esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
PHREEQC employs the law of mass action equations to perform complex geochemical simulations allowing the inclusion of kinetics and rates, and competing reactions between solids, liquids, and gases at equilibrium, details of which can be found in [22]. However, at the time of writing, no models of CO\({}_{2}\)-induced carbonation or changes in phase assemblage due to seawater, NaCl solution, or sulfate ingress into cement have been developed using the PHREEQC geochemical software. Previous work on thermodynamic modelling of carbonation has been undertaken by [PERSON] and co-authors [1] who used the Gibbs free energy minimization software GEMS 3.5 [23] with a general thermodynamic database [24] further expanded with the cemdata18 database [25] containing solubility products of solids relevant for cementitious systems. They modelled mortars with two different types of cement (CEM I and CEM II/B-V according to EN 197-1 [26]) exposed to accelerated carbonation conditions. Thermodynamic predictions of the changing phase assemblages and pore solution chemistries were predicted with increasing levels of CO\({}_{2}\). Thermodynamic modelling of cement-based mortars subject to increasing sulfate levels by [27] demonstrated how the solid hydrates in partially hydrated systems were converted to higher volumes of ettringite, gypsum precipitates, and thaumasite formation as the amount of Na\({}_{2}\)SO\({}_{4}\) solution increased. In the current paper, the phase assemblages have been predicted for a CEM I commercial cement undergoing separate carbonation, sulfate, NaCl, and seawater-induced reactions using the geochemical code PHREEQC. The cemdata18 thermodynamic database was used with several Discrete Solid Phases (DSPs) used to represent the solid solution CSHQ and M-S-H models. Due to the amorphous and poorly crystalline nature of C-S-H in cement with a corresponding range of Ca/Si ratios (0.6-1.7), it displays a strongly incongruent dissolution behaviour [28] where the release of calcium into solution is several orders of magnitude greater than that of silicon. A suitable C-S-H gel solubility model is therefore required to accurately describe its variable composition and solubility behaviour and predict stable phase assemblages, pH, and pore chemistries. While solid solution analysis is possible in PHREEQC, it has a long computational time. The use of DSPs to model stable phase assemblages etc. has been shown by the authors [21;29] to be a suitable replacement with no loss in accuracy with less computational effort.
## 7 Observations and Recommendations
### Recommendations and Research Challenges
#### Harsh Environments  
IWSNs are sometimes deployed in extremely harsh environments such as underground mines [82] and intertidal habitats [83]. Deploying IWSNs in this scenario introduce several monitoring and communication challenges [84]. In addition to the inherent communication challenges such as high signal attenuation and poor link quality, the monitored environment is often dynamic and harsh which can inadvertently affect both the sensor readings and communication networks. In intertidal WSNs for example, packet delivery can be delayed by up to several days which can severely affect the operation of the deployed system [85]. From this discussion it is evident that distinguishing between anomalies and the inherent behaviour of IWSNs deployed these harsh environments is going to be an interesting research challenge for future work.
# Assessing Drivers' Physiological Responses Using Consumer Grade Devices
## 3 Results
### Skin Temperature  
During the experiment, the temperature was rising at what appears to be an exponential curve (see Figure 12).  
Figure 11: HRV pNN50 for each participant in each phase for E4 (**left**) and Faroos 360 (**right**).  
Figure 12: [PERSON] temperature readings from the beginning of the baseline phase. Data for each participant is drawn with different color. Straight lines interpolate the temperature between the end of one phase and the beginning of the next one.  
Figure 10: HRV RMSSD for each participant in each phase for E4 (**left**) and Faroos 360 (**right**).  
Statistically significant differences among phases (Table 3) were found in mean and standard deviation of the skin temperature readings (Figure 13, Figure 14) from the both devices.  
[PERSON] post-hoc test for mean skin temperature measured with E4 showed statistically significant increase between the baseline and the easy driving phase (\(p<0.001\)) and between the baseline and the demanding driving phase (\(p<0.001\)). No statistically significant differences were found between the easy and the demanding driving phase (\(p=0.770\)).  
[PERSON] post-hoc test for _mean skin temperature measure with Faros 360_ showed that it increased statistically significantly for each consecutive phase (\(p<0.001\)).  
[PERSON]'s post-hoc test with Bonferroni correction for standard deviation of the skin temperature measured with E4 showed it increased statistically significantly between the baseline and the easy driving phase (\(p=0.020\)) and between the baseline and the demanding driving phase (\(p=0.020\)). The test did not show any statistically significant differences between the easy driving and the demanding driving phase (\(p=1.000\)).  
Figure 14: Standard deviation of skin temperature for each participant in each phase for E4 (**left**) and Faros 360 (**right**).  
Figure 13: Mean skin temperature for each participant in each phase for E4 (**left**) and Faros 360 (**right**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Skin Temperature Mean** & **Skin Temperature Standard** \\  & & **Deviation** \\ \hline E4 & RMANOVA: & [PERSON]’s nonparametric test: \\  & F(2, 42) = 29.313, \(p<0.001\) & \(\chi\)2(2) = 9.818, \(p=0.007\) \\ \hline \multirow{3}{*}{Faros 360} & RMANOVA with & RMANOVA with \\  & Greenhouse-Geisser correction: & Greenhouse-Geisser correction: \\  & F(1.180, 24.786) = 279.574, \(p<0.001\) & F(1.207, 25.350) = 78.100, \(p<0.001\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical tests’ results for skin temperature.  
Similarly, when analyzing the standard deviation of skin temperature measured with Faros 360, [PERSON] post-hoc test showed it increased statistically significantly from the baseline compared to the easy driving phase (\(p<0.001\)), and from the baseline and compared to the demanding driving phase (\(p<0.001\)). The test did not show any statistically significant differences between the easy driving and the demanding driving phase (\(p=0.473\)).
## 3 Results and Discussion
### Changes in Residual Oil Saturation after Adsorption of a Zwitterionic Surfactant  
To determine whether oil recovery was enhanced via adsorption of a zwitterionic surfactant to PSS-co-MA adsorbed onto nanoscale colloidal dispersions, we analyzed ultimate oil recovery as a function of flow rate and oil viscosity. Colloidal dispersions with the zwitterionic surfactant produced more oil than dispersions without surfactant. In particular, oil recovery at an injection rate of 10 mL/min was higher than that at an injection rate of 0.1 mL/min (Figure 9a). The recovery of 10-cp oil was greater than that of 500-cp oil (Figure 9b). Thus, addition of a zwitterionic surfactant to the colloidal dispersion was advantageous. We explored the stability of Pickering emulsions with and without the zwitterionic surfactant as a function of oil viscosity. As shown in Figure 9c, the preparation containing the zwitterionic surfactant exhibited excellent emulsifying efficiency. In contrast, samples without surfactant exhibited phase separation; the colloidal dispersion adsorbed poorly to the oil-water interface. Thus, zwitterionic surfactant led to EOR; TPS reduced the interfacial tension from 44.1 to 8.7 mN/m from measurement result.  
To explore the effects of viscous and capillary forces on residual oil saturation, we calculated saturation by capillary number (\(N_{c}\)) (Figure 10) using \(N_{c}=\frac{v\eta_{w}}{\sigma_{w}}\), where \(v\) is the interstitial velocity, \(\mu_{w}\) is the viscosity of the colloidal dispersion, and \(\sigma_{w}\) is the interfacial tension between the oil phase and the dispersion. The capillary number depends on the injection rate and oil viscosity. As shown in Figure 10, residual oil saturation decreased with increasing capillary number. In general, capillary forces are negligible compared to viscous forces when the capillary number is higher than \(10^{-5}\); oil recovery is dominated by the viscous force. We found that a colloidal dispersion containing a zwitterionic surfactant reduced the residual oil in rock by more than 10%, versus 5% without the surfactant. The zwitterionic led to EOR. These results highlight that the zwitterionic TPS surfactant used in colloidal dispersions would display the sufficient reduction of residual oil saturation without particles aggregation in porous rock under salinity conditions.  
Figure 9: Oil recovery as a function of (**a**) flow rate and (**b**) oil viscosity in the presence or absence of a zwitterionic surfactant. (**c**) Emulsion stability as a function of oil viscosity in the presence or absence of a zwitterionic surfactant.
## 3 Experiments and Results  
### Construction of Test Platform  
To validate the accuracy of the baseline drift elimination algorithm proposed in this study, a test system that can simultaneously control temperature and strain was developed, as illustrated in Figure 4. The system consists of an electronic universal testing machine, a high-temperature extensometer, a high-frequency induction heating device, mullite and graphite sleeves, a resistance strain gauge, an acquisition system, and a DC power supply.  
At first, a resistance strain gauge is fixed to a nickel-based high-temperature alloy substrate and connected to the system as a variable resistor in a quarter-bridge configuration. The nickel-based high-temperature alloy substrate, with fixed resistance strain gauges, is encased in a graphite sleeve and further enveloped by a mullite sleeve. Subsequently, the top and bottom ends of the nickel-based high-temperature alloy substrate are secured within the fixture of the electronic universal testing machine.  
An electronic universal testing machine is used to perform tensile testing of test pieces, by continuously applying force to the test piece to produce strain. The electronic universal testing machine is entirely controlled through a computerized system. As the force is applied, an extensometer is used to accurately measure the change in length of the specimen so that the resulting strain can be calculated. The high-temperature extensometer is connected to the control software of the electronic universal testing machine, and the two ceramic rods of the extensometer are in contact with the nickel-based high-temperature alloy substrate, which can transmit in real-time the magnitude of the strain generated by the nickel-based high-temperature alloy substrate back to the control software of the electronic universal testing machine. The electronic universal testing machine adjusts the size of the force according to the strain value transmitted back to the high-temperature extensometer, to ensure that the test is conducted in accordance with the predetermined program until the set strain value is reached.  
High-frequency induction heating equipment utilizes a high-frequency electromagnetic field to heat conductive materials. Placed within the induction coil, the graphite sleeve  
Figure 4: The strain- and temperature-testing platform.  
acts as a conductor to generate eddy current heating, while the multite sleeve maintains the temperature seal. With standard \(K\)-type thermocouples inserted into the graphite sleeve, the high-frequency induction heating equipment allows real-time transmission of internal temperature data back to the equipment for temperature control.
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 6 Conclusions  
This paper proposed and studied the anti-interference precoding algorithms that apply the channel inversion technology and use the large-scale fading factor matrix. First, channel-inversion processing was applied to the zero-forcing precoding matrix, and the advanced zero-forcing precoding algorithm is developed. Next, the advanced optimized LSFP is presented, which is based on the optimized precoding algorithm and uses the regularized pre-equalization technology. In addition,  
Figure 8: CDF of MMSE-A-O-LSFP channel can achieve.  
Figure 7: CDF of A-O-LSFP channel can achieve.  
the MMSE pre-equalization algorithm is applied to the optimized precoding algorithm, and the advanced optimal LSFP based on the MMSE is obtained. Furthermore, the asymptotic regime is analyzed when the number of antennas of a base station approaches infinity, and it is shown that the advanced zero-forcing precoding algorithm can completely eliminate all sources of interference and additive noise. Finally, the SINR expressions for anti-interference precoding are derived and the above-improved algorithm is simulated. The simulation results show that the anti-interference precoding that use the channel inversion technology achieves the same communication transmission rate as conventional LSFP when the number of base antennas reducing. That means the improved precoding scheme have an improvement on anti-interference and improving channel transmission rate without increasing the number of base antennas in the practical cellular communication network.  
Conceptualization, W.L. and L.C.; methodology, L.C.; software, Y.L.; validation, W.L., Z.L and Y.L.; formal analysis, L.Z.; investigation, Z.L.; resources, L.C.; data curation, W.L. and Z.L.; writing--original draft preparation, W.L.; writing--review and editing, L.C.; visualization, Y.L.; supervision, L.C.; project administration, L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 3 Attention-Gated Residual U-Net and ECA-ResNet
### Palm Vein Segmentation
#### 3.2.4 Proposed Attention-Gated Residual U-Net  
U-Net [46] demonstrated remarkable success in medical image analysis. The architecture comprises a down-sampling path and an up-sampling path, followed by a skip connection. The proposed attention-aware residual U-Net model's network architecture is illustrated in Figure 6. In the U-Net model, using a pooling layer during the down-sampling process might result in the loss of certain image features. Additionally, the U-Net model employs skip connections to concatenate low-level features with high-level features, which can cause losing spatial details as low-level features often lacking spatial information.  
We have incorporated the attention mechanism into the U-Net model by addressing these challenges. This addition helps suppress irrelevant feature responses and provides a more precise segmentation result. Moreover, deep neural networks often encounter the vanishing gradient problem, where the gradient shrinks infinitely due to repeated multiplication during backpropagation. To overcome this issue, we replaced the convolution process at each level of the encoder and decoder networks with the residual block. The combination of residual blocks and attention mechanism in the skip connection has enhanced the segmentation results of our proposed method, surpassing the baseline methods' performance.  
The contracting path of the proposed model is composed of several residual units. The residual unit includes two 3 \(\times\) 3 convolution blocks and an identity mapping, where each convolution block contains a batch normalization layer, a ReLU activation layer, and a convolutional layer. The identity mapping connects the input and output of the residual unit. In the up-sampling part, the features obtained from the previous residual unit are tuned by computing the attention response using the lower-level features as the attention  
Figure 6: Detailed network architecture of proposed attention-gated residual U-Net segmentation model.  
gate. The tuned output is then concatenated with up-sampled feature maps. This process is continued for each up-sampling stage, and the segmented output image is reconstructed in the final layer. With the benefits offered by the attention gate, the model can correctly predict the vascular shape of vein patterns.
## 2 Proposed Approach
### Data Augmentation Techniques
#### 2.2.2 Augmentation by Rotation  
Because data captured from a microphone array are usually limited, data augmentation for multi-channel data is very important for deep learning. The spatial augmentation method in [32] is a very effective augmentation method that can be used in the FOA format to increase the numbers of DOA labels without losing the physical relationships between steering vectors and observations. Since the spatial responses of the four FOA channels \(\{\mathbf{W},\mathbf{X},\mathbf{Y},\mathbf{Z}\}\) to a source incident from a direction are given by Equations (1)-(4), data for a new direction can be reproduced by the reflection on a specific plane or the rotation of the angle by \(\pm 90^{\circ}\) or \(180^{\circ}\). Whereas azimuths span the whole range of \(\phi\in[-180^{\circ},\,180^{\circ})\), elevations span a partial range of \(\theta\in[-45^{\circ},\,45^{\circ}]\). In order not to go out of the label range, only the reflection is applied for the elevation while the reflection and rotations of \(\pm 90^{\circ}\) can be applied for the azimuth. Then, a total of 16 combinations of rotations are applicable for augmentation, as summarized in Table 1.
# Hand Gesture Recognition on Edge Devices:
## 3 This Survey in the Context of Previous Surveys  
There already exist surveys about most topics mentioned in the introduction (see Section 1), but none have specifically discussed HGR on edge devices with a focus on sensor technologies, processing hardware, and algorithms. Most previous surveys deal with vision-based systems or vision enhanced with depth information, as [6], which is an early survey about gesture recognition in vision-based systems. It already describes the general structure of gesture recognition systems which pertains until today and shows what kind of gestures were recognized before 2019, as well as which research areas gesture recognition relates to. Nevertheless, the field has evolved significantly since 2018. An even earlier publication, Ref. [7] from 2011, is about vision-based human activity recognition and describes HGR as part of such research. Another review about vision-based HGR systems is [8]. It categorizes the types of gestures into \"hand and arm\", \"head and face\", and \"body gestures\" demonstrating how diverse gesture recognition is. Survey [9] gives an overview of artificial intelligence (AI) in IoT hardware until 2020. To this end, the article evaluates edge, fog, and cloud in terms of automated perception through sensor input, reasoning, learning in a distributed network, and AI based-behavior of IoT devices in the environment.  
Refs. [8,10,11] are surveys that deal with radar. Refs. [11,12,13] are about acoustic or ultrasound-based solutions. Applications enabled by WiFi are dealt with in [3]. The majority of surveys available are about vision-based systems, such as [7,14]. On the processing side, refs. [9,15,16,17] are about AI accelerators and AI in IoT. Survey [3] introduces several different sensors for HGR, but all except for the camera-based one are wearable solutions, as opposed to HGR systems considered in this paper.  
\begin{table}
\begin{tabular}{l l} \hline
**Taxonomy** & **Meaning** \\ \hline Coarse vs. fine & Hand or arm gestures vs. finger gestures \\ \hline Complex vs. simple & A gesture that contains at least one turn vs. a gesture that has only one direction \\ \hline \end{tabular}
\end{table}
Table 2: Taxonomy to categorize hand gestures for improved evaluation of the complexity of the task.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
###### Abstract  
GNSS spoofing interference utilizes falsified navigation signals to launch attacks on GNSS systems, posing a significant threat to applications that rely on GNSS signals for positioning, navigation, and time services. Therefore, achieving effective localization of the sources causing spoofing interference is crucial in ensuring the secure operation of GNSS. This article proposes a method for locating GNSS spoofing interference sources using a moving array antenna. Firstly, the proposed method utilizes the inherent characteristics of the double-differenced carrier phase from the deception signals received by the array antenna to effectively extract the spoofing signals. Subsequently, by moving the antenna array, the original carrier phase single-difference data of multiple observation points for deception signals are fused to provide a cost function for direct localization of spoofing interference, and a solution method for the cost function is designed. The proposed method addresses the challenge of extracting and localizing GNSS spoofing interference weak signals, effectively avoiding the data correlation of traditional two-step methods for DOA estimation parameters and ensuring the location accuracy of spoofing interference and the robustness of the method. The effectiveness of the proposed method has been validated through simulation experiments, and its adaptability to factors such as errors in carrier phase measurements has been examined. The method exhibits strong applicability and is well-suited for the hardware platform of the GNSS nulling antenna, thereby enabling it to possess simultaneous capabilities in both anti-interference and spoofing interference localization.  
GNSS spoofing interference; spoofing interference localization; array antenna; double-differenced carrier phase 202315497.12.2.202315497.12.2.202315497.
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
###### Abstract  
The importance of green technologies is steadily growing. Salt-tolerant plants have been proposed as energy crops for cultivation on saline lands. Halophytes such as _Salicornia europeae, Tripolium panomicum, Crithum maximum_ and _Chenopodium quinao_, among many other species, can be cultivated in saline lands, in coastal areas or for treating saline wastewater, and the biomass might be used for biogas production as an integrated process of biorefining. However, halophytes have different salt tolerance mechanisms, including compartmentalization of salt in the vacuole, leading to an increase of sodium in the plant tissues. The sodium content of halophytes may have an adverse effect on the anaerobic digestion process, which needs adjustments to achieve stable and efficient conversion of the halophytes into biogas. This review gives an overview of the specificities of halophytes that needs to be accounted for using their biomass as feedstocks for biogas plants in order to expand renewable energy production. First, the different physiological mechanisms of halophytes to grow under saline conditions are described, which lead to the characteristic composition of the halophyte biomass, which may influence the biogas production. Next, possible mechanisms to avoid negative effects on the anaerobic digestion process are described, with an overview of full-scale applications. Taking all these aspects into account, halophyte plants have a great potential for biogas and methane production with yields similar to those produced by other energy crops and the simultaneous benefit of utilization of saline soils.  
anaerobic digestion; biogas production; _Chenopodium quinao_; co-digestion; _Crichum martitimum_; halophyte composition; inoculum adaptation; plant physiology; _Salicornia europeae_, salinity; _Tripolium panomicum_