esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images
## 6 Results
### KNN Smoothing Comparison  
Figure 20 shows the map generated by the algorithm using the Average-BrayCurtis-0.6 combination (left) and the map obtained after applying the KNN smoothing procedure (right), using a value of \(k=15\). It is worth mentioning that several clusters remain on the new map and only change the originally assigned color. On the other hand, some clusters have disappeared from the map due to the number of points assigned to the cluster having decreased to zero. In this map, it can be seen that the larger the clusters, the greater the likelihood of remaining on the map is, while little clusters tend to be eliminated and their images or points added to a bigger cluster.  
The original idea for executing a smoothing procedure was to decrease the uncertainty of the map by taking into account the categories assigned to the points in a zone; in other words, to create zones with a predominant label and fewer outlier points. Figure 21 shows a zoom-in a zone in the map before (left) and after (right) the smoothing procedure. In this case, the area corresponds to a residential, pedestrian location on the map. The main changes in the map after the smoothing procedure are marked with a red circle. In the left-hand image, the original map, the yellow points correspond to a pedestrian zone and the blue ones to a residential zone. It is notable that, in the limits of these zones, some streets appear that include points of both colors mixed together, for example, a yellow point between three or four blue points. Then, in the right-hand image, the original clusters keep their name and the pedestrian zone only changes color to gray. In this example, we can see that the streets in the borders of the zones now have only one predominant color. Therefore, the smoothing procedure helps to reduce flaws that could emerge in the original semantic mapping procedure.  
Figure 22 shows another comparative example of the results generated by the smoothing procedure. In this example, it can easily be observed that points in \"Villa Franqueza\" street (the diagonal one on the map, marked in red) were grouped in the same cluster, the blue one representing the majority of the points in the zone, correcting the outliers generated in the map.  
Figure 20: Comparison of smoothing results before (**left**) and after (**right**) applying KNN algorithm.
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 5 Performance Analysis and Evaluation
### Datasets  
The analysis has been done based on two types of datasets. The first is Geolife (GEO) ([[https://www.microsoft.com/en-us/download/details.aspx?id=52367](https://www.microsoft.com/en-us/download/details.aspx?id=52367)]([https://www.microsoft.com/en-us/download/details.aspx?id=52367](https://www.microsoft.com/en-us/download/details.aspx?id=52367)), accessed on 12 August 2022 ), provided by Microsoft Research Asia and comprising trajectories involving 178 users from April 2007 to October 2011 across China. Each trajectory is based on a set of GPS-based points ordered in time, comprising longitude, latitude, and altitude. In our study, after an analysis of the different data of GEO, we have randomly selected a total of 380 trajectories from three different users, some of them rejected by the algorithms for having less than twenty points, which is not enough to analyse stop points, and others for not having (according to both algorithms) any outliers.  
A second dataset (PTM) has been considered as an example of a smaller, sparser dataset, collected by our team via smartphones with the tool Persense ([[https://m.apkpure.com/persense-mobile-light/com.senception.persenselight](https://m.apkpure.com/persense-mobile-light/com.senception.persenselight)]([https://m.apkpure.com/persense-mobile-light/com.senception.persenselight](https://m.apkpure.com/persense-mobile-light/com.senception.persenselight)), accessed on 12 August 2022) based on the trajectories of three users in the municipality of Portimao, Algarve, Portugal, over 2 months in 2017, in a total of 36 trajectories. Each trajectory provides latitude, longitude, and speed v.  
For the analysis, we have used the sci-kitlearn ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)), accessed on 12 August 2022) Python implementation of DBSCAN and LOF.
# A Local Information Perception Enhancement-Based Method for Chinese NER
## 4 Experimental Results and Comparison
### Training  
We applied the same pretrained character and lexical embedding as in Lattice-LSTM [7]. The character embedding size and lexicon embedding size are both 50. The multiple heads used in the graph network are 3. We trained our network using the Adamax optimization for all datasets. The decay rate is 0.05. All experiments were conducted on a single NVIDIA GeForce RTX 3090 GPU. Weibo has an optimal learning rate of 0.05, while OntoNotes, Resume, and E-commerce are set to 0.03. Additionally, we set the batch size to 1 for the E-commerce dataset and 8 for the Weibo, Resume, and OntoNotes datasets. OntoNotes' hidden size is set to 300, while Weibo, Resume, and E-commerce are set to 200. These important hyperparameters can be seen in Table 2. Besides, to avoid overfitting the model, we used a parameter setting of a dropout rate of 0.5 in the character embedding layer, word embedding layer, and sequence encoding layer. See Appendix A Table 1 for more hyperparameter settings. Our model uses precision rate (P), recall rate (R), and F1 score as the evaluation metrics of performance.
## 3 Results and Discussion
### The Grating Constant \(\Lambda\)
#### 3.1.2 Impact of the Exposure Duration: \(\Lambda(t_{\mathrm{exp}})\)  
As indicated from looking at Figure 1, the influence of the exposure duration becomes particularly apparent in the case of low recording intensity. Further confirmation of this effect is provided by Figure 2, clearly demonstrating the impact of the exposure duration on the grating constant.  
First, a positive slope of \(\Lambda(t_{\mathrm{exp}})\) can be deduced from Figure 2:  
\[\frac{\partial\Lambda}{\partial t_{\mathrm{exp}}}>0 \tag{5}\]  
Second, this gradient is decreasing with increasing recording intensity:  
\[\frac{\partial^{2}\Lambda}{\partial t_{\mathrm{exp}}}\frac{\partial I}{ \partial I}<0 \tag{6}\]  
_i.e._, in the case of low recording intensity (\(I=5\) mW/cm\({}^{2}\)), an increase of the exposure duration of 50% increases the grating constant by 5%. In addition, as observed before, the higher the intensity of exposure, the less pronounced is the influence of the exposure duration. Until finally, in the case of high recording intensity (\(I=30\) mW/cm\({}^{2}\)), the grating period remains constant, or rather:  
Figure 2: **(a)** Impact of the exposure duration on the grating period: \(\Lambda(t_{\mathrm{exp}})\) for various recording intensities, derived from lateral scans. The strongest dependency is obtained in case of low recording intensity (\(I=5\) mW/cm\({}^{2}\), blue squares). An increase of the recording intensity progressively reduces the impact of the exposure duration: from (\(I=10\) mW/cm\({}^{2}\), green circles) to (\(I=20\) mW/cm\({}^{2}\), orange diamonds) up to (\(I=30\) mW/cm\({}^{2}\), red triangles).  
\[\lim_{I\rightarrow\infty}\frac{\partial\Lambda}{\partial t_{\mathrm{exp}}}=0 \tag{7}\]  
Moreover, the results presented here are in line with results from holographic interferometry, performed during holographic recording of acrylamide photopolymer layers, where higher shrinkage was observed with recordings at lower intensities [29].
## 4 Flexible Route Planning Method Combining Q-learning and Graph Search
### Combination of \(A^{*}\) and \(Q\)-learning
#### 4.2.5 Reward Setting  
The reward used for Q-learning \(r_{t}\) is defined as (12), using the path generated by the A* algorithm. The path explored by the A* algorithm is regarded as the shortest path to the destination. Therefore, if it follows the path and does not collide with an obstacle or another AGV, it is highly rewarded. On the other hand, a negative reward is obtained if the AGV chooses an action that results in a collision with an obstacle or another AGV.  
\[r_{t}=\left\{\begin{array}{l l l}2&if&\text{A}^{*}\text{ path direction and forward action}\\ 1&if&\text{A}^{*}\text{ path direction and non forward action}\\ 0&if&\text{Non A}^{*}\text{ path direction and non stop action}\\ -1&if&\text{Non A}^{*}\text{ path direction and stop action}\\ -2&if&\text{collision with an obstacle or another AGV}\end{array}\right. \tag{12}\]
# A 77 GHz Transmit Array for In-Package Automotive Radar Applications
## 2 Transmit Array Design  
The overall structure of the proposed transmit array is presented in Figure 1. A planar patch antenna surrounded by a mushroom-type EBG (Electromagnetic Band Gap) structure is used as the primary feed. The proposed configuration was designed to be integrated into a \(W\times W\) (12 \(\times\) 12 mm\({}^{2}\)) package using QFN technology. The feed is in the centre of the package, while the package cover is positioned at a distance of \(f\) = 2 mm. The following sections will provide a detailed description of the feed antenna and transmit array.  
Figure 1: Transmit array’s antenna configuration.
# Peak Fitting Applied to Fourier Transform Infrared and Raman Spectroscopic Analysis of Proteins
###### Abstract  
FTIR and Raman spectroscopy are often used to investigate the secondary structure of proteins. Focus is then often laid on the different features that can be distinguished in the Amide I band (1600-1700 cm\({}^{-1}\)) and, to a lesser extent, the Amide II band (1510-1580 cm\({}^{-1}\)), signature regions for C=O stretching/N-H bending, and N-H bending/C-N stretching vibrations, respectively. Proper investigation of all hidden and overlapping features/peaks is a necessary step to achieve reliable analysis of FTIR and FT-Raman spectra of proteins. This paper discusses a method to identify, separate, and quantify the hidden peaks in the amide I band region of infrared and Raman spectra of four globular proteins in aqueous solution as well as hydrated zein and gluten proteins. The globular proteins studied, which differ widely in terms of their secondary structures, include immunoglobulin G, concanavalin A, lysozyme, and trypsin. Peak finding was done by analysis of the second derivative of the original spectra. Peak separation and quantification was achieved by curve fitting using the Voigt function. Structural data derived from the FT-Raman and FTIR analyses were compared to literature reports on protein structure. This manuscript proposes an accurate method to analyze protein secondary structure based on the amide I band in vibrational spectra.  
infrared (IR) spectroscopy; Raman spectroscopy; peak fitting; gluten; zein Article
## 4 Results  
### Model Performance Analysis  
Optimal model parameters were determined utilizing GridSearchCV. Initially, 80% of the dataset was randomly allocated for training. Within this subset, a comprehensive set of 5130 sensitivity parameters was established across the nine pipelines. GridSearchCV implicates a fivefold cross-validation strategy. This method entails training the model on 80% of the subset during each iteration (four out of five times) and validating the residual 20% (once). This procedure is iterated five times to include all samples, with the averaged outcome of validations serving as the performance metric. In total, 25,650 models underwent training to ascertain the optimal configuration. Table 3 presents the results of post-parameter optimization corresponding to each pipeline. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Pipeline** & **Hyperparameter** & **The Best of Values** \\ \hline \multirow{8}{*}{PS-XGB} & n\_components & 6 \\  & learning\_rate & 0.5 \\  & n\_estimators & 200 \\  & max\_depth & 3 \\  & min\_child\_weight & 5 \\  & booster & “gbtree” \\ \hline \multirow{8}{*}{PS-RF} & n\_components & 6 \\  & n\_estimators & 400 \\  & max\_depth & 10 \\  & min\_samples\_split & 10 \\  & min\_samples\_leaf & 4 \\  & max\_features & “sqrt” \\  & bootstrap & False \\ \hline \multirow{8}{*}{PS-NN} & n\_components & 6 \\  & hidden\_layer\_sizes & (400, 300, 200, 100) \\  & activation function & “tanh” \\  & alpha & 0.01 \\  & max\_iter & 7000 \\ \hline \multirow{8}{*}{PFS-XGB} & poly\_degree & 3 \\  & learning\_rate & 0.1 \\ \cline{1-1}  & n\_estimators & 200 \\ \cline{1-1}  & max\_depth & 3 \\ \cline{1-1}  & min\_child\_weight & 5 \\ \cline{1-1}  & booster & “gbtree” \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter result list. Figure 5: An in-depth analysis of reservoir characteristics and production parameters: (**a**) Histograms depicting the distribution and variability of key reservoir properties such as porosity, permeability, and fluid saturation, alongside critical production metrics like oil and gas output. (**b**) Violin plots further elucidating these characteristics, offering a detailed view of their distribution patterns, central tendencies, and dispersion, thus providing a holistic understanding of the reservoir’s behavior and production efficiency. Figure 6 shows the prediction results of validation. The horizontal axis represents the real production calculated by Equation (14), and the vertical axis represents the predicted production. If the predicted production of a sample is equal to the real production, the blue dots will fall on the black diagonal line. The overall prediction results of high-productivity data samples are smaller than the real productivity values, but most of the samples are close to the diagonal. This indicates a good correspondence between predicted production and the red dots.
# Supervised Contrastive Learning for 3D Cross-Modal Retrieval
## 3 Proposed Method
### Loss Functions  
The final training loss of the proposed 3D cross-modal retrieval consists of 3 losses: label prediction loss, CSupCon loss, and Mean Square Error (MSE) loss. Label prediction is employed to enhance inter-class variance. This is given the highest priority. CSupCon loss applies contrastive learning across different modalities. MSE loss is used to emphasize the inter-modal variance to pull each modality. To improve retrieval performance, we incorporate an additive margin named Softmargin into the training process of CSupCon to enhance similarity margins. If the additive margin is denoted as \(\eta\), the exponential similarity function \(E\) can be modified as \(E^{\prime}\) by applying \(\eta\), as follows:  
\[E^{\prime}(i,j)=\exp\big{(}\big{(}sim(v_{i},v_{j})-\eta\big{)}/\tau\big{)} \tag{7}\]  
The loss function \(\ell\) in Equation (8) is also modified as \(\ell^{\prime}\) by applying \(\eta\), as follows:  
\[\ell^{\prime}(i,j,N)=-\log\frac{E^{\prime}(i,j)}{E^{\prime}(i,j)+\sum_{k=1}^ {2N}1_{(i\
eq k)}\cdot E(i,k)} \tag{8}\]  
By employing \(\eta\), we can increase the difficulty of comparing similarities between features, thereby further enhancing the performance of contrastive learning. Therefore, the proposed approach performs \(L_{\text{csc}}\) using the modified \(\ell^{\prime}\) in Equation (8).  
Figure 2: Overview of the proposed method. In the feature extraction stage, an augmented data instance \(x^{\prime}\) and another augmented data instance \(x^{\prime\prime}\) from input \(x\), embedding features \(v^{\prime}\) and \(v^{\prime\prime}\), are extracted from each modality using its corresponding backbone network. The proposed method, cross-modal supervised contrastive learning (CSupCon), pushes the features away for different classes and pulls the features towards each other for the same classes. On the other side, in the fixed projection head (FPH) strategy, the \(v^{\prime}\) features are used to predict semantic labels for classification.  
The loss function of the FPH strategy, \(L_{fph}\), can be represented as Mean Absolute Error (MAE), as shown in Equation (9), where \(P\) represents a classifier that is the same as the projection network.  
\[L_{fph}=\frac{1}{MN}\sum_{m}^{M}\sum_{n}^{N}\lvert P(v_{n}^{m})-y_{n}\rvert \tag{9}\]  
Lastly, the proposed method uses Mean Square Error (MSE) between modalities to enhance inter-modal variation.  
\[L_{mse}=\frac{1}{N\cdot(M-1)!}\sum_{s=1}^{M-1}\sum_{m=s+1}^{M}\sum_{n=1}^{N} \lVert v_{n}^{s}-v_{n}^{m}\rVert \tag{10}\]  
Finally, our entire loss is computed as follows:  
\[L=\alpha L_{csc}+\beta L_{fph}+\gamma L_{mse} \tag{11}\]  
The weights \(\alpha\), \(\beta\), and \(\gamma\) are empirically adjusted.
## 2 Contribution  
The project addressed the issue of securing complex vehicular communications by using a QKD system as the root source of encryption keys. These quantum-generated keys are used to ensure system security instead of using the normally specified public key infrastructure (PKI) certificate-based security [6]. We established a V2I testbed to test the QKD-based vehicle security. The testbed involved designing and implementing a system that integrates the QKD system and key management software for the V2I testbed. This is likely the first practical effort to secure V2I communications using quantum-based security protocols. The motivation is to find a viable alternative to existing certificate-based security for V2I systems.  
Though digital certificates play a pivotal role in internet security protocols, they are not without shortcomings. They rely on third parties and chains of trust that have not always been secure. Additionally, there are considerations of their suitability for Post-Quantum Computing (PQC) security, where future quantum computers might quickly break the encryption systems currently in use. Furthermore, there are concerns about the operational performance of certificate-based systems in safety-critical applications and the management of their lifecycle and revocation [7, 8]. High-performance security is required for ITS applications because of the dynamic and time-sensitive nature of transport applications [9]. As discussed in [10], the IEEE 1609.2 security certificate format used for V2X security [11] was influenced by the Internet's transport layer security (TLS) and was designed to address ITS security performance concerns. Yet, encryption algorithms may need further refinement as decryption delays could impact safety applications, as reported in [12]. Quantum-based protocols can be a solution [13] to address the aforementioned concerns around PKI-based security and performance.  
Figure 1: Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications are considered beneficial to road safety [5]. This example shows the infrastructure communicating the current speed to a vehicle and a platoon of vehicles (public domain from the US Department of Transport).
# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 4 Materials and Methods
### The Construction of Evacuation Simulation Model
#### 4.2.1 Model Parameters  
\(D=\left\{D_{1},D_{2},\cdots,D_{n}\right\}\) is a set of shelters, where \(N=\left\{1,2,\cdots,n\right\}\);  
\(O=\left\{O_{1},O_{2},\cdots,O_{m}\right\}\) is a set of demand points, where \(M=\left\{1,2,\cdots,m\right\}\);  
\(K_{ji}\) is the evacuation route between the demand point \(O_{j}\) with \(D_{i}\), and \(OD_{j}\) is the set of all the routes leading to different shelters from the demand point \(O_{j}\); let \(K_{j}=\left\{K_{j1},K_{j2},\cdots,K_{j\bar{i}_{j}}\right\}\), where \(OD_{j}\to K_{j}\) and the corresponding elements are equal, \(\mathrm{L}_{j}=\left\{1,2,\cdots,\mathrm{l}_{j}\right\}\), \(j\in M\), and \(i\in N\) (the same below);  
\(t\) is the time since the start of the evacuation;  
\(\mathrm{A}_{i}^{j}\) is the number of evacuees received at the shelter \(D_{i}\) by the \(t\)-th second;  
\(d_{i}\) is the capacity of the shelter \(D_{i}\);  
\(T_{i}\) is the time it takes for all the evacuees to evacuate to shelter \(D_{i}\);  
\(a_{j}\) is the number of people to be evacuated at the demand point \(O_{j}\);  
\(d_{l}\) is the longitudinal distance between evacuees;  
\(s_{d}\) is the average travel speed of evacuees;  
\(n_{p}\) is the number of people the horizontal space can accommodate simultaneously;  
\(r_{js}\) is the length of the route \(s\) when the demand point \(O_{j}\) is evacuated, where \(\mathrm{s}\in\mathrm{L}_{j}\) (the same below);  
\(p_{js}\) is the number of people evacuated from route \(s\) at demand point \(O_{j}\);  
\(t_{js}\) is the time it takes for all the evacuees to evacuate from route \(s\) at demand point \(O_{j}\);  
\(F_{js}\) is a dummy variable used to indicate whether the evacuation stops along route \(s\) from demand point \(O_{j}\). The initial value is 0, and 1 means that the evacuation along the route has stopped.
## 2 Model Construction for an SCCNMT
### Notations and Assumptions  
Let \(G\equiv(\mathbf{N},\,\mathbf{A},\,M)\) represent an SCCNMT with \(\mathbf{N}\) being a set of perfect reliable nodes including suppliers, logistics companies, and retail stores, \(\mathbf{A}=\{a_{q}\,|\,q=1,\,2,\,\ldots,\,z\}\) being a set of \(z\) arcs, and \(M=(M_{q}\,|\,\,q=1,\,2,\,\ldots,\,z)\) with \(M_{q}\) being the maximal capacity of each arc. At each arc \(a_{q}\), there are several identical carriers [7; 8; 9]. The capacity vector \(X\) denotes the number of identical trucks in each arc, and \(x_{\text{q}}\) takes a value from \(\{0,\,1,\,2,\,\ldots,\,M_{q}\}\). A cold chain network is stochastic because each truck on the arc may be occupied by the other orders. To evaluate the network reliability of cold chain networks in an SCCNMT, the \(G\) model developed in this research satisfies the following assumptions:  
1. The flow and time units of an SCCNMT are integer [15].
2. No nodes, including suppliers, logistics companies and retail stores, provide inventory service.
3. In such an SCCNMT, only one commodity is provided.  
* The capacity of any component is statistically independent [15].
* Flow on the network G satisfies the flow-conservation law [16].
* The identical type of truck throughout the entire transportation process is used.