esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Resilience to Tampering and Physical Attacks  
Based on the above analysis, if a malicious user captures the drone and physically accesses its memory, they will not be able to recover any of the keys. By following this hybrid (symmetric/asymmetric) method, the proposed approach allows secure logging of transactions that can be only decrypted at the Control Center. The UAV itself will not be able to decrypt the data after encrypting it.  
Moreover, the UAV can encrypt and store information about any suspicious activity (e.g., unscheduled activity in the initial trip plan to access the drone's data or steal the carried items, etc.) as it does with any other transaction, as long as the malicious act does not consist of destroying the drone. It will be one additional block in the chain that can be decrypted and analyzed at the Control Center. Then, adequate measures can be taken, since the time and location of the suspicious activity are known, as they are stored in the corresponding block.  
A slight modification to the proposed approach can be adopted to account for the extreme scenario of destroying the UAV itself (with the impossibility of reporting that immediately, in the absence of connectivity): A copy of the encrypted block of each transaction can be stored at that transaction's location (the actual location where the delivery happened), in addition to storing it at the UAV. If the drone is destroyed between stops no. \(i\) and \(i+1\) for example, a subsequent investigation finds CBC blocks 1 to \(i\) at their respective locations, while finding nothing at location \(i+1\) onwards. This would help narrow down the location of the culprits. By storing only the encrypted block of a given transaction at the corresponding location, without any keys or any of the previous blocks, it will be impossible to decrypt the data. An example is shown in Figure 6 with \(i=4\). The same approach presented in Figure 6 works in other attack scenarios where the drone is rendered un-operational although not necessarily taken down, e.g., jamming attacks. However, it should be noted that in the case of attacks like eavesdropping and jamming, other physical layer techniques can be used to maintain operations. Such techniques include beamforming between the UAV and the target destination, beam nulling in the direction of the jamming signal whenever it can be estimated, or the use of free space optical communications, where data are transmitted over a line of sight between the sender and receiver. Although there is abundant research on each of these techniques, their integration with the proposed approach constitutes an interesting topic for future research.  
Figure 6: Example of trajectory interruption.
# Sequential Recommendations on GitHub Repository
## 4 Experiments  
### Experimental Setup  
#### 4.1.1 Dataset  
This section first describes the pre-processed GitHub dataset set with statistics and evaluates the recommender models and their variations. We then elaborate on the results and analyses of the experiments of the recommendation. For a reasonable comparison, the learning and evaluation code was imported from each appropriate author.  
As mentioned in the Section 3.1, we focus on the interactions between user and repository. Therefore, although a repository has attributes such as timestamp, description, and program language, we exclusively focus on the interactions. In that respect, we ruled out the cold-start users and repositories under \(n\) number of interactions, where \(n\) was set to 40 and 160. The reason behind this is that sparsely interacted items may act as outliers within the dataset and to ensure strong relations among the interactions as practiced in common recommendation systems in [1,8,32,43,46]. The size \(n\) of cold-start in the GitHub dataset indicates that there are no items or users that interact with less than the \(n\) number of inter-activities. Along with \(n\) cold-start, we set the \(L\) previous interactions as 5 and that for target sequence \(T\) as 3. For details, Table 3 shows a brief analysis of the two datasets for our training and experiments. The total number of interactions is revised from the original 37,068,153 interactions to 19,064,945 and 2,968,165, in respect to the \(n\) values. \(n\) can be varied; however, we have set the value to specific numbers to acquire adequate correlativity for modeling recommendation tasks in consideration of the data sparsity. As the sparsity of a dataset significantly affects the modeling of the recommendation, we have defined \(n\) as two values to vary the sparsity. Consequently, the number has dropped from 37 million to a minimum of approximately 2 million, but the amount is still significant for evaluating the recommendation performance. In this dataset, data were split for testing and validation with 20% and 10% of the dataset for each \(n\)-length.  
When applying recommendation systems using a certain dataset, setting the appropriate minimum number of historical interactions and often modeling subsidiary information,  
Figure 4: Model architecture of self-attentive sequential recommendation (SARec) [11].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Descriptive Statistics**} \\ \hline minimum number of interactions & 40 & 160 \\ \hline number of interactions & 19,064,945 & 2,968,165 \\ \hline number of unique users & 230,469 & 14,213 \\ \hline number of unique repository & 102,004 & 8,645 \\ \hline data sparsity & 99.92\% & 97.58\% \\ \hline average interactions per user & 82.72 & 208.83 \\ \hline average interactions per repository & 196.9 & 343.34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of statistics of preprocessed datasets.
## 3 Results
### VOC Profile of TAP Cheeses  
GC-MS was carried out to profile the VOC of the four TAP cheeses object of study. Forty-eight compounds were detected, belonging to seven chemical classes (Table 2).  
Eleven carboxylic acids were detected: acetic, butanoic, hexanoic, octanoic, and decanoic acids were the most representative ones. In Pecorino cheese, concentrations of those acids exceeded 100 mg kg\({}^{-1}\). Among the nine esters found in the cheeses, overall ethyl and butyl esters of octanoic acid were detected at the highest concentrations. Methyl ester of butanoic acid and ethyl ester of hexanoic acid were only detected in Pecorino and Vaccino cheeses. Ethanol (ranging from ca. 3 to ca. 7 mg kg\({}^{-1}\)) and 2,3-butanediol (ca. 2-5 mg kg\({}^{-1}\)) were the most abundant alcohols (12 compounds). Among the six detected ketones, 2-heptanone ranged from ca. 7 (Vaccino) to ca. 28 (Pecorino cheese) mg kg\({}^{-1}\) and 2-nonanone from ca. 8 (Cacioricotta) to ca. 34 (Pecorino cheese) mg kg\({}^{-1}\). The most representative aldehyde among the five detected was nonanal, ranging from ca. 1.1 (Caprino and Cacioricotta cheeses) to ca. 1.6 (Pecorino cheese) mg kg\({}^{-1}\). Three pyrazines were only found in Caprino cheese. The only two lactones detected were caprolactone (only in Pecorino) and dodecalactone (only in Cacioricotta cheese) (Table 2).  
Concentrations of VOC in the cheeses were used as entries for PCA. Considering the two first principal components, the various VOC showed different score contributions, depending on the cheese. Pyrazines, butanal-2-methyl and acetaldehyde characterized Caprino cheese (Figure S3). Some ketones (2-heptanone, 2-undecanone, 2-pentanone, and 2-nonanone), alcohols (e.g., 2-propanol and 2-butanol), and, especially, carboxylic acids (e.g., hexanoic and decanoic acids), contributed to the distinct VOC profile of Pecorino cheese (Figure S4). Nonanoic acid, nonanal, 2,3-butanediol, and some esters (ethyl acetate, ethyl ester of hexanoic acid, methyl ester of butanoic acid, and 1-cyclopentylethyl hexanoate) were characteristic VOC of Vaccino cheese (Figure S5). Some alcohols (1-butanol,3-methyl, ethanol, phenylethyl alcohol), ketones (2-butanone and 2-butanone,3-hydroxy, _alias_ acetoin), and butanoic acid-3-methyl characterized Cacioricotta cheese (Figure S6).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compounds** & **Caprino** & **Pecorino** & **Vaccino** & **Caciorciotta** \\ \hline Acetic acid & 36.170 1  & 84.573 2  & 53.141 2  & 4.558 3 \\ Butanoic acid & 113.145 2  & 266.107 2  & 87.824 2  & 22.280 2 \\ Butanoic acid, 3-methyl- & 0.585 2  & 0.402 2 2 2 2 2 \\ Pentanoic acid & 1.044 2 2  & 3.170 2 2 2 2 2 \\ Hexanoic acid & 221.612 2  & 485.377 2 2 2 2 \\ Hexanoic acid & 0.502 2 2 2 & 0.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 4 Analytical Models with Different Outlier Filtering Criteria
### Analytical Models
#### 4.1.1 Location Error  
The locations of the same cell-RSSI pair are assumed to be normally distributed, and the expected ranges of the longitude and latitude of the \(i\)-th cell-RSSI pair are defined as \(d_{x,i}\) and \(d_{y,i}\) (see Equations (7) and (8)). The location errors may be generated in accordance with these ranges:  
\[\begin{array}{l}d_{x,i}=\int_{\mu_{x,i}-t}^{\mu_{x,i}+t}\left|x_{i}-\mu_{x,i} \right|f\left(x_{i},\mu_{x,i},\sigma_{x,i}\right)dx_{i},\text{ where }f\left(x_{i},\mu_{x,i},\sigma_{x,i}\right)=\frac{1}{\sigma_{x,i} \sqrt{2\pi}}e^{-\frac{\left(x_{i}-\mu_{x,i}\right)^{2}}{2\sigma_{x,i}^{2}}}\\ =\int_{\mu_{x,i}}^{\mu_{x,i}+t}\left(x_{i}-\mu_{x,i}\right)\left(\frac{1}{ \sigma_{x,i}\sqrt{2\pi}}e^{-\frac{\left(x_{i}-\mu_{x,i}\right)^{2}}{2\sigma_{x,i}^{2}}}\right)dx+\\ \\ \int_{\mu_{x,i}-t}^{\mu_{x,i}}\left(\mu_{x,i}-x_{i}\right)\left(\frac{1}{ \sigma_{x,i}\sqrt{2\pi}}e^{-\frac{\left(x_{i}-\mu_{x,i}\right)^{2}}{2\sigma_{ x,i}^{2}}}\right)dx\\ \\ =\frac{2\sigma_{x,i}\left(1-e^{-\frac{t^{2}}{2\sigma_{x,i}^{2}}} \right)}{\sqrt{2\pi}}\\ \\ d_{y,i}=\int_{\mu_{y,i}-t}^{\mu_{y,i}+t}\left|y_{i}-\mu_{y,i}\right|f\left(y_{ i},\mu_{y,i},\sigma_{y,i}\right)dy_{i},\text{where }f\left(y_{i},\mu_{y,i},\sigma_{y,i}\right)=\frac{1}{\sigma_{y,i}\sqrt{2\pi}}e ^{-\frac{\left(y_{i}-\mu_{y,i}\right)^{2}}{2\sigma_{y,i}^{2}}}\\ \\ =\frac{2\sigma_{y,i}\left(1-e^{-\frac{t^{2}}{2\sigma_{y,i}^{2}}} \right)}{\sqrt{2\pi}}\\ \end{array} \tag{8}\]
## 2 Materials and Methods
### MP-AES Determination of Cu and Au and ICP-OES Determination of S in the Complexes  
Sample digestion for determination of Cu, Au, and S: approximately 0.02 g of the complexes was weighed on an analytical balance and dissolved in concentrated HNO\({}_{3}\) (p.a., Chem-Lab NV) for the Cu-containing complex and HNO\({}_{3}\) and HCl (p.a., Fluka AG) for the Au-containing complex. Blank samples were prepared following the same digestion procedure. Determination of Cu, Au, and S concentration was performed with external calibration solutions prepared from monoelemental standards, respectively, 1000 mg L\({}^{-1}\) Cu (Merck, Darmstadt, Germany), 1000 mg L\({}^{-1}\) Au (High-purity Standards, Charleston, England), and 1000 mg L\({}^{-1}\) S (CPachem, Ltd., Bogomilovo, Bulgaria). Cu and Au were measured by microwave plasma-atomic emission spectrometry (MP-AES 4200, Agilent Technologies, Agilent Technologies, CA, USA) on analytical lines 324.754 nm, 327.395 nm, 510.554 nm for Cu and 242.795 nm, 267.595 nm, 312.278 nm for Au using standard measurement conditions. Sulfur was determined by inductively coupled plasma-optical emission spectrometry (ICP-OES iCap 6000, Thermo Scientific, Waltham, MA, USA) on analytical lines 180.731 nm, 182.034 nm, and 182.624 nm in axial mode.
## 5 Vertex Cover
### Lower Bound  
We prove a lower bound for the approximation ratio of a local algorithm for vertex cover with locality distance \(k\). The proof is very similar to the proof given for independent set in Section 4..  
**Theorem 13**.: _Let \(k\) be an integer with \(k\geq 1\). Let \(\mathcal{A}\) be a local algorithm for vertex cover in unit line graphs with a locality distance of \(k\) hops in the setting of location aware nodes. The best performance ratio which can be achieved by \(\mathcal{A}\) is \(\frac{k+1}{k}\)._  
Proof.: Consider the unit line graph \(G_{1}=(V,E)\) consisting of \(6+2k\) vertices (see Figure 13). Let \(VC_{1}\) be the vertex cover which is computed for \(G_{1}\) by \(\mathcal{A}\). Note that an optimal vertex cover for \(G_{1}\) has \(k+3\) vertices. Let \(V_{1}\) be the set of vertices in \(G_{1}\) which are at most \(k-1\) hops away from the very right or the very left vertex (in Figure 13 these are the vertices in the box with the label \"\(k\) vertices\"). If \(k=0\) we define \(V_{1}:=\emptyset\). Let \(V_{2}:=V\setminus V_{1}\) (thus \(|V_{2}|=6\)). We distinguish three cases:  
Case 1: There is no vertex \(u\in V_{2}\) such that \(u\
otin VC_{1}\). So \(|VC|\geq 6+k-1\) and thus the performance ratio of \(\mathcal{A}\) is at most \(\frac{k+5}{k+3}\geq\frac{k+1}{k}\).  
Case 2: There is no vertex \(v\in V_{2}\) such that \(v\in VC_{1}\). Then \(VC_{1}\) is not a vertex cover which is a contradiction.  
Case 3: There is a vertex \(u\in V_{2}\) with \(u\
otin VC_{1}\) and a vertex \(v\in V_{2}\) with \(v\in VC_{1}\). Now consider the graphs \(G_{u}\) consisting of \(u\) and \(v\) respectively and \(k\) nodes each to their left and their right (see Figure  
Figure 13: A local algorithm \(\mathcal{A}\) with locality distance \(k\) has to take the same decisions for the vertices \(u\) and \(v\) in \(G_{1}\), \(G_{u}\) and \(G_{v}\). The black vertices show a possible vertex cover computed by \(\mathcal{A}\). In order to simplify the figure the rectangular boxes represent \(k\) vertices on the line.  
13). Let \(VC_{u}\) and \(VC_{v}\) be the vertex covers computed for \(G_{u}\) and \(G_{v}\) by \(\mathcal{A}\). As the locality distance of \(\mathcal{A}\) is \(k\) and \(u\
otin VC_{1}\) and \(v\in VC_{1}\), it holds that \(u\
otin VC_{u}\) and \(v\in VC_{v}\). If \(k\) is even then \(|VC_{v}|\geq k+1\) whereas the optimal vertex cover for \(VC_{v}\) has only \(k\) vertices. So the performance ratio of \(\mathcal{A}\) is at most \(\frac{k+1}{k}\). If \(k\) is odd then \(|VC_{u}|\geq k+1\) whereas the optimal independent set for \(VC_{u}\) has \(k\) vertices. So the performance ratio of \(\mathcal{A}\) is at most \(\frac{k+1}{k}\).  
**Corollary 5**.: _Let \(k\) be an integer with \(k\geq 1\). Let \(\mathcal{A}\) be a local algorithm for vertex cover in unit disk graphs (UDGs) with a locality distance of \(k\) hops in the setting of location aware nodes. The best performance ratio which \(\mathcal{A}\) can achieve is \(\frac{k+1}{k}\)._
## 4 Discussion
### Limitations and Recommendations for Future Research  
This study has several limitations. First, given the small sample size, the findings cannot be generalized to the entire elderly population. Furthermore, the sample consisted mainly of individuals who had no history of falls in the past year, making it impossible to correlate falls with the motor performance of each DT test. A second limitation is the lack of specific instructions for prioritizing the two tasks, which may have led to some participants focusing more on either the motor task or the cognitive task. Also, cognitive performance was not recorded under the simple activity condition or in the DT tests. Therefore, it was not possible to investigate changes in cognitive performance and the prioritization of the two tasks under the DT conditions. Third, only the cognitive tasks most used in DT gait assessments were investigated. It is not clear whether similar motor behavior would be observed with a secondary task that was either sensory or motor. Fourth, only the time taken to complete each TUG task was noted, without assessing other spatial and temporal parameters of gait, like number of steps or stride length. Although the TUG is easily administered in a clinical setting, this motor task only illustrates one parameter of motor ability, where only the change in completion time between the single test and the DT was analyzed.  
A larger sample of different ages e.g., people over 80 years old, with or without cognitive impairment or with or without balance deficits should be included in future studies. It is crucial to investigate and compare the performance on the DT tests between male and female elderly people, and to investigate any gender differences in lower limb strength, dynamic balance, and balance confidence. Not only the type of cognitive task should be examined, but also the difficulty level of these tasks in the performance of walking with DT. The susceptibility of older individuals to different types of secondary tasks or to more challenging tasks has not been thoroughly explored. Also, further research with additional gait assessments will likely provide more information about older adults' motor performance on the DT tests. Moreover, exploring the application of higher cognitive loads in motor tasks--such as through DT activities with virtual reality (VR) interventions--could be highly beneficial. VR, as demonstrated in previous studies [44], could yield intriguing results by providing immersive and adaptable environments for testing DT scenarios with real-world cognitive and motor challenges. Finally, it seems necessary to conduct a study of older people with a history of falls, not only to increase the sensitivity of the TUG test, for example by adding a cognitive load, but also to create new valid and reliable tests that can be easily applied in a clinical setting.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 3 Related Studies
But if the same is considered in the context of V2V networks, then several constraints are observed, as given below. There is no information on the conflict or cooperation of V2P and V2V systems when vehicles and pedestrians use the same channels and frequencies. It also neglects the issues of growing V2P systems for managing the density of vehicles and pedestrians that are characteristic of metropolises and affecting the reliability of V2V networks. However, generic security and privacy requirements customarily applied to V2P communications, which also include V2V systems, are not considered, even though they play crucial roles in ensuring the dependability of the system as a whole. However, this study provides an in-depth examination of important performance measures for several V2V communication methods. This document aims to offer researchers a comprehensive understanding of the current status of V2V communication technologies and guide future advancement through detailed critical analysis, discussions, and visual izations. This paper offers an extensive review of the primary communication components required for the creation of a V2V communication system and pays special attention to the distinct communication structural frameworks. It goes over the area where Artificial Intelligence (AI) affects the allocation of network dynamics and provides insight into the literature as well as performance outcomes in order to understand the challenges in V2V communication schemes. Thus, while evaluating and identifying significant factors, the review also intends to guide further research in V2V communication and technological progress in the sphere to create a stable and closely-knit framework for ITS. This study gives a detailed analysis of various archetypes of V2V communication architectures, thus making valuable input into the field by elaborating on different V2V systems that help boost the V2V network. They also make a very effective analysis of how these architectures contribute to enhancing V2V communication and are relevant to ITSs. Furthermore, this study has provided a useful research agenda for further investigation of new trends and findings based on new technologies, as well as guidelines for improving existing protocol designs under V2V communication that will be beneficial for future and continuous developments. Also, the inclusion of detailed diagrams with some tables describing the architectures of the models, as well as comparative measurements of their performances, will improve readability and understanding of the content.
## 2 Materials and Methods
### MTT Assay for Cell Viability  
Prior to experiments, cytotoxicity of loliolide in hBM-MSCs was assessed by MTT assay. Briefly, cells were seeded in 96-well plates (1 \(\times\) 10\({}^{3}\) cell/well) and incubated for 24 h. Next, the cells were treated with varying concentrations of loliolide (1, 5, 10, and 25 \(\upmu\)M). Following loliolide treatment, the wells were aspirated and 100 \(\upmu\)L of 1 mg/mL MTT working solution (in distilled water) was added and the plates were incubated for 4 h. After 4 h, the reaction was stopped by adding 100 \(\upmu\)L 100% DMSO. Finally, the absorbance was observed at 540 nm with a MultiSkan GO microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). The viability of hBM-MSCs were calculated as a relative percentage of the untreated control cells.
###### Abstract  
In a previous study, we found that plasma can enhance spore germination and \(\alpha\)-amylase secretion in _A. oryzae_, a beneficial fungus used in fermentation. To confirm this, in the current study, we investigated the effects of plasma on development and \(\alpha\)-amylase secretion using an enlarged sample size and a different plasma source: a plasma jet. There was a \(\sim\)10% (\(p<0.01\)) increase in spore germination upon non-thermal atmospheric pressure plasma jet (NTAPPJ) treatment for 5 min and 10 min, as compared with the control (no plasma treatment). The activity of \(\alpha\)-amylase detected in potato dextrose broth (PDB) media during incubation was significantly elevated in plasma-treated samples, with a more obvious increase upon 10 min and 15 min treatments and 24-96 h incubation periods. The levels of the oxidation reduction potential (ORP) and NO\({}_{\text{X}}\) (nitrogen oxide species) were higher in the plasma-treated samples than in the control samples, suggesting that these two variables could serve as standard indicators for enhancing \(\alpha\)-amylase activity after plasma treatment. Genome sequencing analysis showed approximately 0.0016-0.0017% variations (changes in 596-655 base pairs out of a total of 37,912,014 base pairs) in the genomic DNA sequence of _A. oryzae_ after plasma treatment. Our results suggest that NATPPJ can enhance the spore germination and extracellular activity of \(\alpha\)-amylase, probably by increasing the levels of ORP and NO\({}_{\text{X}}\) to an optimum level.  
Aspergillus oryzae; \(\alpha\)-amylase; enzyme secretion; non-thermal atmospheric pressure plasma +
Footnote †: journal: Applied Sciences
## 4 Adsorption Study  
### Effect of Contact Time on the Removal of RB  
As one of the key parameters, the contact time greatly influences the adsorption process. It is shown that RB dye adsorption onto CC biochar increases with the contact time, until an equilibrium is reached. The sorption capacity of RB using the CCB500, CCB600, and CCB700 biochars at various contact times (0-90 min) and an initial RB concentration of 60 mg/L was studied, and the results are illustrated in Figure 4. The RB adsorption capacity over the first minute is very fast. The adsorption capacity is high due to the efficient collision of the RB dye molecules in the aqueous solution. RB dye removal of 76.2, 79.1, and 100% is recorded for CCB500 after 15 min, for CCB600 after 15 min, and for CCB700 after only 5 min, respectively. After this time, no significant variation is observed as the sorption capacity of the RB dye reaches saturation. The presence of unoccupied adsorption sites makes it relatively simple for dye molecules to interact with the adsorbent surface in the initial step. However, at equilibrium, the adsorption sites are occupied by dye molecules, which leads to a repulsive force on the incoming dye molecules [37].