esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Quasi-Standardized 2-D Dynamic Time Warping
### Quasi-Standardized Two-Dimensional Dynamic Time Warping  
In this paper, we introduce a new variant of Dynamic Time Warping for two-dimensional time series called Quasi-Standardized Two-Dimensional Dynamic Time Warping (QS-2 DDTW) as similarity measure of environment scan between two consecutive time steps, which acts as a kidnapping detector. Let \(\mathcal{P}\) and \(\mathcal{Q}\) define two point clouds extracted based on the reading of environment by range finder sensor at two time instances \(t-1\) and \(t\), respectively. The complete algorithm QS-2 DDTW is shown in Algorithm 3.  
```
1.QS-2 DDTW Algorithm
2. Decompose each dimension of \(\mathcal{P}=\big{[}\ \mathcal{P}_{x}\ \ \ \ \mathcal{P}_{y}\ \big{]}\) and \(\mathcal{Q}=\big{[}\ \mathcal{Q}_{x}\ \ \ \ \mathcal{Q}_{y}\ \big{]}\)
3. Perform variance homogeneity test (dedicated test to be employed depends on normality of the noise) on both dimensions
4.if\(V_{x}<\boldsymbol{\alpha}\) and \(V_{y}<\boldsymbol{\alpha}\) and \(V_{x}>1\) and \(V_{y}>1\)
5. \(\mathcal{P}=\bigg{[}\ \frac{p_{x}-\overline{p}_{x}}{\sigma_{p_{x}}}\ \ \ \frac{p_{y}- \overline{p}_{x}}{\sigma_{p_{y}}}\ \bigg{]}\)
6. \(\mathcal{Q}=\bigg{[}\ \frac{Q_{x}-\overline{Q}_{x}}{\sigma_{0x}}\ \ \ \ \frac{Q_{x}- \overline{Q}_{x}}{\sigma_{0y}}\ \bigg{]}\)
7. End if
8. Construct distance matrix \(\mathcal{D}(\mathcal{P},\mathcal{Q})\in\mathcal{R}^{\mathcal{L}_{\mathcal{P}} \times\mathcal{L}_{\mathcal{Q}}}\) according to \[\mathcal{D}(\mathcal{P},\mathcal{Q})_{i,j}=\sum_{k=1}^{2}\big{|}p_{i,k}-q_{j,k} \big{|}\]
9. Find the minimum cost path using the regular DTW under the constraints stated in Remark 1 on this distance matrix.
```  
**Algorithm 3** QS-2 DDTW algorithm  
The introduction of variance homogeneity test in line 3 has a purpose to selectively decide when to do standardization and when not to do it. This selection is an attempt to improve DTW detector's ability to distinguish between normal and kidnapping condition, i.e., increasing their performance index. Please note that the choice of \(V\) depends on the estimated normality of measurement noise. In order to verify the improvement, the same performance test as in Algorithm 2 is performed. The result of the test is shown in Figure 3.  
The result of unstandardized and standardized versions is also shown for comparison purposes. It is clear from the figure that introducing selection of standardization improves the performance of both 1-D DTW and 2-D DTW as indicated by the absence of negative values while maintaining the high performance level of the unstandardized version. Among these two approaches, however, QS-2 DDTW still outperforms QS-1 DDTW.
## 3 Results  
### RAMMS Model  
For the terrain model, a pre-event DTM with \(5\times 5\) m resolution was used. The initial block has an average depth of 15 m, estimated from the analysis of different epoch DTMs. This method was also useful in defining other parameters such as erosion depth (estimated to be a maximum of 20 m in some parts), deposition height, and the outline of debris flow. RAMMS, based on Voellmy rheology, was used to back-calculate parameters of friction and turbulence. After many iterations, the final best-fitted parameters were determined to be 0.11 for friction and 500 m/s\({}^{2}\) for turbulence.  
For including entrainment, the default value of 1 kPa was used for critical shear stress, and a rate proportionality factor of \(\rm dz/dt=0.1\) m/kPa. Greater values of the proportionality factor tend to overpredict erosion volume. The results of volume and transportation of material with and without entrainment were compared. In the case of Selanac, an estimated (on the field) maximum erosion depth of 12 m was used. A comparison of two epochs of DMT was utilized to define the differences in the transportation zone. Considering entrainment, the deposition heights were slightly larger, and the total volume was relatively greater. It can be concluded that entrainment influenced the outflow of material in the Selanacka river, but generally did not change the model of final heights in the deposition zone (Figure 9b,c, Table 3). The model shows a height in the deposition zone of almost 25.56 m, while the final depths in the deposition zone are almost 17 m (Figure 9c).  
The other model shows changes in velocity (Figure 9d). The maximum velocity was over 33 m/s, which was also predicted in the Selanacka river zone. However, since we have torrential flow in that zone, this part of the terrain outside the first deposition zone cannot be taken as representative, as velocities in this part were probably much greater due to the river's influence.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Debris Flow**} & **Rheology Model** & **Volume of Initial Block** & **Volume of Entrainment** \\  & **(Voellmy)** & **(m\({}^{3}\))** & **(m\({}^{3}\))** \\ \hline Selanac & 0.11; 500 & 453,061.15 & 41,023.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Final results of the parameters in RAMMS model.
## 5 Performance Evaluation of MEC B5G Cellular Networks
### Edge Platform Virtualization Implementation  
The edge computing platform held by the Private/Local Telecom Operator requires that various applications (RAN/Core/Apps) run on shared hardware. Figure 11 shows the architecture that the applications can run on the virtualization platform. In this figure, COTS HW requires a Management (MGMT) GUI function that remotely controls the OS and power supply. For the OS, a general-purpose OS such as Ubuntu/CentOS/RedHat/Windows is used. As a Virtualization Platform, HW has vFPGA/vMEM/vCPU/vGPU/vSSD/vHDD/vNetwork/vNIC/vDriver that can be provided as virtualization as NFVI and can be managed by VIM, and specify it at the time of deployment according to the application requirements for virtualization. An API (Restful/CLI/SSH/YANG) can control VIM for NFVI via MGMT NW. RAN Software/Core Software/MEC Platform/MEC Apps can be operated as an application. RAN Software is divided into vDU/vCU. Furthermore, vCU is separated by CUPS and is divided into vCU-CP/vCU-UP. The interface between vDU and vCU is divided by Split Function (e.g., Split Option 6). Core Software is a 5 GC-based function that can be deployed as needed, such as UPF, AMF, and SMF. MEC Platform and MEC App, etc., mainly indicate applications used by end users when a local breakout is required. As it runs on the same COTS HW as the above application, it can be operated by sharing resources and managing and monitoring it on the virtualization platform. In addition, the virtualization platform is adopted as a single factor without multiplication in order to accommodate virtual applications for high processing performance.
# Drone-Based Parcel Delivery Using the Rooftops of City Buildings: Model and Solution
## 5 Experiments  
### Experimental Design  
To check the feasibility of the proposed DROP-model and evaluate the performance of the proposed heuristic-based solution, we create various problem instances by considering a real situation in one of the districts of Seoul (the capital city), South Korea. The Gangnam District is one of the 25 local government districts that make up the city of Seoul, and it is the third-largest district in Seoul, with an area of 39.5 km\({}^{2}\). As of the 2017 census, Gangnam District had a population of approximately 560 thousand. Residential and commercial areas are located together in this district, and thus, there is a great demand for parcel delivery every day. Along with several private delivery companies, Korea Post is also responsible for the delivery demand in this district. Korea Post, a government organization, usually accounts for approximately 10% of the domestic market share (ranked 4 th) for parcel delivery in South Korea. The Gangnam Post Office (GPO) is one of several post offices in Seoul operated by Korea Post and is located in the northeastern part of the Gangnam District. There are many residential and business buildings within a 6-8 km radius of the GPO, and some of their demand needs to be fulfilled by the GPO. Thus, for our experiments, we constructed a delivery network using the rooftops of the GPO and several nearby buildings, as shown in Figure 6.  
For the experimental delivery network, we create three different problem instances: small (2 drones and 10 destinations), medium (4 drones and 20 destinations), and large problem (10 drones and 50 destinations) instances. For each problem size, 20 different problem instances were randomly generated for testing. The distance and daily demand for each location, used in creating the problem instances, are randomly chosen from real values between 1 and 10 for distance, and integer values between 1 and 5 for daily demand, respectively.  
All experiments were implemented on a personal computer with an Intel(r) Core(tm) i5 3.10 GHz CPU and 8.00 GB RAM. We used GUROBI Optimizer 8.1 ([[http://www.gurobi.com](http://www.gurobi.com)]([http://www.gurobi.com](http://www.gurobi.com))), a commercial optimization tool, to run the DROP-Model to obtain the optimal solution. Additionally, we implemented the proposed heuristic using Python 3.7.1 and Conda 4.7.5.  
Figure 6: Experimental delivery network in the Gangnam district of Seoul.
## 2 Materials and Methods
### Drying Experiments  
Fresh figs (8.0 kg) were selected from the same raw material batch and weighed accurately on an electronic balance. The fresh figs were divided into four groups (2.00 \(\pm\) 0.05 kg each) using a randomized block design. Samples of each group were cut by a food-grade stainless steel slicer (with blade spacing calibrated to 0.5 cm), and fruit slices with uniform thickness were obtained. Each group was dried by a different drying method. Four drying methods are used: hot air drying (HD), vacuum drying (VD), far infrared drying (FID) and vacuum freeze-drying (FD). Briefly, the hot air drying group was dried in an electrically heated blast drying oven (DHG-9030A, Shanghai Bailuda Instrument Co., Ltd., Shanghai, China) [20], and kept at 80 \({}^{\circ}\)C and an air velocity of 2 m/s for 6 h. The vacuum drying group was dried in a vacuum drying oven (Shanghai Jinghong Experimental Instrument Co., Ltd., Shanghai, China) at 60 \({}^{\circ}\)C for 8 h [21]. The far-infrared drying group was dried in a far-infrared dryer (HWG-2, Beijing Zhongxing Weiye Century Instrument Co., Ltd., Beijing, China) with a power of 2.4 kW and a wavelength of 2-15 \(\upmu\)m, kept at 80 \({}^{\circ}\)C for 5 h [22]. The vacuum freeze-drying group was dried for 48 h in a vacuum freeze-dryer (SCIENTZ-10 ND, Ningbo Kanda Biotechnology Co., Ltd., Ningbo, China) with a vacuum of less than 50 Pa and an absolute pressure of 10 Pa in the drying bin [23]. Pre-vacuum drying, fig slices were pre-frozen in a -30 \({}^{\circ}\)C refrigerator (DW-40L278], Qingdao Haier Biomedical Co., Ltd., Qingdao, China) for 12 h. The moisture content of all four dried figs was less than 10% (g/g), which was determined using a moisture meter.
# DATLMedQA: A Data Augmentation and Transfer Learning Based Solution for Medical Question Answering
## 4 Experiments and Results
### Performance Evaluation  
In this section, we present the experimental results and performance evaluation of our system. We especially evaluate the effectiveness of the T5-Small model and data augmentation using the GPT-2 model. The performance of our model running on two test sets (MEDIQA-2019 and TRCEQA-2017) is shown in Table 5. The ALBERT + disease model is the best performance model as studied in [41] and we take it as the SOTA model. Without GPT-2 augmentation, our system can achieve a performance very close to the SOTA model, while after GPT-2 augmentation our system outperforms the SOTA model. For example, our system achieves 80.23% in terms of accuracy and 84.31% in terms of precision, which are superior to 79.49% in terms of accuracy and 84.02% in terms of precision obtained by the ALBERT + disease (SOTA) model on MEDIQA-2019. Similarly, our proposed system shows better performance on TRCEQA-2017 than the SOTA model in terms of accuracy, precision and MRR. The results demonstrate the advantage of using the GPT-2 model and the T5-Small model for medical question answering and question generation tasks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**MEDIQA-2019**} & \multicolumn{3}{c}{**TRCEQA-2017**} \\ \cline{2-7}  & **Acc** & **MRR** & **Precision** & **Acc** & **MRR** & **Precision** \\ \hline T5-Small + XGBoost + disease * & 79.11 & 91.04 & 82.41 & 79.75 & 57.63 & 62.87 \\ \hline GPT-2 + T5-Small + XGBoost + disease * & 80.23 & 92.17 & 84.31 & 80.5 & 58.49 & 63.71 \\ \hline ALBERT + disease * (SOTA) & 79.49 & 90 & 84.02 & 80.1 & 57.21 & 62.4 \\ \hline \hline \end{tabular}  
* * + disease* means that we train BERT through disease knowledge injection before fine-tuning  
\end{table}
Table 5: Experimental results: comparison with SOTA.  
Tables 6 and 7 present the results of getting \(S_{2}\) from Q before and after data augmentation. The test set is MEDIQA-2019. As shown in Table 7, after augmentation using GPT-2, the training loss and validation loss at epoch 3 are significantly reduced, while Rouge-1 (the overlap of unigram (each word) between the system and the reference summaries). Rouge-2 (the overlap of bigrams between the system and the reference summaries), and Rouge-L (the longest common subsequence (LCS)-based statistics) are significantly increased. The results indicate the improvement of generating S with the augmentation using GPT-2.  
demonstrates some full positives. From the results of the two tables, we can learn that we should not count only on cosine similarity to make decisions. That is also why we chose XGBoost, which takes a three-dimensional input and cosine similarity is part of it.
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
## 3 Proposed Algorithm
### Optimizing \(w_{k},\rho_{k},t_{k}\) Given \(\Theta\) and \(P\)  
Since \(\Theta\) and \(P\) appear as products in problem (P2), we define \(Y=P\Theta\) such that optimizing \(\Theta\) and \(P\) is equivalent to optimizing the RIS precoding matrix of \(Y=P\Theta=\mathrm{diag}\big{(}p_{1}e^{i\theta_{1}},\cdots,p_{N}e^{i\theta_{N} }\big{)}\). Next, the optimization objective (15) is transformed into a first-order function using the following lemma. **Lemma 1** (Taylor series expansion formula): _Assuming that the function \(f(x)\) has a derivative of order \(n\) on some closed interval \([a,b]\) containing \(x_{0}\) and a derivative of order \((n+1)\) on the open interval \((a,b)\), the following equation holds for any point \(x\) on the closed interval \([a,b]\)[35]:_  
\[f(x)=\frac{f(x_{0})}{0!}+\frac{f^{\prime}(x_{0})}{1!}(x-x_{0})+\frac{f^{\prime \prime}(x_{0})}{2!}(x-x_{0})^{2}+\cdots+\frac{f^{n}(x_{0})}{n!}(x-x_{0})^{n}+ R_{n}(x), \tag{21}\]  
_where \(R_{n}(x)=o\big{(}(x-x_{0})^{n}\big{)}\) represents the remainder term of the Taylor series expansion formula, which can be ignored in the conversion since it is an infinitesimal term. A Taylor first-order expansion of \(t_{k}^{2}\) using Lemma 1 above gives_  
\[t_{k}^{2}=t_{k_{0}}^{2}+2t_{k_{0}}\big{(}t_{k}-t_{k_{0}}\big{)}. \tag{22}\]  
In addition to this, this paper defines \(W_{k}=w_{k}w_{k}^{H}\) and \(H_{k}=h_{k}h_{k}^{H}\,\forall k\) so that (15) can be transformed into the following form:  
\[(P3):\underset{w_{k},\rho_{k},t_{k}}{\text{maximize}}\,f_{3}=\sum_{k=1}^{K}t _{k_{0}}^{2}+2t_{k_{0}}\big{(}t_{k}-t_{k_{0}}\big{)}. \tag{23}\]  
For (16), we define \(\widetilde{\sigma}=\sigma_{k}^{2}+\frac{\delta_{k}^{2}}{\rho_{k}}\). The following process can be obtained by shifting terms:  
\[\frac{\left|\mathrm{h}_{K}^{H}w_{k}\right|^{2}}{\gamma_{k}}-\sum_{ \begin{subarray} {c}i=1\\ i\
eq k\end{subarray} }^{K}\left|\mathrm{h}_{K}^{H}w_{i}\right|^{2}\geqslant \sigma_{v}^{2}\left\|\mathrm{h}_{r,k}^{H}P\Theta\right\|^{2}+\widetilde{ \sigma},\]  
\[\frac{\text{Tr}\Big{(}\mathrm{h}_{K}^{H}\mathrm{h}_{K}w_{k}^{H}w_{k}\Big{)}}{ \gamma_{k}}-\sum_{ \begin{subarray}{c}i=1\\ i\
eq k\end{subarray} }^{K}\text{Tr}\Big{(}\mathrm{h}_{K}^{H}\mathrm{h}_{k}w_{ i}^{H}w_{i}\Big{)}\geqslant\sigma_{v}^{2}\left\|h_{r,k}^{H}P\Theta\right\|^{2}+ \widetilde{\sigma},\]  
\[\frac{\text{Tr}(H_{k}W_{k})}{\gamma_{k}}-\sum_{ \begin{subarray}{c}i=1\\ i\
eq k\end{subarray}
## 2 Materials and Methods
### Feature Aggregation Wrapper Module
#### 2.1.2 Feature Enhancement  
The FAW module performs a channel-wise segmentation on \(\overline{\mathrm{f}_{\mathrm{ctr}}}\). For the segmented features, the FAW module collects and concatenates the channel features belonging to the same dimension across the temporal dimension T, to obtain sets of channel features \(\mathrm{f}_{\mathrm{chan}}\in\mathrm{R}^{\mathrm{H}\times\mathrm{W}\times \mathrm{T}}\) for C channels. Subsequently, a DCN modified the \(\mathrm{f}_{\mathrm{chan}}\) of different channels, and then a \(1\times 1\) convolution was utilized to aggregate all information modified by DCN, generating an aggregated feature with a size of \(\mathrm{Hf}\times\mathrm{W}\mathrm{f}\times 1\) for each channel. Finally, the inherited features, \(\mathrm{f}_{\mathrm{aggr}}\in\mathrm{R}^{\mathrm{Hf}\times\mathrm{W}\times \mathrm{C}}\), were obtained by concatenating the channel-wise tensor. The features \(\mathrm{f}_{\mathrm{aggr}}\) were then summed pixel-wise with the existing output features, \(\mathrm{f}_{\mathrm{t}}\), to obtain the enhanced features of the imagery in the following equation:  
\[\mathrm{f}_{\mathrm{aggr}}=\mathrm{concat}(\mathrm{Conv}_{1\times 1}( \mathrm{DCN}[\,\mathrm{f}_{\mathrm{chan}}[\mathrm{i}]\,)\,))\,,\ \mathrm{i}=0,1,2,\ldots,\mathrm{C} \tag{3}\]  
\[\mathrm{f}_{\mathrm{ench}}=\mathrm{f}^{\mathrm{(t)}}+\mathrm{f}_{\mathrm{aggr}} \tag{4}\]  
where the DCN is set with a kernel size of 3. The enhanced features, \(\mathrm{f}_{\mathrm{ench}}\in\mathrm{R}^{\mathrm{Hf}\times\mathrm{W}\times 64}\), incorporated motion cues from the previous T frames, and were subsequently fed into the detection heads for object center heatmaps and center offset predictions, while the bounding box predictions were based on the original \(\mathrm{f}^{\mathrm{(t)}}\).
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 6 Discussion of Trends in Photonics Packaging  
The high-cost and low-speed of photonic packaging is probably the most significant bottleneck in developing competitive photonic devices. As spin-out companies work to take devices to market, and large multi-nationals enter the Si-photonics space, there is a growing need to develop automated packaging processes to enable higher volume production. The clear trend is for more compact photonicdesigns with lower insertion-loss, higher levels of integration (electrical and MEMs), scalability to many multiples of 25 Gbps channels, and efficient (ideally passive) thermal stabilization. To meet this challenge, researchers in photonic packaging are transitioning from a legacy of individually customized prototypes towards developing standardized and scalable solutions. While these technology innovations of passive Fiber-to-PIC alignment, more precise flip-chip vertical integration, and better thermal-stack design all play an important role,'soft' developments, such as the publication and adoption of packaging standards, as well as the growth and consolidation of the material and component supply-chain, are equally important.  
The need to create standardized design rules and standards for photonic packaging is slowly gaining recognition in the Si-photonics community. This shift is driven by the benefit of removing application-specific design-burdens from researchers/engineers, and streamlining the design-to-device process. Photonic packaging groups are now working with industry partners to establish packaging design kits (PDKs) and rules (PDRs) that support non-expert users in developing Si-PICs that are compatible with best-practice photonic packaging. This helps new users avoid problems and avoid costly and time-consuming PIC re-designs. Examples of important PDR parameters that are often overlooked by new users are (i) the pitch of grating-coupler arrays on the Si-PIC (which should match the dimensions of the fiber-channels, i.e., 127 um or 250 um); (ii) the minimum pitch of the electrical bond-pads should be \(>\)100 um for DC-connections (to maximize yield of wire-bonding) and \(>\)300 um for RF-connections (to eliminate the need for costly ceramic pitch-reducing interposers); and (iii) the need for an 'exclusion zone' around grating-coupler array, where no electrical connections or phase-sensitive components are placed, in case of epoxy overflow. Although currently at an early stage of development, it is expected that these PDRs will be extended and formalized into standards over time, and rolled in to the design rule checking (DRC) at Si-foundries. Simple photonic packaging PDKs have already been implemented at a software level in the PIC design tools of PhoenixX BV (OptoDesigner) and Luceda (IPKISS) [65; 66].
## 4 Results and Discussion
### Sensitivity of Parameters and Model Calibration
#### 4.2.3 Effect of SP Size  
The allowable size of an SP is limited based on the size of actual particles and the size of the simulated system. Too large an SP can smear out the real solid carbon particle properties. On the other hand, an SP size too small influences the interpolation of the total charge distributed in the flow channel. An optimal SP size is necessary for accurate simulation predictions [48; 63]. The size of SP directly affects the average number of carbon particles in an SP in Equation (4), i.e., increasing the SP size also increases the mass. Despite enough interaction range, this extra weight slows the SP diffusion, affects SP trajectories, and limits charge distribution. However, the rate of change in \(\beta_{Na}\) when decreasing the SP size by 2% and 4% was considerably more prominent, hence improving the availability of carbon particles to charge (Figure 10). The \(\beta_{Na}\) parameter changed little when the SP size increased by 2% and 4%, making the charging of particles difficult.  
Figure 9: Effect of the interaction diameter on the average charge number, \(\beta_{Na}\) with an SP size of 99.319 \(\upmu\)m. Individual calculations were performed using the experimental reference data of EFC charging at a constant voltage of 0.75, 0.5, and 0.25 V.