esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 4 Result and Discussion
### Analysis of Input Parameters  
#### 4.3.1 Comparison of Prediction Results for Different Geological Parameters  
This paper employed the strata area weighting method to determine six key physical and mechanical indices for each ring: natural density, deformation modulus, internal friction angle, cohesion, lateral pressure coefficient, and Poisson's ratio. These indices, along with burial depth, were used as part of the input parameters for the PCA-PSO-LGBM model, yielding excellent predictive results. Additionally, the geological survey report of this project classifies the surrounding rock levels through which the shield tunnel passes into six grades (I-VI), while the dataset used in this paper covers only grades II-IV. The distribution of shield rings within each rock level is shown in Figure 16, indicating that the majority of shield rings are in Grade II rocks, accounting for over 90%, followed by Grade III at 5.7%, and Grade IV with less than 3%. To validate the effectiveness of the strata area weighting method, this section used the rock grade as a characteristic of the geological parameters for each shield ring, combining it with burial depth and other shield excavation parameters as model input. The prediction results are shown in Figure 17. The results indicate that while incorporating the surrounding rock grade as a geological parameter feature yields relatively good predictive performance, it still falls short of the prediction accuracy achieved using the strata area-weighted method. Specifically, the prediction error for the roll angle slightly increased compared to the original model, and the \(MAE\) for the pitch angle increased by 18.8%, with the \(R^{2}\) value decreasing by 0.006. This demonstrates that while the rock grade can reflect some characteristics of the strata through which the shield tunnel passes, its classification is not fine enough to capture more detailed stratigraphic information at the shield cutter face. The strata area weighting method proposed in this paper effectively addresses this issue.  
Figure 16: The number of shield rings contained in different surrounding rock grades.
# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
###### Abstract  
Optimizing the energy consumption of an MEC (Multi-Access Edge Computing) system is a crucial challenge for operation cost reduction and environmental conservation. In this paper, we address an MECS (MEC Server) sleep control problem that aims to reduce the energy consumption of the system while providing users with a reasonable service delay by adjusting the number of active MECSs according to the load imposed on the system. To tackle the problem, we identify two crucial issues that influence the design of an effective sleep control technique and propose methods to address each of these issues. The first issue is accurately predicting the system load. Changes in system load are spatio-temporally correlated among MECSs. By leveraging such correlation information with STGCN (Spatio-Temporal Graph Convolutional Network), we enhance the prediction accuracy of task arrival rates for each MECS. The second issue is rapidly selecting MECSs to sleep when the load distribution over an MEC system is given. The problem of choosing sleep MECS is a combinatorial optimization problem with high time complexity. To address the issue, we employ a genetic algorithm and quickly determine the optimal sleep MECS with the predicted load information for each MECS. Through simulation studies, we verify that compared to the LSTM (Long Short-Term Memory)-based method, our method increases the energy efficiency of an MEC system while providing a compatible service delay.  
MEC energy saving; task arrival rate prediction; spatio-temporal correlation; graph neural network; genetic algorithm +
Footnote †: journal: 2024, 14, 605. [[https://doi.org/10.3390/app14020605](https://doi.org/10.3390/app14020605)]([https://doi.org/10.3390/app14020605](https://doi.org/10.3390/app14020605))
###### Abstract  
In Indonesia, the solar photovoltaic (PV) market is rapidly growing. However, studies on the outdoor performance of PV systems in this tropical rainforest country is lacking. In this work, we compare the performance of PV systems in Indonesia with PV systems in Australia (arid, desert, hot) and Italy (temperate, dry summer, hot summer). Monitoring data from 2008 to 2019, ranging from two to nine years, from fifteen PV systems of six technologies were analyzed. The performance of the PV systems was presented using their performance ratio (PR) and performance loss rate (PLR). PR was calculated using IEC standard 61724, and PLR was calculated using seasonal and trend decomposition, applying locally weighted scatterplot smoothing (STL decomposition) and the year-on-year approach from NREL/RdTools. The results showed that the CIGS (copper indium gallium selenide) system had the highest average PR value of \(0.88\pm 0.04\). The lowest average PR was found in the a-Si (amorphous silicon) PV systems (\(0.78\pm 0.05\)). The p-Si (polycrystalline silicon) systems in the Cfb (temperate, no dry season, warm summer) climate of Italy had a higher average PR of 0.84 than those operated in climates BWh (arid, desert, hot) of Australia and Af (tropical, rainforest) of Indonesia, with the same value of 0.81. The p-Si systems showed the lowest PLR, with a value of \(-0.6\%\)/year. The fastest degradation was experienced by a-Si modules at \(-1.58\%\)/year. The methodological differences in the calculation of PLR using both tested approaches resulted in a significant difference in the degradation value, which demands standardization of the term and calculation methodology.  
degradation; Indonesia; performance loss rate; performance ratio; solar photovoltaic; STL decomposition; year-on-year +
Footnote †: journal: Applied Soft. 2020, 10, 5412; doi:10.3390/app10165412
# Cooperative Reception of Multiple Satellite Downlinks
## 3 Results and Discussion
### Exclusion of the Worst Stream  
Exclusion of the worst stream was the first technique to improve the performance. This technique could be implemented after calculating the BER of all received versions and then deciding the stream with the highest BER value to be the worst quality stream. Then, the VGS formed the combining matrix \(C\) from all versions but the worst one, as illustrated in Figure 8a, and consequently the combining process was done without the worst stream which was holding the system's performance back by introducing more possible errors to the combined stream than the rest streams. Doing so, the detection weight of the better-quality streams increased while nulling the detection weight of the worst stream.
## 1 Introduction
I see the main disadvantage of this process as the fact that the area of hydrate formation is limited by the presence of the liquefiable gas and the methane in gaseous form is vented outside the experimental apparatus. Furthermore, the decompression process during which the hydrate nuclei are formed takes a few seconds, due to which it is not possible to form a large amount of hydrate [21]. A study by [PERSON] et al. focused on hydrate production in an experimental facility constructed in three different conceptual designs. The hydrate formation rate and gas uptake were compared between the different designs. Hydrate formation based on rotary mixing, injection of a water-gas mixture using a nozzle, and a combination of injection and rotary mixing were considered [22; 23; 24]. In the combination of injection and mixing is evaluated as the most suitable of the three different technical methods of hydrate formation and this is due to the largest gas intake into the hydrate structures. Measurements clearly show that injection led to immediate nucleation of the hydrates and additional mixing disturbed the surface tension of the mixture, which also strongly promoted hydrate formation [25]. To accelerate the production of methane hydrates, mechanical rotary mixing is used in most cases to continuously disturb the water-gas mixture. However, during the hydration phase, the viscosity of the mixture increases and hence the energy required to mix it also increases. It should also be mentioned that mixing in the long term may cause disturbance of the structure of the resulting hydrate [26; 27]. A similar study based on the disturbance of the mixture surface area by the reciprocating motion of the impactor was carried out by [PERSON] et al. [28]. In the reactor of an experimental device, a body is moved which continuously impinges on the hydrated mixture and displaces the excess water in the interspace. The main objective is to accelerate the hydrate formation and reduce the total volume of hydrate by displacing the excess water, which has a positive effect on the storage capacity [28]. The above-mentioned studies confirmed that the process of hydrate formation of hydrocarbon gases is feasible and therefore possible under laboratory conditions. Our proposed experimental setup, was based on the findings and measurements of the published articles. The main difference will lie in the use of a pump, which we will use to create an internal circulation circuit of the water-gas mixture. This unique experimental device concept will need to be further analyzed and finally evaluated to see how significantly the process of forming natural gas hydrates can be accelerated by the proposed modification.
## 2 Solar PV Modeling and Characteristics
### Characteristics of a PV System  
Generally, the PV panel characteristics are described by I-V and P-V curves, as presented in Figure 3. It is worth noting that there is a specific operating point at which the output power is at its maximum. This unique point is referred to as the maximum power point (MPP) and is denoted by the letters MPP in the I-V and P-V curves. Due to the PV panel's low efficiency, PV systems must always operate close to the MPP in order to gather the greatest amount of energy [80,81].  
However, the performance of the PV panel can easily be affected by climatic conditions. The short circuit current is linearly dependent on the solar irradiance level, whereas the open-circuit voltage is strongly influenced by the cell temperature [82,83]. Figure 4a depicts the PV characteristics for changing irradiance and Figure 4b depicts the PV characteristics for changing temperature.
## 3 Characterization  
The mineralogical compositions were determined by the techniques of X-ray powder diffraction (XRD) using Philips Panalytical X' Pert PRO powder diffractometer (Almelo, The Netherlands) equipped with X' Celerator detector and with cobalt \(K_{\alpha}\) radiation (\(\uplambda\) = 1.789 A). Tension and current were set to 40 kV and 40 mA respectively. Scans were collected between 3 and 60\({}^{\circ}\) 20 angular range at steep angle of 0.017\({}^{\circ}\). Orientated aggregate samples were scanned from 3\({}^{\circ}\) to 35\({}^{\circ}\) 20 angular range at 1\({}^{\circ}\)/min with a scanning step of 0.01\({}^{\circ}\)/step. The clay fraction was maintained at three different conditions (1 to 3); 1: untreated (natural state), 2: after treatment with ethylene glycol vapors in a desiccator for 24 h at 60 \({}^{\circ}\)C, 3: after heating at 500 \({}^{\circ}\)C for 1 h. The micromorphological features of the samples before and after activation were studied using a scanning electron microscope (SEM) of type Philips XL30 ESEM (Leuven, Belgium) operating with up to 30 kV acceleration voltage field emission gun. Sample clays fragments were previously coated with a thin layer of gold before proceeding to morphological observations. The Fourier transform infrared spectroscopy (FT-IR) was applied using a Perkin-Elmer 983 G IR-spectrometer (Bridgeport, CT, USA) equipped with IR source, KBr beam splitter and DTGS KBr detector. Prior to analysis, samples were lightly ground using a mortar and pestle and sieved again through the 45 \(\upmu\)m sieve. This was done to minimize the scattering, distortion and peak broadening of IR radiation due to larger-sized particles that may have resulted from agglomeration during drying. The spectra were recorded in the spectral range from 4000 to 250 cm\({}^{-1}\). Quantitative chemical analysis of raw material (S\({}_{0}\)) and acid-activated clays were performed using atomic absorption spectrometer (AAS) of type NOVAA 400 (Analytik Jena AG, Jena, Germany)and X-ray fluorescence (ARL\({}^{\circled@}\) 9800 XP spectrometer, Thermo ARL, Ecublens, Switzerland). By X-ray fluorescence, the loss on ignition (LOI) content of dried and powdered clay sample was attributed to the release of water and was measured by calcinations at 975 \({}^{\circ}\)C. Then the dried clay samples were mixed with lithium-tetraborate and lithium iodide and the mixture was fused in a Pt crucible and moulded to a glass disk for chemical analysis.N\({}_{2}\) adsorption/desorption isotherms were measured using Quantachrome Autosorb AS1C (Quantachrome Instruments, Boynton Beach, FL, USA) equipment at liquid nitrogen temperature of \(-\)196.15 \({}^{\circ}\)C. Before adsorption measurements, the samples were degassed in vacuum at 300 \({}^{\circ}\)C for more than 3 h to remove any adsorbed contaminants from the surface and pores of the samples. The Brunauer-Emmett-Teller (BET) method was used to determine the surface areas and pore volume of samples and the pore size distribution was calculated using the BJH method based on the nitrogen adsorption or desorption data.
## 2 Materials and Methods
### Data Collection  
Young football players were monitored during the training sessions using portable GPS devices (STATSports Apex(r), Newry, Northern Ireland). These units operated at a sampling frequency of 18 Hz with an accelerometer (100 Hz), a magnetometer (10 Hz), and a gyroscope (100 Hz), capturing raw data on movement, speed, and distance. Additionally, the devices were equipped. Each GPS unit was securely positioned in a designated pocket on a specialized vessel provided by the manufacturer, situated on the upper back between the shoulder blades. To ensure proper satellite signal reception, all devices were activated 30 min prior to the start of data collection [26]. The validity and reliability of this GPS device's tracking are well established in the literature [6; 32]. To ensure the reliability of the data collected through wearable devices, we referred to previous studies that validated the accuracy of GPS and HR monitors used in this study [6; 32]. Potential measurement errors were minimized by applying data smoothing techniques and discarding outliers beyond a reasonable range based on established thresholds.
## 4 Discussion  
As the results demonstrate for both total biomass and viable cells in the biofilms, BE technology can be an alternative method for biofilm management. The details of the electrical treatment mechanism are still under investigation; however, many hypotheses are suggested that it could induce the molecular vibration of biofilms under alternating electric field application [13], generate surface electrostatic force of the direct current, and induce non-uniformity of the local electrolyte, which are essential to the activation of enzymes [14]. The induced biochemical condition with electrostatic force could make biofilms detach from the surface [15]. Based on the principles of BE technology, the results also show that the electric field application to the biofilms demonstrates significant reduction of the total biomass, as well as increase in the biocidal effect.  
In the total biofilm quantification testing, one of the testing conditions has been shown to lessen biofilm formation as well as reduction of it (#5 condition of Table 2). This is due to the biofilm formation not being consistent with the same conditions. The growth of biofilm can be varied in nature since the DNA expression, transcription, and amino acid formation may be affected even in minor variance of conditions [12; 16]. For further investigation, we plan to control the condition tightly using a microfluidic system or testing in a humidity- and temperature-controlled system. Even if the testing result showed variance of biofilm growth and reduction, it demonstrated with statistical significance that the microcurrent applied system could be effective for biofilm contamination management.  
The total electric power consumption of the prototype system is less than 1 mW, which can be provided by a commercially available battery, typically with electric capacity of 300-2000 mA current with \(-\)1.5-9.0 V. This low power utilization biofilm inhibition system has significant advantage for portable and electrically isolated system development, including the electric automotive industry and home appliances. In particular, in this work, we performed biofilm inhibition testing experiments using a commercialized evaporator that could make a foundation for resolving biofilm contamination issues in the automotive system.  
Future studies with the current system will be conducted to improve the reduction efficiency of biofilms on the automotive evaporator by using high conductivity material and optimizing arrangement of electrodes.  
We believe that the ultra-low power consumption system demonstration might be integrated for electric vehicle HVAC system hygiene management that needs to minimize electric power consumption. Finally, considering this technology does not require any chemical detergent or physical brushing to be applied, this work could impact the development of an eco-friendly biofilm cleaning system.
# Exploring Pre-Trained Models for Skin Cancer Classification
## 3 Materials and Methods
### Considered Pre-Trained Models
#### 3.3.1 ResNet50  
ResNet50 (Residual Network with 50 layers) is a deep CNN that addresses the vanishing gradient problem through residual connections, which allow gradients to bypass certain layers, facilitating more efficient training of deep networks. The architecture consists of bottleneck residual blocks, each comprising convolutional layers, batch normalization, and ReLU activations. Due to its ability to extract hierarchical features, ResNet50 has been widely used in medical imaging, particularly for distinguishing subtle differences between benign and malignant lesions.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Results and Discussion
#### 5.2.5 Transmission Loss  
Transmission loss is measured using Equation (48).  
\[TL=20 logd+\alpha*d*10^{-3} \tag{48}\]  
where \(TL\) is measured in dB, the distance between sender and receiver \(d\) is expressed in meters, \(\alpha\), and the absorption coefficient is measured in dB/km. Equation (48) specifies that the transmission loss is mainly caused by distance dependent attenuation and frequency dependent absorption. Transmission loss badly impacts the receiver's performance. It increases with an increase in attenuation loss and the transmission distance. An acoustic system may operate in a frequency range between 10 and 15 kHz. Although the total communication bandwidth is very low (5 kHz), the system is in fact wideband, in the sense that bandwidth is not negligible with respect to the center frequency. In TBRL, the possible maximum distance exists between two reliable nodes is 70 m; therefore, we took \(d\) as a 70 m and has transmission loss against different frequencies, as shown in Figure 11.  
Figure 11: Maximum Transmission Loss in TBRL.  
Figure 10: Throughput among Different Paths in TBRL.
## 3 Methodology
### Multi-Sensor IF Estimation Algorithm
#### 3.1.3. IF Estimation  
Starting from the time instant \(t_{0}\) first the IF is estimated for the case \(t\)\(>\)\(t_{0}\). Let us initialize \(i=1\) and \(\hat{t}=t_{0}\). Then \(\hat{t}\) is updated as: \(\hat{t}=\hat{t}+T_{s}\), where \(T_{s}\) is the sampling period. To estimate the IF at \(\hat{t}\), we estimate \(f_{0}\) and \(\hat{\alpha}\) that maximize the spatially averaged correlations of multi-sensor signals with the time-shifted and frequency modulated analysis window as:  
\[(f_{0},\hat{\alpha})=\operatorname*{arg\,max}_{f_{A_{l}}}\sum_{k=1}^{M}\biggl{|} \int e^{-\beta\pi ft}x_{k}(t)w_{\alpha_{l}}\bigl{(}(t-\hat{t})\bigr{)}dt\biggr{|}, \tag{13}\]  
The \(\hat{f}_{i}(t)\) at \(\hat{t}\) becomes: \(\hat{f}_{i}(\hat{t})=f_{0}\). Note that search for the maximum frequency is restricted around \(f_{0}-\Delta f\leq f\leq f_{0}+\Delta f\) and search for the rotation order, i.e., \(\hat{\alpha}\) is restricted around \(\alpha_{0}-\Delta\alpha_{0},\alpha_{0},\Delta\alpha_{0}\) where \(\Delta\alpha_{0}=\frac{1}{2L+1}\). This restriction of the search space significantly reduces the computational cost. The process is repeated until \(\hat{t}<T\), where \(T\) is the duration of the signal. We can employ a similar approach for IF estimation at \(t\)\(<\)\(t_{0}\).
## 2 Overall RF Front-End System  
Figure 1 shows the system architecture of the proposed receiver front-end. The proposed work is targeting a 30-40 GHz frequency range broadband input signal with single-ended configuration. The two-stage stagger-tuned low noise amplifier (LNA) provides signal amplification while providing broadband operation bandwidth. The second stage of LNA is loaded with an on-chip transformer, which performs single-to-differential signal conversion. Balun operation in this work is essential not only for its robustness to common-mode noise and interference but also for its good receiver stability. A 30-40 GHz RF signal is then down-converted to a DC-10 GHz IF frequency band by an active push-pull type mixer loaded by 3 rd-order LC ladder filter. An IF signal is delivered to an off-chip 50 \(\Omega\) load through the common-source configured output buffer.  
Figure 1: Receiver front-end architecture: stagger-tuned two-stage low noise amplifier (LNA), down-conversion mixer loaded with 3-stage LC ladder filter, LO buffer, and output buffer.
## 5 Discussion
Another challenge with these documents is that they include email addresses and other identifiable information that must be cleaned up to avoid improperly influencing classification. One way to deal with the aforementioned document noise issue, and potentially improve prediction performance, is to use pretrained word embeddings which learn semantic relationships between words on a much larger document corpus through self-supervised training. As further discussed in Section 2.2, GloVe trained on general documents, as well as Word2 Vec and BERT models trained on legal documents, have been developed and proven useful on other problems [46; 65] or a version trained on legal corpus. However, as shown in Section 4.1.2, existing pretrained embeddings were missing important technical and legal terms necessary to analyze patent litigation. Future work in this area should focus on developing a broader pretrained embedding that can reflect both the technical and specialized legal content required for patent litigation, which is distinct from other forms of litigation as well as patent drafting or non-legal documents. Finally, as machine learning and artificial intelligence methods are applied to the legal field, the academic and practitioner communities must attend to ethical issues. Because lawyers have a personal duty to their clients, offloading tasks to a computer risks breaching that duty [2; 103]. Moreover, from the onset of computational methods for analyzing and predicting litigation outcomes, scholars have questioned whether computers can compete with a lawyer's professional skills and experience [104]. It is unclear how similar computational reasoning models may ever be to human legal reasoning for legal analysis [105]. Explainable and interpretable machine learning and deep models are crucial for practical applications to litigation. This is particularly so where the analysis of litigation outcomes is used to consult clients, given the need for openness and transparency [106].