
For the case of adsorption capacity expressed as molar units (mmol/g) (cf. Table 5), the uptake for Al-TMC toward P\({}_{\text{i}}\) is ca. 70% higher compared to Asi, and similar to the Fe-TMC. By comparison, Cu-TMC shows similar adsorption capacities for both Asi and P\({}_{\text{i}}\).
## 3 Results and Discussion  
### Particle Standard Generation  
The concentration and the size of the particles produced from the miniCAST were reasonably stable during the course of the experiment, and Figure 2 provides a typical time series of the particle number concentration, BC, and PM mass observed during one typical experiment. When switching between the pre- and post-filter locations, the particle number, BC, and PM mass concentrations all changed accordingly. Zeros on the EEPS and DMM were checked by sampling through HEPA filters periodically during which the particle number and mass both dropped to zero (top and bottom panels in Figure 2). This shows no drifting in the electronics over time, ensuring measurements were correct and consistent throughout the experiment.  
Figure 2: Example of the particle number concentration, black carbon (BC), and particulate matter (PM) mass time series from a typical experiment.  
Experiments were performed on all 11 filters, and data were validated by reviewing all measurements thoroughly. Then, all data within each steady-state period were averaged. Figure 3a,b summarize the averaged particle number size distributions measured at the pre- and post-filter locations at various steady-state periods during one typical experiment. Figure 3a shows that the concentration and the size of the particles at the pre-filter location were relatively stable over the course of the experiment, which typically lasted between 1 and 2 h. The size of the particles measured at the post-filter location was consistent, but the particle number concentration pattern varied from experiment to experiment. In some cases, the particle number concentration was stable; however, in other cases, there was a progressive increase in the number concentrations over time (Figure 3b), indicating a loss of particle FE over time. Research on the loading behaviors of electrically active materials have observed that particle penetration through a filter may increase over time (i.e., decrease in FE) from a new state as material is loaded before filtration efficiency begins to improve [19; 39]. More discussions on this will be given in Section 3.4.