esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking Extended Targets: Novel TPMB Filter Driven by Model and Data Collaboration
## 6 Conclusions  
This paper introduces a new robust TPMB filter designed for conditions with uncertain process and measurement noise. By enhancing the adaptive multi-model switching method and performing process noise estimation, we provide robust recursive formulations for TPMB. In terms of model-driven aspects, we have improved the IMM by increasing the matching degree of target motion mode switching through model probability transformation at two adjacent moments. From a data-driven perspective, the GBP in the factor graph for the association of measurement vectors allows the model to adaptively adjust the gating threshold based on the association vector, effectively reducing the computational complexity of data association. Additionally, we present an improved method of importance sampling for IMM-TPMB's ASCKF particles, optimizing the particle degradation issue by estimating the process and measurement noise in the context of target positioning. Simulation studies show that, compared to the GGIW-TPMB, TPMB-BP, TPMB-GBP, and IMM-TPMB-GBP-CKF, the IMM-TPMB-GBP-ASCKF achieves the best overall performance in terms of filtering efficiency and computation time across different target numbers, motion states, and clutter rates.  
Future work includes conducting more extensive tests on different clutter models. Although the algorithm proposed in this paper mitigates particle degradation and running time to some extent, the running time continues to increase with the number of targets, and measurement estimation errors arise due to closely spaced measurements. For scenarios with a high number of measurements and closely spaced measurements, an improved measurement tracking gating method can be employed for processing.  
Conceptualization, Y.Z. and B.L.; methodology, Y.Z. and B.L.; software, J.Z. and Z.L. ([PERSON]); validation, Z.L. ([PERSON]); formal analysis, Y.Z. and B.L.; investigation, Y.Z.; resources, Y.Z.; data curation, Z.L. ([PERSON]); writing--original draft preparation Y.Z.; writing--review and editing, Y.Z. and Z.L. ([PERSON]); visualization, J.Z. and Z.L. ([PERSON]); supervision, J.Z. and Z.L. ([PERSON]); project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research work was supported in part by the Fundamental Research Project of the Educational Department of Liaoning Province (No. JYTMS20230862), and the Natural Science Foundation of Liaoning Province (No. 2020-MS-292).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Finger-Vein Verification Based on LSTM Recurrent Neural Networks
## 2 The Proposed Approach
### Stacked Convolutional Neural Networks and Long Short-Term Memory  
The proposed stacked Convolutional Neural Networks and Long Short-Term Memory (SCNN-LSTM) consist of a CNN model and LSTM model (as shown in Figure 3) and are trained to learn the joint texture and spatial dependency representations for finger-vein texture segmentation. Our SCNN-LSTM takes a sequence associated with \(K\) patches as its input. In SCNN-LSTM, a deep CNN model is built by removing the output layer of an existing CNN model [33] for the vein texture representation. Then we take any patch as an input of the CNN model and it outputs a fixed-length vector representation which is further forwarded to a recurrent sequence learning module (LSTM) to learn the compositional representations in space, as shown Figure 3b.  
Figure 4 shows the architecture of the proposed SCNN-LSTM. As shown in Figure 4, our approach consists of a CNN model and LSTM model. This CNN model (as shown in the red box in Figure 4) consists of three convolutional layers and one fully connected layer. There are 24 kernels of 5 \(\times\) 5 in the first convolutional layer, 48 kernels of 5 \(\times\) 5 in the second convolutional layer, and 100 kernels in the fully connected layer. The LSTM model (the blue region in Figure 4) includes 128 kernels. For SCNN-LSTM training, its input is a sequence of 7 patches with size of 11 \(\times\) 11. Each patch in the sequence is forwarded to CNN model to obtain a 100 dimensional vector. As a result, there are 7 vectors for an input sequence with length of 7. The resulting vectors are taken as an input of LSTM model to obtain a 100 dimensional representative vector. Finally, the output of LSTM model is put into the last layer for classification. The output of last layer is a 2 dimensional vector because there are two classes (vein and background) for vein segmentation. When the input size changes, the width and height of the map in each convolutional layer changes accordingly. Along the forward direction, a patch-based sequence is represented effectively.  
Figure 4: Architecture of SCNN-LSTM.  
Figure 3: Illustration of the SCNN-LSTM model. (**a**) The four orientations for a pixel \(c_{0}\); (**b**) SCNN-LSTM for prediction. A sequence sampled along \(0^{\circ}\) orientation is taken as an input of SCNN-LSTM to predict the probability of the centered point \(c_{0}\) being to vein pattern. The LSTMs share same weights.
## 3 Results
### EDOF CL Use Did Not Affect Binocular Vision  
Three consecutive measurements were obtained of the measurement of the phoria, obtaining a within-subject standard deviation of 0.9 and 1.5 \(\Delta\) for distance and near. No significant variations were found in the measurement of the phoria with the cover test after the use of the CL (\(p\) = 0.368), although there was a slight tendency towards an increase in the exophoria at near (pre-fitting \(-\)2.13 \(\pm\) 0.90 \(\Delta\) vs. 30 days of CL wear \(-\)2.23 \(\pm\) 1.00 \(\Delta\), \(p\) = 0.097). Negative and positive FV blurring, break and recovery points did not experience significant variations with the use of the CL (\(p\)\(\geq\) 0.106), although an increasing trend was observed in the positive FV parameters measured at near (pre-fitting: blur 24.93 \(\pm\) 4.17 \(\Delta\), break 34.20 \(\pm\) 1.96 \(\Delta\), recovery 31.60 \(\pm\) 2.46 \(\Delta\) vs. 30 days of CL use: blur 31.00 \(\pm\) 4.27 \(\Delta\), break 36.92 \(\pm\) 1.65 \(\Delta\), recovery 36.15 \(\pm\) 2.05 \(\Delta\), \(p\) = 0.066) (Table 3).  
Mean magnitude of stereoacuity at the beginning of the study was 23.17 \(\pm\) 1.51 arc seconds. At the last visit of the study, a mean stereoacuity value of 21.27 \(\pm\) 2.26 arc seconds was found. This change in stereoacuity did not reach statistical significance (\(p\) = 0.145), suggesting that the CL does not interfere with stereoscopic vision.
## 3 Deviation-Angle-Panel-Based Antenna Concept
### Improving RSE by Pose Adjustment  
Referring to the second scheme improving the RSE in Ref. [45], the antenna RSE can be improved by simply rotating the regular panel. If combined with the deviation-angle panel, performing the same scheme can further improve the RSE, which is elaborated as follows.  
As shown in Figure 6a, the regular panel is in its initial pose, in which its three corner points \(A,B,C\) are located on plane \(OXZ\), and line segment \(AB\) is parallel to plane \(OXY\), with points \(A\) and \(B\) precisely on the envelope and \(Z\)-axis, respectively. Exert a rotation by \(\theta_{\rm r}\) on the panel about \(A\) until \(D\) is on the envelope to obtain the intermediate pose of the regular panel, as shown in Figure 6b. This pose creates a gap between point \(B\) and the \(Z\)-axis. Then, enlarging the aperture until the gap is filled will increase the RSE, as illustrated in Figure 6c. It can be observed from Figure 6a,b that a smaller \(\angle BAD\) induces a larger angle \(\theta_{\rm r}\) and gap and, eventually, a higher RSE. Since the angle (\(\angle BAD\)) is of critical importance in improving the RSE, we name it as the key angle (KA). Note that the magnitude of the KA cannot be changed by adjusting the pose because it stays fixed if the number \(n\) of regular panels is given. According to Figure 5, the KA decreases with an increase in \(k_{\rm d}\).  
Hence, the KA can be reduced if the regular panel is in place with the deviation-angle panel. As shown in Figure 6c, the green panel \(A_{1}B_{1}C_{1}D_{1}\) is the deviation-angle panel, and it is obvious that its KA (\(\angle B_{1}A_{1}D_{1}\)) is smaller than that of the regular panel. Thus, the length of line segment \(A_{1}B_{1}\) is greater than that of \(AB\), which means that the RSE of the antenna with the deviation-angle panel is greater than that with the regular panel. For ease of expression, hereinafter, the pose of the deviation-angle panel in Figure 6c is named as the compact pose (CP).  
Figure 5: Segmentation scheme of deviation-angle panel. (**a**) Top view. (**b**) Deviation-angle panels. The regular panel \(A_{0}B_{0}C_{0}D_{0}\), colored in gray, is obtained by dividing the reflective surface into \(n\) identical circumferences in radial direction, leaving a circular hub at the center.
###### Abstract  
One of the main factors in triggering cascading outages is the unexpected tripping of backup protection caused by load encroachment (LE). Hence, LE should be identified quickly to avoid unexpected tripping. This paper proposes a fast LE identification method based on the wide-area measurement system (WAMS), applicable to transmission grids with high wind power penetration. Firstly, based on the improved shortest path algorithm, the method searches for critical lines greatly affected by the LE to determine the collected range of wide-area information. Then, by analyzing the hidden dangers of the existing calculation method for the active power estimation value (APEV) in the power system with wind farms, a fast calculation method of the APEV based on the WAMS is proposed to ensure that the existing LE identification criterion is still applicable in the high-penetration system of wind power. Finally, based on the PSASP platform, the wind farm model is integrated into the IEEE 39-bus New England system for simulation. The simulation results show that the method proposed in this paper can quickly search for the critical line set (CLS) and accurately identify the LE under high wind power penetration, effectively avoiding the unexpected tripping of backup protection and preventing cascading outages.  
transmission grids; load encroachment; cascading outages; wide-area measurement systems; critical line sets; high penetration of wind generation +
Footnote †: journal: Applied sciences  
Article
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
## 4 How an End Device Joins a LoRaWAN(tm) Network
### Keys in an End Device before joining the Network  
After commissioning, an end device of a LoRaWAN(tm) network stores in a secure way the following set of keys:  
* The JoinEUI. It is a global application identifier (ID) in the IEEE EUI64 address space (standard for layer-2 port identification released by IEEE. The address space utilizes 64 bit. For more information, see [[https://standards.ieee.org/products-services/regauth/index.html](https://standards.ieee.org/products-services/regauth/index.html)]([https://standards.ieee.org/products-services/regauth/index.html](https://standards.ieee.org/products-services/regauth/index.html))) that uniquely identifies the join server that assists the end device in the processing of the join procedure and the session keys derivation;* The DevEUI. It is another global ID in the IEEE EUI64 address space that is used to uniquely identify the end device;
* The NwkKey. It is a AES-128 root key [9] specific to the end device, which is assigned to the end device during fabrication;
* The AppKey. It is another AES-128 root key [9] specific to the end device that is assigned to the end device during fabrication.
# SOLIOT--Decentralized Data Control and Interactions for IoT
## 4 IoT Requirements
### Data Representation  
While the serialized format of the regarded data object is part of the syntactical aspects, its representation, scheme and meaning of the used vocabulary is generally defined in an information model (as in [9; 12; 22; 30; 33]). A consistent _identification pattern_ (D1) needs to be defined, usually in the form of IRDIs, UUIDs, or URIs. While both IRDIs and UUIDs usually require a certain resolution service, URIs in form of URLs can also directly serve as _resource locators_ (D2, cf. Figure 5).  
An _information model_ needs to define the physical asset, the related Digital Twin, the interrelations its asset, its attributes and the operations it provides [30; 33; 35] (D3). This _core vocabulary_ (D3.1) is then extended with optional _domain specific extensions_ (D3.2) in order to describe specific features, which are  
Figure 5: Overview of Protocol (left) and Data (right) Requirements, positioned in the communication stack.  
not relevant in all use cases. Still, as in the regarded settings a discovery at runtime is required, the resources need to provide _self-descriptive information_ (D4) [22] and outline which of the interaction patterns of F2 to F4 are available for the specific resource (D5) [22]. Potential configuration of the access behavior of a hosting system can also be represented and changed through _security configurations_ or profiles (D6) which might be exposed to users according to their permission setting [12; 36].  
In addition to the publishing of the resource itself and available operation, [PERSON] et al. [22] distinguish between the format of such metadata and payload data and argue for the provisioning of _data schemes_ (D7). In contrast to the metadata, which must follow the specification of the IoT framework, the payload data might be sent in binary or proprietary serializations [12; 22].
# Optimized Implementation of Argon2 Utilizing the Graphics Processing Unit
## 1 Introduction
### Contributions  
Efficient Argon2 Implementation on GPU: The main contribution of this study is the development of a more efficient implementation of Argon2. We improved performance by utilizing both the CPU and GPU, and efficiently distributing tasks. By shifting the processing of several stages from the CPU to the GPU, we significantly reduced the data transfer cost. This new method resulted in faster processing times compared to previous implementations, particularly when dealing with a larger number of passwords and higher levels of parallelism than previous works.  
GPU Shared Memory Utilization: The proposed approach optimally uses the GPU's shared memory, which provides faster memory access. This is particularly beneficial in the computation of process, where a hash value is repeatedly generated until the desired output size is achieved.  
GPU Parallel Processing for Brute-Force Attacks: Another significant contribution is leveraging the GPU's parallel processing capabilities for efficient brute-force attacks. By performing the H function computation on the GPU, the proposed implementation can generate initial blocks for multiple inputs in a single operation, thus resulting in optimal brute-force attacks.  
In-depth Performance Evaluation of Argon2: The performance evaluation reveals that our proposed implementation performs better than existing ones, especially when processing a larger number of passwords and at higher levels of parallelism than previous works.
## 2 Numerical Methods  
### ISAL Image Processing Model  
A well-developed ISAL image processing model comprises three primary assignments: a transmitter side, a receiver side, and ISAL raw data compensation. Ladar transmits a signal to a target and receives an echo signal from it through the atmospheric turbulence. Generally, the phase data of the echo signal are obtained by heterodyne detection, and then phase compensation and self-focusing processing are performed to obtain the target image after aperture synthesis. A concrete illustration is presented in Figure 1.
## 6 Results and Discussion
### Estimation Results of Specific Reference Points  
Figures 8 and 9a-d present the accuracy, precision, and recall results for the identification of specific reference points in a room. For the purpose of visualizing the results, only five points out of the fifteen reference points which formulated a finger print in each room were selected for analysis of precision and recall.  
In Figure 8, the accuracy of the proposed augmented CWT is nearly the same as that of the unaugmented CWT for the Morse wavelet, at 94.93% and 94.80% respectively. However, both feature sets outperformed all the other feature sets, for both Morse and Morlet wavelets. Generally, the Morse-based feature sets performed better than the Morlet. This is significant for the unaugmented CWT-PSD using ANN.  
The precision results arrived at using CNN for both the augmented and unaugmented datasets are high in Figure 9a-d. It is also clear from Figure 9a,c that the ANN-Morlet based precision and recall results are low for the augmented feature sets compared to the ANN-based, Morlet-based precision results for the unaugmented features in Figure 9b.d. We can deduce that ANN-based classification using Morlet-based feature sets does not exhibit improved performance when data augmentation is applied. For the case of this study, we can more specifically deduce that augmentation using Gaussian noise does not improve the performance of ANN-based learning models compared to CNN-based learning models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**CWT without Augmentation Using CNN [13]**} & \multicolumn{6}{c}{**Proposed Augmented CWT Using CNN**} \\ \hline \multirow{5}{*}{True labels} & R1 & 11 & 0 & 0 & 0 & 0 & R1 & 22 & 0 & 0 & 1 & 0 \\  & R2 & 0 & 8 & 0 & 0 & 0 & R2 & 0 & 19 & 5 & 0 & 1 \\  & R3 & 0 & 0 & 9 & 0 &  
Figure 8: Accuracy results for specific reference point estimation.  
Figure 9: Specific reference point precision (**a**,**b**) and recall (**c**,**d**) results for augmented and unaugmented features respectively.  
In summary, for both room prediction and specific reference point estimation, the pattern observed was that there existed better accuracy, precision, and recall performance for Morse over Morlet for ANN, and they were nearly the same for CNN. This result is backed up by the fact that technically, the Morse wavelet belongs to a two-parameter family of wavelets which generates distinct joint-time frequency data space compared to the Morlet, which is a single-parameter family of wavelets. The distinct joint-time frequency numerical data are what composes the CWT-PSD, which is used as input to the ANN model. The joint-time frequency graph which is referred to as the CWT image in this paper is used as input to the CNN model. The close performances of Morse and Morlet when using the CNN are attributed to the similarity of CWT graphical image extracts.
## 8 Conclusions  
This paper examined neural network methods for efficient path loss prediction to address the challenges associated with empirical and deterministic path loss prediction models. An MLP model for path loss prediction has been constructed and validated, considering multi-transmitter scenarios. The inputs to the neural network algorithm vary from a dataset to another, and path loss is the output layer. The MLP models were created to train 14 datasets, and compared to predictions given by six existing empirical models. Two well-known error measures were used to assess the created model: MSE and R-square. Interestingly, the presented MLP models outperformed the other six empirical models and showed the lowest error in various environments, which make them good fits for the measured data. It was found that increasing the number of MLP input variables increased the accuracy of the produced model, which can predict path loss measurements in wireless propagation situations. Therefore, the suggested MLP is quite effective in predicting path fading and is considered a generic data-driven model. Future research will focus on other neural network algorithms, like the radial basis function (RBF) algorithm, and compare it with our MLP models to improve the accuracy of signal loss prediction.  
Conceptualization, M.A. and M.O.; methodology, M.A.; software, M.A.; validation, M.A., M.O. and M.K.; formal analysis, M.A. and M.O; investigation, M.A.; resources, M.A.; data curation, M.A.; writing--original draft preparation, M.A.; writing--review and editing, M.A., M.O. and M.K.; visualization, M.A.; supervision, M.O. and M.K.; project administration, M.O.; funding acquisition, M.O. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Algerian National Agency of Research and Development (DGRSDT-PRFU project number C00L07 UN010120200001).
## 2 Design Aspects
### System Modeling  
The TRNSYS library includes modules (TYPES) that represent the equipment commonly used in energy systems, modules for processing meteorological data, and modules for processing simulation results. The modular structure of TRNSYS gives great flexibility to analyze different types of energy systems. The representation of SACS in TRNSYS, described in the previous section, is illustrated in Figure 5. Table 4 provides information about the most important components of the system, the type of module that represents them, and provides some parameters of the basic design. Figure 5 and Table 4 do not include other components and flows of less importance. The period of simulation in TRNSYS was seven months (cooling season) from 1 April 1 (2160 h) until the end of October (7296 h), with a step time simulation of one minute. Baghdad meteorological data was gained from TMY2.  
Some of the simplifying assumptions used in the calculation are indicated below:  
* The electrical energy consumed by the pumps is neglected.
* Pumps are not supposed to transmit thermal energy to the fluid.
* When the pumps are running the mass, flows remain constant.
* The limit capacity of the chiller is assumed to correspond to a cooling water temperature of 27 \({}^{\circ}\)C.  
In developing the model, the recommendations by some authors who have simulated the behavior of solar absorption cooling systems with TRNSYS, or other applications, have been taken into account [19; 31].
###### Abstract  
This study aimed to evaluate the interaction of environmental variables and Water Use Efficiency (WUE) via multivariate analysis to understand the importance of each variable in the carbon-water balance in MATOPIBA. Principal Component Analysis (PCA) was applied to reduce spatial dimensionality and to identify patterns by using the following data: (i) LST (MOD11A2) and WUE (ratio between GPP-MOD17A2 and ET-MOD16A2), based on MODIS orbital products; (ii) Rainfall baesed on CHIRPS precipitation product; (iii) slope, roughness, and elevation from the GMTED and SRTM version 4.1 products; and (iv) geographic data, Latitude, and Longitude. All calculations were performed in R version 3.6.3 and Quantum GIS (QGIS) version 3.4.6. Eight variables were initially used. After applying the PCA, only four were suitable: Elevation, LST, Rainfall, and WUE, with values greater than 0.7. A positive correlation (\(\geq\)0.78) between the variables (Elevation, LST, and Rainfall) and vegetation was identified. According to the KMO test, a series-considered medium was obtained (0.7 \(<\) KMO \(<\) 0.8), and it was explained by one PC (PC1). PCI was explained by four variables (Elevation, LST, Rainfall, and WUE), among which WUE (0.8 \(<\) KMO \(<\) 0.9) was responsible for detailing 65.77% of the total explained variance. Positive scores were found in the states of Maranhao and Tocantins and negative scores in Piaui and Bahia. The positive scores show areas with greater Rainfall, GPP, and ET availability, while the negative scores show areas with greater water demand and LST. It was concluded that variations in variables such as Rainfall, LST, GPP, and ET can influence the local behavior of the carbon-water cycle of the vegetation, impacting the WUE in MATOPIBA.  
M ARTOPIBA; water use efficiency; principal component analysis 2022  
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