esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents a method of evaluating energy and environmental factors before and after chilled water production system retrofitting at an industrial facility. A general algorithm was used for the analysis of chilled water system retrofitting at a pharmacokinetics factory. Two retrofitting variants based on dual-stage absorption chillers supplied from an existing gas-fueled co-generation plant were identified. The proposed variants, i.e., tri-generation systems, were compared with the basic variant, which relied on electric compression water chillers. An evaluation of the variants was performed on the basis of two criteria: annual primary energy consumption and annual carbon dioxide emission. Variant 2, i.e., with a 1650 kW dual-stage absorption water chiller supplied from an existing gas fueled co-generation plant, was chosen as the optimal variant. It achieved a 370 MWh annual primary energy consumption reduction and a 1140 Mg annual carbon dioxide emission reduction. It was found that increasing the co-generation ratio for the CHP plant powering the pharmaceutical factory resulted in lower consumption of primary energy in variants in which the cooling energy supply system was retrofitted based on absorption water chillers. The threshold values of the co-generation ratio were e = 0.37 for Variant 1 and e = 0.34 for Variant 2. A literature survey revealed that there is limited interest in the application of such a solution in industrial plants. The performed analysis showed that the evaluated systems may nonetheless be an attractive option for pharmaceutics factories, leading to the reduction of primary energy consumption and carbon dioxide emissions, thereby making more electrical power available for core production. The lessons learned during our analysis could be easily transferred to other industrial facilities requiring chilled water production systems.  
energy and environmental analysis; absorption water chillers Article
###### Abstract  
Speech impairment analysis and processing technologies have evolved substantially in recent years, and the use of voice as a biomarker has gained popularity. We have developed an approach for clinical speech signal processing to demonstrate the promise of deep learning-driven voice analysis as a screening tool for Parkinson's Disease (PD), the world's second most prevalent neurodegenerative disease. Detecting Parkinson's disease symptoms typically involves an evaluation by a movement disorder expert, which can be difficult to get and yield varied findings. A vocal digital biomarker might supplement the time-consuming traditional manual examination by recognizing and evaluating symptoms that characterize voice quality and level of deterioration. We present a deep learning based, custom U-lossian model for PD assessment and recognition. The study's goal was to discover anomalies in the PD-affected voice and develop an automated screening method that can discriminate between the voices of PD patients and healthy volunteers while also providing a voice quality score. The classification accuracy was evaluated on two speech corpora (Italian PVS and own Lithuanian PD voice dataset) and we have found the result to be medically appropriate, with values of 0.8964 and 0.7949, confirming the proposed model's high generalizability.  
Parkinson's disease; voice analysis; voice screening; speech signal processing Article  
A Hybrid U-Lossian Deep Learning Network for Screening and Evaluating Parkinson's Disease  
1 Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania  
2 Department of Otorhinolaryngology, Lithuanian University of Health Sciences, 50061 Kaunas, Lithuania  
* Correspondence: [EMAIL_ADDRESS]
## 7 Load Data
Fixed appliances are those that require a constant and uninterrupted power supply to function correctly. Their operation cannot be delayed, shifted, or adjusted without compromising their intended use. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Household** & **Category** & **Quantity** & **Power Usage (W)** & \
\begin{tabular}{c} **Daily Operating** \\ **Hours** \\ \end{tabular} &
\begin{tabular}{c} **Daily Energy** \\ **Consumption (W)** \\ \end{tabular} \\ \hline Cloth dryer & Shiftable & 1 & 2000 & 1 & 2000 \\ \hline Fan & Fixed & 5 & 80 & 19 & 7600 \\ \hline TV & Fixed & 3 & 100 & 10 & 3000 \\ \hline Vacuum cleaner & Elastic & 1 & 700 & 2 & 1400 \\ \hline Water pump & Elastic & 1 & 200 & 3 & 600 \\ \hline Washing machine & Shiftable & 1 & 700 & 4 & 2800 \\ \hline Light & Fixed & 21 & 11 & 17 & 3927 \\ \hline Refrigerator & Elastic & 2 & 100 & 22 & 4400 \\ \hline cloth iron & Fixed & 1 & 800 & 2 & 1600 \\ \hline Space heater & Elastic & 1 & 1500 & 6 & 9000 \\ \hline Air conditioner & Elastic & 2 & 1500 & 11 & 33,000 \\ \hline Microwave & Fixed & 1 & 800 & 4 & 3200 \\ \hline Dish washer & Shiftable & 1 & 1800 & 3 & 5400 \\ \hline Water heater & Elastic & 1 & 1200 & 5 & 6000 \\ \hline \end{tabular}
\end{table} 
Table 3: Power Usage, Operating Duration, and Categories of Household Appliances. Fixed appliances demand a reliable and steady power supply, making it essential to ensure their uninterrupted operation even during periods of fluctuating energy availability. Their energy consumption is typically prioritized in HEMSs to maintain essential services. The categorization of home appliances into Elastic, Shiftable, and Fixed has significant implications for home energy management strategies. By leveraging the flexibility of Elastic and Shiftable appliances, households can maximize the use of RESs, decreasing dependence on non-renewable energy and lowering energy costs. Additionally, these appliances enable households to participate in DR programs, modifying their energy usage in response to the grid signals and energy prices. The proper management of these appliance categories can result in enhanced energy efficiency and lower overall energy consumption, contributing to sustainability goals.
## 3 Temperature Prediction Model for Subgrade
### NARX Network Principle and Structure
A general NARX network model with multiple inputs and one output can be represented as follows:  
\[y(n+1)=f_{0}\left[b_{0}+\sum_{h=1}^{Nh}w_{h0}f_{h}\left(b_{h}+\sum_{k=1}^{NX} \sum_{i=0}^{dx}w_{ikh}x_{k}(n-i)+\sum_{j=0}^{dy}w_{jh}y(n-j)\right)\right] \tag{2}\]  
where \(i=1\), \(2\),..., \(dxj=1\), \(2\),..., \(dy\); \(h=1\), \(2\),..., \(Nlt\); \(k=1\), \(2\),..., \(NX\); both \(f_{h}\) and \(f_{0}\) are the activation functions of the hidden and output layers, respectively. The terms \(w_{h0}\), \(w_{ikh}\), and \(w_{jh}\) are the weights connecting the various neurons. The variables \(b_{0}\) and \(b_{\text{h}}\) are biases, \(N_{h}\) represents the number of neurons in the hidden layer, and \(NX\) indicates the number of network inputs [43; 44]. Table 2 gives the specific settings of the activation functions. As shown in Figure 6, the closed-loop structure uses water content, frost heave, and previously predicted temperature values as inputs for predicting the next temperature value. The critical step in the NARX network model involves training the network to approach the relationship between network input and output parameters, i.e., the connection weights between neurons. Initially, these weights are randomly generated. The network processes the inputted temperature, water content, and frost heave data to generate the network output of temperature through forward propagation. At this stage, the desired temperature targets are the actual monitoring values from the training dataset. The goal of network training is to minimize the discrepancy between the targets and computed outputs as much as possible. Typically, this discrepancy (error) is used in the network's backpropagation process to adjust the weights. This process is repeated until the network's output accuracy meets the preset standards [45]. In this study, the Adam algorithm is employed for open-loop training of the NARX network. The NARX model's performance is primarily determined by three hyperparame  
Figure 6: NARX network closed-loop structure for subgrade temperature prediction. ters: input delay (_dx_), output delay (_dy_), and the neuron number in the hidden layer (_Nh_). Therefore, accurately determining these three hyperparameters is crucial for the neural network's final prediction results. More details will be explored in Section 3.3.
## 6 Forecasting Results and Discussion
### Time Resolution Forecasting  
In this experiment, we considered multiple scenarios of time resolution and time horizons by applying the proposed model after the preprocessing techniques (w/o) outliers. We defined multiple lookback time steps such as 1 h, 24 h, and 48 h for future energy consumption forecasting. The average forecasting performance of all customers for time resolution base is demonstrated in Table 7. All-time resolutions performed relatively similar to each other for all evaluation metrics; however, the one-hour resolution achieved the lowest error rates. We can conclude that the energy consumption behavior of hourly resolution provides a more accurate interpretation of the proposed model compared to daily and bi-daily consumption resolution.
## 5 Numerical Results
### Performance Comparison Guarantee of QoS  
The BS-level QoS satisfaction rate \(q\) is used as a comparison metric because the QoS guarantee is the primary objective of the proposed framework, and the load balancing mechanism is to serve the QoS guarantee. Otherwise, the value of QGJRA-SUL, \(\beta=0\) has an unreasonable advantage over the reward because its reward function removes the penalty item. Figure 5 shows the average QoS satisfaction rate of 100 episodes in the testing. The BS is at full load of about 70 UEs. Compared with other baseline methods, the performance of the proposed QGJRA-SUL is the closest to that of the Optimal. Figure 5 shows an obvious performance gap between QGJRA-SUL and QGJRA-SUL, \(\beta=0\) near the  
Figure 4: The convergence process of DDPG-Agents with different \(\beta\). (**a**) The episode reward with different \(\beta\) in the convergence process. (**b**) The episode QoS satisfaction rate with different \(\beta\) in the convergence process.  
full load. This gap indicates that the load balancing mechanism can help the DDPG-Agent to make better resource reservation for each slice when the slice is close to the full load. QGJRA-SUL, \(\beta=0\) suffers performance degradation, because it has no load balancing mechanism to deal with more complex traffic fluctuations. This means that considering the load balancing between slices is helpful to improve the QoS satisfaction rate under traffic fluctuations. The edge UEs of QGJRA-nonSUL cannot use the SUL band with less path loss, so its performance is mediocre. In addition, QGJRA-SUL can accommodate about 15% more UEs with the same QoS satisfaction rate as that of QGJRA-nonSUL at a load of 60-70 UEs. The framework with SUL achieves a greater QoS satisfaction rate compared to the framework without SUL because of the lower path loss of the SUL band. DNSIE-nonSUL can share resources among multiple slices based on different load thresholds before overload. However, the granularity of this resource adjustment is fixed, which cannot well match the dynamic traffic requirement.
## 1 Introduction
To investigate shear bands, geotechnical testing methods, including conventional triaxial compression and plane strain compression tests, can be used. Because of the different stress states of the specimens in the two tests above, the characteristics of the shear band are different. For example, for quick clay, the shear band emerges before or at the peak deviatoric stress in the triaxial compression test, whereas the shear band emerges after the peak deviatoric stress in the plane strain compression test [15; 26]. For sand, the shear band emerges before the peak deviatoric stress in the plane strain compression test, and the shear band emerges after the peak deviatoric stress in the triaxial compression test [27]. Therefore, the characteristics of the shear bands are also related to the types of soils. Because the clays in slopes are under stress conditions very close to plane strain [26; 28; 29], plane strain compression tests were performed in this study. A study pertaining to the evolution of shear bands in the plane strain compression of a naturally structured clay with a high sensitivity in coastal areas of Zhanjiang City, Guangdong Province, China (Zhanjiang clay), is reported herein. The strain field in the specimen was obtained during the plane strain compression test, and the micro-structures of the clay samples inside and outside the shear band were analyzed via scanning electron microscopy (SEM).
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 3 Analysis of Antenna Configuration
### Circular Arc 3 \(\times\) 3 Transceiver Antenna Array  
A 3 \(\times\) 3 circular arc transceiver antenna array has a similar calculation method to the 2 \(\times\) 2 ones. According to Equation (18), we can obtain one of the optimal channel matrices as:  
\[\mathbf{H_{3\times 3}}\left(\begin{array}{ccc}1&1&1\\ 1&e^{-j\frac{2\pi}{3}}&e^{-j\frac{4\pi}{3}}\\ 1&e^{-j\frac{4\pi}{3}}&e^{-j\frac{8\pi}{3}}\end{array}\right) \tag{36}\]  
Then, the maximum channel capacity can be estimated as:  
\[C_{max}=3 log_{2}(1+\rho) \tag{37}\]  
Therefore, the structure of the antenna is designed in Figure 5.  
In Figure 5, \(T_{1}\), \(T_{2}\), and \(T_{3}\) are transmitting antennas. \(R_{1}\), \(R_{2}\), and \(R_{3}\) are receiving antennas. The antennas \(T_{2}\), \(T_{3}\), \(R_{2}\), and \(R_{3}\) form a parallelogram. \(\theta\) is the central angle of the circle with radius \(r\) of \(T_{1}\), where \(r\) is the distance between the transmitting and receiving antennas.  
This structure satisfies Equations (38) and (39):  
\[R_{1}T_{1}=R_{1}T_{2}=R_{1}T_{3}=R_{2}T_{1}=R_{3}T_{1}=r \tag{38}\]  
\[\left\{\begin{array}{c}R_{2}T_{2}=r+\Delta d_{0}\\ R_{2}T_{3}=R_{3}T_{2}=r+\Delta d_{1}\\ R_{3}T_{3}=r+\Delta d_{2}\end{array}\right. \tag{39}\]  
According to Equation (19), it can be calculated as:  
\[\left\{\begin{array}{c}\Delta d_{0}=-\frac{1}{3}\lambda+k\lambda,keZ\\ \Delta d_{1}=-\frac{2}{3}\lambda+k\lambda,keZ\\ \Delta d_{2}=-\frac{4}{3}\lambda+k\lambda,keZ\end{array}\right. \tag{40}\]  
According to this antenna structure, it is necessary to meet the following formula:  
\[\Delta d_{0}<\Delta d_{1}<\Delta d_{2} \tag{41}\]  
Therefore, it can be obtained as:  
\[\left\{\begin{array}{c}R_{2}T_{2}=r\sqrt{5-4 cos\theta}=r+\frac{2}{3}\lambda +k_{0}\lambda,k_{0}eZ\\ R_{2}T_{3}=R_{3}T_{2}=r\sqrt{3-2 cos2\theta}=r+\frac{4}{3}\lambda+k_{1},k_{1}eZ \\ R_{3}T_{3}=r\sqrt{5-4 cos2\theta}=r+\frac{5}{3}\lambda+k_{2}\lambda,k_{2}eZ \end{array}\right. \tag{42}\]  
Figure 5: Circular arc 3 \(\times\) 3 transceiver antenna array.  
where \(0\leq k_{0}\leq k_{1}\leq k_{2}\). In this configuration, the array element spacing and the distance of the transceiver antennas can be considered as \(rtan\theta\) and \(r\). Since there is no exact solution to the above equations, an approximate solution can be obtained by allowing a 5% error. Since there exists multiple sets of solutions, a set of solutions with the smallest \(\theta\) is picked to obtain a smaller antenna scale. The specific results are shown in Table 1.  
Table 1 shows that when the distance \(r\) is relatively large, there exists an optimal solution for a 3 \(\times\) 3 transceiver antenna array. The central angle \(\theta\) is related to the signal frequency. It can be noticed that the array element spacing decreases as the signal frequency increases. Moreover, the comparable solution for the \(n\times n\) antenna array may be solved in a similar manner, and the ideal antenna configuration can be predicted.
## 3 Results  
### Spectral Analysis  
We performed a spectral analysis on the data samples to characterize the seismic noise recorded at each volcano in terms of its frequency content.  
Examples of waveforms of the seismic noise recorded at the Neapolitan and Colima volcanoes are shown in Figure 5 and Figure S1-S5. As one can observe, the noise has different amplitude levels and frequencies depending on the volcano, on the specific site where the station is located, and on the component of motion.  
The spectral features are summarized in Table 1, where we report the frequency range of the main spectral peaks and their possible origin according to the framework described in the Seismological Overview section. Some considerations arise from this analysis: microseism caused by sea loading (<1 Hz) is observed at all the volcanoes but in two cases it is mixed with the contribution related to hydrothermal (Campi Flegeri) or volcanic (Colima) sources. Anthropogenic noise is usually recorded above the frequency of 1 Hz up to 12-15 Hz, but the specific ranges depend on the volcano and on the site. Moreover, in the case of Ischia, part of the anthropogenic frequency band (1-5 Hz) is superimposed with that of the hydrothermal signal. Finally, at the Neapolitan volcanoes, the effect of human activities on the background seismic signal is strong because the seismic stations are installed in densely urbanized areas. In contrast, this effect is minimal (or even practically absent) at the Colima volcano, where the seismic stations were installed in a sparsely populated region.  
Figure 5: Examples of vertical component recordings of seismic noise and corresponding spectrograms, at Campi Flegeri (ASB2 station), Ischia (IOCA station), Vesuvius (BKSG station), and Colima (COCA station).
## 4 KNN in Wireless Sensor Networks
The WSN nodes are randomly distributed in a 2D plane, and Euclidean distance accurately reflects the actual physical distance between nodes and CHs. The KNN-based clustering approach relies on accurate distance measurements to assign nodes to the nearest CH. Euclidean distance ensures optimal clustering, as demonstrated by the simulation results' reduced energy consumption and improved network lifetime. Euclidean distance is simple to compute and widely supported in clustering algorithms like KNN. Its computational cost is manageable in the study context, where calculations are performed during the clustering phase rather than on individual nodes.
## 4 Discussion
### Soil Moisture and Biomass Were Poorly Related to Synthetic Aperture Radar (SAR) Metrics  
We examined the relationship between Sentinel-1 SAR metrics and soil VWC, as SAR signals are sensitive to soil moisture, but found no significant relationship between VWC and all SAR metrics examined in this analysis (Appendix A). This may be because Sentinel-1 operates at a C-band frequency, which may be more responsive to crop conditions than soil moisture, with two notable studies finding that C-band SAR VV backscatter decreases in response to increasing winter wheat stem height, while soil moisture has less of an impact on backscatter [86; 87]. However, when examined independently, both VV and VH backscatter intensities also showed very poor relationships with cover crop biomass (Appendix D), which is surprising, as shallower incident angles (further off nadir) have been associated with the effective estimation of wheat biomass [41; 88]. Others also demonstrated that C-band SAR failed to detect soil moisture when NDVI exceeded 0.7 [88]. In our study, the use of SAR metrics focused on the spring period (March-May), when we observed high biomass levels, and approximately half of our spring observations had NDVI values greater than 0.7 which likely influenced the relationship between in situ soil moisture measurements and SAR metrics. For samples with an NDVI less than 0.70, it was likely that the combined influence of background soil variability and variability in attenuating structures (small diameter vertical stem growth) [86; 89] culminated in combined backscatter values that could not be related back to either factor in isolation. The ability to interpret and statistically isolate these factors becomes even more challenging when considering factors such as surface roughness, crop residue presence, and crop residue moisture content, all of which influence total backscatter intensity in a given image pixel.
### Research Motivation  
The application of laboratory wettability measurements downhole remains a challenge and not close to being well-established in the industry due to heterogeneity and complex geological structures [16]. NMR logging and well testing have proven to successfully measure important reservoir properties, including wettability [5]. However, these methods are costly and, in relatively low oil price regimes, do not seem feasible to run in each well, giving conventional wireline logs ubiquitous in all wells.  
Conventional wireline logs have a complex and non-linear relationship with surface wettability, but the application of advanced machine learning algorithms may help establish a relationship on a field scale. Many laboratory methods such as spontaneous imbibition, Amatt-Harvey, and contact angle measurements have been used to determine the surface wettability of reservoirs. However, these methods are often incapable of providing reliable results once tested in the field due to the heterogeneity of the reservoir and complex geological structures. As a result, few field scale approaches have been proposed based on well testing and NMR log data analysis, but they are also expensive to acquire. Using wireline logs to help predict this essential reservoir parameter is relatively cheaper and time-efficient than any laboratory wettability method and NMR logging. As such, the need for a cheap and accurate field scale approach for quantitative surface wettability determination has been of interest considering current oil prices.  
Therefore, this research aims to integrate empirical and machine learning techniques in proposing a field scale, time-efficient, and relatively inexpensive alternative approach to determine the wettability of reservoir rocks using wireline logs.
# Sewater Effect on Fatigue Behaviour of Notched Carbon/Epoxy Laminates
###### Abstract  
This paper studies the effect of seawater immersion on the fatigue behavior of notched carbon/epoxy laminates. Rectangular cross-section specimens with a central hole were immersed in natural and artificial seawater for different immersion times (0, 30 and 60 days), being the water absorption rate evaluated over time. After that, fatigue tests were performed under uniaxial cyclic loading using a stress ratio equal to 0.1. After the tests, the optical microscopy technique allowed the examination of the failure micro-mechanisms at the fracture surfaces. The results showed that saturation appeared before 30 days of immersion and that water absorption rates were similar for natural and artificial seawater. The S-N curves showed that the seawater immersion affects the fatigue strength, but there were no relevant effects associated with the type of seawater. Moreover, it was also clear that fatigue life was similar for long lives, close to 1 million cycles, regardless of the immersion time or the type of seawater. On the contrary, for short lives, near 10 thousand cycles, the stress amplitude of dry laminates was 1.2 higher than those immersed in seawater. The failure mechanisms were similar for all conditions, evidencing the fracture of axially aligned fibres and longitudinal delamination between layers.  
composite laminates; open holes; seawater effect; fatigue life; mechanical testing Article