esa-sceva/satcom-chunk-collection huggingface dataset:
# Global and Local Information Adjustment for Semantic Similarity Evaluation
## 3 Materials and Methods
### Similarity Measure  
Global features extracted from Bi-LSTM and local features extracted from the capsule network are generated for each of the two sentences. In this study, the similarity between two sentences is evaluated by applying the Manhattan distance for global features and local features. The Manhattan distance is close to 1 if both vectors are similar and close to 0 if they are dissimilar [2; 3; 4]. The similarity between global and local features is defined as follows:  
In Equations (11) and (12), \(l\) and \(r\) mean two sentences. In Equation (11), \(h_{L}\) is a global feature using the last hidden state of Bi-LSTM. In Equation (12), \(P\) means PrimaryCaps, and \(D\) means the number of dimensions of PrimaryCaps.  
\[Sim_{G}\left(h^{l}{}_{L},\ h^{r}{}_{L}\right)=\exp\left(-\left\|h^{l}{}_{L}-h^ {r}{}_{L}\right\|_{1}\right) \tag{11}\]  
\[Sim_{L}\left(P^{l},\ P^{r}\right)\ =\ \frac{1}{D}\sum_{i}^{D}exp(-\left\|P^{l}{}_{i }-P^{r}{}_{i}\right\|_{1}) \tag{12}\]  
The final similarity value is calculated by the alpha weight (\(\alpha\)) as follows:  
In Equation (13), \(\alpha\) is a weight that can adjust which information to focus on among global features and local features. The value of \(\alpha\) is determined experimentally. Finally, the predicted _Similarity_ of 0.5 or higher determines similarity, and the others determine to non-similarity.  
\[Similarity=\alpha Sim_{G}\left(h^{l}{}_{L},h^{r}{}_{L}\right)+\ (1-\alpha) Sim_{L}\left(P^{l},\ P^{r}\right) \tag{13}\]
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 4 Experimental Results and Analyses  
In this section, the satellite visibility and geometry distribution quality of GNSS and GNSS+ constellations are investigated. Firstly, the performance of BDS-3 and GPS is compared. Then, 3 IGSO satellites of BDS-3 are replaced by 3 QZO satellites and positioning precision of IGSO and QZO satellites are compared. After that, 5 HEO satellites are combined with BDS-3 to establish the BDS-3+HEO constellation and the contribution of HEO is investigated. Finally, the performance of the GNSS+ constellation of GNSS+HEO+LEO is assessed.
# An Anonymous Offline RFID Grouping-Proof Protocol
## 1 Introduction
### Motivation  
In this study, we focus on the offline mode of grouping-proof protocols. There are many works that engage in this mode. At first, the grouping-proof can show the presence of group items as a whole. Note that each single item intends to be sold or transported to other owners. To protect the privacy of these items, the anonymity should be considered as an important security property. In order to do this, the authentication should be anonymous so that any unauthorized third party cannot obtain a tag's identity during the protocol execution. The second point is the secret key distribution. Considering there are a large number of tags in RFID system, the management of secret keys becomes a complicated problem, the use of symmetric encryption schemes is not practical. So the PKI systems are considered. The encryption and decryption in the RSA algorithm need to perform modular exponentiation of great numbers to guarantee security, since the length of the modulus is always larger than 1024 bits, which makes multiplication and division a time-consuming calculation, it is impossible to apply the RSA algorithm in RFID tags in reality. The Ellipse Curve Cryptography (ECC) method is used instead. The point or scalar multiplication is the basic operation for ECC protocols; it  
Figure 1: The offline mode of grouping-proof protocol.  
is easily performed via repeated group operations which is applicable to low-cost RFID tags. The third problem of offline grouping-proof protocol is that the validity check can only be performed by the verifier. That means the invalid grouping-proof will not be found before submission to the background. This problem greatly reduces the response speed to illegal data. Our solution allows the reader to check the tag's identity before submitting the proof data. However, this solution needs the reader to store the tag's identity, which may bring a potential safety hazard about the tag's privacy information. Therefore, it is essentially necessary to find a way to guarantee the legality of grouping-proof without revealing the secret information of tags.
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 5 Results
### Inductive Link Response  
In estimating the channel response, an input single sine wave is swept in frequency, and its amplitude voltage at the receiver coil is recorded. However, one significant deviation is that the source is not resistance-free (unlike Figure 4), but has a 50 \(\Omega\) impedance which decreases quality factor and the peaks, blurring out two peaks of the coupled response discussed earlier Section 3.3. Figure 9 shows the simulated response with a 50 \(\Omega\) source impedance and measured results of the 12 uH coils. The slight deviation between the simulated and measured plots is due to measurement errors, deviation in component values and impedance differences. Figure 10 shows the channel response on the 10 uH coils at 3 different layers. As expected, the 50 \(\Omega\) impedance decreases the quality factor, blurring the two resonance peaks. In addition, a smaller separation distance (less layers of foam) results in more bandwidth because of the higher coupling factor, which is similar to the ideal response in Figure 4. The similarity in bandwidth behavior between Figures 4 9, and 10 indicate everything is in order for scaling up to the 12 MHz range. The peaks are compared other than the tuned frequency because they indicate the bandwidth for the data transfer available as explained in Section 3.3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**12 μH Coil [48 mm by 32 mm Ferrite Plate]**} \\ \hline  & Open-loop voltage & k-factor \\ \hline
1 layer [2.5 mm] 5 V input @ 1 MHz & 3.30 V & 0.80 \\ \hline
3 layer [7.5 mm] 5 V input @ 1 MHz & 1.90 V & 0.46 \\ \hline  & **10 μH Coil [37 mm by 37 mm Ferrite Plate]** & \\ \hline  & Open-loop voltage & k-factor \\ \hline
1 layer [2.5 mm] 5 V input @ 1 MHz & 3.30 V & 0.80 \\ \hline
3 layer [7.5 mm] 5 V input @ 1 MHz & 1.27 V & 0.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Open-loop voltage test to estimate the coupling factor.  
Figure 9: Channel response of the 12 μH transmit and receive coils, tuned individually to 1 MHz using 2.2 nF capacitors.
# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 3 Data Preparation and Evaluation  
We initially performed an evaluation of the completeness of the publicly available dataset with respect to the available EEG data. In turn, we eliminated several subjects that exhibited problems in gathering EEG data, for a resultant set of 11 fully-suitable subject datasets. The subset consisted of 3 female and 8 male subjects with an average age of 24 years (SD = 3.07). Five of the subjects commonly wear vision-correcting devices (with one subject not wearing the device during the experimental session). For comparability with prior results in [2], we evaluated the potential to predict the subject-specific QoE, as well as the set (pre-determined) impairment level (QoS). We employed two different approaches in this performance evaluation, namely (i) a direct EEG-data-based approach utilizing the timely gathered data and (ii) a normalized approach that utilizes z-scores derived over the entire EEG data available per subject. We briefly outline the major statistics employed and our evaluation methodology in the remainder of this section.
## 3 Fabrication Methods and Testing Setup  
### Overall Fabrication Process  
Figure 4a shows the fabrication process for the SAW sensors. First, the 4-inch 128\({}^{\circ}\) YX LiNbO\({}_{3}\) wafer is cleaned with acetone, Iso-Propyl Alcohol (IPA), and De-ionized (DI) water before proceeding with the photolithography process. A negative photoresist (DNR-L300-40) is then spin-coated and patterned using UV exposure. Ti/Pt is deposited at 10/100 nm using an E-beam evaporator, and the remaining photoresist is removed using a lift-off process. Following this, HfO\({}_{2}\) is deposited by ALD between the completed input IDT and output IDT, using a shadow mask for selective deposition on the designated regions.  
The ALD process for the HfO\({}_{2}\) film consists of 300 cycles, each performed at 250 \({}^{\circ}\)C (Figure 4b). Tetrakis(ethylmethylamido) hafnium(IV) (TEMAHf) is used as the precursor for 3 s, followed by an Ar purge for 30 s. H\({}_{2}\)O is then introduced as the reactant for 0.1 s, and a final Ar purge is conducted for 20 s, resulting in the deposition of 30 nm of HfO\({}_{2}\). Finally, the HfO\({}_{2}\) undergoes thermal treatment at 500 \({}^{\circ}\)C for 2 min in rapid thermal anneal (RTA) process under an N\({}_{2}\) environment to complete the device fabrication. The wafer is then diced to create individual sensors, which are connected to the printed circuit board (PCB) pads through wire bonding.  
Figure 3: COMSOL simulation results: (**a**) Eigen frequency study result; (**b**) frequency domain study analysis showing 222 MHz result without sensing material; (**c**) SAW wave propagation after 31 ns with sensing material layer in the cavity; and (**d**) displacement field Y-component response of the SAW-based UV sensor with HfO\({}_{2}\) sensing layer after UV exposure.
## 3 Computational Approach
### Weight Calculation
#### 3.1.2 Time-Based Weight  
Every user bookmarks various resources at different times. As [PERSON] et al. [60] point out, human interests evolve over time. Several research endeavors have delved into the evolution of users' interests, typically harnessing either forgetting functions or designated time windows to discern and monitor fluctuations in user behaviors as time progresses. A notable limitation of the majority of time-window techniques is their predisposition to sideline or overlook information from the distant past [61]. On the other hand, exponential forgetting functions have garnered traction in time-sensitive applications, primarily due to their capacity to subtly downplay the significance of bygone behavioral patterns while still retaining them [62]. CF needs to account for both recent and old bookmarks to comprehend users' preferences and similarities accurately. In this regard, [PERSON] and [PERSON] [27] introduced an innovative adaptive exponential forgetting function tailored to cater to this nuanced need. According to this forgetting function, a person's memory gradually fades over time. The function treats bookmarking time as an ordinal scale. However, the time-based weight (\(W_{time}\)) should account for cardinal time information, as the duration is crucial for understanding a user's interest. Therefore, this paper defines a modified exponential forgetting function as follows:  
\[W_{time}(u,r)=exp(-ln2\times\frac{t}{hl_{l}}) \tag{2}\]  
\[t=\left\{\begin{array}{l}0,t_{i}=t_{l}\\ \frac{t_{l}-t_{l}+1}{t_{l}-t_{l}+1},t_{i}\
eq t_{l}\end{array}\right.\]  
The parameter \(W_{time}\)(\(u\),\(r\)) stands for the time weight, which corresponds to the degree of interest user \(u\) has in resource r. Within this context, \(t\) signifies a relative time point, while \(t_{i}\) denotes the specific day on which a tag was added. The variables \(t_{l}\) and \(t_{f}\) indicate the most recent and earliest tagging days, respectively. For the latest tagging day, \(t_{l}\), \(t\) assumes a value of 0, and for the initial tagging day, \(t_{f}\), \(t\) is set to 1. The value of t remains consistent for identical tagging days. This research introduces the term \(ht_{t}\), which represents the half-life designated for each user. In this scenario, \(ht_{l}\) is set at 0.5. This implies that when \(t\) equals \(ht_{l}\), the time weight \(W_{time}\)(\(u\),\(r\)) reduces to half its original value, or 0.5. Essentially, this function encapsulates half the total duration of a user's tagging activities. Thus, for a specific user, a smaller \(t\) value, corresponding to a more recent bookmark, warrants a greater time weight. Conversely, bookmarks that are more dated will have a diminished time weight, signifying reduced relevance or interest.
# Fractional Order Viscoelastic Model for Stress Relaxation of Polyvinyl Chloride Geomembranes
## 4 Results
### Identification of Model Parameters  
As mentioned above, the fractional two-parameter model would be sufficient to describe the stress relaxation behavior of PVC GMBs. The parameters, \(\beta\) and \(\alpha\), were identified by taking the logarithms of the relaxation stress and relaxation time from the test data and then fitting them with the FOVM. As observed in fitting curves of \(\ln\sigma\) versus \(\ln t\) in Figure 5, the relaxation stresses calculated by the FOVM matched the general pattern and magnitude of the experimental values, exhibiting a pronounced linear relation between them. \(\beta\) has a constant value of 0.125, and \(K\) was calculated by Equation (11). All the model parameters are summarized in Table 2. The correlation coefficients, R\({}^{2}\) (0.991-0.997), at different initial strains and tensile rates were all greater than 0.99, indicating that the FOVM describes the stress relaxation law of PVC GMBs with high accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \(\upsilon\) & \multirow{2}{*}{**Direction**} & \(\epsilon_{\theta}\) & \multirow{2}{*}{\(\beta\)} & \multirow{2}{*}{\(\alpha\)} & \multirow{2}{*}{\(\mathbf{R}^{2}\)} & \multirow{2}{*}{\(K\)} & \(\
u\) & \multirow{2}{*}{**Direction**} & \(\epsilon_{\theta}\) & \multirow{2}{*}{\(\beta\)} & \multirow{2}{  
In Equation (9), as \(t\) reaches positive infinity, \(\sigma(t)\) approaches zero, following the general rule of stress relaxation. For a given relaxation time, the relaxation stress can be accurately predicted in theory. Furthermore, if the relaxation stress for judging the failure of PVC GMBs is provided, the FOVM can predict their service life theoretically. For the transverse specimen tested at 5.0 mm/min, while the relaxation stress approaches approximately 15% of initial stress, the relaxation time at the initial strain of 40.55%, 58.78%, and 81.09% were 212, 375, and 395 d, respectively.
## 4 Heat Transport Pipeline Damage Probability Model  
### Dataset  
#### 4.1.1 Input Data  
Factors affecting damage to heat transport pipelines were identified through damage probability based on attribute information. Using these factors, damage risk probability models were developed. The operator, pipe function, pipe diameter, and sensor wire condition of heat transport pipelines were utilized as input data. Records with missing values were removed before applying the dataset to ML algorithms. A total of 326,788 data points were used for model development.  
Data preprocessing was conducted to efficiently develop the damage probability prediction model. First, the operator and pipe function, which were character strings, were converted into numeric strings using LabelEncoder. Because pipe diameter and sensor wire condition had asymmetrical data distributions, efficient model learning was expected to be difficult [16; 17]. Therefore, a log transformation was applied to better align their data scales.
# Vehicular Localization Enhancement via Consensus
## 6 Conclusions  
A strategy to improve the accuracy of the localization estimate in V2X networks in a consensus-based manner is developed and analyzed. Furthermore, a theoretically intuitive two-step implementation of the proposed strategy is presented and compared in terms of the protocol and the accuracy. The expected accuracy of each step of the strategy is analyzed and the CRLB of the resulting estimator is obtained. While the computations of the strategy are kept very undemanding, the estimator nevertheless achieves its optimum given by the CRLB. The time requirement to fulfill the localization enhancement is thus mainly determined by the propagation delay adjusted by a threshold to the communication distance. Achievement of the optimal estimator is verified by the simulation results under various configurations. Additional insights on the performance changes under various setups are made. With reasonable amount of handicaps, the proposed strategy deviates from the optimum but yet successfully enhances the localization accuracy. The decision making upon the employment of the proposed strategy based on the comparison with the initial MSE is also investigated.  
Conceptualization, All authors; Methodology, H.K.K. and S.H.L.; Software, H.K.K.; Validation, M.K. and H.K.K.; Formal analysis, H.K.K. and S.H.L.; Investigation, All authors; Resources, All authors; Writing-original draft preparation, M.K. and H.K.K.; Writing--review and editing, S.H.L.; Visualization, H.K.K.; Supervision, S.H.L.; Project administration, S.H.L.; Funding acquisition, S.H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by a Korea University Grant, in part by Institute of Information & communications Technology Planning & Evaluation (IIITP) grant funded by the Korea government (MSIT). (No. 2016-0-00208, High Accurate Positioning Enabled MIMO Transmission and Network Technologies for Next 5G-V2X (vehicle-to-everything) Services.), and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1084855).  
The authors declare no conflict of interest.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 2 Related Work
### SSD (Single Slot MultiBox Detector)  
[PERSON] [9] proposed a new object-detection algorithm known as SSD in 2015. Operating within the realm of single-stage detection algorithms, SSDs accomplish target localization as well as classification simultaneously.  
SSD can be divided into SSD 300 and SSD 512, according to the size of the input image. The following is the basic operation process of the SSD algorithm, with SSD 300 serving as the exemplar:  
* The input image (size of 300 pixels \(\times\) 300 pixels) is fed into a pre-training network (VGG-16) to obtain different feature mappings.
* The feature vectors of Conv4\_3, Conv7, and Conv8\_2 are employed to establish object-detection boxes at different scales, concurrently performing their classification.
* The NMS (non-maximum suppression) algorithm is employed to eliminate redundant and inaccurate detections. The final detection outcome is then generated, consisting of the most pertinent and trustworthy bounding boxes for the targeted objects.
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## Network Model and Related Definitions
### Related Definitions  
In order to describe our algorithm more clearly, the relevant definitions are provided as follows:  
**Definition 1**.: _Network lifetime: the round of the first dead node due to energy exhaustion [29; 30; 31; 32; 33]._  
**Definition 2**.: _Forward energy consumption: the distance from the node \(i\) to the sink is defined as the forward distance \(d_{is}\), thus the forward energy consumption \(e_{is}\) can be calculated according to the forward distance \(d_{is}\) and the energy consumption model in [34]. Namely, the model is expressed as:_  
\[E_{t}(\lambda,d) =\left\{\begin{array}{ll}\lambda E_{bs}+\lambda e_{fs}d^{2}&d<d_ {0}\\ \lambda E_{bs}+\lambda e_{mp}d^{4}&d\geq d_{0}\end{array}\right. \tag{1}\] \[E_{r}(\lambda)=E_{bss}(\lambda)=\lambda E_{bss} \tag{2}\]  
_where \(\lambda\) represents the length of the packet, and \(\varepsilon_{fs}\), \(\varepsilon_{mp}\) represent the transmit amplifier, \(d\) is the distance of node sending data, \(d_{0}=\sqrt{\varepsilon_{fs}/\varepsilon_{mp}}\) denotes the threshold of the communication distance, \(E_{bss}\) is the energy consumed when transmitting or receiving \(1\) bit of data. Here, considering that the receiving energy consumption \(E_{r}(\lambda)\) is much smaller than the sending energy consumption \(E_{t}(\lambda,d)\), so we ignore it in the calculation._  
**Definition 3**.: _Node load: under the assumption that the data generation rate of all nodes in the network is the same, the number of descendant nodes of the node \(i\) reflects the load size in some way. At the same time, it is considered that the node itself also generate monitoring data, so the node load \(\text{LN}(i)\) is defined as the number of all nodes (namely descendant nodes) that can transfer data to node \(i\) and also plus itself._  
**Definition 4**.: _The candidate parent set: in the generated minimum routing graph, all parent nodes on the multiple shortest paths of node \(i\) are defined as candidate parent set \(\text{CN}(i)\)._  
**Definition 5**.: _The adjacent node set: a set of nodes, whose nodes are one hop away from the nodes on the initial shortest path of node \(i\) and can transmit data to them, are denoted as \(NBS(i)\)._  
**Definition 6**.: _Transmission energy refers to the energy consumed by the source node sending the data to next hop, and the path energy consumption represents the sum of the transmission energy of each node on the path._
# A System Architecture of a Fusion System for Multiple LiDARs Image Processing
## 3 Proposed System Architecture for Multiple LiDARs Image Processing  
The data fusion system is divided into hardware and software. The hardware has communication interfaces for receiving data from LiDAR sensors, a multi-core CPU and memory for supporting multiprocessing, and communication interfaces to output the data of processing results. The operation procedures of the fusion system are as follows. The client sequentially receives data through sockets, and then the process collects, parses, and converts the data. The converted data are transmitted to the server, where the data are merged, compressed, and transmitted to the external system. Data processing involves a client process for the individual LiDAR sensor and a server process for aggregation and calculation of the point cloud. To process data input from an individual LiDAR sensor, as many clients are created as the number of sensors. The functions of the client process  
Figure 2: Sequential processing vs. multiprocessing.  
are data conversion and parsing. Input data to multiple clients are transmitted to the server using Internal Processor Communication (IPC). The Figure 3 shows the overall block diagram of proposed architecture that consists of multiple clients, a server, and IPC.