esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Our Proposed Methods  
This section details our proposed algorithm for adaptive data hiding and image encryption for HDR images. Figure 1 exhibits the flowchart of our algorithm. In the sender part, two main processes are concurrent image conversion and adaptive message embedding (CICAME) and image encryption, while in the recipient part, there are image decryption and message extraction processes. Here, we briefly highlight these processes and will detail them in the following subsections.  
Figure 1: The flowchart of our proposed method consisting of message embedding and extraction processes.  
To embed a secret message, the sender inputs the demanded target embedding rate (\(ER_{tar}\)), secret message [5], and a source HDR OpenEXR image (\(I_{EXR}\)). The first part of message embedding is converting \(I_{EXR}\) into a new HDR image encoded by the RGBE format. During the conversion, we seamlessly embed secret message {S} into each pixel to generate the stego image (\(I_{S}\)). Next, we encrypt \(I_{S}\) using the encryption key (\(k_{e}\)) to protect the image contents and secret hidden message. The resultant stego encrypted image (\(I_{SE}\)) is then delivered to the recipient through a public channel, such as the Internet. In this way, the secret message is camouflaged during the format conversion and image encryption.  
Image decryption and message extraction are conducted on the recipient side. The receiver first inputs the stego encrypted HDR RGBE image (\(I_{SE}\)). It is then deciphered by the aid of the decryption key (\(k_{d}\)) provided prior by the sender. The image decryption produces the stego HDR RGBE image (\(I_{S}\)) which contains a hidden message. Finally, a secret message {S} is extracted from \(I_{S}\) using the extraction key (\(k_{x}\)), thereby completing the message extraction process. The following subsections detail each step in the message embedding and extraction processes.
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 5 Results  
### Experimental Scheme Design  
ONE (the Opportunity Network Environment) is used as the experimental environment to test the algorithm presented in this paper, and the real data set haggle6-infocom6 [27] is used for performance verification. A total of 3 days of data transmission between 78 mobile Bluetooth devices and 20 fixed devices were collected in the Infocom06 dataset. The experimental parameters are established in Table 3 below, and the Prophet,Epidemic, RDR [19] and FCIM [20] algorithms are evaluated alongside the algorithm suggested in this research.  
The performance of each of the five methods is evaluated under identical circumstances but with varying cache sizes, message generation intervals and message survival times. In this paper, we take into account the following four fundamental metrics: message delivery success rate, average latency, routing overhead and the number of packets dropped. A higher message delivery success rate, a lower average latency, a lower routing overhead and a lower number of packet drops signify a routing algorithm's superior performance. We consider the effect of three parameters: cache spaces, message generation intervals and time to live of messages.  
Data normalization is used in the processing of the results of the experiment. The first processing is Equation (19) because a greater message delivers success rate is preferable, and second processing is Equation (20) as smaller average delay, overhead, and packet drops are preferable.  
\[x^{\prime}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{19}\]  
\[x^{\prime\prime}=1-\frac{x-\min(x)}{\max(x)-\min(x)} \tag{20}\]  
The algorithm's overall score is added by the normalized values under various indexes, and among them, \(\min(x)\) and \(\max(x)\) indicate the minimum and maximum values in a collection of data.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
###### Abstract  
As one of the most promising new energy sources, the lithium-ion battery (LIB) and its associated safety concerns have attracted great research interest. Herein, a comprehensive review on the thermal hazards of LIBs and the corresponding countermeasures is provided. In general, the thermal hazards of the LIB can be caused or aggravated by several factors including physical, electrical and thermal factors, manufacturing defect and even battery aging. Due to the activity and combustibility of traditional battery components, they usually possess a relatively high thermal hazard and a series of side reactions between electrodes and electrolytes may occur under abusive conditions, which would further lead to the thermal failure of LIBs. Besides, the thermal hazards generally manifest as the thermal runaway behaviors such as high-temperature, ejection, combustion, explosion and toxic gases for a single battery, and it can even evolve to thermal failure propagation within a battery pack. To decrease these hazards, some countermeasures are reviewed including the application of safety devices, fire-retardant additives, battery management systems, hazard warnings and firefighting should a hazard occur.  
lithium-ion battery; thermal hazard; management and countermeasures +
Footnote †: journal: Applied sciences
# A Survey on Recent Advanced Research of CPS Security
## 4 Discussion
### Application Scenarios
#### 4.2.4 Smart Homes  
In smart homes, cameras, routers, water heaters, and light bulbs are connected to the home LAN, which can be controlled easily by mobile apps. Such smart devices operate in a relatively trusted environment, so many manufacturers do not give top priority to safety. However, security problems can still cause considerable damage to people's privacy.  
The main security research issues are sensor privacy collection and app vulnerabilities. Researchers have proposed many frameworks that focus on sensor security. Peeves [29] and 6 thSense [30] monitor the changes in sensor data to avoid event sensor faults. Aegis [31] checks sensor-device-user interactions to identify malicious behaviors. In app security checks, static taint analysis [52], inter-app interaction chain analysis [53], and cross-app interaction analysis [55] are useful in finding dangerous actions. Interestingly, as many components are reused in different apps, similarity detection [56] is efficient in vulnerability analysis.
# Performance Optimization of the Low-Capacity Adsorption Oxygen Generator
## 4 Results and Discussion
### The Optimal Oxygen Flux for Different Pressures  
During the measurements, the required operating pressure was set, and the transfer time was gradually increased to find its maximum value, at which the oxygen purity remains at the level of 95%. For the determined transfer time, the oxygen flux was adjusted such that \(\Delta P_{Oxy}\) was 0.5 bar at most. Three measurements were performed for each adsorption pressure from 2.5 to 6.0 bar(g) in 0.5 bar increments. The measurement results are shown in Figure 6.  
In Figure 6, it can be seen that the transfer time increases with increasing process pressure only in the area of lower pressures. From an adsorption pressure of 4.0 bar, it remains constant. This is due to the fact that the adsorption capacity of the bed increases with increasing pressure, but at higher pressures, this increase is slower and slower (see Figure 3). At the same time, with higher pressure, there are more molecules in the gas that can be adsorbed on the bed.  
The maximum oxygen separation capacity is closely related to the adsorption pressure of the bed. This capacity increases with increasing pressure. It can be seen that this increase is relatively linear from 2.5 bar (the lowest pressure tested) to approx. 4.5 bar. This increase in this area is due to a significant increase in the adsorption capacity of the bed (Figure 3) and an extension of the transfer time. For higher pressures, the increase in adsorption capacity is slower, the transfer time remains constant, and the increase in oxygen separation capacity is slower and slower. From the characteristic shown in Figure 6, it can be seen that the required oxygen flux in the PSA generator should be controlled  
Figure 5: Dependence of the separated oxygen purity on the pressure difference between the adsorption pressure and the pressure in the oxygen tank.  
by setting the appropriate adsorption pressure. This pressure may be increased to a value of 5 bar, maximum 6 bar. Above these values, the capacity gains diminish.  
The measurement of the oxygen flux has a certain scatter of values. The random uncertainty of the measurement was calculated as the standard deviation. Taking into account the uncertainty of the flowmeter calibration, the uncertainty of the capacity measurement is 0.05 normal m\({}^{3}\)/h. The optimal transfer times and the average oxygen flux for different adsorption pressures are shown in Table 1.
# ROAD Statistics-Based Noise Detection for DME Mitigation in LDACS
## 3 System and Noise Model
\[I_{pd}(t)=\Sigma_{i=0}^{N_{pd-1}}\Sigma_{l=0}^{N_{i}-1}\sqrt{P_{i,l}}P_{d}(t-t_ {i,l})e^{i2\pi f_{c}t+j\chi_{i,l}} \tag{5}\]  
where \(N_{pd}\) is the total number of interfering DME stations, \(N_{i}\) is the total number of pulse pair in the particular time interval for the ith interfering DME station, \(P_{(i,l)}\chi_{(i,l)}\) are power and phase of the pulse pair, respectively, \(f_{(c,l)}\)--the relative carrier frequency of the \(i\)th interfering DME station and \(t_{(i,l)}\) is the starting time of the \(l\)th pulse pairs of the ith DME station. The methods used to reduce impulse noise work well to reduce DME noise also. The detailed block diagram of the proposed LDACS FL AS receiver is sketched in Figure 4. The first block in receiver removes the cyclic prefix bits associated with the received signal \(r^{\prime}=[r^{\prime}_{t}(0),r^{\prime}_{t}(1),...,r^{\prime}_{t}(N+N_{C_{P }-1})]^{T}\) resulting in signal \(r_{t}=[r_{t}(0),r_{t}(1),...\ r_{t}(N-1)]^{T}\). The nonlinear device ROAD PB detects the DME interference in LDACS signal \(r\) (with the clever use of ROAD statistics) and performs pulse blanking to reduce the bit error rate in received data. In general, the resulting vector can be defined as \(x=f(r)\) or \(x_{t}=f(r_{t})\), where \(f(. )\) is the nonlinear function with enough intelligence to sense DME interference. The nonlinear devices discussed in this paper process the signal \(r_{t}\) in dissimilar ways to sense the DME interference. Moreover, the nonlinear estimators used for the performance comparison of the proposed method utilize one more vector \(\pi_{t}\) to estimate the received signal data. Hence, the definition of function \(f(. )\) varies with different nonlinear devices. The nonlinear device is operated on the signal \(r\) before the DFT processing to block the dispersion of sparse time domain impulses \(p_{t}[n]\) over all the OFDM carriers in the frequency domain.
## 3 Results and Discussion
### Optical Characterization
There is not an increase in the intensity of UV emission at 341 nm and neither in the intensity at 384 nm, which may indicate that there is not a change in the crystalline orientation of the samples; this observation is consistent with the XRD results. Peak intensity at 616 nm may change due to electronic capture of Mn. The electrons captured by Mn cannot be bound by surface oxygen vacancies and defects to produce excitons, which decreases excitonic PL intensity [22]. The values of hydrodynamic dimension and Z potential presented in Table 3 were obtained through DLS. The results of the hydrodynamic dimension reflect a monomodal distribution, which indicate that the particle size is regular in suspension. According to the results shown in Table 3, sample Nb\({}_{2}\)O\({}_{5}\):Mn (2.5 wt.%) submitted at 700 \({}^{\circ}\)C presents more interaction with the solvent used in this test. This behavior would affect its photocatalytic performance, since the recovery of the sample from aqueous medium is difficult. In terms of electrostatic stability, the particles of the samples obtained at 700 \({}^{\circ}\)C are stable in aqueous medium due to their Z potential being minor than \(-\)30 mV. Figure 8: Kubelka–Munk transformed reflectance spectra of annealed samples at 700 \({}^{\circ}\)C.
## 2 Materials and Methods
### Preparation of CCNF by Colloid Milling  
Sample CC was preliminarily cut short by a household cooking machine (Model: JYL-G12E, Joyoung Co. Ltd., Jinan, China), and sieved through a 40-mesh screen, and then was dispersed in deionized water at a concentration of 2 wt%. The pretreated CC slurry was ground by a vertical colloid mill (Model: JM50, Chenghua packaging machinery Co., Shantou, China). The clearance between the rotor and the stator of the colloid mill is in a range of 0.1 to 5 mm, with 10 stages adjustable, and the rotating speed is 3000 rpm. Meanwhile, the pH of the CC suspension was adjusted to 1, 3, and 7, respectively, with dilute sulfuric acid. The grinding process started from the large clearance to the small. The CC sample suspension underwent grinding once at each clearance stage, and then was repeatedly milled 10 times at the minimum clearance. After grinding, the slurry was centrifuged by using a centrifugal machine (Model: LD-4, Kexi instrument Co. Ltd., Guangzhou, China) for 15 min at 4000 rpm. The centrifugal precipitate was removed, and the solid content of the supernatant was tested by dry weighting method. The supernatant was sealed in plastic containers and refrigerated at 3 \({}^{\circ}\)C before use.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(W_{\text{urea}}/W_{\text{cell}}\) & **Temperature (\({}^{\circ}\)C)** & **Time (h)** & **DS** \\ \hline
0.334 & 135 & 3 & 0.113 \\
0.334 & 140 & 3 & 0.123 \\
0.334 & 145 & 3 & 0.136 \\
0.334 & 150 & 3 & 0.139 \\
0.334 & 140 & 2 & 0.098 \\
0.334 & 140 & 4 & 0.143 \\
0.334 & 140 & 5 & 0.109 \\
0.259 & 140 & 3 & 0.055 \\
0.451 & 140 & 3 & 0.133 \\
0.485 & 140 & 3 & 0.171 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conditions and results of cellulose carbamate (CC) synthesis.
## 3 Materials and Methods
### Antiviral Properties  
Antiviral activity tests were conducted for the investigated extracts using enveloped human herpesvirusirus 1 (HSV-1--ATCC(r) VR-1493(tm)) and nonenveloped human adenovirus 5 (HAdV-5--ATCC(r) VR-5(tm)). Fractions of _I. aquifolium_ Silver Queen were tested using EN 14476 [47]. This standard describes a quantitative suspension test for the evaluation of virucidal activity in the medical area (Phase 2/Step 1), mixing one part by volume (0.1 mL) of test virus suspension of HSV-1 (at a concentration of 10\({}^{12}\) TCID\({}_{50}\)) or HAdV-5 virus (at a concentration of 10\({}^{8}\) TCID\({}_{50}\)) with one part by volume of interfering substance (0.1 mL), and PBS (with bovine albumin), which simulates the non-sterile conditions under which the test product will potentially be used, and eight parts by volume of PLGA (polyphenol and phenolic acid extracts), SAP (saponin extract), or TERP (higher terpenoid extract) (concentration of 0.5% in distilled water for PLGA and SAP and 5% ethanol:water for TERP). Aliquots were taken at a specified contact time (60 min) and serial dilutions (up to 10\({}^{-12}\)) of each mixture were prepared. In eight repeats, 50 \(\mu\)L of each dilution was added to the microtiter plate containing a monolayer of confluent HeLa cells (ATCC(r)--CCL-2 TM--human cervix carcinoma; ATCC, Manassas, VA, USA) or A549 cells (ATCC(r)--CCL-185 TM--human lung epithelial carcinoma cells). The plate was observed daily for up to 4 days under an inverted microscope (Olympus Corp., Hamburg, Germany; Axio Observer, Carl Zeiss Microlmaging GmbH, Munich, Germany) for the development of viral cytopathic effect. Residual infectivity was then determined by comparing the titre (determined using TCID\({}_{50}\)) of the virus treated with the substance and the titre of the control virus. According to the standard, a substance has antiviral effectiveness if the titre is reduced within the recommended exposure time by \(\geq\)4 log10 steps (inactivation \(\geq\) 99.99%).  
In parallel, cytotoxicity tests were carried out as part of the procedure given in the standard and as a baseline for assessing the cytopathic effect caused by the virus. Since previous cytotoxicity tests using the SRB method identified the highest nontoxic concentrations, the virucidal assays used appropriate dilutions of fractions as starting points (concentration range 1-200 ug*mL\({}^{-1}\)). A number of dilutions, from 10\({}^{-1}\) to 10\({}^{-12}\), were prepared from each fraction, and 50 \(\mu\)L of each dilution was added to a microtiter plate containing a monolayer of confluent HeLa cells or A549 cells. The results were read in parallel with the virucidal results.
# A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business
## 5 Conclusions, Limitations, and Directions for Further Research  
### Conclusions  
This study systematically reviewed the literature on blockchain technology adoption by businesses. First, we conducted a bibliometric analysis to understand current research trends, such as the core themes and recent advances. Second, an in-depth literature study was conducted to explore further the literature and broaden the information sources. This significantly contributed to the establishment of our conceptual framework on blockchain technology adoption by businesses, which explains the entire review. The following are some of the most important conclusions of this review:  
Recent research on the implications of blockchain adoption has focused extensively on the factors that drive blockchain adoption by businesses and barriers to adoption. These studies combined various technology adoption models with other models, such as value creation and economic models. Several variables influence a business's adoption of blockchain, categorized as technological, organizational, individual, and environmental. Individual, technological, and environmental aspects were identified as the subjects of high investigation. However, prior research has paid minimal attention to organizational factors, such as power concentration, administrative procedures, hierarchical levels, human resource coordination, accessible resources, worker relationships, and firm age, which must be thoroughly investigated.  
Business operations have been a primary consideration in the study of the role of blockchain adoption by businesses. Blockchain technology is primarily projected to benefit businesses by improving their business process efficiency; however, there has been a paradigm shift towards social responsibility, sustainability, and progression towards the fourth industrial revolution. Furthermore, value creation through technology research includes company performance, decision-making, strategy, and digitalization.  
The supply chain has received much attention, but new growing themes and advances, including environmentally friendly supply chains, virtual environments, and digital identities are attracting interest. Furthermore, the integration of blockchain technology with other technologies, such as artificial intelligence (AI) and e-commerce, and its potential in extensive data management and the Internet of Things is investigated, implying a high emphasis on companies' attempts and interest in achieving automation and data exchange in manufacturing technologies and processes through the Fourth Industrial Revolution (Industry 4.0). Several technological, organizational, and environmental barriers have been highlighted in the literature. Numerous studies have revealed that the government significantly affects blockchain adoption through regulations, such as human rights, innovation support, and stakeholder trust.
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 3 Results and Discussion in Axle Recognition  
The axle recognition model was constructed using Transformer-GRU to locate the time points of axle entering and exiting platforms so as to calculate the number of axles, axle loads, and gross vehicle weight [22; 23; 24; 25]. The input sequence was the weighing signal collected by the WIM sensors, and the label sequence was the 0-1 sequence, where the output value 1 represented the time points when the axle entered and exited platforms.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Load Disaggregation Algorithm (LDA)
#### 4.3.3 Learning Strategy  
When the features are extracted from the signal there are several algorithms that can be selected for the training process. They can roughly be split up in two categories optimization algorithms and pattern recognition algorithms. The optimization algorithms relies on a database of appliance info, and tries to find the subset of the database that is most likely to be in use.  
\[M=\underset{x\in P(D)}{argmin}(|\sum_{i=0}^{I}x_{i}-\hat{y}|) \tag{7}\]  
This is illustrated in Equation (7) were a set of appliances \(\mathcal{M}\) that correspond to the measured signal \(\hat{y}\) needed to be found. This is achieved by searching the known database of all appliances \(\mathcal{D}\). This is done by taking the powerset of the database and finding the subset that minimizes the error. It can therefore be seen as a minimization problem, where the desired set \(\mathcal{M}\) is the arguments to the minimum solution. This approach is fine for small amounts of appliances, but for large databases and many appliances in use at the same time this would take far too long to be practical.  
Due to this, most researchers use pattern recognition techniques instead. These have the benefits of being more scalable, and be usable even if only parts of the device database is known. The basic concept is to create a statistical model that can be used to classify appliances. For this kind of training it is easy to collect a big training set, but it requires the groups to be manually labeled afterwards. Many algorithms are based on clustering techniques and Hidden Markov models (HMM) [42; 43; 44; 45; 46; 47]. HMM have proven very effective when classifying Type-1 and Type-2 appliances. This is due to its ability to model state changes as an imported part of the classification. It is shown that using clustering techniques can greatly improve the classification, and reduce the training time. Artificial neural networks are also showing great promise, and is a topic that is currently in focus by many researchers [43].