esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Priority-based Coverage-aware Congestion Control (PCC)
### System Model and Design Considerations
For instance, if the queue in a sensor node is near fully occupied and dominated with _Non-Event_ packets, when an _Event_ packet arrives, it is better to drop _Non-Event_ packets because _Event_ packets are more important. To address _Coverage Fidelity_, we can consider the scenario in Figure 3, where node \(B\) is closer to node \(C\) than node \(A\), and the sink is at the rightmost end. If node \(A\) generates packet \(P_{A}\) and node \(B\) generates packet \(P_{B}\) simultaneously, \(P_{B}\) will normally arrive at node \(C\) earlier. When \(P_{A}\) arrives at node \(C\) whose queue is highly utilized, \(P_{A}\) may be dropped while \(P_{B}\) remains in the queue. This results in unfairness to different sensors. To mitigate this our proposed method checks the status of all packets in the queue and selectively drops packets according to an optimization algorithm, which will be introduced in the next section. With the help of a list of pointers to packets in the queue, it is feasible to drop intermediate packets with much lower complexity than expected. Figure 3: Queue scheduler that allows dropping intermediate packets. 4. Resource Efficiency: Another important concern in a WSN is resource efficiency since sensor nodes usually have limited power and channel bandwidth [9; 10]. In a WSN, packets from sensors far from the sink normally consume more network resource than those from nodes nearby the sink. In our proposed method, we give preference to maintain packets from remote nodes since those packets have consumed more network resource and have lower probability to reach the sink when the intermediate relaying node experiences severe congestion. Therefore, when the same information is collected from different sensors in the network, our mechanism can efficiently utilize network resources by reducing the average number of point-to-point transmissions. 5. MAC/PHY Link Quality: In a multihop WSN, the interference among neighboring links can severely reduce the transmission opportunities in MAC layer. In addition, in a WSN, wireless link qualities, such as noise and channel fading, are quite distinct according to locations, obstacles, etc. The link condition at MAC/PHY layer can also influence the success probabilities that packets reach the sink. In respect to resource efficiency, packets traveling through low quality links consume higher system resource since they require more re-transmission due to MAC collisions or more transmission time due to lower PHY layer transmission rate. Therefore, our mechanism will give packets traveling though poor quality links from remote nodes higher probability to reach the sink.
# A Multiple-Choice Machine Reading Comprehension Model with Multi-Granularity Semantic Reasoning
## 5 Results and Analysis  
### Experimental Results  
Most of the answers to the questions in the \(C^{3}\) dataset used in this paper require a combination of semantic reasoning, some of which need reasoning on a single sentence, and others require multiple sentences to be considered together to find the appropriate answer. Therefore, this dataset can better verify the effectiveness of multi-granularity reasoning.  
To illustrate the effectiveness of the proposed model, the test results are compared with the test results of several models, and the detailed experimental comparison results are shown in Table 2.  
From the table above, we can see that our proposed model achieve improvements of 0.634%, 0.838%, and 0.736% over the benchmark BERT model on \(C^{3}_{M}\)-_test_, \(C^{3}_{D}\)-_test_, and \(C^{3}\)-_test_, respectively, which indicate that the introduction of the multi-granularity module has a significant improvement over the benchmark BERT model. The results of our experiment also suggest that the fused multi-granularity semantic reasoning method we propose can improve the reasoning ability of the model. By convolution and maximum pooling on the convolution window size of 2, 3 and 4, our model can extract the local multi-granularity feature information and then combine it with the global granularity feature information, which can achieve the effect of reasoning from multi-granularity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parm Type** & **Parm Value** \\ \hline Batch size & 32 \\ Learning rate & \(2\times 10^{-5}\) \\ Epoch & 10 \\ Max Length & 512 \\ Dropout & 0.1 \\ Gradient Accumulation Steps & 4 \\ Optimization functions & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameter settings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{C^{3}_{M}}\)**-_Test_ & \(\mathbf{C^{3}_{D}}\)**-_Test_ & \(\mathbf{C^{3}}\)**-_Test_ \\ \hline Random & 27.8 & 26.6 & 27.2 \\ Distance-Based Sliding Window & 45.8 & 40.4 & 43.1 \\ Co-Matching & 48.2 & 51.4 & 49.8 \\ ERNIE & 63.7 & 64.6 & 64.1 \\ BERT & 64.6 & 64.4 & 64.5 \\ Our model & 65.234 & 65.238 & 65.236 \\ \hline \hline \end{tabular} \({}^{\mathrm{a}}\)Results are measured by accuracy, using percentages (%).  
\end{table}
Table 2: Comparison table of experimental results \({}^{\mathrm{a}}\).  
We have also counted the testing results of each round on the validation set to verify the relationship between the number of training rounds and model performance. The results are shown in Figures 3 and 4.  
From Figures 3 and 4, we can see that the model converges faster in the first three rounds of training, and the model convergence becomes steady when the training process reaches the third round. The model achieves the best performance at the fifth round, which indicates that the proposed model can achieve a better convergence effect and can complete the training of the model with fewer training rounds.
# A Network Architecture and Routing Protocol for the MEDIcal WARNING System
## 6 Implementation and Performance Evaluation  
Here, a proof-of-concept implementation of the MEDIWARN communication system on COTS devices is presented. The aim is to show the feasibility of the proposed network architecture and to assess its performance.  
As shown in Figure 3, each end node consists of a number of sensors connected to a Philips IntelliVue MP5 monitor that shows the detected vital parameters and sends them through a Raspberry Pi.  
The MP-RPM is implemented on the Raspberry Pis, which act as relay nodes. The implemented version of MP-RPM allows setting a static value of \(upd\_period\). However, an algorithm to dynamically change the \(upd\_period\) at runtime can be very helpful in a dynamic context and will be addressed in future work.  
The sink, i.e., the MEDIWARN Virtual Biosensor in the architecture shown in Figure 1, consists of a Raspberry Pi that collects sensor data and a server that processes the data.  
Since the paper deals with the MEDIWARN network architecture, here, we assess the proposed communication system without addressing the logic implemented by the virtual biosensor.
## 2 Results
Figure 4 shows the Z-scan results of _p_-Si at the wavelength of 2.4 \(\upmu\)m. Because of the detectors' poor sensitivity at this wavelength range, the fluence was increased by an order of magnitude. The use of the longer wavelength required an increase in the scanning range from 5 mm to 30 mm to capture the stretched Z-scan features for both the closed and the open aperture cases. Due to the low level of the TPA at 2.4 \(\upmu\)m, the open aperture experiments, as shown in Figure 4b, encountered a high noise level and problems preventing achieving the optimal alignment. The combination of these factors led to a distortion of the symmetrical shape about zero and a higher absolute error in the determination of the TPA coefficient. By fitting the experimental results, we obtained the nonlinear coefficients \(\beta=5.0\pm 0.5\times 10^{-3}\) cm/GW and \(n_{2}=-3.5\pm 0.4\times 10^{-4}\) cm\({}^{2}\)/GW, indicating that the nonlinear refraction was even stronger for the longer wavelength range and that nonlinear absorption process was further restrained. Figure 4: The experimental (dots) and fitting (solid lines) results of the Z-scan measurements for the 15 \(\upmu\)m-thick _p_-Si sample for a pulse with wavelength of 2.4 \(\upmu\)m and the incident fluence of 0.5 (magenta), 0.55 (blue), 0.6 (green) and 0.65 \(\upmu\)J (red), respectively. (**a**) Closed aperture Z-scan (normalised transmittance with a 500-\(\upmu\)m aperture). (**b**) Open aperture Z-scan (normalised transmittance without aperture). \[T_{close}(z)=1+\frac{4(\frac{z}{z_{0}})\Delta\Phi}{(1+(\frac{z}{z_{0}})^{2}) (9+(\frac{z}{z_{0}})^{2})}-\frac{\beta I_{0}L_{eff}}{2\sqrt{2}}\frac{(3-( \frac{z}{z_{0}})^{2})}{(1+(\frac{z}{z_{0}})^{2})(9+(\frac{z}{z_{0}})^{2})}\quad; \tag{2}\]0 Nonlinear phase shift, \(\Delta\Phi\), as a function of the incident fluence for _c_-Si (blue) and _p_-Si (red). The squares represent the experimental results, and the solid lines are calculated with \(n_{2}\) obtained from the fitting results.
## 2 LMMSE Estimator within GNSS Augmentation Systems  
Similar to aviation applications of local area and wide area differential GNSS, the railway scenario can take advantage of well surveyed GNSS reference stations to generate differential corrections for pseudorange measurements obtained by a standard GNSS receiver. In the vicinity of railway lines, every GNSS equipped RS is able to derive pseudorange measurements between itself and each individual Space Vehicle (SV) in view. On the other hand, these RSs have well known positions as part of an augmentation system infrastructure. Differential corrections are hence generated, which are fairly accurate but affected by noise.
## 5 Machine Learning Algorithms
### Speech Emotion Recognition Models
#### 5.1.3 Recurrent Neural Networks: LSTM  
LSTM (long short-term memory) is a widely used deep learning architecture that excels at processing sequential data due to its capability to retain information over extended time steps. This makes LSTMs particularly effective for tasks involving time-sensitive or sequential data, such as speech recognition, language translation, and time series forecasting, where maintaining context over long sequences is crucial. The authors proposed four architectures [34] based on the RAVDESS dataset:* Stacked Time Distributed 2D CNN-LSTM integrates several 2D convolutional layers with an LSTM layer, enabling the effective learning of spatial features from sequences of images while also capturing temporal relationships.
* Stacked Time Distributed 2D CNN-Bidirectional LSTM with Attention improves upon this by adding a bidirectional LSTM and an attention mechanism, which enhances the model's ability to consider context from both preceding and subsequent frames in the input data.
* Parallel 2D CNN-Bidirectional LSTM with Attention employs parallel 2D CNNs to simultaneously extract spatial features from multiple inputs and channels them into a bidirectional LSTM with Attention, enhancing the model's focus on critical information throughout the input sequence.
* Parallel 2D CNN-Transformer Encoder leverages parallel 2D CNNs for feature extraction, followed by processing with a Transformer encoder that utilizes self-attention mechanisms, thereby facilitating a deeper understanding of intricate patterns in sequential data.  
The comparison of the presented classification models for emotion recognition along with their most important parameter, accuracy, is presented in Table 2.
## 4 Developing SNEP Model in AVISPA
### Simplification of SNEP Protocols  
#### 4.1.1 Number of Keys  
In original SNEP protocols, when \(A\) and \(B\) communicate, they use separate keys for encryption and MAC calculation and also in opposite directions. Thus, a set of four keys (_i.e._, \(K_{AB},K_{BA},K^{\prime}_{AB}\) and \(K^{\prime}_{BA}\)) is derived from the master secret, \(\chi_{AB}\). This provides the separation of cryptographic primitives. However, AVISPA does not differentiate among symmetric keys, and thus, to keep our model simple, yet not deviating from the original specification, we reduce the number of keys between \(A\) and \(B\) to one. Since, AVISPA does not allow key derivation, we simply pass the same symmetric keys to \(A\) and \(B\).
## 2 Background and the Related Work
### Openflow  
OpenFlow (OF) enables routing decisions and the transmission of rules such as load balancing, security, and quality of service (QoS) from the SDN controller to devices in the data plane. Southbound API is a widely used SDN protocol for facilitating communication between network devices and the SDN controller. The three primary components of an OpenFlow network architecture are the OpenFlow controller, OpenFlow protocol, and OpenFlow switch [17]. One or more flow tables containing flow entries are present in OpenFlow switches. Match fields and actions are included in each flow entry, and the controller fills the tables. The match fields are made up of a packet's header data, including the source and destination IP and MAC addresses, port numbers, and other details. Each action determines the packet's instructions. Along with basic flow data, like IP and port numbers, OpenFlow's flow table also offers a number of statistical features, including packet and byte numbers, duration, and so on [18].  
An OpenFlow switch looks up the appropriate command in its flow table when a packet arrives at the switch (for example, discarding or forwarding the packet). The packet is sent to the controller, a process known as packet-in, if there are no instructions in the flow table. Based on the packet's payload, header information, and statistics, the controller chooses the appropriate instruction. The packet is sent back to the switch, a process known as packet out, after the instructions have been decided. The flow table is updated in the interim. Subsequent packets from the same flow are treated in accordance with the instruction.  
When load balancing is used, the architecture enables a global view of the network and ongoing evaluation of the load on various links. This makes it simple to alter forwarding rules so that the load is shared equally. The basis of OpenFlow networks is the ability to conceive all traffic as flows, which means that different flows will have different entries in a flow table and that different rules can be defined for each entry. More specifically, the OpenFlow protocol facilitates information exchange between network devices in the forwarding plane and the SDN controller.
## 2 Laser Control of Ring Currents in a Molecule Having a Single Aromatic Ring
### Laser-Polarization Effects on Ring Currents within the Frozen-Nuclei Approximation
#### 2.2.2 Three-Level Model Analysis of Optical Excitation
This requires  
\[e^{i2\delta}\tan\beta=-\frac{\left(e^{i\theta}\mathbf{\mu}_{X}-\mathbf{\mu}_{ Y}\right)\cdot\mathbf{e}_{+1}}{\left(e^{i\theta}\mathbf{\mu}_{X}-\mathbf{\mu}_{ Y}\right)\cdot\mathbf{e}_{-1}}, \tag{12}\]  
where \(\theta\) is the relative phase between \(\Omega_{X}\) and \(\Omega_{Y}\), that is, \(\theta\equiv\arg(\Omega_{Y}/\Omega_{X})\). The condition \(|\Omega_{X}|=|\Omega_{Y}|\) can be fulfilled with the ellipticity angle \(\beta\) and orientation angle \(\delta\) determined by the absolute value and argument of the right-hand side of Equation (12), respectively. To render Equation (11) more compact, we introduce an alternative pair of superposition states:  
\[|U_{\pm}\rangle\equiv\frac{1}{\overline{\Omega}}(\Omega_{X}|X\rangle\pm\Omega _{Y}|Y\rangle)=\frac{\Omega_{X}}{\overline{\Omega}}\Big{(}|X\rangle\pm e^{i \theta}|Y\rangle\Big{)}, \tag{13}\]  
where \(\overline{\Omega}\equiv\Big{(}|\Omega_{X}|^{2}+|\Omega_{Y}|^{2}\Big{)}^{1/2}= 2^{1/2}|\Omega_{X}|=2^{1/2}|\Omega_{Y}|\). One can easily find that they are normalized and orthogonal to each other. We rewrite Equation (8) in terms of these superposition states as:  
\[|\Psi(t)\rangle=c_{G}(t)|G\rangle+e^{-i(\omega t+\varphi)}[c_{+}(t)|U_{+} \rangle+c_{-}(t)|U_{-}\rangle], \tag{14}\]where  
\[c_{\pm}(t)\equiv\frac{1}{\overline{\Omega}}\Big{[}\Omega_{X}^{*}e^{i(\Delta\omega t +\varphi)}c_{X}(t)\pm\Omega_{Y}^{*}e^{-i(\Delta\omega t-\varphi)}c_{Y}(t)\Big{]}. \tag{15}\]  
Then, the time evolution of the new coefficient vector \(\mathbf{D}(t)\equiv(c_{G}(t),c_{+}(t),c_{-}(t))^{\mathrm{T}}\) follows:  
\[\frac{d\mathbf{D}(t)}{dt}=\frac{i}{2}\left( \begin{array}{ccc}0&\overline{ \Omega}f(t)&0\\ \overline{\Omega}f(t)&0&2\Delta\omega\\ 0&2\Delta\omega&0\end{array} \right)\mathbf{D}(t) \tag{16}\]  
with the initial condition \(\mathbf{D}(0)=(1,0,0)^{\mathrm{T}}\).
# Polarization Effect on the Performance of On-Chip Wireless Optical Point-to-Point Links
## 4 Conclusions  
An integrated dielectric Vivaldi antenna has been proposed for point-to-point on-chip communication links. The antenna is constructed with a SiN slot waveguide tapered according to a Vivaldi profile, and it is directly coupled to an input SiN strip waveguide. The design criteria to optimize the antenna gain are defined in relation to the geometrical parameters of the Vivaldi-shaped slot waveguide and to the antenna length. The designed antenna exhibits the same high gain for both the TE and TM polarizations, (i.e., 23.8 dB and 23.52 dB for the TE and the TM modes, respectively).  
The analysis of point-to-point communication links between two Vivaldi antennas has been reported by considering the electromagnetic propagation in on-chip multilayer structures, with different thicknesses and different refractive indices of the cladding layer. It has been shown that, when the antennas are fed with TM-polarized modes, lower propagation losses are achieved.  
Given that the Vivaldi antenna is a non-resonant structure, it can allow broadband communication between two nodes in OWiNoCs, thus allowing the implementation of WDM schemes in on-chip communication.  
Conceptualization: G.C., G.B., F.F., M.B., V.T. and V.P. Methodology: G.C., G.B., F.F., M.B., V.T. and V.P. Software: G.C. and G.B. Validation: G.C., G.B., F.F., M.B., V.T. and V.P. Investigation: G.C., G.B., F.F., M.B., V.T. and V.P. Writing--original draft preparation, G.C., G.B., F.F., M.B., V.T. and V.P. Writing--review and editing: G.C., G.B., F.F., M.B., V.T. and V.P. Supervision: G.C. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the \"IMPACT\" project, funded by the Apulia region under the call \"Progetti di ricerca scientifica innovativi di elevato standard internazionale (art. 22 della legge regionale 30 November 2019, n. 52)\", by the project \"Integrated Magneto-Biplasmonic Circulator For Silicon Platform (IMPACTS)\", funded under the call Galileo 2022/ Galilee 2022, and by the European Union under the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on \"Telecommunications of the Future\" (PE0000001--program \"RESTART\").  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
Figure 15: Transmittance in dB, which is calculated at the receiver waveguide as a function of wavelength for the TE (solid curve) and the TM (dashed curves) polarizations. The link distance is \(\mathrm{d_{link}}=100\)\(\mathrm{\SIUnitSymbolMicro m}\), and a multilayer structure with PMMA as cladding polymer and cladding thickness \(\mathrm{d}=3\)\(\mathrm{\SIUnitSymbolMicro m}\) is considered.
## 3 Proposed Methods
### Long-Term Learning  
#### 3.3.1 Cluster Linear Regression  
To investigate the relationship between the PM\({}_{2.5}\) concentration and the explanatory variables, some existing works deployed various forms of linear regression, such as multiple linear regression [20] and dynamic multiple regression [17]. We devise another form of linear regression, namely, the cluster linear regression, which partitions the feature space into several clusters and interprets the relationship between the features and the response variable for each cluster by linear regression. The clustering process is conducted by a decision tree with six features. The features are selected by reference to the maximal information gain principle where the feature reducing the most regression RMSE (and thus with better interpretation capability) is selected next in the decision tree. To avoid selecting redundant features, the collinearity between the next feature and the features already selected is tested. In details, the clustering starts with the initial feature set \(I\), consisting of all 20 features listed in Table 2 and an empty set \(J\) of features for clustering. For each feature variable \(x\) contained in \(I\), we find the optimal threshold to partition \(x\) values into two subgroups and interpret all the plots (\(x\), PM\({}_{2.5}\)) in each subgroup by linear regression. The optimal value of the threshold is the one which minimizes the sum of the RMSE for the two subgroups. After all features in \(I\) have been examined, the best feature \(\hat{x^{*}}\) with the minimum sum of the RMSE is selected. We then test the Pearson collinearity between \(x^{*}\) and any variables already contained in \(J\). If their absolute Pearson correlation is all no greater than 0.7, we update the two feature sets by \(I=I-\{x^{*}\}\) and \(J=J\cup\{x^{*}\}\). Otherwise, we only perform \(I=I-\{x^{*}\}\) and test the next best feature \(x^{*}\) with the minimum sum of the RMSE. The process is iterated until \(J\) contains six features resulting in 64 clusters, i.e., the decision tree has six levels. As the amounts of our nine-year long-term data are huge, the maximal level of the decision tree is set to six by trading off the regression interpretability and the computational efficiency.
# An Empirical Recommendation Framework to Support Location-Based Services +
## 6 Discussion and Lessons Learned  
In our work, we proposed a system that considers the user's interest profile generated from their previous activities. To generate the user's interest profile, we used the DBSCAN clustering to form the parent category of the check-in spots. Then a collaborative filtering method is used to find the unvisited places that can also be a point of interest for a user to complete the user profile. We considered top-k users with a similarity score of more than 0.5 to identify the unvisited places. We introduced a grid-based structure to map the POIs into different grids to reduce the computational complexity. Finally, the two way of recommendation is generated. We considered the current location of the user and the physical distance between the suggested POI location and the user location for the recommendation. To provide the shortest distance we measured the manhattan distance for the single user. In the case of group recommendations, we analyzed all users' profiles of the group to find similar interests. Then their centroid is calculated to provide the recommendations at an optimal distance from all the users of the group. We tested our system in two categories for real world users and evaluated them accordingly.  
There are some features that can be added to our system for better improvement. For example, our work does not provide the facility with information about traffic congestion update on the road between the user's location and their destination. In the future, we will consider this to provide a congestion-free path to the user.