esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### The Self-Assembly Approach  
#### 2.1.1 Template-Monomer Interaction Studies  
Molecular modelling (MM) analysis (Supplementary Figures S1-S5) identified potentially favourable hydrogen bond interactions (<2.8 A) [48] between **1**, by virtue of the nitrogens of the piperazine ring, and the FMs **6**, **7** and **12-15** (see Figure 2 for FM structures) at **1**-FM ratios <1:2. Subsequent **1**-FM NMR interaction studies, i.e., \({}^{13}\)C NMR titration and Job plots (Supplementary Figures S9-S15), showed minimal or unfavourable interaction with **13-15**, but they did confirm favourable interactions with **6**, **7** and **12** at **1**-FM ratios <1:2.  
Figure 2: Chemical structures of functional monomers examined in this study: acrylic acid (**6**), methacrylic acid (**7**), acrylamide (**8**), methacrylamide (**9**), 4-vinylpyridine (**10**), styrene (**11**), itaconic acid (**12**), 2-hydroxyethyl methacrylate (**13**), \(N\),\(O\)-bismethacroyloyl ethanolamine (NOBE) (**14**) and 7-hydroxy-4-methylcoumarin acrylate (**15**).  
These interactions were demonstrated by significant upfield shifts in the resonance signals (2.5-6 ppm) of the carbons in the piperazine ring of **1** in the presence of FMs **6**, **7** and **12**. The resonance shifts could be attributed to the hydrogen-bond interactions between the adjacent nitrogens and the acidic moieties of the FMs. Conversely, the carbonyl carbons of FMs **6**, **7** and **12** also exhibited a downfield movement (deshielding) of their signals, an expected effect of the hydrogen-bond interactions. The Job plots generated for **1** and FMs **6**, **7** and **12** also showed well-defined 1:1 stoichiometries (Supplementary Figures S9-S11), further confirming favourable **1**-FM interactions.  
Although some interaction between **1** and our selected XL agents, ethylene glycol dimethylacrylate (EGDMA), trimethylolpropane trimethylacrylate (TRIM) and divinylbenzene (DVB), were noted from computer simulations (Supplementary Figures S6-S8), \({}^{13}\)C-NMR titration experiments presented very minimal **1**-XL interactions (Supplementary Figures S14 and S15), making them ideal for promoting **1**-FM association and the formation of template imprints.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 4 Advanced Theories and Methods for TD-NIRS
### Perturbation Theory
#### 4.5.4 TD-Perturbation Using the TD-RTE  
For more general treatment of the perturbation theory, [PERSON] and [PERSON] started from the TD-RTE for the Stokes vector of the radiance, \(\mathbf{I}=\{U,Q,U,V\}\), where \(I,Q,U,\) and \(V\) are the Stokes parameters describing the polarization state [86]. For simplicity of discussions, they first defined the operator of the RTE, \(\mathbf{L}\), for \(\mathbf{I}(t,\,\mathbf{r},\,\mathbf{n})\) at point \(\mathbf{r}\) in the direction \(\mathbf{n}\) at time \(t\), and its adjoint operator, \(\mathbf{L}^{\star}\), for the Stokes vector of the adjoint radiance, \(\mathbf{I}^{\star}(t,\,\mathbf{r},\,\mathbf{n})\), and then expressed an optical measurement of \(E\) (such as reflectance or transmittance) as the scalar product of a response function (equivalent to the Green's function) of \(\mathbf{R}\) and the radiance of \(\mathbf{I}\), \(E=\mathbf{<R}^{\star}\), \(\mathbf{\text{L}>}\) (superscript + denotes the transpose of the vector). Finally, they derived two basic equations for the perturbation approach as,  
\[\delta E\approx-<\mathbf{I}_{b}^{\star+},\delta\mathbf{L}^{\star}\mathbf{I}_{ b}>\text{and}\,\frac{dE}{dp}=-<\mathbf{I}_{b}^{\star+},\frac{d\mathbf{L}}{dp} \mathbf{I}_{b}> \tag{39}\]  
where subscript \(b\) denotes the background medium and \(p\) is a parameter such as \(\mu_{a}\) and \(\mu_{s}{}^{\prime}\). It is very difficult to explicitly give analytical solutions of the TD-RTE for the response function of \(\mathbf{R}\), because Equation (39) is expressed implicitly, but the perturbations of Equation (39) are given by the direct and adjoint solutions of the RTE, \(\mathbf{I}_{b}\) and \(\mathbf{\Gamma}_{b}^{\star}\), and for the background medium.
## 5 Experimental Results and Analysis
### Experimental Dataset
#### 5.1.2 SCB-U Dataset  
The SCB-U dataset [44] encompasses six common classroom behaviors: 'raising hand','reading', 'writing', 'using phone', 'bowing head', and 'learning'. The SCB-U dataset was compiled from actual classroom surveillance footage and expanded using \"frame interpolation\" techniques to ensure diversity and precise annotation. Despite the relatively singular  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Behaviors** & **Labels** & **Train** & **Val** & **Test** \\ \hline
1 & write & 1025 & 452 & 491 & 82 \\
2 & read & 1075 & 810 & 139 & 126 \\
3 & lookup & 5725 & 3620 & 1656 & 449 \\
4 & turn\_head & 1025 & 748 & 117 & 160 \\
5 & raise\_hand & 725 & 561 & 82 & 82 \\
6 & stand & 94 & 50 & 30 & 14 \\
7 & discuss & 242 & 172 & 50 & 20 \\ \hline \hline \end{tabular}  
* The table summarizes the SCBehavior dataset, including the number of labels and the distribution across training, validation, and test sets.  
\end{table}
Table 2: SCBehavior Dataset. The dataset includes the number of labels and the division into training, validation, and testing sets.  
Figure 6: The seven types of student behaviors are well-documented and displayed, demonstrating that our dataset encompasses a diverse range of classroom environments and student actions. This dataset can be effectively utilized for tasks related to classroom behavior recognition.  
perspective of the dataset images, its high annotation density and accurate simulation of real classroom environments make it invaluable for model training and evaluation.
## 3 Results and Discussion
### Sensing Performance  
In order to quantify the potassium sensing properties of this novel combination of materials, the device was immersed into aqueous solutions containing different salts in varying concentrations, but at a constant pH value (Figure 3). Ammonium chloride, potassium chloride, potassium nitrate and sodium chloride were tested in concentrations between 10 \(\mu\)M and 0.5 M. In order to determine the sensitivity of the photonic crystal film alone, KCl solutions were also applied to a PAM film without valinomycin/PVC membrane coverage. The presence of K\({}^{+}\) ions induces a color change at K\({}^{+}\) concentrations between 0.1 mM and 0.1 M. The response is virtually instantaneous (\(<\)10 s), and the effects are fully reversible without the need for adding any other reagents. In comparison to an ionic strength sensor without PVC film [26], response times are marginally longer (\(\sim\)2-5 s), due to the ion-selective membrane coverage. The logarithmic plot is linear in the concentration range between 1 and 50 mM, and the slope is 53 nm per one log order of [K\({}^{+}\)]. This obviously also covers the typical concentration of K\({}^{+}\) in serum (\(\sim\)4 mM). In addition, the sensor is very selective in that monovalent cations of approximately the same size (ammonium and sodium) do not cause a signal change over the whole concentration range (Figure 3). This also confirms that the PVC membrane prevents the direct contact of the analyte with the PhC film. The corresponding anion appears not to have an effect, since KCl and KNO\({}_{3}\) give the same response.  
It is useful to have a look at the PhC-based sensor for K\({}^{+}\) described by [PERSON] _et al._[30]. It is composed of a single-layer consisting of poly(N-isopropylacrylamide) (PNIPAM) modified with a crown ether. The maximum of the reflected wavelength shifts from 500 to 700 nm if [K\({}^{+}\)] is increased from 0 and 40 mM, and its lower limit of detection is rather high (5 mM). By comparison, the sensor film presented here undergoes a smaller spectral shift (from 530 to 440 nm), but has a much higher dynamic range  
Figure 3: The effect of the salt concentration on the wavelength of the reflected light of colloidal PhCs in a hydrolyzed PAM hydrogel covered with a K\({}^{+}\)-selective membrane. The film was immersed into solutions of KCl, KNO\({}_{3}\), NaCl and NH\({}_{4}\)Cl of varying concentrations. Each data point represents the average of five measurements; the error bars display the respective standard deviations.  
(from 0.1 to 100 mM). In addition, the PNIPAM-based sensor does not respond linearly, and solutions containing ammonium cations (which are known to bind to certain crown ethers) have not been examined [30]. Finally, the maximum wavelength of reflection of PNIPAM is known to be highly temperature-dependent. We therefore believe that our system has distinct advantages.
# Magnetic Localization Method for Vehicles Based on Transformer
## 3 Methods
### Transformer Model Structure
#### 3.2.2 Decoder  
As shown in Figure 6, the deep magnetic features extracted from the encoder, along with the previous positions, are inputted into the decoder. Similar to the encoder, shallow position features with sizes of [\(B\),\(iD\)] are extracted from the previous positions by a simple convolutional layer and a positional embedding layer, where \(i\in[1,W]\). The attention mask is a predefined tensor, which is described in detail below. Then, the position features pass through \(N_{2}\) Transformer decoder layers and are transformed into deep position features. Finally, the position features are projected to the coordinates of the target position (\(x\),\(y\)) in 2 dimensions.  
Since the decoder relies on the previous positioning results, it is implemented in a recurrent manner to obtain the final location. Specifically, given a new magnetic sequence, the position of the first point in this sequence is predicted first by executing the decoder once, where [\(-1\), \(-1\)] is chosen as the initialized input. Then, the position of the second point can be computed by running the decoder again. This recursive procedure can be applied to the entire sequence. Therefore, in the online phase, the decoder is operated \(W\) times to obtain the final position.  
The decoder layers have the same structure as those in [30] and are slightly different from the encoder layers, as shown in Figure 7. The decoder layers replace the self-attention layer with a masked self-attention layer and have an extra cross-attention layer.  
**Masked Self-Attention Layer**: This layer is a variant of the self-attention layer. The only difference is that an attention mask \(Mask_{attl}\) with a size of \([i,i]\) is added to the attention computation. \(Mask_{attl}\) is an upper triangular matrix, and Equation (5) shows an example of \(Mask_{attl}\) when \(i=5\).  
\[Mask_{attl}=\begin{bmatrix}0&-\inf&-\inf&-\inf&-\inf\\ 0&0&-\inf&-\inf&-\inf\\ 0&0&0&-\inf&-\inf\\ 0&0&0&0&-\inf\\ 0&0&0&0&0\end{bmatrix} \tag{5}\]  
Figure 6: Structure of the decoder.  
Figure 7: The structure of a decoder layer.  
This mask is applied in the attention computation, as shown in Equation (6):  
\[\text{MaskAttention}(Q,K,V,Mask_{att})=\text{softmax}(\frac{QK^{T}}{\sqrt{d_{k}}}+ Mask_{att})V \tag{6}\]  
For point \(k\) in the input sequence, -inf means that the attention response is not activated from a point with \(i>k\), and the attention response from \(i\leq k\) remains. This design ensures that only the previous input contributes to the output at the current moment and that a future input does not influence past outputs.  
**Cross-Attention Layer**: This layer differs from self-attention layers in terms of the sources of \(Q\), \(K\), and \(V\). In the self-attention layer, \(Q\), \(K\), and \(V\) come from the same sources as those of the input. However, in the cross-attention layer, \(Q\) is generated by the input as well, while \(K\) and \(V\) are generated by the deep magnetic features extracted from the encoder.
## 3 Results
### Electrochemical Characterization  
Chronoamperometry and cyclic voltammetry (CV) experiments were performed to investigate the suitability for potential-controlled adsorption as well as the electrochemical reactions between the monoliths' surface and the electrolyte environment. Within this framework, maleic acid as a negatively charged electrolyte and target molecule is frequently studied for electrosorption onto carbon electrodes [30; 31; 32; 36; 58]. [PERSON] et al. [36] and [PERSON] et al. [31] already observed reversible maleic acid binding onto a particulate CNT electrode in aqueous systems, thus ensuring its suitability to study the surface-liquid interface of our novel monolithic CNT electrodes.  
Figure 7a,b show an initial high current due to the electrodes' double-layer charging. As the double layer extends, the current profile declines until a nearly constant value is achieved. The curves do not reach zero. Hence, a remaining faradaic current is observed for untreated and oxidized monoliths, confirming deviations from an ideal capacitive system. Consequently, the studied monolithic electrodes display a resistance, which can be related to the observed faradaic current. This observed equilibrium current is generated by redox reactions causing constant electron flow on the electrode and is illustrated in Figure 7c. Hereby, an increased current response for monoliths comprised of oxidized CNTs is visible compared to untreated CNT monoliths. The higher faradaic current results from redox reactions between functional groups on the surface of the oxidized nanotubes [59; 60]. This assumption can be further confirmed regarding the measured cyclic voltammograms of untreated and oxidized CNT monoliths in Figure 8. As the successive scans remain nearly congruent, no irreversible reactions are expected for the untreated CNTs. Moreover, its smooth shape approximates a capacitive behavior. Monoliths consisting of oxCNTs, on the other hand, behave slightly differently. A greater CV area can be observed, indicating an enlarged capacitance. At around +550 mV, a redox peak is passed through, assigned to oxidized groups on the monolith's surface. Moreover, the voltammetry scans for oxCNT monoliths pictured in Figure 8 behave slightly inconsistent. Thus, further irreversible reactions on the surface-electrolyte-interface might be displayed. While the increased current response of oxCNT demonstrates improved electrosorption capability, indicated irreversible reactions might negatively impact a reversible process. The following section examines how these interrelationships are manifested in a practical electrosorption process.
# Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling
## 4 Experimental Analysis
### Baselines  
According to the topic of intent detection and slot filling in a task-oriented dialog system, we tested many studies on ATIS that focus on the intent-detection or slot-filling tasks to serve as baselines.  
* **RNN-LSTM:** [PERSON] presented an approach that jointly modeled slot filling, intent detection, and domain classification in a single bidirectional RNN with LSTM cells [26].
* **Attention-based RNN:** [PERSON] and [PERSON] studied using an RNN for the NLU task, with particular attention on modeling the output sequence dependencies [27]. The authors proposed to model the slot-label dependencies using a sampling approach by feeding the sampled output labels back to the sequence state.
* **Slot-gated:** [PERSON] proposed a slot-gated model that introduces an additional gate that leverages the intent context vector to improve the slot-filling performance [28].
* **CAPSULE-NLU:** [PERSON]. [PERSON] proposed a capsule-based neural network model that accomplished slot filling and intent detection via a dynamic routing-by-agreement scheme [37].
* **Joint BERT:** Q. [PERSON] proposed a joint intent-classification and slot-filling model based on BERT [33].
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 5 The Applications of Electrosprayed PCL Particles
### Control Release and Drug Delivery  
In the PCL matrix, the slow controlled release behavior, compared to other biodegradable polymers, allows vectorizing the drug to the suitable cells. Furthermore, this polymer shows a high selective permeability towards small drug molecules, and drug degradation in an acidic medium may be prevented. Thus, PCL electrospray particles are suitable for long-term delivery extending throughout more than one year [172], especially in the biomedical field for parenteral and oral drug deliveries, in which anti-hypertensive, antibiotic, anti-inflammatory drugs are entrapped [94; 128; 161; 164]. The applications of electrospray particles in the field of biomedical engineering depend in part on their morphology. Thus, interactions between cells and biomaterials are promoted when particles have a gully-shaped surface with wrinkles [173]. Semi-spherical particles with a porous structure are used in multi-stage biological and drug delivery scaffolding [174]. Also, their high specific surface area coupled with low density allows them to be used for pulmonary drug delivery.  
There are two main methods to encapsulate the drug via the electrospray route, according to their solubility and the desired morphology and final used end, i.e., (i) dispersion of the active substance in the PCL solution to obtain microspheres, and (ii) the use of a coaxial system for the production of core/shell structure [95]. The release behavior depends on the particle size as well as the surface area/volume ratio, to allow the drug diffusion [95; 127], and burst release is reached from tinier particles [94]. The release rate significantly increases for thin layer shell or porous membrane as well as the wrinkled surface structure and is homogeneous for a monodispersed particle size. The polymer degradation rate depends on the structure and morphology of the particles [18; 19; 95]. Thus, it increases with decreasing the size of microcapsules, and wrinkled and porous morphology induce its degradation [175], and a high specific contact area improves interactions between particles and cells [17].
## 4 Analytical Model and Evaluation
### Relation between the Analytical Evaluations and Simulations  
To verify the mathematical abstractions that were presented in this section, it is both useful and common to validate the analytical results with simulations. Here we give a brief explanation of the simulations but the detailed explanation of the simulation environment can be found in the next section. The experiment includes 25 randomly placed anchors and 1 MN moving in a random trajectory.  
Figure 6: Probability of packet loss, disconnections of individual links, and handoff in time. Comparing figures (a,b), we can observe higher packet loss probability stemming from fluctuations in the link quality. Comparing Figures (a,c), the higher packet loss probability is stemming from the increased velocity.  
The simulations showed the packet loss increases by path loss variance as illustrated in Figure (a)a. The simulation results are aligned with the probability of packet loss in the analytical evaluation in Figure (a)a.  
As depicted in Figure (b)b, increasing the velocity of the MN also impacts the reception ratio of the data packets in the same way as the analytical evaluations suggested (in Figure (c)c).
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 4 Evaluation
### Backscatter Communication
#### 4.4.2 Simulation Study  
In the real-world experiments, we did not have full control over all variables, i.e., the channel impulse response, the extent to which the tag clock varies, and the amount of noise in the signal. To characterize the influence of various parameters on the receiver, we also implemented a channel simulation incorporating multipath propagation, time-varying SFE, and noise levels. In Figure 16 left, we show the influence of different channel impulse responses on the output SNR of the receiver. We compare three channel impulse responses: an ideal channel without ISI and two previously recorded channels. Gaussian noise was applied after convolution with the impulse response to make SNRs comparable between different channels. We observe that the impulse response greatly influences the output of the receiver. With an ideal channel, the SNR of the raw signal and the SNR of the equalized signal are up to 10 dB larger than with impulse response IR1, and channel IR2 produces less noise at the equalizer output than IR1. In Figure 16 right, we generated a gaussian distributed time-varying SFE similar to the observed variations from the actual low-power oscillator shown in Figure 11. We varied the standard deviation of the simulated SFE and measured the Bit Error Rate (BER) over 5000 simulated messages (1 million bits). The results indicate that even with an SFE close to zero, the decoder without PLL has a high BER (\(5\times 10^{-3}\)). We attribute this high BER to incorrect estimation of the SFE during preamble detection, accumulating into larger phase errors throughout the message. In direct comparison, a decoder with PLL significantly reduces the bit error rate by more than two orders of magnitude to roughly \(1\times 10^{-5}\). However, the gain of the PLL plays an important role. With an SFE deviation in the range of \(2\times 10^{-4}\) (200 ppm), which we observed on the low-power oscillator, the decoders with medium to high gain PLL achieved BERs of about \(3\times 10^{-4}\).
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 5 Evaluation and Results  
In this section, we present the validation tests of SD6 WSN. Functional assessment was performed in a real testbed and the performance evaluation in the COOJA/Contiki simulation environment. The use of COOJA allows collection of data, either by the log of the UART of the emulated motes or by the capture of the packets transported between them in the same format of real networks.  
We performed two experiments to evaluate the 6 LoWPAN network, with SDN (SD6 WSN) and traditional (RPL) routing approaches. As in the other proposals of the SDN approach, ours uses the 6 LoWPAN protocol stack as the message delivery protocol (in the control plane), just as the OpenFlow approach uses the TCP-IP protocol stack to deliver its control messages. The first experiment simulates a typical AMI network scenario in residential areas, with nodes distributed along a straight path on both sides of the street. Here, the multi-hop characteristic prevailed, with a high number of jumps between the 6 LBR and the most distant node. The main objective of this experiment is to evaluate the overhead introduced by SD6 WSNP, with respect to RPL. The goal is to compare the competing approaches in the communication scenario most favorable to RPL, that is communication traffic occurs only from nodes to 6 LBR and vice versa. To evaluate the performance of our approach in this condition, we measured the overhead incurred by SD6 WSNP messages and the end-to-end delay. We expected that the overhead would not be much higher, not excessively increasing network charge with  
Figure 12: Complete 6 LBR in a mote with Ethernet interface.  
the additional control messages, thus providing similar average latency. In the second experiment, we evaluate a different traffic model. We select a classic grid topology to evaluate network performance between peer-nodes. The goal is to compare the competing approaches in a communication scenario that tends to appear more frequently in solutions aimed at the Internet of Things, where this type of communication is expected. To compare the performance of the two approaches, we compare the average (one-way) communication latency between pairs of randomly-chosen nodes.