esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Loose Ends  
The Relay system developed through this research may serve as a pivotal stride towards achieving tangible IoT interoperability, a feat that remains largely elusive in contemporary settings. The Relay, designed with an emphasis on simplicity, ease of deployment, usability, and efficacy, empowers a broad spectrum of users to implement it with minimal complexities, thereby accelerating the realization of genuine IoT harmonization.  
Through a proper selection and integration of hardware, software, and infrastructure components, including the FiPy module, the NB-IoT infrastructure and the HA ecosystem, our research met its technical objectives. At the same time, we are facilitating the real, deep integration IoT needs to become a usable part of society's daily life.  
The Relay's design, highlighted by its straightforward yet thoughtfully intended implementation, underscores its innovative approach to dismantling the barriers that currently hinder seamless IoT communication. Immediate next steps involve a deeper characterization of the Relay, including time responses and latency variations due to its presence, eventual packet loss and error rate, and performance stability. So far, we carried out qualitative, naked eye evaluation procedures where it seemed to perform as expected; however, further, more precise tests must be realized, such as those carried out in [14].  
Figure 7: Two custom templated sensors natively displayed in the HA.  
Deployment of communication relays like this, as well as the adoption of standardized IoT protocols, will gain market interest in the long term. Although big players and industry leaders may initially be reticent to forego their control on proprietary systems, open ecosystems end up driving larger market and investment figures, with new, often unexpected relations among different players. This may eventually lead to enormous expenditures in the research and innovation of such fields, which big players and investors may also take advantage of.  
However, the IoT is evolving rapidly, which may be an issue from a standardization perspective, as the diverse and evolving nature presents a challenge in establishing timely harmonious, global standards. To help overcome these barriers, stakeholders like standardization bodies and industry leaders must keep collaborating to foster IoT harmonization, thereby unlocking the full potential of IoT interoperability and connectivity.  
By providing a clear pathway for the ad hoc development and deployment of such relays in real-world scenarios, these findings may lay a foundation for future advancements in IoT interoperability, highlighting the synergy between diverse technologies, tools, and software systems in overcoming the complexities of IoT ecosystems.
## 8 Performance Evaluation
### Handoff Overhead
#### 8.2.2.1 Location Update Signaling Cost  
The dotted line in Figure 22 shows a possible path followed by a MN when it enters and exits a PAN. Figure 23 shows the LU cost (in bits) incurred due to the MN's movements along the same path, at different speed. It shows that as the speed increases, LU cost does not change. This is due to the fact that even if the speed of the MN increases, it associates with the same number of SNs that lay in its path [34]; and thus these SNs send the same number of LU messages. Also, the signaling cost of DLoWMod to send an LU message is less than that of LoWMod. This is because for DLoWMod, an LU message travels less number of hops to reach the GW. Also the MSPs in DLoWMod limit the frequency of sending LUs to GW.  
Moreover, it can be seen that the signaling overhead of the proposed schemes is significantly less as compared with HMIPv6. This is because the adaptation layer packets have been used for sending LU, instead of IP packets (as in the case of HMIPv6). In HMIPv6 the size of the LU message is 68 bytes [33].
# Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series
## 3 Data and Methods
### One-Class Support Vector Machine  
One-Class Support Vector Machine (OCSVM) [48] is a supervised, one-class classification method based on the SVM framework. It only requires training data from the target class and, when used with the Radial Basis Function (RBF), the setting of two parameters: y and \(\
u\). Like other SVMs, it uses a kernel function to map samples into a higher dimensional feature space. In a binary SVM, a hyperplane is then generated that seeks to maximize margins between the hyperplane and the closest training samples. The separating hyperplane is the decision boundary between the two classes, and the closest training samples are the vectors supporting it. In an OCSVM, the decision boundary tries to delineate the samples of the target class from the origin in the mapped feature space. The y parameter determines the width of the RBF kernel, whereas \(\
u\) is the lower limit of the amount of support vectors and the upper limit of the fraction of training samples that are classified as outliers. Tuning of the parameters is usually more difficult than in other SVMs due to the unavailability of samples from the outlier class [66].  
Figure 3: Temporal profile of TS\({}_{\text{EVI}}\) and TS\({}_{\text{LSWI2130}}\) of two rice paddies ((a) Lake Xingkai, Heilongjiang province; (b) Lu’an, Anhui province), a corn field ((c) Bohai Bay, Tianin province) and aquaculture ponds ((d) Dongting Lake, Hunan province).  
For our OCSVM model, we scaled all input samples by scaling to unit variance and removing the mean, so that no feature can dominate the learning and classification process. The classification was performed using the _scikit-learn_ package (see Appendix A). We chose to collect samples from the target, as well as from the outlier class to overcome the OCSVM parameter selection problem and perform a grid search to select the optimal values. A grid search was performed using 25% of our _rice_ and 50% of our _no_rice_ reference data. As the optimization criterion, we chose the f1-score as a balanced measure between recall and precision, which is an appropriate measure for imbalanced classification tasks [66], and the RBF kernel, which is a standard kernel suitable for classification tasks [45; 67]. The best set of parameters for the RBF kernel was determined with a parameter grid of 200 linearly-spaced y values ranging from y = [0.025,..., 0.5] and v values v = [0.05, 0.075, 0.1, 0.125, 0.15], which resulted in a grid search over 1000 parameter combinations. The model with the best performance was then used to classify paddy rice areas throughout China for the years 2002, 2005, 2010 and 2014. The resulting classifications are referred to as OCSVM\({}_{2002}\), OCSVM\({}_{2005}\), OCSVM\({}_{2010}\) and OCSVM\({}_{2014}\), respectively, and have a value of one where paddy rice was detected as at least one crop during that year and a value of zero for all remaining pixels, including masked ones.
# On the Use of Deep Learning for Video Classification
## 3 Video Classification
### Video Data Modalities  
As compared to images, videos are more challenging to understand and classify due to the complex nature of the temporal content. However, three different modalities, i.e., visual information, audio information, and text information, might be available to classify videos in contrast to image classification, where only a single visual modality can be utilized. Based on the availability of different modalities in videos, the task of classification can be categorized as a uni-modal video classification or a multi-modal video classification, as summarized in Figure 2. The existing literature has utilized both of these models for the video classification task, and it is generally believed that models utilizing multi-modal data perform better than the models based on uni-modal data [20, 21]. Moreover, the visual description [22] of a video works better than the text [23] and the audio [24, 25] description for the classification purpose of a video.
## 2 Atmosphere Channel  
### Scintillation Index with Aperture Averaging  
The scintillation index in the receiving aperture is an important coefficient to describe the fluctuation of the optical signal. For satellite-to-ground optical downlink, when the height of the satellite is over 20 km, the scintillation index can be expressed as [19]:  
\[\sigma_{I}(d)=8.70k^{7/6}(H-h_{0})^{5/6}\sec^{11/6}(\zeta)\times\text{Re}\int_{ h_{0}}^{H}C_{n}^{2}(h)\bigg{[}\Big{(}\frac{kd^{2}}{16(H-h_{0})\sec\zeta}+i\frac{h-h_{ 0}}{H-h_{0}}\Big{)}^{5/6}-\Big{(}\frac{kd^{2}}{16L}\Big{)}^{5/6}\bigg{]}dh \tag{1}\]  
\(k\) is the wave number of the optical signal and it is defined as:  
\[k=\frac{2\pi}{\lambda} \tag{2}\]  
\(d\) is the diameter of the receiving aperture. \(\lambda\) is the wave length. \(H\) and \(h_{0}\) are the altitude height of the satellite and the receiver. \(\zeta\) and \(L\) are the zenith angle of the satellite and the distance from the ground receiver to the satellite. \(C_{n}^{2}(h)\) is the refractive-index structure parameter. Here, the Hufnagel-Valley (H-V) model is employed as [19]:  
\[C_{n}^{2}(h)=0.00594\Big{(}\frac{w}{27}\Big{)}^{2}\Big{(}10^{-5}h\Big{)}^{10} \exp(-\frac{h}{1000})+2.7\times 10^{-16}\exp(-\frac{h}{1500})+A\exp(-\frac{h}{10 0}) \tag{3}\]  
\(h\), \(w\), and \(A\) are the altitude, the rms windspeed and the nominal value of \(C_{n}^{2}(0)\). The rms wind speed depends on the ground wind speed and the slew rate associated with the satellite moving relative to the observer on the ground. \(A\) can be estimated by the altitude of the ground station. If \(d\) in Equation (1) is 0 (point receiver case), Equation (1) reduces to the scintillation index described as [18; 19]  
\[\sigma_{I}=2.25k^{7/6}\sec^{11/6}(\zeta)\int_{h_{0}}^{H}C_{n}^{2}(h)(h-h_{0})^ {5/6}dh \tag{4}\]
## 4 Matrix Converters in the Connection of Renewable Energies to the Grid
### Matrix Converter Applications for Integration of Photovoltaic (PV) Systems
#### 4.1.2 Unidirectional 3 \(\times\) 2 Matrix Converter Topology Application  
The CSI topology is equivalent to an unidirectional 3 \(\times\) 2 matrix converter and can be considered as such. For the connection of PV arrays to the SG, this single-stage unidirectional topology, as shown in Figure 11, has several advantages compared to the more conventional VSI and it has regained attention in the last years [56; 57]; it can be noted that the input/output voltage gain of the matrix converter can be beneficial in this application since it allows to boost the PV voltage allowing more flexibility on the PVs' configuration [58].  
The electrolytic capacitor used in VSI topologies is widely considered to be the least reliable component in PV systems. Using the 3 \(\times\) 2 matrix converter a dc link inductor is used, instead of the electrolytic capacitor, for increased robustness, reliability and longer lifetime of the PV system [57; 59; 60; 61]. Also, a filtered DC link current is more suited to a PV system application as it reduces the stress on the PV arrays [57; 59; 60]. In addition, the 3 \(\times\) 2 matrix converter has an inherent voltage step-up capability with a minimum admissible transfer ratio of 1.155 which increases the PV array voltage range and allows the grid voltage level to be matched without the need for extra conversion stages [58; 62]. This single-stage topology improves the reliability of the system and reduces the cost and losses [59; 63].  
Figure 10: Typical application of matrix converter in the integration of renewable energy to the grid.  
Novel configurations have been proposed in the literature to avoid earth leakage currents into the grid without using a bulky and costly isolation transformer [57; 64; 65].  
This unidirectional \(3\times 2\) matrix converter topology can be used to compensate PQ issues in the connection of PV arrays to the SG with current harmonics compensation [54] and fault voltage ride-through (FVRT) capability [66]. However, the compensation capability of a shunt-connected converter is limited, therefore PV converter topologies including shunt and series connections to the SG for current and voltage compensations have been considered in the literature [67; 68; 69]. This leads to a unified power quality conditioner (UPQC) topology [70] where the PV array is connected to the dc link.
## 5 Related Works
Research has been conducted to overcome this problem, and techniques such as triangulation techniques and semi-supervised methods have been proposed; however, there is still a need for strong cross-lingual learning [39]. To overcome the need of usingcross-lingual learning, the authors of [39] propose a novel solution to train NMT models. They suggest relying on a monolingual corpus for entirely unsupervised model training. The authors used a standard model architecture: an encoder-decoder system with attention mechanisms where the encoder and decoder contained a two-layer bi-directional recurrent neural network, and the attention mechanism used was a global attention method with the general alignment function [39]. Three critical aspects allowed the MT system to be translated with an unsupervised approach, and this included a dual structure, a shared encoder, and fixed embeddings within the encoder. Furthermore, two strategies allowed the NMT system to predict translations in a monolingual corpora, which would have been otherwise impracticable since the authors did not use a parallel corpus. First, they use the principle of denoising autoencoders that train the system to reconstruct a corrupted input to its original form. More specifically, they switch the word order of the input sentences so that the system can learn to retrieve the correct order [39]. Next, they use an adjusted on-the-fly back-translation method so that given a particular input sentence in the source language, the system can use inference mode with greedy decoding to translate the input to the target language [39]. After conducting both automatic and human evaluations, the experiments conclude that there is a significant improvement in translation over a baseline system that performed word-by-word substitution. The system was also able to effectively model cross-lingual relations and output accurate and excellent quality [39]. They further showed that moving on from a strictly unsupervised case by incorporating a small parallel corpus has the potential to further improve the quality of translations [39]. Another translator that successfully utilizes neural network architecture for translation is DeepL. DeepL is a neural machine translation service that advertises its enhanced performance compared to competitor tech companies such as Google Translate and Microsoft Translator. It separates itself from the competitors by improving the neural network methodology in four different areas: the network architecture, training data, training methodology, and the size of the network [9].
## 4 Discussion
Additionally, deep learning has been applied to predict the SAR values at the tip of conductive leads along clinically relevant cardiac and DBS paths during 1.5 T and 3 T MRI [58; 59; 60]. This achieves a fast method for estimating the safety of patients with only knowledge of the lead's geometry and MRI RF coil features, enabling the study of the impact of the implant's placement and imaging conditions. Nevertheless, this methodology is constrained to homogeneous human models, leading to variations in the predicted electric field compared to its actual counterpart within the human body. This work has several limitations. Initially, the investigated paths refer to CIEDs, such as pacemakers (PMs), implantable cardioverter defibrillators (ICDs) and cardiac resynchronization therapy (CRT) devices, for which the transvenous implantation is used (endocardial systems). Therefore, this work does not include epicardial systems, which are stitched directly to the myocardium and connected to an IPG placed in the abdomen. Moreover, the extracted electric fields along the paths were normalized to an established safety limit (SAR or \(\text{B}_{1}{}^{+}\text{RMS}\)) intended for implant-free patients and not always suited for patients with implants [50]. Another limitation of our study is that it focused on the induced electric field along the cardiac paths considering the different dielectric properties of the tissues surrounding the implanted devices without calculating the subsequent RF heating. Since the different thermal properties of tissues could induce high temperatures even in tissues with low deposited power, complimentary work is required. Furthermore, since the values of electric fields within the six anatomical models varied widely due to their different morphological and anatomical characteristics, and as local quantities can vary more than 10 dB depending on the anatomy [61], studying a larger number of models could cover the patient population in any safety evaluation.
# Machine Learning Applications and Uncertainty Quantification Analysis for Refload Tests
## 2 Methods and Data Preparation
### Machine Learning Model
#### 2.3.3 Deep Neural Networks  
DNNs, composed of multiple hidden layers between the input and output, can estimate the unknown underlying function that maps inputs to outputs of the given datasets. Our DNN model employed the Adam optimization algorithms to justify the learning rates [PERSON] et al. (1984). Furthermore, the output of SPACE prediction's accuracy is positive real numbers. Therefore, the ReLU activation function was documentationally recommended to be most appropriate [PERSON] et al. (1984). Moreover, the mean squared error (MSE) as the loss function to track the accuracy of the DNN during the training process was deployed; the selected hyperparameters of the DNN model are presented in Table 3.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Quantity** & **DNN** & **LR** & **RF** \\ \hline Number of hidden layers & 2 & - & - \\ Number of nodes in the hidden layer & 256, 128 & - & - \\ Loss function & MSE & MSE & MSE \\ Activator & ReLU & - & - \\ Optimizer & Adam & - & - \\ Training samples & 20,000 & 20,000 & 20,000 \\ Testing samples & 4000 & 4000 & 4000 \\ Number of trees & - & - & 200 \\ Random seed number (random state) & - & - & 10 \\ Number of epochs & 200 & - & - \\ Validation split & 10\% & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: The selection of the hyperparameters in the machine learning models.  
Figure 2: The dependency in the regression tasks: (**a**) the linear dependency, (**b**) the nonlinear dependency. The red line indicates the linear fitting method and the black dots represent the data to be fitted.  
It can be revealed that the DNN predictions may have some noisy results even after sufficient epochs when the MSE reaches an acceptable value. To mitigate these noises, the ModelCheckpoint in Keras [38] during the training process was utilized. This allowed us to capture the best accuracy achieved, ensuring that the best prediction of DNNs could always be obtained despite the presence of noise.
# Context-Aware Systems Architecture in Industry 4.0: A Systematic Literature Review
## 3 Methodology
### Review Procedures  
Figure 1 shows the flowchart according to the PRISMA methodology and then describes the steps explicitly.  
1. We identified records by using the Google Scholar search tool. Search filters were applied, including a specific date range, a preference for the English language, and all types of articles, including review articles. At this stage, a NodeJS script automated the extraction of 2751 records (see Supplementary Materials for the dataset). Expression: (architecture) (\"industry 4.0\" OR \"industrie 4.0\") \"context aware\"
2. Records for which full access to the content was not possible, did not provide an abstract, or were not classified as journals or conference articles were automatically excluded. After this step, 649 records were ignored.
3. The titles of each record were automatically analysed, and we excluded 378 records that included the words \"review(s)\", \"survey\", and \"systematic\". At the end of this step, 1724 records remained.
4. Records that explicitly included terms associated with other application domains and unrelated to the manufacturing sector were excluded from the review. Considering that this systematic review focused on the I4.0 paradigm, we eliminated records that  
Figure 1: Flowchart of the systematic review.  
\begin{table}
\begin{tabular}{c} \hline \hline
**Criterion** \\ \hline Articles without access to the full version or an abstract \\ Articles associated with other domains \\ Articles that do not explicitly incorporate the context-aware concept or do not \\ describe an architecture \\ \hline \hline \end{tabular}
\end{table}
Table 3: Exclusion criteria.  
did not include the concepts associated with and identified by [PERSON] et al. [29] and the term \"context\" in the title or abstract. After this process, 1463 records were eliminated, leaving 261 that were subjected to manual evaluation.
5. At this stage, we first manually read the abstracts of each article identified, and then we read the articles that required a more in-depth analysis for decision-making. We excluded articles classified as literature reviews (30), articles focused on areas of study that did not contribute to the design of the architecture and the respective discussion of the context-aware concept (119), articles related to other domains of applicability (63), articles with the absence or duplication of software architectures (12), and articles with no discussion of the concept of context (8), and articles (4) were also excluded due to the impossibility of obtaining relevant information for the state-of-the-art review. In the end, 25 articles remained.
## 3 The Main Parameters and Indices of MOT
### Conventional Performance Indices
#### 3.2.3 Atomic Temperature  
The temperature of cold atoms determines the velocity distribution of atomic groups. When atoms escape from the MOT with transverse velocity, thermal diffusion occurs, affecting subsequent test performance [37]. The temperature of the cold atom is determined by the cooling mechanism and influenced mainly by the laser intensity and the detuning.  
A common method for determining the temperature of cold atomic groups is the time-of-flight method [46]. This method involves allowing the confined cold atomic group to fall freely under the influence of gravity, irradiating the falling atomic group with a detection laser beam and receiving a pulse signal containing velocity distribution information of the cold atomic group on the photodetector. By fitting the measured velocity distribution with Formula (9), the temperature of the cold atomic group can be obtained.  
\[\Delta v=2\sqrt{\frac{2k_{B}T}{m}\ln 2} \tag{9}\]  
where \(\Delta v\) is the velocity distribution width of the half-high value.