esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 5 Numerical Results
### Impact of \(\rho_{a}\) on the Authentication Performance  
We show in Figure 7 ROC variations under the settings of (SNR = 10 dB, \(N_{r}\) = 128, \(L\) = 20, \(k_{\phi}\) = 2 dB, \(k_{\theta}\) = 2 dB, \(k_{\alpha}\) = 2 dB) at different correlation coefficients of path gain. From Figure 7, we can see that the authentication performance (\(P_{f}\), \(P_{d}\)) under \(\rho_{a}\) = 0.9 outperforms that of the others, and the performance under \(\rho_{a}\) = 0.7 is the poorest. This is because a larger \(\rho_{a}\) means that the communication entities move slower. When \(P_{f}\) is fixed, \(P_{d}\) increases when \(\rho_{a}\) increases. Moreover, the authentication performance (\(P_{f}\), \(P_{d}\)) under \(\rho_{a}\) = 0.9 can satisfy the practical authentication requirements.  
Figure 6: Performance comparison between the scheme in [16] and our scheme with the settings of (SNR = 5 dB, \(N_{r}\) = 128, \(L\) = 20, \(k_{\phi}\) = 2 dB, \(k_{\theta}\) = 2 dB, \(k_{\alpha}\) = 2 dB).  
Figure 7: ROC curves under different correlation coefficients of path gain with the settings of (SNR = 10 dB, \(N_{r}\) = 128, \(L\) = 20, \(k_{\phi}\) = 2 dB, \(k_{\theta}\) = 2 dB, \(k_{\alpha}\) = 2 dB).
## 3 Results and Analysis
### Effect of Steel Grade on Ultimate Strength
It can be clearly seen that, for stiffened plates with large column slenderness ratios, the ratio \(\Delta\sigma_{U}/\Delta\sigma_{Y}\) is, in general, small, i.e., increasing the yield strength (using higher grade of steel) has little effect on improving the ultimate strength. In comparison, increasing the yield strength can effectively increase the ultimate strength of stiffened plates with small column slenderness ratios, and the ratio \(\Delta\sigma_{U}/\Delta\sigma_{Y}\) approaches 1. For stiffened plates with very small column slenderness ratios, the ultimate strength may even linearly increase with the yield strength. For column slenderness ratios \(0.4<\lambda<0.8\), the ratios of the ultimate strength to the yield strength \(\Delta\sigma_{U}/\Delta\sigma_{Y}\) are somewhat dispersed, as shown in Figure 12. This is due to the different buckling collapse modes associated with the combinations of plate and stiffeners of various sizes. In general, the ratios of the ultimate strength to the yield strength of the stiffened plates are between 0.2 and 0.8, with a relatively higher distribution around 0.4. This implies that, for common stiffened plates in ships, the average effectiveness of increasing the yield strength on improving the ultimate strength of the stiffened plates is around 0.4. In other words, for most of the ship stiffened plates, the ultimate strength will increase by 40 MPa if the yield strength is increased by 100 MPa.
## 3 Results and Discussion
### Volatile Compound Analyses
#### 3.3.2 Acids  
Concerning the analysis of volatile acids in wine samples, several compounds reveal substantial variations in concentration among the three samples (Table 4). Wines that were inoculated with the Zb-A19Y5 strain showed the highest concentration of propanoic acid and propanoic acid, 2-methyl, which imparts an acidic, cheesy aroma, of octanoic acid, with its characteristic waxy notes, and finally oleic acid, which offers a slightly fatty aroma [64]. Meanwhile, the wine that was fermented with the Tc-A9Y2 yeast strain shows its significant differences in its hexanoic acid concentration, known for its pungent, sweaty odor, and octanoic acid, with its characteristic sweaty notes. Finally, the wine that was inoculated with the Zb-K29Y2 strain has the largest concentration of hexadecanoic acid, a fatty acid with the potential for a subtle wax or candle-like aroma, while pentadecanoic acid can contribute to a creamy and waxy aroma. These variations underscore the intricate and diverse nature of wine's flavor and aroma, reflecting the complex interplay of these compounds in defining the sensory experience. It has been noted that fatty acids can influence the complexity of wine aromas, and this influence is contingent on the concentration of acids in the wine [65], although it is not linked to the overall quality of the wine [66]. For instance, at concentrations ranging from 4 to 10 mg/L, fatty acids may contribute to enhancing the complexity of wine, but the elegance of the wine aroma could be negatively impacted when their concentration exceeds 20 mg/L [67].  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Zb-A19Y5** & **Tc-A9Y1** & **Zb-K29Y2** \\ \hline
1-Butanol, 3-methyl-(Isoamyl alcohol) & 376.23 \(\pm\) 4.42 a & 534.38 \(\pm\) 39.20 ab & 604.33 \(\pm\) 76.8 b \\
1-Hexanol & 7.15 \(\pm\) 1.64 a & 5.58 \(\pm\) 1.03 a & 10.21 \(\pm\) 0.11 a \\
1-Hexanol, 2-ethyl- & 4.72 \(\pm\) 2.30 a & 6.59 \(\pm\) 3.06 a & n.d. \\ Phenylethyl Alcohol & 246.63 \(\pm\) 15.09 a & 276.16 \(\pm\) 2.00 a & 319.92 \(\pm\) 36.05 a \\ Behenic alcohol & 2.7 \(\pm\) 0.13 & n.d. & n.d. \\
3,3,6-Trimethyl-1,4-heptadien-6-ol & 2.68 \(\pm\) 0.20 & n.d. & n.d. \\ Tryptophol & 21.21 \(\pm\) 0.81 b & 20.62 \(\pm\) 0.80 b & 8.76 \(\pm\) 0.74 a \\ Ethanol, 2-phenoxy- & n.d. & n.d. & 0.78 \(\pm\) 0.09 \\ \hline n.d.: not detected. & & & \\ \end{tabular}
\end{table}
Table 3: Higher alcohols (mg/L) of wines produced in pilot scale fermentations (\(\pm\)SD) with different non-_Saccharomyces_ strains (Zb-A19Y5, Tc-A9Y1, and Zb-K29Y2). Different letters in each row indicate the significance of One-way Analysis of Variance (ANOVA), which was followed by [PERSON]’s post hoc test (\(p<0.05\)).
###### Abstract  
The study presents the first application of the Connected Linear Network (CLN) package implemented in MODFLOW-USG to an existing Ground-Source Heat Pump (GSHP) system. The numerical element was specifically adapted by the authors in a previous study to simulate vertical Borehole Heat exchangers (BHEs) and is here applied for the first time to evaluate the heat transfer in Milano subsurface induced by a GSHP system. The evaluation of interference between geothermal systems and wells is an important topic, especially in densely populated areas, which has scarcely been explored in the literature. Specifically, the aim is to evaluate the thermal perturbation and the possible interference between BHE systems and the drinkable water wells of the Armi pumping station managed by MM S.p.A. The simulation results show moderate groundwater thermal perturbation: approximately 3 \({}^{\circ}\)C at 100 m downgradient of the borefield and, furthermore, a limited impact (maximum 1 \({}^{\circ}\)C) in just two wells of the Armi pumping station. After 3 years of GSHP system operation, the thermal perturbation can extend for kilometers, but with limited variation in groundwater temperature (lower than 1 \({}^{\circ}\)C). Although the predicted groundwater temperature variation is not critical, the real-time monitoring of temperatures coupled with numerical modeling is essential to prevent thermal interference and optimize GSHP system performance.  
Keywords:groundwater; ground-source heat pump system; numerical modeling; heat transport modeling; borehole heat exchanger; shallow geothermal resource; drinkable water wells +
Footnote †: journal: Applied sciences
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 4 Experimental Setup
### Implementation Details  
The input features of the network are 23-dim Mel-frequency cepstral coefficients (MFCCs), and pitch features were appended, which were extracted from 25 ms frames with 10 ms overlap, mean-normalized over a sliding window of up to 3 s. The energy-based voice activity detection (VAD) is applied to filter out nonspeech frames.  
The MUSAN [47] and RIRs [48] are used as noise sources to enhance the training data, and the amount of training data is doubled. The augmentation method followed the same recipe described in [PERSON] toolkit [49].  
We used the ASV-Subtools toolkit [50] for our experiments; all models are trained with a cosine learning rate decay scheduler with the Adam optimizer, the learning rate set to 0.001 and probabilistic linear discriminant analysis (PLDA) [51] for back-end scoring without score normalization.  
To prevent overfitting, we applied a weight decay on all weights in the model of \(1\times 10^{-3}\). The mini-batch size for training is 64.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **Layer Context** & **Total Context** & **Input \(\times\) Output** \\ \hline frame1 & \{t\(-\)2,t+2\} & 5 & 26 \(\times\) 512 \\ frame2 & \{t\(-\)2,t,t+2\} & 9 & 512 \(\times\) 512 \\ frame3 & \{t\(-\)3,t,t+3\} & 15 & 512 \(\times\) 512 \\ frame4 & \{t\} & 15 & 512 \(\times\) 512 \\ frame5 & \{t\} & 15 & 512 \(\times\) 1500 \\ statistics pooling & \{0,T\} & T & 1500 \(\times\) 3000 \\ segment6 & \{0\} & T & 3000 \(\times\) 512 \\ segment7 & \{0\} & T & 512 \(\times\) 512 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The embedding TDNN architecture. x-vectors are extracted at layer segment6.
# An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks
## 4 Theoretical Model for Our Fault-Tolerant WSN
Let us now consider a second scenario, in which a WSN is composed of a set of non-fault-tolerant clusters presented by the Cluster Heads (\(CH1,\ CH2,\ CH3\)) and one fault-tolerant \(FT_{BS}\). During time \([t_{1}-t_{2}]\), the set of clusters sense the field information and transmit data to the BS. At time \(t_{3}\), the BS receives, aggregates, and sends data to the end user. During the time \([t_{4}-t_{5}]\), Cluster Head \(CH1\) stops functioning due to energy depletion, causing cluster isolation, but \(CH2\) and \(CH3\) keep operating by sending data to the BS. At moment \(t_{6}\), \(FT_{BS}\) receives and transmits data to the end user. During time \([t_{7}-t_{8}]\), Cluster Head \(CH2\) depletes its energy and then stops; therefore, only one Cluster Head (\(CH3\)) collects and transmits the data packet to the BS. At moment \(t_{9}\), the Base Station exhausts its residual energy and is immediately replaced by its backup: \(FT_{BBS}\). The latter entity continues operating by receiving the collected data and transmitting the data packets to the end user. At moment \(t_{10}\), Cluster Head \(CH3\) exceeds its energy and hence, stops operating as \(CH1\) and \(CH2\). At that time, all clusters will be disconnected from the entire WSN and all environmental data will be lost. Therefore, _safety_ and _liceness_ have been violated in this scenario. Hence, we can conclude that to construct a _fault-tolerant WSN_, it has to be composed of a set of _fault-tolerant clusters_ and a _fault-tolerant Base Station_. \(\square\)
## 3 Results
### Step 0: Identify Case Study and Analysis Accident Data
#### 3.1.4 Categorization and Probability  
The primary purpose of analyzing the UAS accident and incident reports was to inform the risk matrix analysis. To this end, each report was classified according to the main contributing factors, namely Human Factors, Hardware, Software, Procedure/Policy, and Environment/Weather. This classification scheme was adopted to ensure compatibility and consistency in the analysis of data from both the AAIB and ASRS databases.  
In cases where the primary cause was not clearly stated or fell outside the predefined categories, the report was considered as noise data and excluded from further analysis. For incidents/accidents involving multiple contributing factors, a qualitative assessment was conducted to determine the primary cause based on the available information in the report. The results were presented in the following order:  
* Human Factors: 246 cases (Pilot error, inadequate training, fatigue, loss of situational awareness, etc.)
* Hardware: 80 cases (Mechanical failures, structural defects, engine malfunctions, loss of power, etc.)
* Procedure/Policy: 53 cases (Non-compliance with regulations, lack of standard operating procedures, etc.)
* Environment/Weather: 28 cases (Strong winds, turbulence, icing, bird strikes, etc.)
* Software: 22 cases (Autopilot glitches, navigation errors, software bugs, etc.)  
These priorities and categories are applied as weights in the risk matrix.
# The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment
###### Abstract  
Sinkholes are global phenomena with significant consequences on the natural- and built environment. Significant efforts have been devoted to the assessment of sinkhole hazards to predict the spatial and temporal occurrence of future sinkholes as well as to detect small-scale deformation prior to collapse. Sinkhole hazard maps are created by considering the distribution of past sinkholes in conjunction with their geometric features, controlling conditions and triggering mechanisms. Quantitative risk assessment then involves the statistical analysis of sinkhole events in relation to these conditions with the aim of identifying high risk areas. Remote sensing techniques contribute to the field of sinkhole hazard assessment by providing tools for the population of sinkhole inventories and lend themselves to the monitoring of precursory deformation prior to sinkhole development. In this paper, we outline the background to sinkhole formation and sinkhole hazard assessment. We provide a review of earth observation techniques, both for the compilation of sinkhole inventories as well as the monitoring of precursors to sinkhole development. We discuss the advantages and limitations of these approaches and conclude by highlighting the potential role of radar interferometry in the early detection of sinkhole-induced instability resulting in a potential decrease in the risk to human lives and infrastructure by enabling proactive remediation.  
sinkholes; geohazard; inventory; monitoring; prediction; SAR interferometry 1
## 3 Results
### The Hinge Region of ER\(\alpha\) is Required for its Mono-ADP-ribosylation by PARP7  
In order to confirm the mono-ADP-ribosylation of ER\(\alpha\)'s transactivating AF-1 domain, we performed a co-immunoprecipitation assay with three truncated variants of ER\(\alpha\) (Figure 7A). COS-1 cells were transfected with GFP-PARP7 and FLAG-ER\(\alpha\) variants ABC, ABCD, and CDEF. Since only CDEF contained a ligand binding domain, the samples were not treated with E2. As expected, we did not observe any mono-ADP-ribosylation in ER\(\alpha\) CDEF, which lacked the A/B domain (Figure 7B). Surprisingly, no mono-ADP-ribosylation was detected in the ABC variant, although it contained the AF-1 domain. We did, however, detect mono-ADP-ribosylation of the ABCD variant, with an intensity comparable to the DMSO-treated sample in Figure 5A. This could imply that the D domain, or hinge region, is required for the modification to occur on ER\(\alpha\).  
Figure 7: The hinge region of ER\(\alpha\) is required for mono-ADP-ribosylation by PARP7. (**A**) A schematic representation of the truncated variants of ER\(\alpha\). (**B**) PARP7 co-immunoprecipitationoprecipitated with all three ER\(\alpha\) variants. Only ER\(\alpha\) ABCD was mono-ADP-ribosylated. COS-1 cells were transfected with GFP-PARP7 and either 3 xFLAG-ER\(\alpha\) ABC, 3 xFLAG-ER\(\alpha\) ABCD, 3 xFLAG-ER\(\alpha\) CDEF. Co-immunoprecipitation was carried out with anti-FLAG, and membranes were blotted with anti-FLAG, anti-GFP and anti-ADP-ribose.
## 4 The Proposed New SMO Variant
### The SMO Basic Algorithm
#### 4.1.3 Global Leader Phase (GLP)  
In this stage of the SMO algorithm, which starts immediately after the LLP, all members of the spider monkey group change their positions using information received from both the Global Leader and local group members. This SMO step generates a new global spider monkey solution as follows:  
\[X_{SM_{new}}^{i,j}=X_{SM}^{i,j}+rand(0,1)\times\left(X_{GL\ SM}^{i}-X_{SM}^{i, j}\right)+rand(-1,1)\times\left(X_{SM}^{r,j}-X_{SM}^{i,j}\right) \tag{6}\]where \(X^{j}_{GL\;SM}\) represents the \(j\)th component of the global leader position, which is chosen randomly from the set \(j\in\{1,2,\ldots\ldots\,\mathrm{D}\}\). The generation of \(X^{i,j}_{SM_{new}}\) follows a random selection mechanism based on a random value called \(prob_{i}\), which is calculated using the following formula [31]:  
\[prob_{i}=\frac{fitness_{i}}{\sum_{i=1}^{N}fitness_{i}} \tag{7}\]  
It is evident from Equation (7) that the spider monkey with a better fitness value has a greater chance of improving itself, where \(fitness_{i}\) represents the fitness value of the \(i\)th spider monkey. Furthermore, just as in the LLP, the existing solution \(X^{ij}_{SM}\) is replaced by \(X^{i,j}_{SM_{new}}\) if the new one is superior. Algorithm 2 provides a detailed explanation of the process of generating a new solution and updating the existing one in the GLP SMO algorithm.  
```
Input:\(prob_{i}\) Output:\(X^{ij}_{SM_{new}}\)
1:function SMO-GLP
2: for each member spider monkey\({}_{i}\in\) whole SM group do
3: if \(\mathrm{rand}(0,1)<prob_{i}\) then
4: Select a \(j\) index randomly from the set \(\{1,2,\ldots\ldots\,D\}\)
5: Compute a novel spider monkey solution \(X^{ij,j}_{SM_{new}}\) using (6).
6: if \(f_{obj}\Big{(}X^{ij}_{SM_{new}}\Big{)}<f_{obj}\Big{(}X^{ij}_{SM}\Big{)}\)then
7:\(X^{ij,j}_{SM}=X^{ij}_{SM_{new}}\)
8: end if
9: else
10:\(X^{ij,j}_{SM_{new}}=X^{ij}_{SM}\)
11: end if
12: end for
13:endfunction
```  
**Algorithm 2**Global Leader Phase (GLP)
## 1 Introduction
Thus, this work explores the most recent knowledge and development achieved in the field of targeted retrieval of cloud microphysical parameters, starting from observations performed in the VIS, IR, and MW wavelengths in either a stand-alone or complementary manner, with the aim of finding answers to the above-mentioned challenges. This review provides an overview of the techniques developed for retrieval with observations in each of the bands (MW and IR) individually, with their characteristics and main results. This work highlights the achieved improvements and discusses how some techniques have evolved, transformed, and merged to address specific challenges without going into the specific details of the individual strategies. This paper is organized as follows: After a brief introduction to the key quantities considered for cloud microphysics research, the methods used to retrieve these cloud properties from IR-only and MW-only satellite observations are reviewed in Section 2. Section 3 is dedicated to methods developed to integrate MW and IR observations in order to improve the accuracy of cloud products, and Section 4 summarizes the main cloud parameter databases and retrieval methods. Section 5 discusses the main points and presents conclusions, highlighting the gaps and new challenges found during this review, and it finally suggests desirable future research work in this field.
## 3 Materials and Methods
### Methodology: Hand-Object Interaction Detection  
In order to evaluate the usefulness of the depth estimation algorithms in a more realistic setup, we proceeded to use MiDaS for depth estimation in a task where the objective is to detect hand-object interactions based on, Section 3.4 (setup experiment B). We estimated Depth using the MiDaS Deep Learning model from the 2D RGB images to proceed with our analysis. Later, all recorded data were labelled for interactions, such as hand-cup and hand-apple. Therefore, the successes of interaction detection were measured by the type of depth data used by the algorithm. To determine the reliability of depth estimation by MiDaS and compare it with the results of RealSense, we used other Deep Learning models to detect the hand (MediaPipe [24]) and the objects (YOLOv3 [25]). MediaPipe detects 21 3D landmarks and YOLO provides a list of detected objects and the position of bounding boxes indicating their position in the scene. Therefore, in each frame, we compared the interaction between hand and object and check the two below criteria to report an interaction. Since we wanted to keep the analysis simple, we limited ourselves to two criteria, namely the difference in depth between the hand and the object and the difference in pixels in the 2D image. To simplify the measurement of depth, we average over the depth values of the pixels of a region smaller than the detected object (see Figure 8). So, we resized the bounding rectangle and created a smaller rectangle over the surface of the detected object and then average over the depth values of the pixels within the smaller rectangle. For the hand, we also selected three landmarks, drew a bounding rectangle over them, and averaged over the rectangular surface of the hand.  
Figure 8: Left: RGB image \(\mid\) Right: depth image based on Intel RealSense camera.  
Therefore, in each frame there are the results of hand and object detection, as well as the results of the depth difference between the hand and the object, and the pixel difference in the RGB image between the hand and the object. To decide which image is showing the interaction between the hand and an object, we set a threshold value for the depth difference in the RealSense system and also a different value for the threshold in the MiDaS system because they have different units. However, we chose the same value for the pixel difference because the RGB images are used in both systems are the same. We computed the precision, F1-score and sensitivity to compare the performance of a system using MiDaS and RealSense as our reference.
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
###### Abstract  
Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach.  
autonomous underwater vehicle; artificial intelligence; environmental monitoring; energy-aware path planning; wireless sensor networks; underwater communication 2024 IEEEexample:BSTcontrol