esa-sceva/satcom-chunk-collection huggingface dataset:
# Magnonic Metamaterials for Spin-Wave Control with Inhomogeneous Dzyaloshinskii-Moriya Interactions
## 2 Analytical Model
### Total Internal Reflection  
Analogously to the case of electromagnetic waves in photonics or acoustic waves in phononics, spin waves are also expected to be completely reflected by the interface when a spin-wave travels from a _denser_ medium with a higher refractive index to a _thinner_ medium with a lower refractive index known as TIR. TIR occurs when the incident angle \(\theta_{i}\geq\theta_{c}\), where \(\theta_{c}\) is often called the critical angle. When \(\theta_{i}=\theta_{c}\), the refracted spin-wave travels along the interface between the two media or the angle of refraction \(\theta_{t}\) is \(\pi/2\). According to the magnonic [PERSON]'s law in Equation (4), the critical angle can be expressed as  
\[\theta_{c}=\arcsin\left(\frac{\mathbf{k}_{g,t}-\delta}{\mathbf{k}_{g,i}} \right), \tag{5}\]  
where \(\delta=\delta_{i}-\delta_{t}\). Specifically, Equation (5) shows that \(\theta_{c}\) equals \(\pi/2\) when the DMI is homogenous (\(D_{1}=D_{2}\)), i.e., all incident spin waves are fully transmitted and no reflection occurs. Furthermore, when \(\delta\) (the difference between DMI in two regions) is chosen to be large enough, a gap falls in between the two isofrequency circles and TIR occurs at all incident angles (i.e., \(\theta_{c}=0\)). Equations (4) and (5) are the main analytical results in our paper.  
Figure 2: (**a**) Schematic illustrations of reflection and refraction of spin-wave at an interface between two different media in wave vector (\(\mathbf{k}_{x}-\mathbf{k}_{y}\)) space. The pink and green circles indicate the individual frequency contours of the allowed modes in the same-color-coded media A and B, respectively. The color-coded arrows denote the spin-wave vectors \(\mathbf{k}\) propagating in each medium, as indicated by the incident (pink) and refracted (green) rays. The blue arrow denotes the critical angle. (**b**) Phase diagrams of critical angle \(\theta_{C}\) in the \(D_{1}-D_{2}\) plane. No TIR exists in the white regions. (**c**) Critical angle \(\theta_{c}\) as a function of DMI constants \(D_{2}\) with a fixed DMI constant \(D_{1}=4\times 10^{-3}\,\mathrm{J/m^{2}}\). The symbols (red squares) are simulation data, and the solid curve represents the analytical results of Equation (5).
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
## 5 A Producer Mobility Link Service in NDN Face
### PMLS Control Message  
The PMLS introduces three types of control messages that can be used to recover a broken link: data segments, status reports, and Face update messages. They follow the Type-Length-Value (TLV) structure to meet the common link-layer packet format of the NDN face [18]. They are only used between peer PMLSs.  
_Data Segment:_ A Data segment carries a segment of an Interest or Data packet. It includes the segment sequence number.  
* Nonce: All data segments belonging to one data packet have the same nonce field of the data packet. This nonce value is directly derived from the nonce value in the received interest packet that uniquely identifies the interest packet.
* Sequence number: It means the relative position of the data segment.
* Last segment flag: It means whether there are more data segments to be sent. In other words, it is set to 1 when it is the last data segment.
* Payload: Partial content of the Data packet.  
_Face update:_ A Face update is used to notify a peer PLMS of the new transport endpoint address after the NMP has moved.  
* Nonce: The value from the nonce field of an instance of the transaction structure.
* Address information: A new transport endpoint address. It is used to transmit a status report to the peer PMLS.  
_Status Report:_ A status report message is used to notify the peer PLMS of the current exchange state of a transaction.  
* Nonce: The value from the nonce field of an instance of the transaction structure.
* Missing segment number and length: Content lost due to the movement of the producer.
## 3 Methods
### Classification Algorithms  
#### 3.2.1 Logistic LASSO  
In binary classification studies, the dichotomous response variable \(y_{i}\) is usually coded as 1 for cases and 0 for controls. In order to model logistic regression, the probability \(p_{i}=Pr(y_{i}=1)\) of case \(i\) given the predictor vector \(x_{i}\) can be expressed as:  
\[p_{i}=\frac{e^{\beta_{0}+x_{i}^{T}\beta}}{1+e^{\beta_{0}+x_{i}^{T}\beta}}, \tag{2}\]  
Figure 1: Overview of the proposed framework.  
The parameter vector \(\Theta=(\beta_{0},\beta_{1},\ldots,\beta_{p})^{T}\) is usually estimated by maximizing the log-likelihood function:  
\[L(\Theta)=\sum_{i=1}^{N}[y_{i}log(p_{i})+(1-y_{i})log(1-p_{i})] \tag{3}\]  
Regularization methods such as shrinkage or penalized regression could reduce the likelihood of overfitting by modifying the loss function with a penalty term to shrink the coefficients in the regression towards zero and selecting the nonzero variables in the final model [25]. This approach has the additional advantage of selecting the most important variables for the predictions from a large and potentially multicollinear set of features, resulting in a more relevant and interpretable set of predictors [26; 27; 28]. The logistic LASSO model involves L1 penalty term, resulting in:  
\[L(\Theta)+\lambda\sum_{j=1}^{p}|\beta_{j}| \tag{4}\]  
In order to tune the penalty constant \(\lambda\), without introducing bias and overfitting, we used an inner round of k-fold validation within each training round, with \(k=3\). The AUC metric was computed for the test set as the performance measure to tune \(\lambda\). We used the \"glmnet\" package to fit the logistic LASSO regression.
## 4 Results
### Annotation Statistics and Dataset Distribution  
The finalized annotated datasets from the Mediavida forum contained a total of 1001 dialogues annotated for sexism and 989 dialogues annotated for racism (see Table 7). The sexism dataset exhibited a class imbalance, comprising 221 positive examples (22.1%) and 780 negative examples (77.9%). The racism dataset had a slightly higher proportion of positive instances, with 299 examples labeled as racist (30.2%) and 690 labeled non-racist (69.8%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Sexism Dataset** & **Racism Dataset** \\ \hline Total annotated dialogues & 1001 & 989 \\ Positive examples (\%) & 22.1\% & 30.2\% \\ Negative examples (\%) & 77.9\% & 69.8\% \\ Mean tokens per example & 133.3 & 123.7 \\ Median tokens & 103 & 94 \\ Maximum tokens & 957 & 784 \\ Cohen’s Kappa (\(\kappa\)) & 0.55 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Annotation statistics for sexism and racism dialogues.  
In terms of textual characteristics, dialogues annotated for sexism had an average length of 133.3 tokens per example, with a median of 103 tokens and a maximum length of 957 tokens. Dialogues annotated for racism were shorter on average, containing 123.7 tokens per example, a median of 94 tokens, and a maximum of 784 tokens. The observed distribution highlights that sexism-related discussions tended to be more extensive, reflecting possibly deeper conversational contexts.  
Inter-annotator agreement measured through Cohen's Kappa yielded moderate agreement for sexism (\(\kappa=0.55\)) and substantial agreement for racism (\(\kappa=0.79\)), confirming annotation reliability and guideline effectiveness.  
In addition to sexism and racism, initial annotation efforts were undertaken for two other bias categories: homophobia and aporophobia. The homophobia dataset consisted of 2153 examples but contained only 20 positive instances (0.9%), while the aporophobia dataset had 1352 examples with just 9 positive cases (0.7%). Due to the extremely limited presence of these labels in the data, indicative of the relatively infrequent explicit appearance of these biases within the analyzed dialogues, further dataset creation and subsequent model training for these two categories were not pursued.
## 1 Introduction
Thus, the expressions necessary to calculate the general CoM, the shaking force, and the shaking moment of the system can be obtained in a direct and automated way. Another advantage is that the force balancing conditions, directly expressed in terms of the local coordinates of the CoM of the bodies, are obtained directly from the terms in the mass matrix. In the authors' perspective, this contributes to making the method based on the optimal control of the robot-manipulator center of mass faster and easy to perform. In this article, the natural coordinates are used to obtain the analytical expressions to calculate the position of the general CoM, the shaking force, and the shaking moment of the 3 RRR planar parallel manipulators (PPM). The expressions to calculate the shaking force are used for the force balancing of the mechanisms applying the method based on the optimal control of the global CoM of the manipulator, as presented previously in References [23; 30; 31]. As a first approach, the acceleration of the global CoM is reduced using \"bang-bang\" law, proposing to partially balance the the manipulator without any addition of supplementary masses. As a second approach, a two-stage method is proposed. The first stage consists of the optimal redistribution of the driving links' masses using counterweights, securingconvergence of the end-effector trajectory and the CoM of the manipulator. This mass redistribution is done without increasing the total mass of the original system, keeping the masses of the driving links the same value. In a second stage, the optimal control of the end-effector acceleration according to the \"bang-bang\" law is developed. The expressions for the shaking moment are used to verify the behavior of the mechanism before and after the force balancing. The rest of the article is organized as follows: in Section two, a description of the modeling using fully Cartesian coordinates (natural coordinates), stressing the importance of the mass matrix of the body, is presented. Special attention is given to the mass of a link represented with two and three \"basic points\". Additionally, a general and easy to automate method to calculate the global CoM, the shaking force, and the shaking moment is introduced. In Section three, the method described in Section two is applied to calculate the shaking force balancing conditions of the 3 RRR PPM and the partial force balancing following the approaches described in the previous paragraph. In Section four, all analytical expressions developed in Section three are used in a numerical example for an specific manipulator. Comparative results from computer simulations are presented. Finally, conclusions are given in Section five.
## 3 Performance Evaluation and Discussion
### Topology Evaluation and LoD Representation
We first use a Monte Carlo sampling method [26] to obtain a set of points over \(\mathcal{X}\) and, for each point on \(\mathcal{X}\), it searches the closest point (face or edge) on the other mesh \(\mathcal{Y}\). By this way, we can calculate the Mean and RMSE between the sampling points on the sampled mesh \(\mathcal{X}\) and their closest points on the target mesh \(\mathcal{Y}\). These two measures are regarded as the criterion to determine the similarity of two shapes. In our case, \(\mathcal{X}\) represents a tunnel model constructed by a series of profiles at a specific interval. A target mesh \(\mathcal{Y}\) denotes a tunnel model that is created by the screened Poisson surface reconstruction algorithm [27] using the raw point clouds. As shown in Figure 12a-g, the target tunnel meshes denoted by gray color are superimposed onto a series of sampled tunnel models with different colors. Through the superimposition, we qualitatively evaluate the similarity by visually inspecting the differences of these two shapes. Meanwhile, we also use the two well accepted statistical measures above to make quantitative evaluations. To this end, we first fix the rotation angle interval \(\Delta\varphi\) with a specific value to analyze the variations of Mean and RMSE of the generated tunnel models with respect to the split intervals at 0.2 m, 1.0 m, 2.0 m, 4.0 m, 6.0 m, 8.0 m and 10.0 m. The results are shown in Figure 13, and we unexpectedly discover that, for each polyline corresponding to a specific rotation angle, the Mean and RMSE first decrease and then increase with increasing split interval. This means that the high density of profiles does not guarantee producing a tunnel model with high geometric accuracy. Actually, in our scenario, the split interval of 4.0 m can get the tunnel model with the minimum Mean and RMSE errors. On the contrary, we fix the split interval with a specific value and compare geometric variations with regard to the different rotation angle intervals \(\Delta\varphi\), i.e., \(3^{\circ}\), \(6^{\circ}\), \(9^{\circ}\) and \(12^{\circ}\). The results are shown in Figure 14, where we find that the geometric errors are substantially increasing with the increased rotation angles. The bottom polyline in yellow in Figure 14a corresponds to the tunnel model at a 4.0 m split interval. Therefore, we can make a safe conclusion that extraction of profiles at the space of 4.0 m can guarantee the reconstructed tunnel model with the minimum geometric error. However, in practice, the user should totally make a trade-off between the geometric accuracy, compactness and the specific applications to finally determine how many profiles are needed. Figure 13: The comparisons of those generated and the reference tunnel model at different \(\Delta d\) and \(\Delta\varphi\).
# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
### Water Quality Sensors
The hardware architecture, implemented on a Raspberry Pi microcomputer, includes a camera as an image sensor and an Intel Neural Compute Stick (NCS) for optimized inference execution and reduced power consumption. The study makes a significant contribution to the field of PA by introducing an efficient, autonomous system for detecting pests in orchards. A study in [111] highlights the growing need for PA to meet global food demand while minimizing environmental impact and optimizing resource use. Focusing on the challenge of cost-effective technology integration for farmers, the paper presents a wireless sensor network using low-cost soil moisture sensors to improve irrigation processes in PA. Each wireless node comprises four soil moisture sensors measuring moisture at different depths using a mechanism based on mutual induction. Several prototypes were tested, with the optimum design featuring a 1:2 winding ratio with 15 and 30 arrows at 93 kHz. A specific communication protocol was developed to enhance system performance. The proposed system was tested on a real cultivated plot, evaluating coverage and the Received Signal Strength Indicator (RSSI) to assess losses due to vegetation. The paper also details the network protocol, message exchange, data collection, and control algorithm, focusing on a distributed network structure with sensor and actuator nodes. This comprehensive approach addresses the need for affordable and reliable precision farming systems, marking a significant contribution in this field. In the same context, the authors of [112] address a prevalent problem in apple orchards, focusing on combating the threat of the coding moth, an important pest for apple crops. Leveraging advances in IoT sensing devices with embedded ML algorithms, the proposed system offers extensive real-time data collection and analysis capabilities, specifically targeting coding moth detection. The innovative approach uses neural network algorithms close to sensors that automatically identify coding moths. The system captures trap images, pre-processes them, and uses a neural network to classify the insects, rapidly informing the farmer as soon as they are detected. In addition, the application runs on a low-power platform, using a compact solar panel for energy autonomy, guaranteeing continuous, unattended operation over low-power wide-area networks. The study highlights the importance of a low-power platform for rapid IoT prototyping with ML algorithms. In the study, a Raspberry Pi3 board and an Intel Movidius NCS are used for pre-processing and implementation of the neural network, respectively. A detailed analysis of the network model, parameters, and limitations in light of hardware constraints is provided. System performance is thoroughly evaluated, with discussions on power consumption aimed at achieving a zero-energy balance.
# Centralized Energy Prediction in Wireless Sensor Networks
###### Abstract  
Resource Constraints in Wireless Sensor Networks are a key factor in protocols and application design. Furthermore, energy consumption plays an important role in protocols decisions, such as routing metrics. In Software-Defined Networking (SDN)-based networks, the controller is in charge of all control and routing decisions. Using energy as a metric requires such information from the nodes, which would increase packets traffic, impacting the network performance. Previous works have used energy prediction techniques to reduce the number of packets exchanged in traditional distributed routing protocols. We applied this technique in Software-Defined Wireless Sensor Networks (SDWSN). For this, we implemented an energy prediction algorithm for SDWSN using Markov chain. We evaluated its performance executing the prediction on every node and on the SDN controller. Then, we compared their results with the case without prediction. Our results showed that by running the Markov chain on the controller we obtain better prediction and network performance than when running the predictions on every node. Furthermore, we reduced the energy consumption for topologies up to 49 nodes for the case without prediction.  
software-defined networking; energy consumption; centralized prediction Article
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 2 Recent Advances on Catalysts for Syngas Production
### Transition Metals-Based Catalysts
#### 2.2.2 Cobalt Catalysts
[84] have evaluated the DRM activity of 13 wt% Co-based catalysts supported on activated carbon-modified WC (WC-AC) and reported high stability of the prepared catalysts. According to the authors, at 800 \({}^{\circ}\)C, both CH\({}_{4}\) and CO\({}_{2}\) conversions were maintained over 90% for a 50 h test with minor activity drop, while H\({}_{2}\)/CO ranged from 0.90 to 0.92 during the whole test. The authors evaluated that an optimal 6.5 wt% W loading on the support was responsible for decreasing the activation energy of the reactants and boosted the catalytic activity. Furthermore, [PERSON] et al. [85] also evaluated the activity of the Co/WC-AC catalyst and reported the effects of La-promotion. Although no significant improvement on H\({}_{2}\)/CO ratio was observed, the authors showed that a 10-20 wt% La addition to the catalyst could prevent catalyst sintering and the formation of poorly active CoWO\({}_{4}\) phase on the support, which could lead to catalyst deactivation.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Defining Message Format and Interval  
The UAVs must periodically exchange hello messages, global pheromone map, and location information for successful movement and communication.
## 3 Results  
For the numerical study, a 21-node sample distribution network has been employed which is 13.8 Kv and has 20 feeder section which is shown in Figure 8. In this sample, six wind turbines which are able to produce 500 kVA are set up in buses 14, 15, and 18 to 21. The network characteristics and constraints such as voltage deviation and HV/MV substation capacity are derived from [16]. The load demand, electricity price, and wind speed data are simulated by the Monte-Carlo simulation. The normal distribution function for the load demand and electricity price and the Weibull distribution function for the wind speed data are taken into account. The load, electricity price, and wind speed data are for Ontario, Canada [28]. The parameters of the sample network are shown in Table 2. It is assumed that 30 PEVs are connected to each demand node. The information on arrival and departure time, travel distances, and type of vehicles are based on the NHTS database [29]. To show the performance of the proposed charging method, three different approaches including uncoordinated, smart G2V, and smart V2G are considered.
## 2 Method  
In this study, the real-time LEO satellite clocks are proposed to be determined and provided to users with the following steps:  
1. Set a timer and loop through the steps (2)-(4);
2. Use real-time GNSS orbits, clocks and LEO satellite on-board GNSS observation to determine the near-real-time LEO satellite clocks based on the kinematic model, including GNSS product selection and the clock re-referencing;
3. Predict the LEO satellite clocks in the ultra-short term, ensuring that users obtain LEO satellite clock products in real time;
4. Broadcast the low-order polynomial parameters to users at regular intervals.  
The flowchart of real-time clock determination is illustrated in Figure 1. The principles of the steps are described briefly in the following subsections.
## 2 Materials and Methods
### Isothermal Titration Calorimetry  
The ITC MicroCal PEAQ-ITC200 apparatus (Malvern, Worcestershire, UK) was used for the calorimetric titration at 36.6 \({}^{\circ}\)C, as described previously [30]. The treatments included 19 injections, with 150 s between injections. A stirring speed of 250 rpm and 10 val\(/\)s of reference power were used. A measuring cell of 0.2 mL was filled with degassed MAO-A solution, 100 \(\mu\)mol/L, and titrated with each of the propenylbenzene derivatives at a concentration of 10 mmol/L or/and 5-HT (10 mmol/L). The enzyme and compounds were prepared in methanol LCMS, pH 7.6. Control experiments were performed by injecting MAO-A into the buffer in an identical manner and the resulting heat changes were subtracted from the measured heats of binding. The binding and thermodynamics constants were determined using the \"One Set of Sites\" fitting model. Stoichiometry (n), enthalpy changes (\(\Delta\)H), Gibbs free energy changes (\(\Delta\)G), entropy changes (\(\Delta\)S), the dissociation constant (K\({}_{\rm D}\)), the binding constant (K\({}_{\rm A}\)), and the inhibition constant (K\({}_{i}\)) were calculated from the ITC titration nonlinear least squares saturation curve fitting carried out using MicroCal PEAQ-ITC200 software 1.30. The K\({}_{i}\) values for the oxidation of 5-HT catalyzed by MAO-A were calculated by using the Michaelis-Menten equation (Figure 2). To assess the inhibition activity, the signals of 5-HT oxidation in the presence of inhibitors were subtracted from the signals of 5-HT oxidation without inhibitors, while considering the enthalpy recorded during the interactions of the inhibitors with MAO-A.  
Figure 1: Structures of the propenylbenzenes: **1a–5a;** diols: **1b–5b;** hydroxy ketones: **1c–4c**.  
The inhibition activity was calculated as [(\(\Delta\)H \(-\)\(\Delta\)H\({}_{\rm i}\))/\(\Delta\)H] \(\times\) 100%, where \(\Delta\)H\({}_{\rm i}\) is the signal of 5-HT oxidation in the presence of an inhibitor, and \(\Delta\)H is the signal of 5-HT oxidation without an inhibitor. The percentage of inhibition of 5-HT oxidation at a given concentration of the ligand was calculated and it was used to calculate the concentration of ligands that inhibited 50% of the oxidation reaction (IC\({}_{50}\)) [30]. The curves are provided in the supplement (Figure S1).