esa-sceva/satcom-chunk-collection huggingface dataset:
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.11. e-GOTHAM  
The project \"e-GOTHAM\", which involved participants from five countries, also addressed issues relating to grid management and integration of RES. The scope has been to divide the grid into microgrids with increased communication capabilities, which would enable collaboration between the different parts and with the overall power grid. Interoperability mechanisms have been addressed as well as algorithms for the coordination of the central and local controllers. The different elements under test, like prosumers, micro-grids, and hardware devices communicated through a middleware architecture, which has been constructed for the project's realization. The information exchanged has been formatted through the xml language. Semantic treatment of the information has taken place through a component of the middleware architecture, which has been specifically devoted for this scope. The project entailed also a Graphical User Interface through which the energy manager could configure energy sources and production plans in simulation algorithms, monitor and control maintenance plans, observe consumption data from smart meters installed in different locations [108; 109].
## 3 Results and Discussion
### Waveform Analysis  
#### 3.3.1 Principal Frequency Analysis  
After conducting FFT frequency statistics on the three types of strong dynamic load waves, it can be observed that fault-type mine seismic waves exhibit two main frequencies, roof-type mine seismic waves exhibit three main frequencies, while coal pillar-type mine seismic waves exhibit four main frequencies. The specific numerical values of the main frequencies can be found in Tables 3-5.  
Based on the statistical results, it can be observed that the first main frequency of the three types of strong dynamic loads is relatively concentrated. By using the first main frequency, the approximate frequency boundary lines for the three types of signals can be determined to be around 30 Hz and 60 Hz. If we use the first main frequency as the basis for identifying the type of mine seismic event, signals with main frequencies in the range of 0-30 Hz can be identified as fault-type dynamic loads, those in the range of 30-60 Hz as roof-type dynamic loads, and those in the range of 60-80 Hz as coal pillar-type dynamic loads.
## 3 Results and Discussion
### Nutrient Content
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**N Source**} & \multicolumn{6}{c}{**N Level**} \\ \cline{2-9}  & **N-50** & **N-70** & **N-90** & **Mean** & **N-50** & **N-70** & **N-90** & **Mean** \\ \hline \multicolumn{9}{c}{**Pot Cultivation**} \\ \hline NH\({}_{4}\)NO\({}_{3}\) & 78.4 \(\pm\) 6.4 **\({}^{\text{\text{\tiny{\text{\tiny{\text{\tiny{\text{\text{\tiny{\text{\text{\text{ \text{ \text{ \text{ \  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**N Source** & **N Level** & **N** & **P** & **K** & **Ca** & **Mg** \\ \hline \multirow{6}{*}{NH\({}_{4}\)NO\({}_{3}\)} & N-50 & 2.82 \(\pm\) 0.09 b\({}^{\rm{ce}}\) & 0.44 \(\pm\) 0.03 ab & 5.32 \(\pm\) 0.16 b\({}^{\rm{bc}}\) & 2.31 \(\pm\) 0.19 b\({}^{\rm{bc}}\) & 0.35 \(\pm\) 0.02 b\({}^{\rm{b-d}}\) \\  & N-70 & 3.24 \(\pm\) 0.09 d & 0.51 \(\pm\) 0.05 b\({}^{\rm{b-d}}\) & 5.53 \(\pm\) 0.20 b\({}^{\rm{b-e}}\) & 2.31 \(\pm\) 0.12 b\({}^{\rm{bc}}\) & 0.38 \(\pm\) 0.01 d\({}^{\rm{d-f}}\) \\  & N-90 & 3.87 \(\pm\) 0.03 e & 0.47 \(\pm\) 0.07 ad & 6.25 \(\pm\) 0.29 f  & 2.83 \(\pm\) 0.20 f  & 0.38 \(\pm\) 0.02 f  \\  & Mean & 3.31 \(\pm\) 0.44 B** & 0.47 \(\pm\) 0.06 AB & 5.70 \(\pm\) 0.46 b\({}^{\rm{BC}}\) & 2.48 \(\pm\) 0.30 AB & 0.37 \(\pm\) 0.02 b\({}^{\rm{b-d}}\) \\ \hline \multirow{6}{*}{Ca(NO\({}_{3}\))\({}_{2}\)} & N-50 & 2.89 \(\pm\) 0.09 b\({}^{\rm{bc}}\) & 0.47 \(\pm\) 0.01 a\({}^{\rm{c}}\) & 5.44 \(\pm\) 0.05 b\({}^{\rm{b-d}}\) & 1.73 \(\pm\) 0.02 a\({}^{\rm{c}}\) & 0.33 \(\pm\) 0.02 a\({}^{\rm{c}}\) \\  & N-70 & 3.34 \(\pm\) 0.16 d  & 0.51 \(\pm\) 0.01 b\({}^{\rm{d-d}}\) & 4.68 \(\pm\) 0.11 a\({}^{\rm{a}}\) & 2.50 \(\pm\) 0.05 b\({}^{\rm{b-d}}\) & 0.36 \(\pm\) 0.02 c\({}^{\rm{c}}\) \\  & N-90 & 3.73 \(\pm\) 0.20 e  & 0.54 \(\pm\) 0.02 cd  & 5.99 \(\pm\) 0.26 g  & 3.66 \(\pm\) 0.07 i  & 0.41 \(\pm\) 0.02 f  \\  & Mean & 3.32 \(\pm\) 0.38 B  & 0.51 \(\pm\) 0.03 BC  & 5.37 \(\pm\) 0.56 A  & 0.37 \(\pm\) 0.07 Ba  \\ \hline \multirow{6}{*}{NH\({}_{4}\)NO\({}_{3}\)} & N-50 & 2.87 \(\pm\) 0.25 b\({}^{\rm{ce}}\) & 0.62 \(\pm\) 0.03 e  & 5.78 \(\pm\) 0.40 c\({}^{\rm{f}}\) & 3.45 \(\pm\) 0.18 b\({}^{\rm{hi}}\) & 0.38 \(\pm\) 0.01 f\({}^{\rm{ef}}\) \\  & N-70 & 3.64 \(\pm\) 0.10 e  & 0.49 \(\pm\) 0.02 b\({}^{\rm{b-d}}\) & 5.85 \(\pm\) 0.27 c\({}^{\rm{f}}\) & 2.62 \(\pm\) 0.16 b\({}^{\rm{de}}\) & 0.46 \(\pm\) 0.02 g  \\  & N-90 & 3.83 \(\pm\) 0.12 e  & 0.50 \(\pm\) 0.01 a\({}^{\rm{d-d}}\) & 5.95 \(\pm\) 0.12 d\({}^{\rm{d-f}}\) & 3.18 \(\pm\) 0.09 b\({}^{\rm{sh}}\) & 0.53 \(\pm\) 0.01 b\({}^{\rm{h}}\) \\  & Mean & 3.45 \(\pm\) 0.45 B  & 0.54 \(\pm\) 0.06 C  & 5.86 \(\pm\) 0.30 C  & 3.08 \(\pm\) 0.07 D  \\ \hline \multirow{6}{*}{NaNO\({}_{3}\)} & N-50 & 2.78 \(\pm\) 0.12 b\({}^{\rm{b}}\) & 0.55 \(\pm\) 0.02 d  & 5.22 \(\pm\) 0.29 b\({}^{\rm{b}}\) & 3.10 \(\pm\) 0.01 f\({}^{\rm{g}}\) & 0.32 \(\pm\) 0.01 ab\({}^{\rm{b}}\) \\  & N-70 & 3.35 \(\pm\) 0.12 d  & 0.46 \(\pm\) 0.02 ab\({}^{\rm{ab}}\) & 5.36 \(\pm\) 0.36 b\({}^{\rm{bc}}\) & 2.76 \(\pm\) 0.20 b\({}^{\rm{de}}\) & 0.29 \(\pm\) 0.02 a\({}^{\rm{a}}\) \\  & N-90 & 3.75 \(\pm
## 2 The Normal-Mode Helical Antenna Design Considerations
\[\Gamma=\frac{\sqrt{\left(\mathrm{R-z_{0}}\right)^{2}+\mathrm{x}^{2}}}{\sqrt{ \left(\mathrm{R+z_{0}}\right)^{2}+\mathrm{x}^{2}}} \tag{3}\]  
where  
R: load resistance;  
Z\({}_{0}\): source characteristic impedance;  
X: impedance reactive components. Mismatch Loss = \(-10\) Log(\(1-\Gamma^{2}\))  
According to Figure 3, the helical antenna exhibits a very narrow operational bandwidth of around 1 MHz, as expected, due to mismatch losses. In this regard, the gain of the antenna is significantly reduced to \(-24\) dB at 30 MHz. Figure 4 presents the high-impedance plot of the helical antenna, which causes the load mismatch attenuation. A passive helical antenna will not function effectively in a wideband mode unless coupling losses caused by the complex impedance of the helical antenna are compensated [18; 19]. One approach is to employ a high-Q passive matching network to match the impedance of the helical antenna to 50 \(\Omega\) when it operates off the resonant frequency [20]. However, this may not be a practical approach for a broadband antenna, which must operate efficiently across the entire frequency range. An alternative approach is to convert the passive helical antenna into an active one. This involves employing a high-input-impedance low-noise amplifier (LNA) to compensate for the coupling losses of each helical antenna across a wide frequency range. Although there is a mismatch loss at 25 MHz between  
Figure 2: The C.P. radiation pattern of the helical antenna. the 50 \(\Omega\) impedance of the helical antenna and the high input impedance of the LNA, the power gain of the entire active system, which includes the gain of the beamformer active circuit, compensates for that loss, as discussed in Section 3.1. In the following section a detailed discussion on the high-impedance LNA design methodology is provided.
# Implementing Many-Lights Rendering with IES-Based Lights
## 4 Method  
The main goal of the paper is to investigate the feasibility of the introduction of photometric-based light sources in a real-time Many-Lights technique, thus considering the management and the effect of a non-uniform emission in the computation of both direct and indirect illumination components.  
In this section, we describe the main contributions of the paper. In particular:  
* we implemented a parser for the IES file format, which creates a polygon mesh representing the photometric solid of a luminaire. This mesh is then used in the rendering process to influence the direct and indirect illumination components;
* we extended the Reflective Shadow Maps technique [8] using cube maps in order to address the omnidirectional emission of point lights. The original technique as proposed in [8] considers only directional lights, using a standard 2D texture image to store the RSM data;
* we weighted the emission from the VPLs on the basis of the parsed data from the IES files. This affects not only the direct illumination component (leading to a more realistic rendering of the area and surfaces closer to the light source) but also the indirect illumination component because the placement of the VPLs is tuned by the actual volume of emission, which is no longer perfectly spherical but described by the data from a real luminaire (Figure 2).
# DIFshilling: A Diffusion Model for Shilling Attacks
## 2 Related Work
### GAN-Based Shilling Attacks  
GAN-based shilling attacks exploit the powerful generative capabilities of GANs to create fake user profiles or ratings, manipulating RS outcomes. Through adversarial training between a generator and a discriminator, GANs can synthesize realistic fake user profiles, which are injected into the RS to execute the attack [33]. The generator's objective is to produce fake data that are statistically similar to real user profiles or ratings, making it difficult for detection systems to identify them. Meanwhile, the discriminator is trained to distinguish between real and generated data. During training, the generator continuously improves its ability to generate indistinguishable data while the discriminator simultaneously enhances its detection accuracy. Recent studies have also integrated GANs for shilling attacks. [PERSON] et al. [20] modeled shilling attacks as a general-sum game between the RS and fake user generators, utilizing DCGAN [34] to generate fake userprofiles. AUSH [9] trained its generator and discriminator using a combination of reconstruction loss, shilling loss, and adversarial loss to consider user segments, attack cost, and detectability. Leg-UP [21] extended AUSH by applying more direct loss functions and leveraging a surrogate model, further enhancing attack transferability and invisibility. Defense mechanisms against GAN-based shilling attacks can be broadly categorized into detection-based defenses, robust learning algorithms, and adversarial training [23]. Detection-based defenses aim to identify and filter out malicious profiles using anomaly detection and behavioral analysis, though increasingly sophisticated attacks challenge their effectiveness. Robust learning algorithms enhance RS resilience by integrating noise-resistant and anomaly-aware techniques, mitigating the impact of adversarial inputs. Adversarial training further strengthens defenses by exposing models to attack-like patterns during training, improving their ability to recognize and counter manipulated data. A combination of these approaches enhances the overall robustness of RS against evolving adversarial threats.
## 1 Introduction  
Today's Internet architecture faces some well-known limitations and many ongoing research activities exist to define the so-called _Next Generation Internet_ (NGI). As security was not a major concern while developing the current architecture, it is agreed that it needs to be an integral part of future concepts. This is because the Internet has transformed from a communication means where users can exchange files and messages to the basis of today's economy with billions of participants.  
To compensate for the lack of security of the original design, many add-ons have been defined over time. Each newly identified security risk resulted in a new fix which was applied to one of the OSI layers. The outcome are many specific security solutions spread over all layers ranging from IPSec encryption at the network layer up to HTTPS in the application layer for example. One of the design goals for a Next Generation Internet architecture is to anchor the security functionality in the network layer to provide a clean and consistent approach for all participants without any need for further application layer security. Especially trust has always been an issue in today's communication infrastructure. Significant additional effort had to be put into establishing a trustworthy communication between two entities. Our approach for a Next Generation Internet is designed to support this from day one.  
The _Hierarchical Internet Mapping Architecture_ (HiiMap) [1] is a clean-slate approach based on the locator/identifier-split principle [2]. It is a highly scalable and reliable approach with built-in mobility support to also foster network-enabled mobile devices.  
Our approach for a Next Generation Internet not only takes into account the emerging needs for a more flexible communication architecture but also a fair and equal mapping scheme distributed over all participating countries [1]. This guarantees the continuity of the Internet as a global means of communication devoid of political and social disputes.  
In this article we outline our security approach for the HiiMap architecture and evaluate it according to the recommendations of the Internet Engineering Task Force (IETF) standard for Authentication, Authorization and Accounting (AAA) [3].  
The remainder of this article is structured as follows: Section 2 discusses related work before we give a brief overview of the HiiMap architecture in Section 3. In Section 4 we detail the security approach and give an evaluation of it in Section 5. Section 6 concludes this article.
## 3 Colloidal Silica Transport Through Soil Stratum
### Evaluation Method for CS Grouting Quality
#### 3.4.4 Unconfined Compression Strength  
A rudimentary unconfined compression strength was adopted by many researchers to evaluate the grouting quality and degree of stabilization achieved ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). It has been widely acknowledged that the unconfined compression strength is positively correlated with the cyclic strength for both liquefiable and improved soils. Since the unconfined compression test is easier to operate and handle, it has been adopted as an index indicator test to reflect the cyclic strength of the targeted soil specimen and further evaluate the coverage of CS grout. In addition, when larger particles exist in soil  
Figure 16: Checking grout quality using the methyl red indicator ([PERSON] et al., 2016).  
samples, the unconfined compression strength is difficult to obtain with an unconfined test. In this instance, an unconsolidated undrained (UU) triaxial test can be used to obtain the Mohr's stress circle. Then, the shear strength can be deduced from the failure line and finally the unconfined compression strength can be calculated by multiplying the shear strength by 2 [37].  
In summary, four methods, including the colored tracer chemical method, the silica content method, the chloride concentration method, and the unconfined compression strength method, were proposed in the previous literature to evaluate the coverage of CS grout and to make judgements concerning grouting quality. The colored tracer chemical method is the most simple and direct method, by which visual judgement can be conducted by color variation in the grouting process. However, CS concentration and grouting quality at a given place cannot be evaluated quantitatively with this method. The unconfined compression strength method can be applied to evaluate the grouting quality properly and quantitatively, however, the mechanical test, which is more complicated than the physical index test, needs to be performed here. Silica content and chloride concentration can be measured quantitatively by a simple physical index test, and they are perfect indicators of CS coverage. Therefore, the silica content method and chloride concentration method have been mostly adopted in experiments and practices to judge grouting quality.
## 1 Introduction
The proposed PLA approach is designed to mitigate spoofing attacks and unauthorized access by verifying sensor nodes based on their physical attributes. By combining RSSI, BL, and altitude, this method ensures the authenticity of sensor nodes during wireless communication. Integrating both machine learning (ML) models, such as LR, RF, and KNN, and deep learning (DL) models like MLP and CNN, enables the system to analyze the collected physical attributes and enhance detection accuracy for malicious nodes attempting to spoof legitimate sensors. The key contributions of this research are as follows:  
1. We introduce a multi-attribute PLA scheme for LoRaWAN-based WSNs, using RSSI, BL, and GPS-reported altitude to strengthen device authentication. This additional physical layer of security aims to reduce susceptibility to spoofing attacks in resource-constrained networks. 2. ML models, including LR, RF, and KNN, and deep learning models, including MLP and CNN, are applied to analyze physical attributes (RSSI, BL, and altitude), aiming to improve the accuracy of detecting nonlegitimate nodes attempting to impersonate legitimate sensors. 3. The PLA scheme is tailored specifically for low-power LoRaWAN-based WSN environments, evaluated using a partially synthesized dataset that integrates real-world RSSI values from the LoRaWAN at the Edge Dataset (LoED). This ensures that the dataset reflects practical conditions, making the scheme applicable to real-world scenarios like forest fire detection, where reliable, low-energy security measures are essential. 4. We provide a detailed evaluation of our proposed scheme, assessing its effectiveness in detecting spoofing attacks by using standard classification metrics such as accuracy, precision, recall, and F1-score. Additionally, we evaluate computational efficiency in terms of training time and memory usage, ensuring suitability for low-power LoRaWAN environments. The rest of this paper is structured as follows. Section 2 reviews related work on PLA schemes and the integration of ML and DL techniques. Section 3 outlines the methodology for developing the multi-attribute PLA scheme. Section 4 describes the implementation of the PLA scheme. Section 5 presents the evaluation of the models used for detecting spoofing attacks. Section 6 concludes the paper and discusses future work.
## 3 Method of Features' Extraction
### Passband Power  
The selection of the band used for identifying band power pBP was made based on the averaged spectrum for the recorded AE signals. Figure 3 presents an averaged spectrum for 1000 AE signals recorded and the spectrum of a selected signal fragment where no acoustic emission was observed. For both spectrums, the transfer function of the selected detector (R3\(\alpha\)), given in the calibration datasheet, was taken into account.  
The presented spectrum was used to select a signal band with a frequency ranging from 15 kHz-30 kHz for further analysis because the power of the signal recorded within the band increased significantly when acoustic emission occurred.
## 2 Study Area  
The SPOT6 study area is located on the high elevation, internally drained Altiplano-Puna Orogenic Plateau [28] in the Southern Central Andes at \(\sim\)24.5\({}^{\circ}\)S and 67\({}^{\circ}\)W (Figure 1A). The SPOT6 data are centered on the Pocitos Basin, about 100 km west of the Eastern Cordillera in northwestern Argentina, with a minimum elevation of \(\sim\)3600 m and enclosed by steep, high-relief mountains, such as the Nevado Queva with a peak elevation of 6140 m (Figure 1B). The Eastern Cordillera forms an orographic barrier on the windward, eastern sides, generating hyperarid conditions to the west [29; 30]. Vegetation cover is high on the eastern flanks of the Cordillera, and high fluvial erosion rates drive landscape evolution. In contrast, erosion rates on the Altiplano-Puna are low, and landscape evolution is partly controlled by diffusive processes [17; 31]. The basin hosts a large salt flat (Salar de Pocitos), covering \(\sim\)375 km\({}^{2}\). In contrast to its steep surroundings, the salar has no relief (Figure 1B,C) but does have large textural variations due to salt patches and wind-blowndust. This diverse environment makes the Pocitos Basin an ideal target for investigating the influence of image texture on stereo-derived DEMs on large areas of flat and mountainous terrain. Further, the absence of cloud and vegetation cover provides optimal conditions for studying the bare Earth's surface with optical remote sensing data, and, for this reason, we have carried out other DEM quality studies in this region [10; 15].
# Effects of Phase Shift Errors in Recurrence Plot for Rotating Machinery Fault Diagnosis
## 3 Results
### Phase Shift Effects on Multi-Source Functions
In conclusion, the velocity with phase shift is different from the velocity without the shift for functions with many vibration sources. The small phase shifts that were considered can represent just one of the errors generated from the integration of the acceleration or from the signal conditioning process. Another conclusion is that the phase plane has a high degree of sensibility to phase shift changes. The main remark is that, when there is more than one vibration source, the double integration of the acceleration can affect the phase plot due to the phase shift errors in trigonometric solutions.
###### Abstract  
Background: Processing whole-slide images (WSI) to train neural networks can be intricate and labor intensive. We developed an open-source library dealing with recurrent tasks in the processing of WSI and helping with the training and evaluation of neuronal networks for classification tasks. Methods: Two histopathology use-cases were selected and only hematoxylin and eosin (H&E) stained slides were used. The first use case was a two-class classification problem. We trained a convolutional neuronal network (CNN) to distinguish between dysembryoplastic neuroepithelial tumor (DNET) and ganglioloma (GG), two neuropathological low-grade epilepsy-associated tumor entities. Within the second use case, we included four clinicopathological disease conditions in a multilabel approach. Here we trained a CNN to predict the hormone expression profile of pituitary adenomas. In the same approach, we also predicted clinically silent corticotroph adenoma. Results: Our DNET-GG classifier achieved an AUC of 1.00 for the ROC curve. For the second use case, the best performing CNN achieved an area under the curve (AUC) of 0.97 for the receiver operating characteristic (ROC) for corticotroph adenoma, 0.86 for silent corticotroph adenoma, and 0.98 for gonadotropin adenoma. All scores were calculated with the help of our library on predictions on a case basis. Conclusions: Our comprehensive and fastai-compatible library is helpful to standardize the workflow and minimize the burden of training a CNN. Indeed, our trained CNNs extracted neuropathologically relevant information from the WSI. This approach will supplement the clinicopathological diagnosis of brain tumors, which is currently based on cost-intensive microscopic examination and variable panels of immunohistochemical stainings.  
brain; pituitary adenoma; dysembryoplastic neuroepithelial tumor; DNET; ganglioloma; deep learning; digital pathology; convolutional neural network; computer vision; machine learning; convolutional neural network; CNN