esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Feasibility of Using Industrial Urea to Mitigate Thermal and Shrinkage Cracking in Concrete
## 3 Experimental Design
### Methods  
The slump and air content of unhardened concrete were measured in accordance with ASTM C143 [29] and ASTM C231 [30], respectively. To measure the setting time, a penetration resistance test was conducted according to ASTM C403 [31] by using fresh mortar with coarse aggregates being removed using a 4.75-mm sieve immediately after mixing fresh concrete. To measure the compressive strength of concrete, a cylinder specimen with dimensions of 100 mm \(\times\) 200 mm was made in accordance with ASTM C39 [32], and water curing at a temperature of 21 \(\pm\) 2 \({}^{\circ}\)C up to a certain age was implemented. A specimen with accelerated carbonation was produced in a 100 mm \(\times\) 100 mm \(\times\) 400 mm cubic mold. This specimen was cured for 28 days, and then, its two ends were coated with epoxy resin to ensure that carbon dioxide (CO\({}_{2}\)) could penetrate only into the specimens in a two-dimensional model. The specimen was inserted in an accelerated carbonation tester produced to maintain a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C with relative moisture of 50 \(\pm\) 5% and CO\({}_{2}\) concentration of 10 \(\pm\) 0.5%, and the carbonation depth was measured using phenolphthalein solution. In general, for experimentally evaluating the shrinkage strain of concrete, the shrinkage of concrete is calculated by measuring the shrinkage length of a specimen; however, this may cause experimental errors owing to external environmental factors and specimen damage during curing. In this experiment, a strain gauge (Tokyo Sokki Kenkyujo, Tokyo, Japan) was embedded in the center of a mold with dimensions of 100 mm \(\times\) 100 mm \(\times\) 400 mm, and the shrinkage was measured subsequently using the data-logger at 30-min intervals was collected and recorded automatically. For measuring the hydration heat of concrete simply and easily, a T-type thermocouple was installed at the center of a mold (internal dimension: 220 mm \(\times\) 220 mm \(\times\) 230 mm, insulation thickness: 25 mm) made of compressed polystyrene for maintaining insulation. Then, the concrete temperature owing to hydration heat was measured automatically for up to seven days of aging at 30-min intervals using a device connected with the thermocouple. While measuring the shrinkage strain and hydration heat, the specimen was kept in an environment with a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and relative moisture of 50 \(\pm\) 5%.
## 2 Study Area and Datasets  
### Study Area  
Shenmu City, a county-level city under the jurisdiction of Yulin City in Shaanxi Province, is situated on the western bank of the Yellow River in Northern Shaanxi. It holds the distinction of being the largest county-level city in Shaanxi Province by area. Spanning a coal-bearing region of 4500 square kilometers, which represents 59% of the county's total area, Shenmu boasts over 50 billion tons of proven coal reserves, earning its status as China's foremost coal-producing county [24]. Geologically, the study area lies within the northern Shaanxi platform depression, encircled by fold-fault bundles, and features a nearly north-south rectangular shape. Its strata predominantly comprise Carboniferous-Permian and Mesozoic systems, lacking significant magmatic activity and undergoing minimal metamorphic alteration. Consequently, the area is endowed with abundant mineral resources, including sedimentary bauxite, coal, and petroleum [24]. Notably, the western mining region of Shenmu experiences substantial impacts from coal and other fossil resource extractions, leading to prevalent mining-induced subsidence and collapse issues (as depicted in Figure 1c).  
The study's focal area was the western mining region of Shenmu City (longitude 108\({}^{\circ}\)24'E to 109\({}^{\circ}\)46'E, latitude 34\({}^{\circ}\)48'N to 35\({}^{\circ}\)54'N) shown in Figure 1a, measured according to the map scale with an area of approximately 2.8 \(\times\) 10\({}^{3}\) km\({}^{2}\). The region is predominantly flat, featuring elevation variations from 1100 to 1300 m with a relative height difference of about 200 m. The terrain predominantly ascends from the southeast to the northwest and primarily consists of desert grassland areas, interspersed with dense hygrophytic vegetation in beach lands (illustrated in Figure 1b).
Sensors_**2013**, _13_, 10981-11006; doi:10.3390/s130810981  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors.  
_Article_  
**Temperature-Compensated Clock Skew Adjustment**  
**[PERSON] *, [PERSON] and [PERSON]**  
Computer Architecture, Electronics and E.T., University of Cordoba, Cordoba 14071, Spain;  
E-Mails: [EMAIL_ADDRESS] (J.M.P.); [EMAIL_ADDRESS] (J.O.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +34-957-212-039.  
_Received: 8 June 2013; in revised form: 15 August 2013 / Accepted: 19 August 2013 / Published: 20 August 2013_  
**Abstract:** This work analyzes several drift compensation mechanisms in wireless sensor networks (WSN). Temperature is an environmental factor that greatly affects oscillators shipped in every WSN mode. This behavior creates the need of improving drift compensation mechanisms in synchronization protocols. Using the Flooding Time Synchronization Protocol (FTSP), this work demonstrates that crystal oscillators are affected by temperature variations. Thus, the influence of temperature provokes a low performance of FTSP in changing conditions of temperature. This article proposes an innovative correction factor that minimizes the impact of temperature in the clock skew. By means of this factor, two new mechanisms are proposed in this paper: the Adjusted Temperature (AT) and the Advanced Adjusted Temperature (A2T). These mechanisms have been combined with FTSP to produce AT-FTSP and A2T-FTSP. Both have been tested in a network of TelosB motes running TinyOS. Results show that both AT-FTSP and A2T-FTSP improve the average synchronization errors compared to FTSP and other temperature-compensated protocols (Environment-Aware Clock Skew Estimation and Synchronization for WSN (EACS) and Temperature Compensated Time Synchronization (TCTS)).  
**Keywords:** clock skew; WSN; synchronization; temperature; oscillators; tuning-fork; FTSP; AT-FTSP; A2T-FTSP
## 4 Conclusions  
From this study, we can conclude the following: Higher acceleration values can be observed in packages that are in a higher stacked position. This is true for all orientations. The highest acceleration values were in the lateral direction: \(4.16g\), \(4.93g\), and \(5.94g\). In the vertical direction, the highest was \(3.99g\). Higher acceleration levels were observed when there was more free movement space in the package layout method.  
In terms of vertical orientation, the most intense vibration levels were found in the frequency range of 8-50 Hz; furthermore, this was amplified in the higher layers. Below 8 Hz, the vibration intensity was almost the same in the higher layers as on the floor, and around 40-50 Hz, a cross-frequency could be found above which the intensity decreased rapidly below the floor intensity. In all package positions, the highest vibration levels occurred in the vertical direction, followed by lateral and longitudinal.  
The highest overall Grms values (in the frequency range of 1-200 Hz) were observed in the third layer, and in each case, were highest in the vertical direction followed by the lateral and longitudinal directions.  
The kurtosis statistical values for the recorded vertical vibration events were from 1.18 to 6.76, which presents a non-Gaussian nature of random vibration in every package position.  
The results of this paper can provide information about such a test environment for engineers that can simulate the vibration circumstances of small stacked packages in parcel delivery transportation, thereby helping to find and design an optimal packaging that simultaneously serves to protect the goods and reduce the environmental impact of the packaging used.  
We want to draw the attention of managers to the fact that the presented results can be used practically immediately in day-to-day design and testing practice. They are also encouraged to apply the newly developed packaging testing guidelines, which can also help avoid damage to goods, thereby generating greater profits or resulting in lower costs for companies. The results of this study will also help companiesmaintain a competitive advantage for a particular product in the market, or help them enter into a new sales channel that they have not used before.  
**Author Contributions:** [PERSON] and [PERSON] designed the research. [PERSON] conducted measurements and experiments; [PERSON] and [PERSON] developed and analyzed the data and presented results. [PERSON] wrote the original draft; [PERSON] finalized the draft. This research project was conducted under the supervision of [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Utilizing Cost-Effective Determination Techniques to Authenticate Cosmetics
## Materials and Methods
### MALDI-TOF Analysis  
The utility of cosmetic ingredient analysis and authentication by MALDI-TOF has been identified by previous explorations [12; 16]. Given the unique characteristics of cosmetics, an efficient analysis methodology was developed. Firstly, 0.1 mL or 0.1 g of uniform sample was taken and diluted with TBS buffer (laboratory-made, generally 10 mmol/L Tris containing 0.9% NaCl, and the pH was adjusted to 7.4 with 1 N HCl) at a 1:20 volume or mass ratio. Then, the suspension was sonicated for 20 min. Subsequently, it was refrigerated at 4 \({}^{\circ}\)C for an hour. The solution was centrifugated at 5000 rpm for 10 min to obtain the supernatant. Then, 10 g/L of 3-hydroxypyridinic acid solution (50% acetonitrile + 50% 0.1% TFA aqueous solution) was added into the corresponding sample solution in a 1:1 ratio to formulate a matrix sample solution.  
The matrix sample solution of 2.5 \(\mu\)L was positioned onto the 96-well target plate, repeated twice for each sample point, and the target plate was heated until dry. Afterwards, this target plate was sent to MALDI-TOF mass spectrometry for subsequent analysis. The parameters were set as follows: the source voltage was 20 kV, laser frequency was 1 kHz, laser energy was 9 \(\mu\)J, linear cationic mode was employed, the relative molecular mass scanning range was 100-1200 \(m/z\), the scanning speed was 0.5 mm/s, and the sample analysis time was 40 s/hole. Spectral analysis was performed using QuanTOF 1.0 software.  
Figure 1 shows the comparison of the MALDI-TOF mass spectra of two groups of samples from the same brand but different purchase locations. Minor differences could be displayed on the spectrum through comparison, and the percentage of differences could be obtained through subsequent statistical comparisons.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 2 Methodology
### Signal Conditioning Circuit  
During physiological signal acquisition, ambient electromagnetic interference can significantly affect low-amplitude ECG signals, potentially compromising subsequent signal analysis [37; 38; 39]. Additionally, myoelectric interference, baseline drift, and high-frequency noise generated during individual movement can further degrade ECG signal quality, ultimately affecting the accuracy of physical health assessments. Therefore, the design of efficient filtering circuits is critical in ECG monitoring systems. In this study, the CEMS integrates three key filtering components, as shown in Figure 3: a notch filter for suppressing industrial frequency interference at 50 Hz (Figure 3a), a fourth-order linear-phase low-pass filter with a cutoff frequency f\({}_{\text{c}}\)= 100 Hz to eliminate high-frequency noise (Figure 3b), and a fourth-order linear-phase high-pass filter with a cutoff frequency f\({}_{\text{c}}\)= 100 Hz to remove baseline drift (Figure 3c). These filters effectively suppress various types of noise while preserving the critical features of the ECG signal, thereby enabling high-quality ECG acquisition.  
The frequency response of the filter circuit is illustrated in Figure 4. It exhibits pass-band characteristics within the ranges of 0.05-50 Hz and 50-100 Hz, while effectively attenuating signals in the remaining frequency bands. Notably, the notch filter demonstrates a pronounced suppression effect at 50 Hz, achieving a gain of \(-\)40.9 dB, which efficiently eliminates industrial frequency interference and ensures high-quality acquisition of the user's ECG signals.  
Figure 4: Frequency response of the filter circuit.  
Figure 3: The topology of the built-in filters in the CEMS. (**a**) Notch filter. (**b**) The fourth-order linear-phase low-pass filter. (**c**) The fourth-order linear-phase high-pass filter.
## 5 Conclusions  
The current study introduced a new approach for detecting routing attacks in VANETs, leveraging incremental online learning algorithms trained on data generated in real-time. Our study focused on assessing the performance of two algorithms, namely Adaptive Random Forest (ARF) and K-Nearest Neighbors, with regard to Accuracy, Precision, Recall, and F1-score metrics. The training and testing time of both classifiers were also analyzed. Our research particularly addressed the detection of black hole attacks, which pose a significant threat to the AODV routing protocol. Data used for attack detection are collected from simulating realistic VANET scenarios using two well-known simulators, namely SUMO and NS-3. Further, essential features, which are relevant in capturing the behavior of VANET nodes under a black hole attack, are monitored over time.  
The findings show that incremental learning is a promising solution in time-critical applications like attack detection in highly dynamic environments such as VANETs, as it allows continuous and real-time learning. Further, the results show that Adaptive Random Forest (ARF) can be successfully applied to classify and detect black hole nodes in VANETs. ARF outperformed KNN with respect to all performance measures. However, ARF required more time for both training and testing in comparison with KNN.  
While these conclusions have shown promising results, the detection of intrusions using online incremental machine learning remains a challenging problem due to the constantly evolving nature of attacks and the need to continuously adapt the models to new threats. It is worth noting that the choice of features used to represent the network traffic data has a significant impact on the performance of the classifier. Further, the performances of incremental learning classifiers seem to vary depending on various factors like the dataset used, the complexity of the problem, as well as the type of the incremental learning framework used.  
As a next step, we intend to lead an in-depth study by tuning the algorithms' parameters in order to enhance their performance as well as the overall required time for training and testing.  
Finally, it is crucial to acknowledge the challenges of real-time attack detection in VANETs, owing to the highly dynamic nature of the network and the rise of sophisticated attack strategies. Accordingly, our next priority is to implement a hybrid model that incorporates ARF with other well-known algorithms like online SVM for more effective and precise detection of potential threats and attacks in VANETs.  
Conceptualization, S.A. and S.E.H.; methodology, S.A. and M.-A.E.H.; validation, M.H.; formal analysis, S.A., M.H. and S.E.H.; investigation, S.A. and S.E.H.; resources, S.A. and S.E.H.; data curation, S.A. and S.E.H.; writing--original draft preparation, S.A., M.H. and S.E.H.; writing--review and editing, S.A., M.H. and S.E.H
## 4 OWS Q-Learning Algorithm
### OWS Q-Learning Algorithm
#### 4.3.2 Optimize Learning Rate  
The learning rate \(\alpha\) controls the learning progress of the model in the process of iteration and determines the degree to which the newly obtained information overwrites the original information [60]. \(\alpha=0\) means that the agent cannot do anything to learn and only uses prior knowledge, and \(\alpha=1\) means that the agent only considers the latest information and ignores prior knowledge [60]. The speed and accuracy of the convergence of the Q-learning algorithm are seriously affected by the learning rate, if the value of the learning rate is too large, the learning will accelerate, but the noise in the environment will also be absorbed, so the Q value will fluctuate greatly, and it is difficult to converge [59]. On the contrary, if the value of the learning rate is too small, then the Q value will converge slower, making it difficult for the training of the agent to keep up with changes in the environment [59]. In most Q-learning applications, the learning rate is usually set as a constant, usually \(\alpha\) takes a value between 0.01-0.1, and a constant learning rate is oftenused, such as taking \(\alpha=0.1\) in experiments, so it cannot meet the needs of dynamic and fast learning.  
If the learning rate is fixed in practice, when the algorithm reaches convergence, the Q value will swing in a large area near the optimal value. However, when the learning rate decreases with the increase in the number of iterations, the algorithm will converge, and the Q value will swing in a smaller area near the optimal value. In this paper, the learning rate \(\alpha\) is optimized, so that \(\alpha\) decayed along with the number of training rounds. In the early stage of training, the learning rate \(\alpha\) is larger, the new information covers the original information less, and the agent considers less prior knowledge, with the increase in training rounds, the learning rate gradually decreases, the new information covers the old information more, the agent considers more prior knowledge, \(\alpha_{1}\) is used to represent the improved learning rate. The optimization result of the learning rate is as follows:  
\[\alpha_{1}=\frac{1}{k+10} \tag{18}\]  
where \(k\) is the number of training rounds episodes, \(k=0,1,\dots\)\((k\in Z)\), \(\alpha_{1}\) with the dynamic change in the number of training rounds of the agent, the value of \(k\) is small when the agent first starts training, in order to ensure that the value of \(\alpha_{1}\) is meaningful, the denominator part is set to \(k+10\) after multiple experimental comparisons.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 3 Preliminaries
### Optimization Goal
#### 3.3.2 The Lifetime of the FANET  
When the remaining energy of a node is less than 20% of the initial value of the node, it is regarded as a dead node and removed from the FANET [21]. If the number of dead nodes is too large, the performance of the FANET will be greatly reduced. So when the number of dead nodes reaches half, the network will be considered invalid. The lifetime of the FANET can thus be expressed as  
\[T=t_{e}-t_{s}, \tag{2}\]  
where \(t_{s}\) is the time when the FANET starts working, and \(t_{e}\) represents the time when the FANET becomes invalid.  
Compared with CMs, CHs undertake more tasks and consume more energy. Therefore, if the energy consumption of CH can be reduced, the lifetime of the FANET will be greatly improved. Relying on the scheme with dynamic transmission power of CHs, the CH can flexibly adjust its transmitting power according to the distance to different CMs, avoiding redundant energy consumption and saving resources. As a result, the rate of node death is slowed down and the lifetime of the FANET is increased.
# Research on an Improved SOM Model for Damage Identification of Concrete Structures
## 4 Experiments and Results Analysis
### Neuron Topology Analysis for RPC Bending Fatigue Damage SOM Network Model
Therefore, all unknown neuron damage categories are determined based on the topology grayscale algorithm, which further improves the network identification accuracy. Compared with the traditional SOM model, the identification accuracy was improved by 4.69%.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 6 Fog Computing and Data Management Use Cases in Smart Cities
### Fog Computing in Smart Healthcare
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Application** & \multicolumn{1}{p{142.3 pt}}{**Computing Model (Fog/Cloud)**} & **Fog Nodes’ Role** & **Benefits** \\ \hline Alert and management [55] & emergency architecture & Fog/cloud notification process & Optimize the emergency notification process & Overall delay reduced six times compared to a cloud-only solution \\ \cline{3-4}  & & & Alert emergency services and the victim’s family members & \\ \cline{3-4}  & & & Offload resource-intensive tasks & \\ \hline Mobile system [56; 57] & health & Fog/Cloud & Capture user and mosquito sensor data & Quick identification of any newly infected user or risk site. \\ \cline{3-4}  & & & Fuzzy k-nearest neighbor (FKNN) based classification model [57] & Provide data storage and pre-processing & Reduced system latency. Improved response and execution times. \\ \hline Monitoring of patients suffering from chronic diseases and other health service [58; 59] & Fog/cloud disease and other health & & Aggregate and analyze data collected by edge devices & Increasing the efficiency of the entire system \\ \cline{3-4}  & & & Distribute processing tasks to edge devices & \\ \cline{3-4}  & & & Manage data pipeline from data acquisition to data analytics on the cloud & \\ \hline Dynamic distribution and scheduling of health tasks [60] & Fog/cloud tasks [60] & Perform computations tasks (data analysis, context management, critical control) & Reduce application delays and costs and meet their time constraints \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fog computing-based healthcare applications.
## 2 Materials and Methods
### Blood Samples Collection and Analysis  
At exasanguination, a blood sample was collected from a subsample of 24 randomly selected pigs for each experimental group (\(n\) = 72). Blood was collected in lithium heparin tubes and immediately stored at +4 \({}^{\circ}\)C to be transferred to the laboratory of the Department of Veterinary Medicine of the University of Bologna. Blood tubes were then centrifuged (at 2000\(\times\)\(g\) for 20 min), and plasma was separated and stored at \(-20\)\({}^{\circ}\)C, pending subsequent analysis for cortisol, CK, and aldolase.  
Cortisol was used in this study as an indicator of acute pre-slaughter stress (e.g., during handling, loading, transport and/or restraint) [21]. Creatine kinase (CK) was chosen as a subacute (8-48 h) indicator of intense physical activity (including aggressive behaviors and activity during the last on-farm stages and on the truck) [22; 23; 24]. Lastly, aldolase was taken as a long-term indicator of muscle damage (48-72 h) also correlated to meat quality traits [25; 26; 27; 28].  
The quantitative determination of serum cortisol was made using a commercial Elisa kit (orb566639, Biorbyt Ltd., Cambridge, UK) and is expressed as ng/mL.  
Creatine kinase and aldolase concentrations (both expressed as U/L) were measured using two commercially available kits (CK Nac Liquid and Aldolase, Sentinel Diagnostics, Milan, Italy), and serum CK and aldolase concentrations were determined with a spectrophotometer (Ultrospec 3000, Pharmacia Biotech, Milan, Italy). The intra-assay CV was 5.61%, 4.21% and 4.98% for plasma aldolase, CK and cortisol, respectively.