esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Joint Resource Optimization for NOMA-Enabled Vehicular Networks
### Proposed Optimal Resource Optimization
#### 3.2.2 Formulation (Phase B): Transmission Power Control  
The Phase A formulation determines the optimal RSU-VU association, VU-channel assignment and NOMA-OMA switching using the binary decision variable \(\mathbf{X}\). While Phase A also estimates optimal power consumption, to satisfy the S(I)NR requirements, it does not perform actual power allocation. Given the output \(\mathbf{X}\), from Phase A, Phase B proceeds to allocate the optimal power for each user, ensuring that all quality of service (QoS) requirements are met. Specifically, Phase B incorporates the following constraint Equation (11), derived by rearranging the Shannon capacity formula, to fulfill the QoS requirements. In the following constraint, \(r^{h}_{u}\) and \(r^{l}_{u}\) represent the QoS requirement (bits per time slot) for VU \(u\) when classified as a high-power or low-power user, respectively, after Phase A. The term \(\Delta_{f}\) denotes the per-channel bandwidth. The rest terms including \(g_{bcu}\), \(p^{h}_{bcu}\), \(p^{l}_{bcu}\) and \(\sigma\) are referred as the same as before.  
\[\forall b,u:\quad\sum_{c}g_{bcu}\cdot p^{h}_{bcu}\quad\geq\Bigl{(}2^{r^{h}_{u }/\Delta_{f}}-1\Bigr{)}\Biggl{(}\sum_{c}g_{bcu}\cdot p^{l}_{bcu}+\Delta_{f} \times\sigma\Biggr{)}, \tag{11}\] \[\sum_{c}g_{bcu}\cdot p^{l}_{bcu}\geq\Bigl{(}2^{r^{l}_{u}/\Delta_ {f}}-1\Bigr{)}\Delta_{f}\times\sigma\]  
The Phase B formulation reuses some of the formerly defined constraints: Equations (5)-(7). The objective function of the Phase B formulation is also the same as that of Phase A, Equation (9). It is worth nothing that the second-phase problem for a particular RSU \(b\) is independent of the rest RSUs. Thus, for reducing the computational complexity, the second-phase problem can be solved in a distributed, per-RSU manner.  
In sum, the optimization problem P. (12) (i.e., Equations (12a) and (12b)) given below is the complete formulation of the Phase B solution we propose.  
\[\min_{\mathbf{X},\mathbf{P}} \sum_{b}\sum_{c}\sum_{u}\left(p_{b\text{c}u}^{h}+p_{b\text{c}u}^{l}\right)\] (12a) s.t. \[\text{constraints (\ref{eq:P1})-(\ref{eq:P1})}\] \[\forall b,c: \sum_{c}g_{b\text{c}u}\cdot p_{b\text{c}u}^{h} \geq\left(2^{\ell_{u}^{h}/\Delta_{f}}-1\right)\left(\sum_{c}g_{b\text{c}u} \cdot p_{b\text{c}u}^{l}+\Delta_{f}\times\sigma\right), \tag{12b}\] \[\sum_{c}g_{b\text{c}u}\cdot p_{b\text{c}u}^{l}\geq\left(2^{\ell_{ u}^{l}/\Delta_{f}}-1\right)\Delta_{f}\times\sigma\]
## 5 Experiments
### Model Configurations  
We use XLM to obtain the transformer baseline, and implement layer-wise coordination transformer and consistent pretraining translation transformer based on the codebase of XLM ([[https://github.com/](https://github.com/)]([https://github.com/](https://github.com/))  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Chinese–English** & **English–German** \\ \hline \multirow{3}{*}{Parallel data} & News Commentary v12 & News Commentary v12 \\  & CWMT Corpus & Common Crawl corpus \\ \cline{1-1}  & UN Parallel Corpus V1.0 & Europarl v7 \\ \cline{1-1}  & & Rapid corpus of EU press releases \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parallel corpus of Chinese–English and English–German on WMT17.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{3}{c}{**Chinese–English**} \\ \hline \multirow{3}{*}{size} & Parallel data & Chinese monolingual data & English monolingual data \\  & 227 K & 9 M & 20.6 M \\ \cline{1-1}  & News Commentary v12 & CWMT corpus & News Crawl 2016 \\ \hline \multicolumn{3}{c}{**English–German**} \\ \hline \multirow{3}{*}{size} & Parallel data & English monolingual data & German monolingual data \\  & 270 K & 4.5 M & 4.5 M \\ \cline{1-1}  & News Commentary v12 & News Crawl 2016 & News Crawl 2016 \\ \hline \hline \end{tabular}
\end{table}
Table 2: All training data in our experiments.  
facebookresearch/XLM). For simplicity, we use LWCT to represent layer-wise coordination transformer and CPTT to represent consistent pretraining translation transformer in the rest of this paper.  
We set embedding size to 512 and feed-forward hidden size to 2048 for all models. For transformer model and CPTT model, we use 6 layers for both encoder and decoder. For LWCT model, we tried two configurations, one using 6 layers encoder to ensure that the same pretrained model is used as other models, and the other using 12 layers encoder to guarantee the similar number of parameters in NMT model. During inference, we use beam search with a beam size of 4 and a length penalty of 0.6 for all NMT models.  
We use Adam [32] optimizer with learning rate \(10^{-4}\) for both pretraining and translation stage, and train all models on 2 V100 GPUs. For pretraining models, the batch size is set to 192 and a sequence in a batch contains 256 tokens. For NMT models, a batch contains about 20,000 tokens. Dropout [33] is set to 0.1 for all tasks. we follow XLM to set other settings.
# Soft Contrastive Cross-Modal Retrieval
## 3 Methodology
### Soft Contrastive Learning  
Contrastive learning [57] aims to associate different types of data (such as images and text) to achieve the goal of cross-modal retrieval, which is the mutual retrieval and matching of data in different modalities. Contrastive learning obtains a shared embedding space by learning the similarity between images and texts so that related images or texts are close to each other in the common space, while irrelevant samples are far away from each other. Undoubtedly, contrastive learning focuses on learning the common features between similar instances and distinguishing the differences between non-similar instances. Compared with generative learning, contrastive learning does not need to pay attention to the tedious details of the instance. It only needs to learn to distinguish the data in the feature space at the abstract semantic level. Consequently, the model and its optimization become simpler and have better generalization capabilities.  
However, traditional contrastive learning utilizes the deep network model to pay much attention to noise samples or negative samples when overtraining, as a consequence, leading to the poor generalization ability of the model and poor retrieval results on new and unknown test data. This is because when overtraining or when the model is particularly complex and contains huge parameters, there will be sharp edges between different sample types in the common subspace as shown in Figure 3. During training, the mapping of the deep neural network makes each anomalous sample be divided into a specific boundary, resulting in an extremely tortuous and sharp classification boundary.  
To better solve this problem, we use the soft contrastive loss to optimize the model parameters. In the process of sample mapping, the boundaries between classes become smooth. Despite it being a problem of model complexity or overtraining, soft contrast loss can help the model obtain smooth boundaries, thus greatly improving the generalization and robustness of the model as shown in Figure 4. Hence, the soft contrastive loss function is calculated by Equation (4) as follows:  
\[\begin{split}\mathcal{L}_{sel}&=-\log\left(\frac{e^ {\tau\cdot\mathsf{S}(\mathbf{I}_{i},\mathbf{T}_{j})}}{e^{\tau\cdot\mathsf{S}(\mathbf{I}_{i},\mathbf{T}_{j})}+\sum_{k=1}^{N}e^{\tau\cdot\mathsf{S}(\mathbf{I}_{i},\mathbf{T}_{k})}} \right)\\ &=-\tau\cdot\mathsf{S}(\mathbf{I}_{i},\mathbf{T}_{j})+\log\left(e^{\tau \cdot\mathsf{S}(\mathbf{I}_{i},\mathbf{T}_{j})}+\sum_{k=1}^{N}e^{\tau\cdot\mathsf{S}( \mathbf{I}_{i},\mathbf{T}_{k})}\right)\end{split} \tag{4}\]  
where \(\tau\) is an adjustable temperature parameter, controlling the distribution range of similarity. Equally, \(\mathbf{I}_{i}\) and \(\mathbf{T}_{j}\) are the embedding of the image and text, \(\mathsf{S}(\mathbf{I}_{i},\mathbf{T}_{j})\) denotes the cosine similarity between the text and image, and \(N\) is the total number of samples in the batch. The proof is shown in Appendix A.  
Figure 3: Illustration of the effect of overtraining or overfitting in feature embedding learning.
## 3 Results
### Reticulated Porous ZTA Specim Prepared by Coarse Zirconia Particles
#### 3.1.2 The Effect of the Thickener on the Reticulated Porous ZTA Specim  
Controlling the viscosity by managing the solid loading level only requires additions of alumina and zirconia particles to the point that the desired viscosity is reached, but this approach is not efficient. Therefore, the solid loading content was fixed at 66 wt.%, and the viscosity was controlled by adding a thickener. The thickener acts to increase the uniformity when mixed with water [24]. By suppressing the sedimentation of the slurry, the viscosity of the upper and lower positions of the slurry and the dispersion state remain the same for a long period of time to stabilize the viscosity of the slurry. In addition, the thickener improves the mechanical strength of the material by increasing the thickening properties, preventing separation between materials, and retaining the water necessary for hydration [23]. In addition, when polyvinyl alcohol as an organic binder and methyl cellulose as a thickener are used together, more effective improvement of the compressive strength becomes possible [24,25]. Therefore, the solid loading content was fixed at 66 wt.%, and the viscosity was controlled with the addition of a thickener, as mentioned above. When the thickener was added, the viscosity increased by approximately 100 times, as shown in Figure 3b. The thickener content was fixed at 2.5 wt.% and led to the highest compressive strength given the optimized viscosity.  
The compressive strength outcomes at pore densities 25, 45, 60, and 80 for the reticulated porous ZTA specimens are shown in Figure 3c. As the PPI was increased, the pore  
Figure 3: Viscosities at a shear rate of 0.01 s\({}^{-1}\) of the ZTA slurry, and the compressive strengths: (**a**) 57–66 wt.% solid loading, (**b**) 0.5–3.0 wt.% MC, and (**c**) compressive strengths and densities of reticulated porous ZTA specimens with pore densities of 25, 45, 60 and 80.  
density of the reticulated porous ZTA specimens increased, resulting in higher compressive strength. However, when the pore density was 80, the viscosity of the ZTA slurry was high, and the compressive strength decreased. As the pore density of the reticulated porous ZTA specimens approaches 80, preparation becomes difficult owing to the high pore density of the sacrificial polymer template, also making it difficult to remove the residual slurry deep inside the sacrificial template. In addition, closed pores form inside the sacrificial polymer template due to residual slurry after the squeezing process, thereby reducing the compressive strength [26]. Therefore, it was confirmed that the reticulated porous ZTA specimens can be prepared up to a pore density of 60.
## 3 Information and Communication Technology Solutions for Precision Agriculture
### Multi-Purpose Platforms
#### 3.4.3 Smartphone Applications  
Smartphones are extensively used across the world nowadays, and there is a plethora of mobile applications supporting various PA practices. [PERSON] et al. demonstrate a mobile application which can be used for irrigation scheduling in cotton crops. This application estimates the crop root zone soil water deficits (RZSWD) based on weather station data, various crop and soil parameters, as well as on irrigation applications. After the user has inserted location of crops, soil type, and details about the irrigation strategy employed, the application informs the user when RZSWD surpasses a certain threshold; rain is reported by a nearby weather station or when phenological changes take place. Thus, the application facilitates decision-making around irrigation and can contribute to increased productivity and resource saving [102]. [PERSON] et al. present a smartphone application for executing nitrogen leaching simulations and conducting assessments of nitrogen loss risk while also correlating results with certain observed values. Using this application, farmers can make better decisions regarding nitrogen management and optimize the use of resources [103]. [PERSON] analyzes a mobile application for the early diagnosis of plant diseases. Based on image processing, classification, and segmentation techniques, this application enables farmers to insert photos of their plants in order to identify diseases. Evaluation of this application indicated acceptable accuracy. Early detection of diseases provided by the application contributes to the minimization of the financial cost and the environmental impact [104]. [PERSON] and [PERSON] demonstrate a smartphone application for the measurement of fractional green canopy cover (FGCC). This parameter is of great importance in PA as it can be used to monitor canopy growth, interception of light, as well as the portioning of evapotranspiration. Several image processing techniques are utilized to estimate FGCC. Users can insert images and/or videos as inputs to the application. Evaluation of the application revealed importantly faster operation as compared to other similar software tools while also maintaining high accuracy [105].  
The table below summarizes the results of the present literature review utilizing our proposed categorization method, which is described in Section 2.
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 4 Methodology
### Training  
For this paper, there are six (6) state estimators to pit against each other:  
* Weighted Least Squares-State Estimation;
* Standard LSTM;
* Loss Modified LSTM;
* 25% Pruned LSTM;
* 50% Pruned LSTM;
* 75% Pruned LSTM.  
To ensure consistency of structure among comparisons, the Modified and Pruned LSTMs followed the Standard LSTM's structure and were trained separately from zero, that is, transfer learning between models is not used. All networks are trained over about 13,000 data points corresponding to two hours' worth of measurement values. Half of the data are used for training, the other half are hidden and used for validation. It is important that the training process does not see the validation set so that the aim of generalization is reliably tested. A third hour of the dataset, hidden from the training process, is used for final evaluation which is shown in Section 5. The optimizer used is the adaptive moments algorithm with a learning rate of 0.0001 to aid in preventing explosive and vanishing gradients during the training process. The loss function used is the weighted least squares for the loss modified LSTM and standard mean square error for the rest. The training session repeats for 30 epochs, with batch sizes of 9 for the DC subgrid and 68 for the AC subgrid due to limitations in implementing the WLS loss function in the TensorFlow framework. The length of the training session should be kept minimum so as not to overfit into the dataset. The models are evaluated for learning fitness depending on the ratio between the final training and validation loss values. Ideally, these values should be as close to each other as possible. These configurations are kept the same everywhere for consistency.
# A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM
## 3 Doppler Compensation for Multicarrier Modulation  
In this section, an algorithm to compensate for Doppler is described. In comparison to the algorithm described in [18], a common Doppler compensation is applied to reduce computational  
Figure 4: Examples of implementation of time-varying delay using TVFDL.  
Figure 3: Comparison between different resampling methods applied to a 512-Hz sine wave with a sampling frequency of \(F_{\mathrm{s}}\) = 10,240 Hz.  
complexity. First, in Section 3.1, a mathematical model is developed to represent the effect of time-variance on the received signal, and then, in Section 3.2, signal processing techniques at the receiver to compensate time-scaling are developed.
# Poly(\(\varepsilon\)-caprolactone)-Ionic Liquid Composite as Piezoionic Mechanical Sensor
## 2 Materials and Methods  
Poly(\(\epsilon\)-caprolactone) (PCL) (Mn = 45,000 g mol\({}^{-1}\)), chloroform (CHCl\({}_{3}\)), and dimethyl-formamide (DMF), which were used for the electrospinning process of the polymer, were furnished by Merck/Sigma Aldrich KGaA (Darmstadt, Germany) and were used without purification. 1-Ethyl-3-Methylimidazolium tetrafluoro borate (EMIMBF\({}_{4}\)) was purchased from Fisher Scientific Italia/Alpha Aesar (Segrate, Italy). The conductive silver-based varnish spray used as the electrode was furnished by Tifoo (Regensburg, Germany). Electrospinning was carried out by using a commercially available apparatus (Starter Kit 40 kV Web, Linari Nanotech srl, Pisa, Italy), comprised of a high-voltage power supply, a syringe pump, a syringe, a stainless steel blunt-ended needle connected with the power supply electrode, and a grounded drum collector covered with aluminum foil. PCL electrospun scaffolds were produced by dissolving the PCL at a concentration of 20% \(v/v\) in a solution of CHCl\({}_{3}\)/DMF (4:1). The polymer solution was poured into a 20 mL syringe equipped with 21-gauge needle (inner diameter = 0.8 mm). A voltage of 15 kV was applied through a high-voltage power supply. The perpendicular distance between the syringe tip and the grounded drum collector was set at 15 cm. The flow rate of 3 mL/h was controlled through the pump speed. The electrospinning process was carried out at 25 \({}^{\circ}\)C and at 57% relative humidity.  
To fabricate the devices, electrospun PCL membranes (2 cm \(\times\) 5 cm) were cut, dried in an oven at 40 \({}^{\circ}\)C, soaked with EMIMBF\({}_{4}\) for 24 h, and left again in an oven for 24 h at 40 \({}^{\circ}\)C. PCL/EMIMBF\({}_{4}\)/Ag devices were fabricated by spraying a conductive varnish on both sides of the membrane twice. After every deposition, the membranes were dried in an oven for 5 min (T = 40 \({}^{\circ}\)C). The device thickness was determined using a caliper (accuracy \(\pm\)1 \(\upmu\)m).
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Monitoring and Control through Smart Metering  
#### 4.2.1 E2 SG  
Another project, where smart meters play an important role has been the E2 SG project, in which organizations or institutions from nine countries participate. Its scope has been to design mechanisms for an efficient smart grid monitoring and control. The project addressed issues of grid sensing, smart metering and communication, including network monitoring by using phasor measurement units (PMUs), smart meters application and implementation of an architecture for data collection, storage and management. The smart meters that have normally been used, worked with a low power wireless transceiver. Another topic of the project has been smart conversion, like optimum power transfer to the grid and best possible interfacing of energy generating nodes with the AC grid and efficient interfaces from the end user to the grid for energy reduction. Advanced grid control methods have been examined as well as the topology of the grid [78].
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 4 Scheduling for Adaptive Time-Triggered Systems
### Conditional Timed Context Agreement Model  
For each node of the multi-schedule graph (MG), there is a combination of context events as depicted in Figure 4. These events occur over time at predefined points in time if they occur. Each node in the MG represents a schedule for the system, which consists of schedules for each end system and each switch. For a subset of these endsystems and switches, the schedule differs from the schedules of the predecessors. Those endsystems need to be informed about the occurrence of the context event using context messages, while the other endsystems, where the schedules remain unchanged, do not require the context information of the MG node.  
Therefore, we can define the communication requirements for the context messages:  
* **Time:** To ensure correct schedule transitions, context messages must be _received_ before the predefined schedule switch time, denoted as \(t_{\text{switch}}\). Given a known communication delay \(dx_{i}\), the message must be _sent_ early enough so that it arrives on time. The latest possible transmission time is therefore calculated as: \[t_{\text{send}}=t_{\text{switch}}-dx_{i}\]This ensures that the message arrival time \(t_{\text{receive}}=t_{\text{send}}+dx_{i}\) satisfies \(t_{\text{receive}}\leq t_{\text{switch}}\), maintaining timing correctness and system-wide consistency.
* **Content:** Context messages need to contain information about those context events that enable the activation of the schedule associated with an MG node.
* **Sender:** The sender of a context message is the endsystem, where the context event is observed (e.g., endsystem, where a task exhibiting slack is executed).
* **Receiver:** The receivers of a context message are all endsystems, where schedules changed compared to the predecessors in the MG.
## 7 Experimental Results
### Performance Comparison with the Existing Schemes  
#### 7.2.1 Impact of Sink Speed  
Figure 5a shows a comparison of DSR according to the sink speed. The end-to-end distance between the source and the sink is fixed at 300 m. Since SPEED and THVR employ data delivery speeds calculated from the distance between the source and the initial static location of the sink, the moving sink may not receive the data within the desired time deadline even though the data is delivered to the initial location of the sink on time. EAR2, however, forwards data with the modified delivery speeds that consider sink mobility, so the probability of deadline success is higher than with the other schemes.  
Figure 5b indicates the EC of the proposed scheme with those of the other protocols according to the sink speed. We keep the distance between the source and the sink at 300 m. In SPEED and THVR, as the sink speed increases, the data path for footprint chaining from the initial location of the sink to the moved location increases, and the interval between sink location updates decreases. This makes the energy consumption gradually increase. The energy consumption of THVR is higher than that of SPEED due to the initialization cost of discovering two-hop neighbors, but they have a similar increasing pattern. EAR2 consumes the same energy for initialization as SPEED but exploits the restricted flooding to support sink mobility. Because the \(EA\) is exponentially proportional to the sink speed, EAR2 consumes more energy than the other schemes as the sink moves faster.