esa-sceva/satcom-chunk-collection huggingface dataset:
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 3 Protocol Design and Description
### Cryptographic Primitives Election  
Once security services and the cryptographic primitives that help to preserve them have been reviewed, it is necessary to specify which of these primitives will be used in the future. It is sought that three security services will be preserved: confidentiality,integrity, and authentication, through four cryptographic primitives. Encryption: used to provide confidentiality to the information [6], so it can travel securely. Digital signature: fundamental primitive to provide authentication, any entity that receives information can know its identity [7]. Key Encapsulation Mechanism (KEM): an agreement that encrypts the information using techniques designed to secure the keys of symmetric cryptography for transmission using asymmetric algorithms. Hash functions: it maps binary strings of information of an arbitrary length and converts them into binary strings of fixed length [6]. It is computationally simple to get from any input to any output. However, getting from any input to a hash value is computationally impossible.  
These primitives are used to take advantage of the capabilities of each device. Before fully defining the location of each of these primitives, it is necessary to know the capabilities of the entities that will be used in the protocol. In [33; 34], the minimum capabilities that the used equipment must have, are presented, as well as their different characteristics, such as the range of data that they handle, the frequency at which they work, and the range of data presented by each of the devices.
## 3 Image processing and evaluation
### Image tracking algorithm
#### Variation limits:  
To validate an image tracking result, variation limits are set for all the tracking parameters, i.e.  
\[\begin{array}{l}\left|\frac{S_{2,n^{\prime}}-S_{1,m^{\prime}}}{S_{1,m}} \right|\leq\phi_{5}\text{, \ \ }\left|\frac{B_{2,n^{\prime}}-B_{1,m^{\prime}}}{B_{1,m}}\right|\leq\phi_{6} \text{, \ \ }\\ \left|\frac{\Theta_{2,n^{\prime}}-\Theta_{1,m^{\prime}}}{\Theta_{1,m}} \right|\leq\phi_{6}\text{, \ \ }\left|\[D_{r}(m,n) = c_{s}\Bigg{[}\frac{S_{1,m}-S_{2,n}}{S_{2,n}}\Bigg{]}^{2}+c_{s} \Bigg{[}\frac{B_{1,m}-B_{2,n}}{B_{2,n}}\Bigg{]}^{2}+ \tag{12}\] \[c_{o}\Bigg{[}\frac{\Theta_{1,m}-\Theta_{2,n}}{\Theta_{2,n}} \Bigg{]}^{2}+c_{o}\Bigg{[}\frac{\theta_{1,m}-\theta_{2,n}}{0.5\pi}\Bigg{]}^{2}\]  
If image pair _(m',n')_ is determined with equations (8) but not fulfill condition  
\[D_{r}\big{(}m^{\prime},n^{\prime}\big{)}=\min\left\{\begin{array}{ll}D_{r} \big{(}i,n^{\prime}\big{)}&for\ i=1,2,3\cdots,M_{o}\ and\\ \big{(}X_{1,i}-X_{2,n^{\prime}}\big{)}^{2}+\big{(}Y_{1,i}-Y_{2,n^{\prime}} \big{)}^{2}\leq R_{s}^{2}\end{array}\right\}, \tag{13}\]  
the tracking is not reversible. A tracking result without reversibility may be considered as a failure.
## 2 Materials and Methods
### Proposed Method for Collecting and Processing Information for IRI Calculation
#### 2.3.1 Data Collection  
The first step at this stage is to define the path you intend to model. Limits must be set for the length and width of the area to be surveyed, as this has a direct impact on the dimensions of the model and, therefore, on the available computational processing capacity. There are six flight parameters to consider: (1) height or height range covered by the UAV team; (2) flight speed, which should be constant while capturing the image; (3) setting a timer for the camera, automating the recording of images achieve continuity between them; (4) overlap between the captured images; (5) camera focus or angle, which should range from 0 degrees (Nadir) to 45 degrees (oblique images); (6) calculating the GSD parameter as it allows us to know the width and length in meters that each image represents depending on the flight height and the intrinsic characteristics of the camera used, and this parameter allows us to know the dimensions that a pixel represents.  
Concerning the definition of the flight plan, the direction used will be parallel to the longitudinal axis of the road in both directions. Weather conditions will affect the survey campaign, the presence of strong winds or rain are reasons to postpone the flight, as they affect the stability of the aircraft as well as the quality of the images. When conducting the campaign, it is important to know the available image storage capacity, in addition to the battery capacity of the UAV equipment, in order to avoid possible inconveniences during the flight exercise, such as reaching the memory limit or running out of power for the equipment. It is necessary to determine whether the positioning system to be used will be \"manual\" using well-defined ground control points (GCP) checkpoints within the area to be surveyed or will be \"automatically\" by connecting to the global GPS network system. Moreover, complete visualization of UAV equipment by the pilot must be ensured.
###### Abstract  
In battery management systems, state of charge (SOC) estimation is important for ensuring the safety and reliability of batteries. Currently, there are various methods for estimating SOC, and the neural network method is the most popular. However, when the battery's SOC is low (below 20%), the uncertainty in neural network parameters can lead to significant bias in SOC estimation. To address these problems, this study proposes a method based on genetic algorithm (GA) optimization of a time-serialization convolutional neural network (TSCNN) model. First, the population is initialized according to the optimized hyperparameters of the TSCNN model, whereby the experimental data are converted into time-series data. Subsequently, neural network models are built based on the population, thereby using the effect of the network as the fitness function for GA optimization. Finally, an optimized network structure is obtained for accurate SOC estimation. During the optimization process, the optimized data exhibited abnormal phenomena, usually manifested as exceeding the data limits or being zero. In the past, abnormal data were discarded and new data were regenerated; however, this reduces the correlation between data. Therefore, this study proposes a check function to enhance the correlation between the data, converting abnormal data into normal data by limiting the data range. To the best of our knowledge, it is the first time that a GA is being proposed to optimize the time-series length of a convolutional neural network (CNN) while the neural network parameters are optimized so that the time-series length and neural network parameters achieve the best match. In the experimental results, the maximum error was 4.55% for the dynamic stress test (DST) dataset and 2.58% for the urban dynamometer driving schedule (UDDS) dataset. When the battery SOC was below 20%, the estimation error did not incur a huge error. Therefore, the optimization method proposed for the TSCNN model in this study can effectively improve the accuracy and reliability of SOC estimation in the low-battery state.  
TSCNN; SOC; GA +
Footnote †: journal: Applied Sciences
# Performance of Ore Sand as Aggregate for Interlocking Blocks
## 3 Results  
### Compressive Strength of the Blocks  
Figure 4 shows the results of compressive-strength tests for the compared blocks. The compressive strength of the block with IOT is similar to that of the conventional block. After 15 days of curing, the ore sand block presented a strength of 37.87 MPa. The Brazilian standard [24] establishes that this strength must be 80% (28 MPa) of the expected strength for light traffic paving. Both blocks meet this requirement. After 28 days of curing, the ore-sand block presented strength higher than that established in the standard (35 MPa) and higher than that of the conventional block. Therefore, the results meet the minimum requirements of the Brazilian standard, as well as of the requirements of the European and British standard BS EN 1338:2003 [43], the Indian standard IS 15658 [44], and the Egyptian standard ESS 4382 [45], which establish a strength of 30 MPa as the minimum acceptable. However, the American standard ASTM C936 [46] specifies a value of 50 to 55 MPa. A cone-shaped fracture with vertical crack [47] was identified in the compression tests for both blocks analyzed.  
The blocks constructed with ore sand were homogeneous and dense (Figure 5). The limestone sand has fine granulometry and works as a filler in the block. Thus, the total replacement of this material by ore sand that had finer granulometry and that was well graded and more uniform resulted in better packing, reducing the voids and resulting in better mechanical performance of the ore-sand blocks compared to the limestone-sand blocks [48; 49]. According to [PERSON] et al. [50], ore sand reduces the porosity of the material by increasing the contact surface between the particles, promoting performance that is satisfactory and even superior compared to that of blocks made with natural sand.  
Figure 4: Comparison of the compressive strengths of the blocks.  
The results obtained for mechanical strength show that the blocks have similar performance and can be considered products of the same standard category. They are therefore comparable in the environmental-performance assessment.
## 3 Results and Discussion
### Effect of the ZnO Thickness  
ZnO applied as an ETL in an n-i-p device as the first layer that absorbed the incident light. Therefore, the thickness of the ZnO layer was optimized in the range of 100-1000 nm. The current density-voltage (\(J\)-\(V\)) curves with respect to the ZnO thickness are shown in Figure 3. At a ZnO thickness of 300 nm, the highest values of \(\mathrm{V_{oc}}\), \(\mathrm{J_{sc}}\), FF, and PCE were 1.04 V, 23.3 mA/cm\({}^{2}\), 80.2%, and 19.4%, respectively. Then, as the ZnO thickness increased, each output parameter values showed a saturated behavior. The reason that the PCE  
Figure 2: (**a**) Current density–voltage (\(J\)–\(V\)) and (**b**) quantum efficiency (QE) curve for ITO/ZnO/CsPbI\({}_{\mathrm{s}}\)Br\({}_{3-\mathrm{x}}\)/NiO\({}_{\mathrm{x}}\)/Au structure.  
increased with the ETL thickness from 100 nm to 300 nm may be attributed to the increase in dissociation of the photo-generated holes and electrons and reduced recombination in the photovoltaic solar cells [39]. When the thickness exceeded 300 nm, as recombination also increased as the thickness of ZnO increased, the PCE did not significantly increase and was considered to show a behavior of saturation. A similar behavior can be found in [39].
## 2 Theoretical Analysis  
In order to understand the carrier dynamics inside the active region of LDs, the carrier transportation mechanisms are investigated. Figure 1 illustrates the carrier transport process in the InGaAsSb/AlGaAsSb 3 QWs LD.  
As shown in Figure 1, the electrons injected from the N-side into the active region are captured by the first quantum well, and are transferred to the next quantum well by tunneling or diffusing the barrier. The process is repeated until the electrons flow into the P-side. The energy discontinuity between the waveguide and cladding layers could prevent the minority carriers from following into the high-doped area. But for antimonide materials, the small conduction band offsets reduce the carrier confinement, and the high mobility of carrier electrons further accelerate the minority carriers spilling over the active region. The minority carrier concentration \(N_{1}\) is calculated by using the following equation [10]:  
\[N_{1}=\frac{1}{2\pi^{2}}(\frac{2m}{\hbar^{2}})^{\frac{3}{2}}\int_{\Delta E_{c} }^{\infty}\frac{(E-E_{c})dE}{1+\exp(\frac{E-E_{n}}{kT})} \tag{1}\]  
where \(\hbar=\frac{\hbar}{2\pi}\), \(h\) is Planck constant, m is electron effective mass, \(F_{n}\) is the Fermi level, and \(k\) is the Boltzmann's constant. Equation (1) indicates that the concentration of minority carriers decreases with increase of the conduction band offset \(\Delta E_{c}\).  
The electron leakage current density \(J_{1}\) can be estimated by using the following formula, if only considering the thermionic emission diffusion:  
\[J_{1}=e\frac{D}{L}N_{1} \tag{2}\]  
where \(D\) and \(L\) are the electron diffusion coefficient and length at the P-side respectively. When the carriers are injected into the active region, they can either recombine radiatively or not radiatively. The lowest threshold current density \(J_{2}\) is modeled as Equation (3).  
\[J_{2}=e\frac{d}{\tau}N_{th} \tag{3}\]  
where \(\tau\) is the electron average lifetime and \(d\) is the thickness of the active region. Thus, the laser threshold current density \(J_{th}\) is calculated as follows:  
\[J_{th}=J_{1}+J_{2}=e\frac{D}{L}N_{1}+e\frac{d}{\tau}N_{th} \tag{4}\]  
From Equation (4), it is known that the laser threshold current density can be reduced with the decrease of carrier leakage. Therefore, a large band offset between the waveguide and cladding layers should be designed elaborately to block the minority carriers from flowing into the high-doped area in order to improve the opto-electrical properties of 2 \(\upmu\)m InGaAsSb/AlGaAsSb LDs.  
Figure 1: Schematic diagram of carrier leakage of InGaAsSb/AlGaAsSb 3 QW LD.
## 1 Introduction  
Utilities for various purposes, such as communication lines, water and sewage pipes, power pipes, gas pipes, and heat transport pipes for heating, are usually installed under the pavement. However, underground utilities are difficult to manage because of their complexity and invisibility. During the five years from 2014 to 2019, 13 gas leakage accidents occurred in Seoul, and 10 of them were cases in which gas pipes were damaged during other excavation work [1]. The destruction of underground utilities resulted in high social costs being paid nationwide and increased public anxiety [2].  
According to several studies, including the Federal Highway Administration (FHWA) report, the biggest threat to project completion delays and budget shortages in highway planning/design/construction is incorrect utility location information [3, 4]. In other words, an underground utility management system can minimize the risk to the project caused by excavation work. Damage caused by natural disasters can be reduced as well by identifying and recording the location information of underground utilities as necessary.  
Especially in Korea, due to the competition among private operators, overhead communication lines on utility poles have become crowded. For urban aesthetics and safety, a project to move the communication cables underground is in progress. The communication lines are to be installed at a depth of 40-50 cm by the trenching excavation method. Since this is relatively shallow, it is expected to come into conflict with other utilities, and it is necessary to manage their location and history.  
Various solutions have been developed and proposed. According to the literature, UHF RFID is determined as a proper alternative. However, research on applications of UHF RFID under the pavement are limited. For field application, verification studies should be conducted.  
In this study, we aim to determine the feasibility of using the UHF RFID tag for utility management installed under a pavement. The limitations are presented through a laboratory experiment, such as the depth of the installation and the materials of pavement.  
We determine the signal reach by simulating a multilayer medium representing the road substructure and installing tags at different depths. This study makes a novel contribution to the literature by proposing the applicability of the UHF RFID tag in a multilayer structure composed of heterogeneous materials, which was not considered in previous studies.
## 2 Materials and Methods  
### Communication Network Topology  
To collect information about the movement of human-body segments while walking, five real-time IMU motion tracking units were attached to the segments of a human's lower limbs, and a master device was attached to a backpack worn by the subject. The devices were configured to communicate in a star-shaped network topology, shown in Figure 1. The structure of the network itself is very simple. The devices communicate in master-slave mode over the UWB radio channel. The device at the centre of the network, i.e., the master device, coordinates the response of the remote IMU devices by sending the triggering packet to which each IMU device responds in a timed manner. The set of devices constitutes the WSA facility for providing real-time feedback of the sensory information while walking.  
Figure 1: Star network topology of Wearable Sensory Apparatus for tracking the motion of body segments while walking.  
Multiple remote devices communicate with the master device based on the principle of time-slot sharing. Since they communicate on the same radio channel and only one device can transmit data at a time, precise time synchronization between all the devices is a basic requirement for reliable data transfer. Time synchronization is achieved by a synchronization packet sent by the master device, to which the timers of the local IMU devices are aligned for a particularly delayed response.
## 4 Performance Optimization and Testing
### Computational Balance between CPU and DCU  
The \"Dongfang\" supercomputing system is equipped with high-performance hardware resources for each compute node, including 32 CPU cores and 4 DCU accelerators. In this paper, a hybrid parallel strategy was adopted, utilizing the Pthread library to launch multiple threads on the CPU for computation while additional threads were used to invoke the DCUs for computation. We used 28 CPU cores to compute 1/5 of the grid, meaning each core handled a smaller grid segment. This approach fully leverages the multicore parallel processing capabilities of the CPU, accelerating the computation process. Additionally, we reserved 4 CPU cores, creating one thread per core to invoke the DCUs for computation. Each DCU also processed 1/5 of the grid, with single-step computation times shown in Figure 14. As illustrated, the computation speed of a single DCU is significantly faster than that of the CPU, reaching 16 times the speed of the CPU. This clearly demonstrates the efficiency of DCUs in handling specific computational tasks.  
the parallel efficiency in strong scalability tests increased to over 90%, indicating that the program's performance can maintain efficient growth as more compute nodes are added.  
Figure 16: Comparison before and after computational balance.  
Figure 14: Comparison between CPU and single DCU.  
Figure 15: Comparison between CPU and single DCU after optimization.
## 1 Introduction
In this study, we adopted a composite method, in which an enzyme was adsorbed on a carrier and then crosslinked with glutaraldehyde (GA) treatment. Since the stabilization by GA treatment depending on the type of carrier and enzyme, the stability of immobilization due to the difference in GA concentration between Accurel MP 100 and Phospholipase A\({}_{1}\) was investigated. The objectives of the study were to examine the effects of the optimized reaction conditions on hydrolysis between immobilized PLA\({}_{1}\). Particularly, we used Accurel as a carrier to develop a higher immobilized enzyme. Moreover, we investigated phospholipid hydrolysis using an immobilized industrial-class PLA\({}_{1}\) on Accurel MP100. The latter was made of a polypropylene-based hydrophobic granular support [25]. We solved this \begin{table}
\begin{tabular}{c c c c c c} \hline
**Enzyme** & **Carrier** & **Substrate** & **Solvent** & **Reaction** & **Ref.** \\ \hline Lipase from _Candida rugosa_ & Polypropylene & Triolein & Isooctane & Hydrolysis & [19] \\ \hline Lipase from _Candida rugosa_ & Zeolite & Triglycerides & Supercritical carbon dioxide & Hydrolysis & [20] \\ \hline Lipase B from _Candida antarctic_ & Silica support & rac-1-phenylethanol & Ionic liquid/hexane & Synthesis & [21] \\ \hline Lipase B from _Candida antarctic_ & Acrylic resin & & & & \\  & immobed 150 & Sorbic acid & Glycerol &esterification & [22] \\ \hline Phospholipase A\({}_{2}\) & Ion exchange support & Lecithin & Triton & Hydrolysis & [23] \\ \hline Phospholipase A\({}_{1}\) & Microporous resin & Phosphatidylcholine & Supercritical carbon dioxide & Synthesis & [24] \\ \hline \end{tabular}
\end{table} 
Table 1: Previous hydrophobic substrate reactions using immobilized lipases and phospholipases. problem of the water content under the immobilized enzyme reaction and obtained optimized water content. Moreover, we solved the problem about the impact of the enzyme immobilization process and the water content in the organic phase on the kinetic data to achieve maximization on reactivity and repeated productivity of immobilized PLA\({}_{1}\) for a long period of time.
# Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection
## 4 Approach
### Overview of Our Framework  
Due to the lack of smoky vehicle data, there are not any pure deep learning algorithms for smoky vehicle detection. Instead, background modelling and proposal detection are normally used in existing methods. Here, we propose a novel cascaded smoky vehicle detection pipeline without traditional pre-processing steps, illustrated in Figure 4.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Database** & **Type** & **Total Samples** & **Annotation** & **Available** \\ \hline KMU Fire-Smoke [45] & video & 10 & category & public \\ Mivia Fire-Smoke [46] & video & 149 & category & public \\ SmokeVideos in [18] & video & 25 & block labels & public \\ VSD(Set4) [47] & patches & 10,617 & category & public \\ \hline SEU-SmokeVeh [7] & videos & 102 & temporal segments & private \\
**LaSCoV** (Ours) & image & 75,000 & bounding boxes & public \\
**LaSCoV-video** (Ours) & video & 163 & temporal segments & public \\ \hline \end{tabular}
\end{table}
Table 1: The comparison between existing datasets and ours.  
Figure 3: Some examples in existing datasets [7, 18, 45, 47].  
Our cascaded framework was comprised of three modules, namely a light-weight smoke detector, a vehicle matching module, and a short-term spatial-temporal network. The light-weight smoke detector, i.e., YOLOv5 tiny, was re-designed from the famous YOLOv5 pipeline, which has a minimum width and depth in the architecture and largely uses depth-wise and point-wise convolutional operations to replace traditional convolutional operations. The light-weight smoke detector detects smoke regions in each video frame and may be confused by shadow and non-road regions since their appearances are very similar to vehicle smoke, see Figure 4. The vehicle matching module is activated once a smoke region is detected in a frame. This module first runs a vehicle detector to obtain vehicle regions, which can be optimized together in YOLOv5 tiny. We roughly used the off-the-shelf (on COCO dataset) YOLOv5n, which can detect three vehicle categories, i.e., car, bus, and truck. With the assumption that each smoke region corresponds to a vehicle, the vehicle matching module then computes the Intersection-over-Union (IoU) ratios and pair-wise distances between smoke regions and vehicle regions. If the minimum distance was smaller than a given threshold or the IoU was larger than a threshold, we made the given smoke region and the certain vehicle a smoke-vehicle pair. The smoke-vehicle matching module can remove these false positive regions easily, such as non-road and other non-vehicle regions, see Figure 4. To further remove hard false positives, another short-term spatial-temporal model was applied on the spatial-temporal extent of the remaining regions. Overall, our framework is accurate and effective and largely leverages prior knowledge and deep learning technology.