esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### InSAR  
The InSAR method is a powerful active remote sensing system deployed on satellites designed to acquire high-resolution images of the Earth's surface using microwave radiation [1]. SAR data consist of two essential components: amplitude, representing the intensity of the returned signal, and phase, encoding the relative distance and characteristics of the signal. These components form the basis for advanced analytical techniques such as InSAR, which has revolutionized the monitoring of ground movements and subsidence. InSAR operates by performing interferometric analysis on SAR images acquired at different times. Comparing the phase information of two SAR images generates interferograms that reveal phase differences caused by variations in the wave path to specific ground targets. These differences correlate with surface movements during the acquisition interval, enabling precise surface subsidence measurements at the mm scale. A variant of InSAR, called Persistent Scatterer Interferometry (PSI), further enhances the technique by focusing on stable scatterers over time, allowing for even more accurate long-term subsidence monitoring. Consequently, InSAR enables precise surface subsidence measurements, offering a robust method for detecting natural geophysical processes and anthropogenic ground displacements. InSAR has transformed the ability to monitor surface subsidence over large areas with millimeter-scale precision. It utilizes satellite platforms like Sentinel-1A/B, TerraSAR-X, and Capella, which provide high spatial resolution and frequent revisit intervals, facilitating continuous ground motion monitoring [2]. These attributes make InSAR a critical tool for early warning systems, detecting subsidence, structural instabilities, and other geohazards [3; 4]. The technique has demonstrated its efficacy in various applications, including identifying subsidence caused by groundwater depletion [5], frost-heave cycles [6], and karst-related ground motion [7]. Moreover, InSAR has been instrumental in assessing slope stability in mountainous highways [8] and monitoring thermal-induced subsidence in bridges [9]. While InSAR excels in surface monitoring, its inability to detect subsurface anomalies, such as voids or cavities, represents a significant limitation. This gap underscores the need for complementary methods like GPR and ERT, which provide detailed subsurface imaging. An integrated approach links surface subsidence data with underlying geological or structural causes, offering a more comprehensive understanding of geohazards and ground subsidence processes.
# Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs
## 1 Introduction
Even though [19] provides an extensive review of magnetic susceptibility measurements applications for reservoir characterization, most of the discussed results were based on laboratory rather than logging measurements. As a consequence, to our knowledge, this type of borehole logging tools can not be found for commercial use in oil industry nowadays. In this paper we propose to overcome this lack of direct measurements by estimating the magnetic susceptibility of the rocks along the borehole using the magnetic data acquired by the magnetometer included in the orientation tools of imaging logs.
## 4 Results
### Stability Assessment  
The 16 critical profiles can be seen in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Susceptibility Category**} & \multicolumn{8}{c}{**Stations in Rock**} \\ \cline{2-9}  & **GS02** & **GS04** & **GS08** & **GS10** & **GS13** & **GS14** & **GS15** & **GS16** \\ \hline III & & 15.0 & 15.00 & 15.0 & & & \\ \hline IV & 16.0 & & & 17.5 & 17.5 & 17.5 & 17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Assessment of detachment susceptibility in rock mass.  
Figure 8: Zoning plan according to susceptibility to landslide and detachment.  
Figure 9(a),b shows examples of profiles (CP12, Figure 9) in the SLIDE program in static and pseudostatic conditions. For the stability assessment, the geotechnical parameters of Table 16 were used, which were obtained from the results of Table 11. In some profiles, colluvium's presence was taken into account--its values of density, cohesion and friction angle present in a previous work were used [33].  
Table 17 shows the comparison of the susceptibility analysis and the safety factors (SF) of the stability analysis in the SLIDE program (obtained from critical profiles in Figure 5). The safety factor was found to be between 0.76 and 2.64. Regarding susceptibility levels, the profiles CP10-CP16 range from high to very high susceptibility, while the profiles CP01-CP09 range from low to high.  
Figure 10: _Cont._  
Figure 9: Critical profiles for stability assessment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density (kN/m3)** & **Cohesion (kPa)** & **Friction Angle (Degrees)** \\ \hline Colluvium & 21.00 & 10.00 & 25.00 \\ Shale & 26.00 & 14.00 & 42.00 \\ Sandstone & 28.00 & 20.00 & 38.00 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Geotechnical parameters used in stability analysis. Colluvium data from [33].  
Figure 10: CP12 profile results in (**a**) static condition and (**b**) pseudostatic condition.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Critical Profile** & **Susceptibility Coefficient (SC)** & **Static Condition (SFstatic)** & **Pseudostatic Condition** \\ \hline CP01 & Low to high & 2.19 & 1.71 \\ \hline CP02 & Low to high & 3.66 & 2.64 \\ \hline CP03 & Low to high & 1.62 & 1.25 \\ \hline CP04 & High & 1.36 & 1.25 \\ \hline CP05 & High & 1.42 & 1.12 \\ \hline CP06 & High & 1.59 & 1.21 \\ \hline CP07 & High & 2.26 & 1.64 \\ \hline CP08 & Medium to high & 2.85 & 2.14 \\ \hline CP09 & Medium to high & 2.15 & 1.59 \\ \hline CP10 & Very high & 1.15 & 0.76 \\ \hline CP11 & Medium to very high & 2.07 & 1.16 \\ \hline CP12 & High & 1.25 & 0.82 \\ \hline CP13 & Medium & 1.51 & 0.88 \\ \hline CP14 & High & 2.43 & 1.26 \\ \hline CP15 & High & 1.48 & 1.00 \\ \hline CP16 & Medium to high & 1.69 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Results obtained from the stability analysis considering the 16 critical profiles.
## 1 Introduction
### Organization  
The remainder of the paper is organized as follows. Section 2 gives the system model, including the V-MISO and V-MIMO, multiple assets in view, and channel modeling. Section 3 analyzes the path diversity, system outage probability, and investigates the ASER and the system capacity of the multiple HAP network. Section 4 shows the numerical simulations and results. Finally, Section 5 summarizes our conclusions and provides next steps to this work. Table 1 lists the primary notations used in the paper.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 1 Introduction
They are mostly based on dynamics, self detection, and machine learning. Machine learning is an application of artificial intelligence that provides a system with the ability to learn and improve from the experience automatically. Classification is one of the renowned approaches of data mining, which is a subset of machine learning. It separates data into different classes explicitly and helps in decision making [4]. According to the information on data, there are three categories of machine learning techniques:  
* Supervised learning: Data mining techniques are applied to data with a predetermined label of the classes. * Unsupervised learning: The techniques are applied to non-labeled data. Data are classified with no previous knowledge. * Semi-supervised learning: This is a hybrid of supervised and unsupervised learning. For the purpose of fault detection, Support Vector Machine (SVM), Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), Stochastic Gradient Descent (SGD), Random Forest (RF), and Probabilistic Neural Network (PNN) classifiers, as shown in Figure 1, are used to classify the sensed data into two cases, i.e., normal or abnormal case.
## 4 Results
### General Applications of RIP
#### 4.3.3 Neuromuscular Functioning Associated with Respiration  
Neuromuscular illnesses encompass a wide range of conditions that impact muscle function, either by affecting the voluntary muscles directly or by impacting the peripheral nervous system or neuromuscular junctions indirectly. Respiratory failure in neuromuscular illnesses might manifest as an abrupt occurrence or in a chronic, progressive manner [8]. Monitoring of respiratory activity in neuromuscular diseases can be expensive, time-consuming, and upsetting for patients. Better evaluation methods, such as in-home assessments, are being investigated. Assessment with PneuRIP\({}^{\text{TM}}\) might be a more efficient way to assess a patient's lung health. In [10], an outpatient clinic environment was set up and the efficacy of PneuRIP\({}^{\text{TM}}\) in adolescents with neuromuscular illnesses was evaluated and their RIP parameters were compared with those of age-matched healthy individuals. The PneuRIP\({}^{\text{TM}}\) technique has been used to successfully evaluate work of breathing (WOB) indices and respiratory patterns and has proven to be effective in recognizing abnormalities in WOB indices and breathing patterns in patients with neuromuscular illnesses. RIP can detect swallowing in both healthy and neurologically affected subjects, which is crucial for identification of choking and subsequent respiratory arrest [83; 32]. Difficulty in swallowing associated with neuromuscular pathology has been shown to be detectable with RIP with 95% sensitivity and 99% specificity [83]. Increased intercoastal muscle activity in patients with spinal cord injury at cervical level has been successfully monitored with RIP in comparison with normal subjects, which activity has been undetectable with electromyography (EMG) [84].
## 4 Experimental Results
### Ablation Experiment
#### 4.2.3 Ablation Experiment for Anomaly Score Judgment  
In Tables 4 and 5, SpoofScore\({}_{\mu-\sigma}\) is used as the anomaly judgment score, which improves the anomaly judgment performance of the model. This subsection conducts ablation experiments on the previously designed different anti-spoofing scores, as shown in Table 6. It can be seen from the table that \(\sigma Score\) has poor performance as an anomaly judgment score. However, by introducing \(\mu Score\) to complement \(\sigma Score\) and jointly conduct anomaly judgment on the input images of the model, it can be observed that the performance of SpoofScore\({}_{\mu-\sigma}\) is better than simply calculating the distribution mean \(\mu\) and variance \(\sigma\) of the test samples. When the attack presentation classification error rate (APCER) is 10%, SpoofScore\({}_{\mu-\sigma}\) is 2% higher than the single anomaly score \(\mu Score\) and 4.5% higher than \(\sigma Score\) in terms of the AUC. Compared with the judgment based on the reconstruction distance, the spoof score proposed in this section shows a stronger discriminative ability in the anti-spoofing task.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Contribution Weight** & **EER** & **AUC** & \(\mathbf{BPCER_{10}}\) \\ \hline Single-path reconstruction & \(\times\) & 0.1676 & 0.9034 & 0.1781 \\ \hline + Single-branch segmentation & \(\times\) & 0.1552 & 0.9326 & 0.1224 \\ \hline + Single-branch segmentation & \(\surd\) & 0.1227 & 0.9381 & 0.1678 \\ \hline + Three-branch segmentation & \(\times\) & 0.1020 & 0.9579 & 0.0964 \\ \hline + Three-branch segmentation & \(\surd\) & **0.0668** & **0.9748** & **0.0562** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study on weighted module in segmentation network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **EER** & **AUC** & \(\mathbf{BPCER_{10}}\) \\ \hline Transformer baseline & 0.1865 & 0.9028 & 0.2267 \\ \hline CNN baseline & 0.2158 & 0.8674 & 0.3253 \\ \hline + Variational structure & 0.1176 & 0.9588 & 0.0938 \\ \hline + Self-attention & 0.1533 & 0.9251 & 0.1786 \\ \hline + VAM & **0.0668** & **0.9748** & **0.0562** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study on variational attention module.
## 4 Segmentation Performance Evaluation
### Spatial Area-Based Metrics  
Spatial area-based metrics compare the semi-automatic segmented regions with the manually segmented ones (\(\mathcal{R}_{A}\) and \(\mathcal{R}_{T}\), respectively) by calculating the overlapping percentage of area between the two masks obtained from the segmentation of the image \(\mathcal{I}\). Recalling some basics on statistical decision theory measures, the regions containing 'true positives' (TP), 'false positive' (FP), 'false negatives' (FN), and 'true negatives' (TN) are defined as:  
\[\mathcal{R}_{\text{TP}} =\mathcal{R}_{A}\cap\mathcal{R}_{T}\] \[\mathcal{R}_{\text{FP}} =\mathcal{R}_{A}-\mathcal{R}_{T}\] \[\mathcal{R}_{\text{FN}} =\mathcal{R}_{T}-\mathcal{R}_{A}\] \[\mathcal{R}_{\text{TN}} =\mathcal{I}-\mathcal{R}_{T}-\mathcal{R}_{A}\]  
When validating the segmentation results, the two most used area-based metrics are the Dice Index (DI) and the Jaccard Index (JI), defined in Equations (20) and (21), respectively. DI and JI are used to describe how much similar the manual (ground-truth) and the semi-automatic segmentations are: the greater they are, the higher is the overlapping percentage between the two masks.  
\[\text{DI}=\frac{2\cdot\mathcal{R}_{\text{TP}}}{\mathcal{R}_{A}+\mathcal{R}_{T}} \tag{20}\]  
\[\text{JI}=\frac{\mathcal{R}_{A}\cap\mathcal{R}_{T}}{\mathcal{R}_{A}\cup\mathcal{ R}_{T}} \tag{21}\]  
Sensitivity and specificity--defined in Equations (22) and (23)--represent the portion of positive pixels (foreground) and negative pixels (background) correctly detected by a segmentation method with respect to the ground-truth, respectively.  
\[\text{Sensitivity}=\frac{\mathcal{R}_{\text{TP}}}{\mathcal{R}_{\text{TP}}+ \mathcal{R}_{\text{FN}}} \tag{22}\]  
\[\text{Specificity}=\frac{\mathcal{R}_{\text{TN}}}{\mathcal{R}_{\text{TN}}+ \mathcal{R}_{\text{FP}}} \tag{23}\]  
False Positive Ratio (FPR) and False Negative Ratio (FNR)--defined in Equations (24) and (25)--denote the presence of false positives and false negative compared to the reference region, respectively.  
\[\text{FPR}=\frac{\mathcal{R}_{\text{FP}}}{\mathcal{R}_{\text{FP}}+\mathcal{R }_{\text{TN}}}, \tag{24}\]\[\text{FNR}=\frac{\mathcal{R}_{\text{FN}}}{\mathcal{R}_{\text{FN}}+\mathcal{R}_{ \text{TP}}}. \tag{25}\]
## 5 Implementation and Experiments
### Assessment through Personalized Content
#### 5.2.1 Experiment of Personalizing Foundations for Different Proficiency Levels  
This experiment generates foundational material for a topic for a student at level 1, i.e., novice, and for another student at level 10, i.e., subject expert. The foundational material generated from GPT for the student at level 1 is shown in Figure 11.  
Figure 10: User interface of the eLearning system.  
As shown in the figure, the foundation only includes a preliminary concept of 'loop' constructs including 'for' loop. This content is self-explanatory, and the example is easy to understand and intuitive.  
Now, the foundational material generated from GPT for the student at level 10 is shown in Figure 12.  
The generated foundation presents a summary of basic constructs including 'for', while', and 'break'. And it also presents advanced techniques for using loop constructs, and understanding the given example requires some basic proficiency in Python language. This foundational content should reasonably challenge students at level 10 but will be too challenging for beginners to understand.  
Figure 11: Foundational material from GPT for a student at level 1.  
Figure 12: Foundational material from GPT for a student at level 10.  
Through the two contents of foundational material generated for different levels of students, we can clearly observe that the GPT content for the foundations is well personalized for each proficiency level.
# A Simplistic Downlink Channel Estimation Method for NB-IoT
## 1 Introduction  
Narrowband Internet of Things (NB-IoT) is a low-power wide-area network (LPWAN) technology designed to enable efficient machine-to-machine (M2M) communication. It emerged from the LTE technology as LTE-NB1 and LTE-NB2 standards, introduced in 3 GPP specifications Release 13 and 14, respectively, [1, 2]. It is assumed that NB-IoT user equipment (UE) will operate for several years without battery replacement. Thus, NB-IoT devices require a special design to maintain low-power consumption. This refers not only to optimal hardware implementation, but also to the development of efficient and low-complexity physical layer processing algorithms. It is especially important to optimize downlink procedures, as receive algorithms are generally more complex than transmit ones. Well-designed algorithms should be kept simple, still providing acceptable receive quality in challenging propagation conditions. IoT devices are likely to be installed in locations with a low signal-to-noise ratio (SNR), like basements, underground facilities, factory sites, etc. [3].  
One of the fundamental procedures of downlink signal processing is channel estimation, which provides information about the distortion of the received signal, caused mainly by multipath fading, carrier frequency offset and phase noise [4, 5, 6, 7]. This information is further utilized in the channel equalization step, which compensates for channel impairments to provide phase-amplitude alignment of a complex constellation for further demodulation and decoding. To make channel estimation possible, the transmitted signals contain reference elements (pilots) which are known to the receiver. Channel distortion of these elements may be estimated and extended to other components of the received signal.
# Improving Data Communication of Enhanced Loran Systems Using 128-_ary_ Polar Codes
## 1 Introduction
The main contributions of this paper are as follows:  
* The design of a 128-_ary_ polar code that is compatible with the 128-_ary_ PPM employed in the eLoran system;
* The proposal of an SS demodulation method, based on a correlation receiver, designed for 128-_ary_ PPM to provide the requisite soft information for the 128-_ary_ polar decoder;
* A demonstration that the proposed 128-_ary_ polar code with SS demodulation achieves an error performance gain of approximately 9.3 dB compared to conventional eLoran data encoding (RS codes with conventional demodulation). The origins of this significant gain are analyzed in the subsequent sections. The remainder of this paper is organized as follows. Section 2.1 details the conventional eLoran data encoding and modulation methods, while Section 2.2 presents the design of the 128-ary polar code for the eLoran system. Section 3 provides the simulation results comparing error performance. Finally, Section 4 discusses the findings and concludes this paper.
## 1 Introduction  
Follicular lymphoma (FL) is the second-most common non-Hodgkin lymphoma (NHL) worldwide, accounting for about 35% of all adult lymphoid neoplasms [1]. The clinical and histological features of FL are greatly variable. Most cases present with nodal disease and are histologically characterized by a nodular to nodular and diffuse proliferation of atypical B cells with germinal center (GC) phenotype. The neoplastic population includes small- to medium-sized centrocytes and variable numbers of large nucleolated centroblasts. Over 85% of FLs carry the t (14;18) (q32;q21), which leads to the juxtaposition of the _BCL2_ and _IGH_ genes and to the overexpression of the anti-apoptotic protein Bcl2 [1]. This genetic event confers a survival advantage to the B cells and (together with other molecular derangements) promotes neoplastic transformation [1].  
Aside from such a classical presentation, clinical-pathological variants of FL are recognized [2], including site-specific entities (e.g., testicular and duodenal-type FL), pediatric forms (i.e., pediatric-type FL), and tumors with unique histological and cytogenetic features (i.e., diffuse FL with 1p36 deletions lacking the _BCL2_ translocation) [2, 3]. Rare histological presentations also include FL with Hodgkin/Reed-Sternberg (HRS)-like cells [3], Epstein-Barr virus (EBV)-positive FL [4] and Castleman disease-like FL [5]. In all such instances, this diagnosis is based upon a high degree of suspicion and on careful histological evaluation.  
In our practice, we faced a further misleading presentation of FL, characterized by inguinal lesions with extensive necrosis, HRS-like cells and areas of high-grade evolution. Such an unusual picture is not documented in the literature and expands the spectrum of necrotizing nodal lesions. To increase awareness of this entity, we present the clinical-pathological features of four inguinal necrotizing FLs, specifically addressing their differential diagnosis and possible pathogenic mechanisms.
# The Complex Relationship between Virulence and Antibiotic Resistance
## 5 Biofilms Enhance the Transmission of Virulence and Antibiotic Resistance
### Horizontal Transfer of Virulence Genes  
Similar to the transfer of antibiotic resistance genes, virulence genes can be acquired via a variety of mechanisms, including spontaneous mutations and deliberate exchange of genes through horizontal gene transfer. Horizontal gene transfer allows the transmission of genes both between individuals of a single species and between multiple species. Of course, such a transfer is facilitated by proximity; thus, when bacterium convert from planktonic, free floating individuals, to a sessile collective, transfer via plasmids is hastened (Figures 2 and 3) [197; 62; 198]. In a study of the role of plasmid-mediated horizontal gene transfer of antibiotic resistance, samples of the gut microbiome from hospitalized patients and healthy volunteers, 46 genes were identified. Multiple commonalities were identified among the individual patients. Surprisingly, one antibiotic resistance gene was also identified in a single healthy volunteer. Unfortunately, the study was unable to identify the source of resistance (i.e., nosocomial or normal flora) [199]. In addition to horizontal gene transfer due to proximity, other environmental factors (e.g., acidic or alkaline environments) also play a role in the transfer of genes, as evidenced by the increase in tetracycline resistance genes in wastewater sludge when grown under acidic environmental conditions, revealing an increased potential for a horizontal gene transfer of tetracycline resistance genes located on a plasmid [200].