esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Diversity Indices of the Microbiome of the Striga-Infested Sampling Sites  
The microbial community of _Striga_-infected rhizosphere soil and bulk samples of the two sampling sites were evaluated using Pielou evenness and Shannon diversity. With Kruskal-Wallis one-way analysis of variance, alpha diversity of microbial communities indicated insignificant differences (\(p>0.05\)) between the five taxonomical groups (phylum, class, order, family, and genus) (Table 2). In addition, the PCoA analysis revealed the composition of the _Striga_-infected soil and bulk samples sites showed a more significant difference between Es1, Es2, Es3, and Ec compared to Ms1, Ms2, Ms3, and Mc (Figure 6). The PCoA inference was validated by analysis of similarity (ANOSIM) (\(p=\)0.01, R = 0.52).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & **Es1** & **Es2** & **Es3** & **Ec** & **Ms1** & **Ms2** & **MS3** & **Mc** & \(p\)**-Value** \\ \hline \multirow{3}{*}{Phylum} & Simpson\_1-D & 0.767 & 0.75 & 0.755 & 0.74 & 0.73 & 0.719 & 0.711 & 0.714 & \\  & [PERSON]\_H & 1.936 & 1.834 & 1.88 & 1.836 & 1.77 & 1.733 & 1.694 & 1.709 & 0.998 \\  & Evenness\_e\_H/S & 0.289 & 0.269 & 0.273 & 0.2613 & 0.24 & 0.2357 & 0.227 & 0.23 & \\ \hline \multirow{3}{*}{Class} & [PERSON]\_1-D & 0.672 & 0.658 & 0.641 & 0.657 & 0.79 & 0.77 & 0.797 & 0.783 & \\  & [PERSON]\_H & 1.651 & 1.603 & 1.564 & 1.684 & 1.94 & 1.878 & 1.981 & 1.929 & 0.976 \\  & Evenness\_e\_H/S & 0.2084 & 0.199 & 0.1911 & 0.215 & 0.28 & 0.262 & 0.29 & 0.275 & \\ \hline \multirow{3}{*}{Order} & [PERSON]\_1-D & 0.793 & 0.772 & 0.777 & 0.792 & 0.74 & 0.764 & 0.732 & 0.756 & \\  & [PERSON]\_H & 1.952 & 1.884 & 1.894 & 1.949 & 1.88 & 1.915 & 1.845 & 1.892 & 0.998 \\  & Evenness\_e\_H/S & 0.2817 & 0.263 & 0.266 & 0.281 & 0.26 & 0.272 & 0.253 & 0.265 & \\ \hline \multirow{3}{*}{Family} & [PERSON]\_1-D & 0.852 & 0.843 & 0.845 & 0.851 & 0.87 & 0.875 & 0.87 & 0.874 & \\  & [PERSON]\_H & 2.256 & 2.234 & 2.237 & 2.272 & 2.38 & 2.386 & 2.378 & 2.349 & 1 \\  & Evenness\_e\_H/S & 0.382 & 0.374 & 0.375 & 0.388 & 0.43 & 0.435 & 0.432 & 0.491 & \\ \hline \multirow{3}{*}{Genus} & [PERSON]\_1-D & 0.815 & 0.768 & 0.76 & 0.887 & 0.85 & 0.857 & 0.861 & 0.849 & \\  & [PERSON]\_H & 2.314 & 2.152 & 2.143 & 2.605 & 2.41 & 2.438 & 2.434 & 2.323 & 0.991 \\ \cline{1-1}  & Evenness\_e\_H/S & 0.405 & 0.344 & 0.341 & 0.541 & 0.45 & 0.458 & 0.456 & 0.408 & \\ \hline \end{tabular} \(p\)-Value was based on Kruskal-Wallis one-way analysis of variance. Es—Eruwa rhizosphere sample, Ec—Eruwa bulk (control) sample, Ms—Mbuzini rhizosphere sample, Mc—Mbuzini bulk (control) sample.  
\end{table}
Table 2: Alpha diversity indices of microbiomes across the sampling sites.
# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## 4 Comparative Experiment on Deep Learning Model Performance
### Determination of Training Epochs  
The training duration for the deep learning model encompassed approximately 53 min/200 epochs for both datasets, ORG and TRANS. Throughout this training process, the values of the loss function were recorded for both the training and the validation datasets at every epoch, as shown in Figures 8 and 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Image batch size & 4 \\ Learning rate & 0.0001 \\ Optimization algorithm & ADAM [47] \\ Convolutional network & ResNet—50 layer [48] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters of Faster R-CNN.  
Figure 8 shows the dynamic progression of the loss function across the ORG dataset. In Figure 8, it becomes evident that the classification and regression losses exhibit a tendency to converge harmoniously throughout the entirety of the epochs. However, an observation arises from the validation dataset's loss curves, as depicted in Figure 8, revealing divergence post approximately 100 epochs in the former, and a stagnation without noticeable decline in the latter. Consequently, a selection of 100 epochs emerges as the optimal training epoch for the ORG case. The analogous approach is applied to the TRANS dataset, as depicted in Figure 9. Here, similar trends of convergence in classification and regression losses within the training dataset are apparent throughout the epochal evolution. Notably, the validation dataset's loss curves follow a pattern similar to that observed in Figure 8, with divergence commencing around 50 epochs in Figure 9a and a sustained lack of discernible decrease in Figure 9b. Thus, a proper choice designates 50 epochs as the optimum training epoch for the TRANS case.
# Experimental Evaluation of the Packet Reception Performance of LoRa
## 5 Results and Analysis
### Parameter Setting of the Transmission Scheme
We assumed that the packet length of the data to be sent is 32 bytes, and substituted the PER under different parameters into Equation (12). The results show that the successful transmission time is the shortest when the node is configured with SF = 7, BW = 250 kHz, and CR = 4/5. We also proved the correctness of the conclusion through experiments. Figure 13b shows the result of the transmission time when the packet length is 32 bytes with different SNRs. Compared with the original parameter configuration, the communication time after parameter optimization is reduced by about 50%. Figure 13: _Cont._
## 2 Methods and Data
### Pseudo-Measurement Generation
#### 2.2.4 Matching the Datasets to Loads of the Network  
During state estimation, each load of the network is randomly paired with a dataset from the pseudo-measurement database, taking into consideration whether it is a residential or a controlled one. Each residential load's dataset is selected from the cluster whose consumption boundaries match the AAC of the load (load AACs are obtained from the network input data). The same method is applied for the input data of the load flow validation, with the exception that this input is selected from even week time periods, while DSSE input is from odd weeks.  
During the process of state estimation, measurements are defined for network nodes instead of individual loads; thus, measurement values corresponding to the same network node are aggregated. The uncertainty of the aggregated measurement was defined as a sum of the uncertainty of individual load measurements, employing a Gaussian Mixture Model.
# Experimental Investigation of Controlled and Uncontrolled Rectifiers for Low-Power Wind Turbines
## 3 Rectification Types  
The rectification process converts alternating current into a direct current using semiconductors such as diodes and thyristors or silicon-controlled rectifiers (SCRs) [18]. Although there are many topologies for rectifier circuits, the systematic study requires their classification based on some common characteristics. For example, rectification can be classified based on the number of AC phases and controllability [13; 19]. The classification of rectifiers is shown in the Figure 3.  
The wind turbine generator can have a single-phase or three-phase output. Single-phase systems are attractive because of their simplicity and reliability. However, three-phase generators are gaining popularity because they are more efficient and have higher power density [20]. The three-phase alternator can extract more power than a single-phase system for the same size. However, a three-phase rectifier needs many more semiconductor devices; therefore, overall losses and system reliability are less compared to a single-phase system. Rectifiers are further differentiated based on controllability [13]. Any rectifier can be uncontrolled, half-controlled, or fully controlled. Uncontrolled rectifiers use only uncontrolled power devices (diodes) to provide a fixed DC output. Uncontrolled rectifiers are the most straightforward and, therefore, the most widely used rectifier circuits [21]. They comprise only an input filter capacitor, semiconductor diodes, and an output filter. Therefore, their output cannot be regulated with varying AC input or because of the load reactance.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 5 Stream Classification Algorithms and Architectures
### Algorithms
#### 5.2.6 Support Vector Machines  
The Support Vector Machine (SVM) [89] is a well-known offline classification algorithm. SVMs are based on finding the maximum marginal hyperplane that separates training data of different classes (see Figure 7f). Finding the hyperplane is a convex optimization problem, which can be solved using the Lagrangian formulation. Using SVM algorithms in big data settings comes with complex calculations, making the traditional SVM unsuitable for the stream scenario. An approach that adapts the idea of the SVM algorithm to the stream classification problem is the Core Vector Machine (CVM) [147]. Here, a minimum enclosing hypersphere is used, which represents the divisive data records. Calculations are then based on the representative hypersphere instead of the original data records, making calculations more efficient. This idea forms the basis for other SVM-based algorithms such as StreamSVM [148]. Here, the CVM is extended so that one pass over the data instances is sufficient. The main drawback of the SVM is the low learning and prediction speed, such as the inability to react to changes in the data stream. Further, SVM models are extremely difficult to interpret [121]. Further, the performance of SVM-based classification models depends on a proper parametrization, e.g., choosing a suitable Kernel [89].
## 3 Circuit Description
### Dtc  
Figure 6 shows the block diagram of an RC delay-based digital-to-time converter. To support the fractional-N functionality of the PLL using the QE values obtained from the delta-sigma modulator, a carefully controlled resistor-capacitor (RC)-based digital-to-time converter (DTC) architecture is employed, ensuring stable and predictable delay characteristics across the supported resolution settings [21; 22]. This design incorporates a 10-bit control architecture for the DTC, with the control bits divided into two segments: the lower 5 bits (LSB) are binary encoded, while the upper 5 bits (MSB) use thermometer coding. This hybrid configuration combines the area efficiency of binary encoding with the linearity of thermometer encoding, thereby enhancing timing accuracy.  
The DTC supports three resolution settings: 1.1 ps, 750 fs, and 360 fs. These resolutions are achieved by adjusting the timing characteristics of the delay line through resistor switches. At the most precise 360 fs resolution, the DTC delivers a phase noise performance of approximately \(-150\) dBc/Hz at a 1 MHz offset, suitable for targeting high frequencies above 5.7 GHz in wideband PLLs. The 750 fs resolution setting provides a phase noise performance of \(-146\) dBc/Hz at a 1 MHz offset, targeting frequencies from 2.7 to 5.7 GHz. The 1.1 ps resolution setting offers a phase noise performance of \(-140\) dBc/Hz at a 1 MHz offset, covering frequencies from 2 to 2.7 GHz. The resolution and control bits of the DTC must be designed to match the period of the DCO and the quantiz  
Figure 5: Block diagram of delay-locked loop.  
of the DSM. For instance, if a second-order delta-sigma MASH11 with a QE range of two is used and the DCO period is 300 ps, the DTC must cover a range of 600 ps, which is twice the DCO period. The coverage range of the DTC is determined by multiplying the DTC resolution by two raised to the power of the number of bits, necessitating the selection of an appropriate DTC resolution for the frequency band. In RC slope-based DTC designs, the relationship between DTC resolution and phase noise indicates that lower resolution improves phase noise, thus the appropriate DTC resolution must be selected based on the application. The designed DTC exhibits an integral nonlinearity (INL) of 4.5 LSB, demonstrating efficiency in performance. These design specifications ensure that the DTC provides high accuracy and minimal phase noise, enhancing the overall performance and reliability of the wideband PLL system.  
The relationship between DTC resolution and phase noise in an RC slope-based DTC indicates that lower resolution improves phase noise. Thus, for high-frequency applications in wideband PLL designs, reducing the DTC resolution enhances PLL performance. With these design specifications, the DTC ensures high accuracy and minimal phase noise, contributing to the overall performance and reliability of the wideband PLL system.
# A Classification Tree for Modeling Ground Fractures from Subsidence
## 3 Modeling of Ground Cracking
### Exploration and Expression of Variables  
From geotechnical and geophysical campaigns (49 geotechnical boreholes, aerial photogrammetry of the area, and 31 measurements of environmental seismic noise), important parameters were defined. This information was mapped in 2D/flat units (squares of 121 m\({}^{2}\)) georeferenced to the center of each unit. The square is selected for the ease of translating its information into pixels and voxels. Water content W%, Arterberg limits (liquid limit, LL; plastic limit LP; and plasticity index, IP), number of blows of SPT NSPT (average for the first 35 m of the column), materials classification according to the USCS (Unified Soil Classification System), cohesion/friction values (average in the first 35 m), and the presence of a semirigid layer embedded in the clay matrix are the basic geotechnical inputs. Additionally, data concerning soft deposit thickness (clay strata), amplification, and soil period were included. The depth and slope of the basement that underlies the clay formation was also included. Some examples of these maps are shown in Figures 5-8. The included anthropic parameters are the housing level (number of floors and type of structure), the size of the streets (number of lanes), the position relative to the pumping wells, and the groundwater level. A summary of the inputs is listed in Table 1.
## 1 Introduction
#### Significance of Speaker Anonymization  
There are many contexts where speaker anonymization plays a crucial role:  
Figure 1: Preserving privacy strategies in cloud-based speech applications (from [17]).  
* **Privacy Protection and Ethical Data Use** The preservation of personal privacy is critical in a time when personal data are increasingly being stored digitally. If voice recordings are not sufficiently anonymized, they can be used for malicious activities such as identity theft and spying. Voice anonymization techniques allow voice data to be used while hiding the speaker's identity.
* **Legal Compliance** Organizations handling voice data are required to abide by the increasingly strict data protection regulations being introduced by governments and regulatory bodies. In order to guarantee that sensitive voice data are handled in a way that complies with legal requirements, voice anonymization can be a crucial part of compliance strategies.
* **Enhanced Security** Malicious actors may use exposed voice data to commit fraud or identity theft. The risks of data breaches and unauthorized access are reduced by anonymizing voice recordings.
* **Versatile Applications** In addition to protecting one's privacy, voice anonymization makes it possible to use voice data for a variety of purposes without sacrificing the data's security or confidentiality. This covers everything from enhancing call center and voice assistant functionality to supporting law enforcement organizations in protecting the privacy of their witnesses.  
In summary, removing voice prints from recorded audio data is crucial from a variety of technological and ethical points of view. Nevertheless, voice anonymization presents important ethical challenges. For instance, one key concern is the potential misuse of these techniques, such as in deepfake scenarios where malicious actors may use voice conversion to impersonate others. Striking a balance between the legitimate use of voice anonymization and preventing unethical use is a complex ethical dilemma. Furthermore, the issue of informed consent is paramount. When voice data are collected, individuals should be informed about how it will be used, including whether or not speaker anonymization will be applied.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 2 Geodetic and Satellites Input Data
### SRTM Model  
Map products derived from SRTM data were sampled to 1 arc-sec by 1 arc-sec grid (30 m by 30 m approximately), with a linear relative height error of less than 10 m [38]. We used a mosaic based on six SRTM Global Version 3 files, Digital Object Identifier (DOI) number://10.5066/F7 PR7 TFT. The SRTM model was used to estimate the elevation change over time (\(\Delta h\)) with respect to the ICESat profiles. To keep consistency between the data sets, elevation values were converted to WGS84 ellipsoid with EGM96 geopotential model [13]. The final product was projected according to UTM system, zone 18S, with a resolution of 30 m.
# Unobtrusive Vital Sign Monitoring in Automotive Environments--A Review
## 3 ECG Monitoring in a Car  
The electrocardiogram (ECG) is a biopotential, with the body floating with respect to the ground. Hence, its measurement requires at least two electrodes and a differential amplifier. The ECG reflects electrical and--to some extent--mechanical function of the heart muscle. It is fair to say that it is the most valuable physiological signal when it comes to heart diagnostics [18; 19].  
Classical ECG monitoring employs well-conducting adhesive electrodes made from silver with a thin silverchloride surface coating being in direct contact to the skin. Such electrodes are coupled to the body surface by well-conducting gels and are typically cable-bound, which prohibits regular use in non-clinical scenarios. Electrochemically, such electrodes are termed _non-polarizable_. The combination of a difficult to dissolve metal and its metal chloride make these electrodes less sensitive to ion concentration changes and hence their polarization is very stable. The electrode mainly behaves like a resistor.  
Figure 1: Overview of physiological sources, effects, unobtrusive and non-contact sensors, and obtainable vital signals, modified and extended from [3] (see Figure 1).  
Plain metal electrodes (like stainless steel) are different in that they represent _polarizable_ dry electrodes. While cheap and simple, their polarization depends on the salt concentrations of the surroundings (e.g., skin) and hence their polarization is not stable. Electrically, such electrodes mainly behave like capacitors.  
Low conduction or capacitive electrodes have no direct galvanic contact to the body. Typically, capacitive electrodes are separated from the body by cloth or other textile layers. As the measured ECG signals are at least one order of magnitude smaller than with galvanic electrodes, capacitive electrodes typically have an operational amplifier directly at the electrode. The compensation of common mode by an active driven ground is much more important than in the galvanic case.  
For the car, electrodes can be placed at various locations, including the steering wheel (conductive), the backrest (capacitive, both thoracic or lumbar) and the seat (capacitive). For reduction of common mode, driven ground electrodes can be added. While thoracic placement of low-contact electrodes has the advantage of a larger signal strength, this location may also be the one which is subject to the biggest motion artifacts. Figure 2 gives an overview of published electrode locations for heart rate monitoring in the car.
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 3 The Proposed VQoSRR Framework
### Network Model Architecture
#### 3.1.1 Application Plane  
* **Video Service**: This work evaluated the video performance using two different video formats, standard definition and high definition (HD ready falls into this category), all encoded with H.264/AVC. We developed video service applications (both client and server) in Python. The TCP protocol was used to exchange messages between client and server, while the RTSP and RTP over UDP protocols were used to transmit video data. A client application could request a video and specify its resolution and bitrate. However, the server streamed videos over the network using the GStreamer RTSP server. GStreamer is a GNU LGPL-licensed software library and application that allows the reading, converting, recording, editing, and streaming of audio and video files [28].
* **Policy Manager** is responsible for defining and reflecting policy rules to the controller and the queue manager. This paper defined these policy rules: 1. As thresholds for HD and SD video streams, packet loss rate and bandwidth were used as QoS parameters. 2. For different traffic types, this work developed three types of service categorization: the first group needs quality of service requirements to be met (called group A); the second group can accept acceptable performance guarantees (called group B); the third group does not require any QoS guarantees (called Best effort).
* **Queue Manager** allows the configuration of queues and ports, in addition to other characteristics.
# Microbial Colony Detection Based on Deep Learning
## 2 Related Work
### Microbial Colony Identification  
Microbial colony identification has been the focus of in-depth research, and a variety of methodologies have been investigated to achieve the accurate and automated recognition of microbial colonies. In the early stages of research, traditional non-deep learning methods were widely used for this purpose. These methods involved handcrafted features and classical machine learning algorithms to classify and count microbial colonies based on their visual characteristics [37; 38].  
However, with the emergence of deep learning, there has been a shift towards leveraging its powerful capabilities in microbial colony identification. In particular, CNNs have shown astonishing performance in picture identification tasks. Researchers have applied CNN-based approaches to classify and count microbial colonies, using CNNs' capacity to automatically extract discriminative features from data [39].  
Comparative studies have also been conducted to assess the effectiveness of deep learning methods in comparison to traditional approaches. These studies have highlighted the advantages of deep learning in achieving higher accuracy and robustness compared to traditional methods, while also acknowledging the insights provided by traditional feature engineering techniques [40].  
Overall, deep learning algorithms have changed the field of microbial colony identification by allowing for more precise and automated assessments. The combination of classical methodologies and deep learning advancements has significantly advanced the field of identifying microbial colonies, offering valuable solutions for applications in microbiology, agriculture, and healthcare.