esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Description and Processing  
### Data Description  
Descriptive statistics were generated for all variables (velocity, longitudinal acceleration, latitudinal acceleration, acceleration, and jerk) as shown in Table 2. The total number in the experimental sample is 479,552. Velocity varies from 0 to 95.78 km/h. Acceleration has a wide range, and it can be seen as the acceleration and deceleration process of the truck. The means of the longitudinal acceleration and latitudinal acceleration are 0 since, during driving time, the truck maintained a constant speed most of the time. The SD of velocity remains at a high value because the velocity varies frequently in the route. The other SDs are small, reflecting the truck travelled smoothly overall.  
Figure 3 shows a boxplot of the above-described factors in this study. It can be seen that velocity is concentrated in the range of 15-40 km/h. This indicates that drivers tended to drive slowly in most cases. However, there remains a part of the time when the truck was travelling at high speed, even up to 95 km/h. This poses certain safety risks. The average value of lateral acceleration is 0, and most of the variation is concentrated within 0.5 m/s\({}^{2}\), indicating that the truck drivers mostly maintained presence in a single lane while driving. However, there are still some abnormal latitudinal acceleration values, indicating that the drivers turned at large angles while driving. Reasons for this may include cornering, lane  
\begin{table}
\begin{tabular}{c c c} \hline
**Intersection Type** & **Counts** & **Existing Area** \\ \hline Yield & 189 & Rural \\ Stop & 206 & Urban \\ Two-phase signalized & 162 & Urban and Rural \\ Multiphase signalized & 177 & Urban \\ \hline \end{tabular}
\end{table}
Table 1: Intersection composition.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Min** & **Max** & **Mean** & **SD** \\ \hline Velocity (km/h) & 0 & 95.78 & 28.03 & 15.71 \\ Longacc (m/s\({}^{2}\)) & \(-\)3.63 & 7.40 & 0 & 0.17 \\ Latacc (m/s\({}^{2}\)) & \(-\)8.32 & 1.82 & 0 & 0.07 \\ Acc (m/s\({}^{2}\)) & 0 & 10.81 & 0.13 & 0.13 \\ Jerk & \(-\)5.61 & 10.42 & 0 & 0.13 \\ \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of kinematic variables.  
changing, and avoiding emergencies. The values for longitudinal acceleration are mostly concentrated in the range of \(-1\) to \(1.5\,\mathrm{m}/s^{2}\), indicating that the driver was basically driving normally. However, there are some higher values of acceleration variation, which indicate there was some aggressive behavior during driving sessions. As a result, the boxplot of acceleration illustrates that these heavy-duty truck drivers exhibited some aggressive behavior during their driving sessions.
# Data Analysis of the TK-1G Sounding Rocket Installed with a Satellite Navigation System
###### Abstract  
This article gives an in-depth analysis of the experimental data of the TK-1G sounding rocket installed with the satellite navigation system. It turns out that the data acquisition rate of the rocket sonde is high, making the collection of complete trajectory and meteorological data possible. By comparing the rocket sonde measurements with those obtained by virtue of other methods, we find that the rocket sonde can be relatively precise in measuring atmospheric parameters within the scope of 20-60 km above the ground. This establishes the fact that the TK-1G sounding rocket system is effective in detecting near-space atmospheric environment.  
Keywords:TK-1G sounding rocket; near space; data analysis +
Footnote †: journal: _atmosphere_
## 3 Sensor Data Security Estimator
### Proposed Security Metrics
#### 3.2.1 Key Strength Probability of the Cryptography Algorithm
Security strength values for symmetric and asymmetric algorithms can be found in periodic reports provided by organizations, such as NIST [16], or projects, like EncryptII [17], among others. In case the security strength of a cryptographic algorithm (for a given key) is unknown, \(s\) will correspond to the size of the key used. Likewise, the value of \(f\) can be determined according to the algorithm used and will correspond to the higher publicly available key test throughput for that algorithm. For example, the customized machine, Copacobana Rivyera, designed to break the DES (Data Encryption Standard) algorithm, has a throughput \(f=292\) billion keys per second and can find a DES key in less than a single day [18]. The value of \(f\) is publicly available for each algorithm, but in case this test rate is not publicly known for a given algorithm, its value can be set according to the rates usually achieved when trying to break any algorithm with the same key size using distributed computing (e.g., worms, grid computing or cloud computing), as described in [17]. We highlight that SDSE obtains the parameter \(t\), for each node, in execution time, whereas parameters \(s\) and \(f\) are gathered from SDSE's database. **Practical Importance of \(\boldsymbol{P_{F}(t)}\)**. The cryptographic strength probability is important to help sensor network administrators choose suitable key sizes that assure the desired level of protection for the network, besides assisting them in deciding on when a sensor key must be renewed. This metric also allows different cryptographic algorithms to be compared with each other. Moreover, as this metric takes into account the time \(t\) of key activation at each node, it is possible to use this metric in real time in order to inform network users about the security the cryptography provides to each individual sensor node. **Discussion**. Although in traditional networks, key lengths of \(128\), \(192\) and even \(256\) bits or more can be used, choosing proper key sizes for sensor networks is a critical task, since sensors have limited computational resources and might not always be able to store large keys with high security strengths. Thus, smaller keys have to be employed. For example, TinySec, a security architecture developed for TinyOS, uses the Skipjack algorithm [19] by default, which has a maximum key size of \(80\) bits. Skipjack is also employed by Minisec [20], another platform developed for TinyOS that provides security for sensor networks. Therefore, since sensor keys are typically small, it is important to have a system like SDSE to monitor, in real time, the status of the security provided by the keys of the sensors nodes.
## 2 Materials and Methods
### Biofertilizer  
The biofertilizer was produced from residual cladodes of nopal (_Opuntia focus-indica_) by anaerobic fermentation, using rabbit manure as inoculum. _Opuntia focus-indica_ is originally from Mexico, currently present all over the world, whose abundance and high water content (approximately 90%) make it an ideal substrate for anaerobic digestion [48]. Opuntias are an important biomass source in arid and semi-arid areas where they thrive by efficiently using scarce water, characteristic of these places, thanks to the crassulacean acid metabolism (CAM) they possess. In Hidalgo, there are 560,160 hectares planted with _Opuntias_, which generate an average of 6 Mg\(\cdot\)ha\({}^{-1}\) annually as pruning residues, which are not used, causing environmental and public health problems [49].  
The biofertilizer was produced in three 60 L batch reactors, which were fed with an organic load rate (OLR) of 1.5 g VS\(\cdot\)L\({}^{-1}\)d\({}^{-1}\), and operated at a hydraulic retention time of 50 days at room temperature (about 20 \({}^{\circ}\)C). pH of the feed was adjusted (at 6.5) with KOH. The inoculum was prepared mixing fresh rabbit manure and water (1:3 \(w\)/\(v\)). The mixture was kept under anaerobic conditions for 50 days before being utilized for inoculating bioreactors. For the biofertilizer characterization, the methods described by [PERSON] et al. [50] were applied to quantify organic carbon by acidic oxidation with potassium dichromate, total nitrogen by Kjeldahl method, and total phosphorus by Klett-Summerson method. The chemical oxygen demand was determined by method 5220 of the Standard Methods [51]. The contents of Ca, K, Mg, Na, Cu, Fe, and Zn were quantified by atomic absorption spectroscopy (AAS) (Agilent, Santa Clara, CA, USA) in a sample previously digested in a microwave oven (CEM, Matthews, NC, USA) applying the EPA 3051 method [52]. Aucins were analyzed according to Glickmann and Dessaux [53], and for gibberellins, Graham and Henderson [54] methodology was applied. All the above determinations were made in triplicate. To quantify N-fixing microorganisms, tenfold selected dilutions (10\({}^{-3}\)-10\({}^{-5}\) in 0.9% NaCl) were poured by triplicate on plates containing Ashby-Sucrose agar, then plates were incubated at 28 \({}^{\circ}\)C for 7 days. To evaluate the total microflora in the biofertilizer, the mesophilic aerobes (MA) were quantified pouring tenfold selected dilutions (10\({}^{-5}\)-10\({}^{-7}\) in 0.9% NaCl) by triplicate on plates with standard count agar (BD Bioxon, Estado de Mexico, Mexico), which were incubated at 37 \({}^{\circ}\)C for 48 h. The presence of _Salmonella_, _E. coli_, and fecal coliforms was investigated as well. The same dilutions used for MA were poured on plates with eosin methylene blue agar (EMB, Dibico, Cuattilan Izcalli, Mexico) to quantify fecal coliforms and on violet red bile agar MUG (VRB-MUG, Difco, Waltham, MA, USA) for _E. coli_ and total coliform bacteria counting. After, the plates were incubated at 37 \({}^{\circ}\)C for 24 h.
# Rehabilitative Ultrasound Imaging Evaluation in Physiotherapy: Piloting a Systematic Review
## 3 Results
### Additional Analysis  
The number of studies is increasing every year (Figure 3), with an upward trend and a peak in publications in 2014 and 2015. Activity continued in the subsequent years, and picked up significantly in 2017. Evidently, it is not just a passing trend [42] even today. Therefore, it is a line of research on a global level, which is of interest, and in the light of the evidence analyzed, can be potentially very useful for the evaluation and evidence of the techniques used in physiotherapy.  
The global distribution of the scientific production according to the articles reviewed over the last five years as full-texts shows a predominance in the United States and Australia (30% and 17%, respectively) followed by China and Japan (7%), and finally the rest of the countries in a bracket \(\leq\)4.9%, which includes Spain (Figure 4).  
Figure 4: Pie plot global evidence in % over total.  
Figure 3: Publications alongside years.  
The graph gives an idea of the main research drivers and the countries involved in this lumbar region technique at present.  
The analysis of the articles according to the type of study provided us with evidence that can be split into two groups:  
1. Randomized Clinical Trial. [PERSON] 2015.  
In this paper, a parallel RCT comparing two post-operative rehabilitation protocols following lumbar disectomy was developed. The methodology concerning design, recruiting processes, randomization, allocation, intervention, and measurements were done according with the objective and primary purposes. The results were expressed as a percentage change in thickness, but it would be wise if the measurements were included. There was an excellent study on follow-ups after samples along six months. This research demonstrates that exercise is an excellent approach for low back pain treatment, and ultrasound is a perfect tool with validity to obtain measurements related to the patient's improvement. Interesting results described no differences between specific and general exercise. The cross-sectional areas and muscle morphology were evaluated by sonography, and the two groups improved in the measurements.  
An important limitation was recognized by the author in that there was no control group.
2. Non-Randomized Trials.  
This section ranges from case reports to quasi-experimental studies. Most of them are prospective observational studies, cross-sectional, and valid by intra and inter-tester, which support or confirm working methods (Figure 5).  
The intraclass correlation results were compiled in a comprehensive table for direct observation and better evaluation was performed (Table 3).  
Figure 5: Distribution of the evidence published classified by type of studies.
## 2 Results
### Volatile Compounds  
Wine volatile compounds were affected by cluster zone ABA application in this study, but the impact varied between the two vintages (Table 2). In 2018, ABA wines had higher levels of (\(E\))-3-hexen-1-ol, 1-hexanol, 2-heptanol, benzyl alcohol, \(\beta\)-damascenone, \(\beta\)-ionone, ethyl decanoate, ethyl dodecanoate, and nerol than CON. The levels of isoamyl alcohol, 2-phenylethanol, linalool, bound-form 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN), and vitispirane were lower in wines from ABA-treated fruit compared to CON. The ABA treatment caused higher total concentrations of esters, C\({}_{13}\)-norisoprenoids, and terpen in 2018 GRBD wines (Table 2). In 2019, higher concentrations of propanol, ethyl decanoate, and ethyl undecanoate and lower concentrations of 3-methylbutanoic acid, hexanoic acid, 2-phenylethanol, ethyl 2-methylbutanoate, ethyl 2-methylpropanoate, ethyl 3-methylbutanoate, ethyl octanoate, ethyl propionate, isoamyl acetate, isobutyl acetate, phenethyl acetate, and 3-isobutyl-2-methoxyyrazine (IBMP) were observed in wines with the ABA treatment compared to CON. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Compound 1**} & \multicolumn{3}{c}{**Vintage 2018**} & \multicolumn{3}{c}{**Vintage 2019**} \\ \cline{2-7}  & **ABA** & **CON** & \(\boldsymbol{p}\)**-Value 2** & **ABA** & **CON** & \(\boldsymbol{p}\)**-Value 2** \\ \hline caffe acid & 3.8 \(\pm\) 0.3 & 4.2 \(\pm\) 0.4 & 0.214 & 4.1 \(\pm\) 1.4 & 5.0 \(\pm\) 0.8 & 0.598 \\ caffeoyltartaric acid & 20.3 \(\pm\) 0.7 a  & 23.6 \(\pm\) 0.5 b  & \(<\) 0.001 & 9.7 \(\pm\) 5.4 & 13.1 \(\pm\) 3.1 & 0.219 \\ catechchin & 67.0 \(\pm\) 6.0 & 71.8 \(\pm\) 6.2 & 0.093 & 34.3 \(\pm\) 2.6 & 35.3 \(\pm\) 3.3 & 0.183 \\ epicatechin & 83.6 \(\pm\) 7.5 & 81.0 \(\pm\) 5.0 & 0.500 & 91.4 \(\pm\) 15.0 & 83.5 \(\pm\) 25.4 & 0.526 \\ ferraric acid & 7.2 \(\pm\) 1.3 & 7.3 \(\pm\) 1.3 & 0.901 & 1.7 \(\pm\) 0.1 & 1.6 \(\pm\) 0.1 & 0.322 \\ gallic acid & 9.4 \(\pm\) 0.8 & 9.3 \(\pm\) 0.5 & 0.741 & 6.5 \(\pm\) 0.5 & 6.8 \(\pm\) 0.4 & 0.275 \\ malvidin-3-o-glucoside & 124 \(\pm\) 2 a  & 141 \(\pm\) 10 b  & 0.002 & 263 \(\pm\) 14 & 266 \(\pm\) 21 & 0.732 \\ p-coumaric acid & 3.9 \(\pm\) 0.4 & 3.8 \(\pm\) 0.8 & 0.899 & 4.3 \(\pm\) 1.1 & 4.9 \(\pm\) 1.0 & 0.389 \\ resveratrol & \(<\)1 & \(<\)1 & \(<\)1 & \(<\)1 & \\ trans-coutaric acid & 8.3 \(\pm\) 1.2 & 8.0 \(\pm\) 0.3 & 0.517 & 9.6 \(\pm\) 0.7 a  & 10.8 \(\pm\) 0.5 b  & 0.009 \\ vanilla acid & 10.1 \(\pm\) 1 & 11.4 \(\pm\) 1.5 & 0.127 & \(<\)1 & \(<\)1 \\ \hline \end{tabular}
\end{table} 
Table 1: Major phenolic compounds measured in wines from GRBV-positive Pinot nori vines under exogenous abscisic acid application (ABA) to the fruit zones and no abscisic acid control (CON) during two seasons.
## 6 Conclusions  
In order to solve problems such as low detection accuracy for current strip steel surface defect detection algorithms in practical applications, this paper proposed an effective steel surface defect detection algorithm, DEW-YOLO, on the basis of YOLOv8. The algorithm introduces deformable convolution into the C2f network structure, which constitutes the C2f-DCN and replaces the C2f in the backbone network with the C2f-DCN. This allows the model to realize adaptive adjustment of the sensory field, which enhances the model's ability to extract features for multiple classes of defects. Then, the model fuses the explicit visual center of CFPNet to capture the long-range dependence of features and improve the model's ability to detect small targets. Finally, the CIOU loss function was replaced with the WIoU loss function to improve the accuracy of the common mass anchor frames and enhance the fitting ability of the bounding box losses. Extensive experiments and comparisons with other algorithms validated the feasibility of the proposed method and highlighted the sophistication of the methodology of this paper, as it effectively improved the detection accuracy and maintained the detection speed. The mAP of this algorithm reached 80.3% on the NEU-DET strip surface defects dataset, which was 3.9% higher than the original algorithm, and a real-time detection speed of 91 frames/s could be realized in the defect detection of strip production, which made it more efficient and flexible in actual strip production. Although the improved model had a slight increase in computations compared with the original model, it offered significantly better detection accuracy and still remained suitable for deployment on mobile devices. The proposed method is applicable to the current demand for surface defect detection in steel strips.  
Using computer vision technology to address surface defect detection in steel strips has practical significance and provides reference value for defect detection problems in other industrial sectors. In future work, the detection precision of the proposed method will be further developed, and the model will be optimized for lightweight deployment to enhance detection speed.  
Conceptualization, J.L. and M.C.; methodology, J.L.; software, J.L.; validation, J.L.; formal analysis, J.L. and M.C.; investigation, J.L.; resources, M.C.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, J.L. and M.C.; visualization, J.L.; supervision, M.C.; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant No. 61863009, and the Guangxi Key R&D Program, Guike-AB22080093.
## 3 Wireless Communication Technologies
### Short-Range Communication Technologies
#### 3.1.3 ZigBee (IEEE 802.15.4)  
ZigBee technology is normally used in the low data rate applications that require high battery life and secure networking. It covers a distance ranging from 10 to 100 m. Compared to Bluetooth and Wi-Fi, it is less expensive and simpler. It operates in the 2.4 GHz frequency range with a data rate of 250 kbps. It has 16 channels and each requires 5 MHz of bandwidth. In [13], the authors presented indoor localization of a quadcopter using ZigBee. The results authenticate that the localization system using ZigBee is viable, effective, and easy deployable. [PERSON] et al. [14] analyzed UAV landings, where ZigBee was tested for communication and position estimation. The study demonstrates, as an outcome, a significant decrease in error and an accurate estimation of the position. [PERSON] et al. [15] proposed a hybrid scheme where ZigBee was used for intra-cluster communication. The simulation results reveal that ZigBee can be a potential candidate for low-data-rate FANET applications.
## 3 Feature Extraction Methods and Indicator Development
### Development of a Similarity-Based Indicator  
A new indicator was developed to quantify the feature difference between Segment 1 and Segment 2, which serve as a mutual instantaneous baseline for each other to combine debonding-sensitive features and realize comprehensive assessment. As formulated in Equation (10), the indicator is a weighted summation of \(p\) features. Since different features differ in contribution to the identification effect, weights were introduced to quantify the contribution of different features. The weight of each feature was calculated by normalizing the difference in feature values between the two segments in the early stage of monitoring.  
\[F=b_{1}f_{1}+b_{2}f_{2}+\ldots+b_{1}f_{1}+\ldots+b_{p}f_{p}\ (l=1,2,\ \ldots,\ p) \tag{10}\]  
The \(l\)th weight is expressed as \(b_{l}=\left|f_{seg1}^{ear_{l}}-f_{seg2}^{ear_{l}}\right|/\sum_{l=1}^{p}\left| f_{seg1}^{ear_{l}}-f_{seg2}^{ear_{l}}\right|\). \(f_{seg1}^{ear_{l}}\) represents the \(l\)th feature value of Segment 1, \(f_{seg2}^{ear}\) represents the \(l\)th feature value of Segment 2 where \(\sum_{l=1}^{p}b_{l}=1\). The superscripts 'ear' and 'cur' denote the early stage and current stage of monitoring, respectively, and the subscripts'seg1' and'seg2' represent Segment 1 and Segment 2, respectively. The weights can characterize the importance of a feature to the identification, and the contribution to the identification increases with increasing weight. In the subsequent analysis, all of the adjustable constants were equal. The current values of the indicator for the two segments can be expressed as:  
\[F_{seg1}^{cur}=b_{1}f_{seg1}^{cur_{1}}+b_{2}f_{seg1}^{cur_{2}}+\ldots+b_{p}f_{ seg1}^{cur_{p}} \tag{11}\]  
\[F_{seg2}^{cur}=b_{1}f_{seg2}^{cur_{1}}+b_{2}f_{seg2}^{cur_{2}}+\ldots+b_{p}f_{ seg2}^{cur_{p}} \tag{12}\]A significant difference between \(F_{seq1}^{cur}\) and \(F_{seq2}^{cur}\) implies that a distinct anomaly happens to one of the track segments, based on the assumption that the two track segments do not encounter damage at the same time.
## 4 Utilizing the Marx Generator in Biomass Processing
### Application of the Marx Generator for PEF Treatment of Milk Casein
#### 4.2.2 Particle Size and \(\zeta\)-Potential Analysis  
Table 2 shows the effects of PEF treatment generated using a Marx generator (in both ranges) on the particle sizes of CSMs. The particle sizes of protein molecules increased after PEF treatment. This could be probably due to the partial denaturation or aggregation of CSM's globular structure after PEF treatment [44]. Similar results were observed in our recent study [26] in which PEF treatment (10 kV/cm) increased the size of CSMs using another type of PEF generator. The \(\zeta\)-potential of CSM proteins (Table 2) increased after PEF treatment. There was no significant change in the \(\zeta\)-potential after PEF treatment. The increase in \(\zeta\)-potential may increase the electrostatic repulsions, altering the functional properties of proteins [45]. PEF treatment could generate free radicals, increasing the absolute \(\zeta\)-potential value around protein molecules [4].  
The changes in the fluorescence spectra of proteins were considered an indicator for the changes in the proteins' tertiary structure induced by the microenvironment polarity changes in the tyrosine (Tyr) and tryptophan (Trp) residues [46; 47]. To verify that the alterations in CSM's structure resulted from PEF treatment, Figure 10 illustrates the shifts in fluorescence spectra between untreated and PEF-treated protein samples, revealing a decrease in fluorescence intensity following PEF treatment. This suggests that tryptophan moieties were less exposed to excitation, as lower emission intensities were observed. Typtophan's fluorescence is reliant on the polarity of its microenvironment. In the tertiary structure, tryptophan residues were buried in nonpolar and hydrophobic regions. After unfolding the protein structure, tryptophan residues were exposed to aqueous polar media, quenching their fluorescence signals. Typtophan's fluorescence quenching could also be related to the tertiary structure's conformational changes, including the dissolution of hydrophobic interactions and/or the crosslinking of amino acid side chains [44]. In another study, the fluorescence intensity of the PEF-treated (4-10 V/cm) soy protein isolate was higher than the native untreated protein [48]. This could be due to the differences in the nature and structure of proteins and/or the different applied electric field strengths.