esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytics on Anonymity for Privacy Retention in Smart Health Data +
## 2 Related Work  
Much research in data privacy addresses issues on anonymity [8; 9; 10; 21; 22; 24; 25; 26; 27; 28; 29]. Many aim to measure anonymity [11; 12; 13; 14], whereas some are concerned with the utility of the result [24; 25; 29]. Majority of the metrics that are concerned with anonymization quality [11; 13; 14] use [PERSON]'s entropy to quantify average information [13; 14]. Work in [13] uses entropy to estimate the average number of correct re-identifications (of individuals) based on binary queries. More correct responses help increase the attacker's information about the individuals in the database and reduce the anonymity of the individuals. [PERSON] et al. proposed a measure [14] to estimate an average information loss when an attacker acquires additional information through querying. Again, the more information the attacker gains (or average information loss), the less anonymity users have. This makes it easier for the attacker to breach disclosure and identify the users. Our paper suggests a method to analyze the data using this latter measure to pinpoint areas in the data that are susceptible to privacy attacks. Some anonymization techniques consider the utility of the data after the anonymization for evaluation [24; 25; 29]. Among those, work in [25] considers information loss and calculates utility accordingly whereas some considers classification accuracy to measure utility [24; 29].  
A large body of research in anonymity concerns with anonymization techniques to transform a given data set into a more anonymous form for privacy preservation [8; 9; 10; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Most of these techniques find anonymized data (via generalization) that complies with \(k\)-anonymity requirement [8; 9; 10; 19; 20] to guarantee that each group of unique critical attribute values has at least \(k\) records to prevent individuals from being reidentified easily. Some anonymization uses exhaustive search to find the minimal \(k\)-anonymization with minimal distortion [9; 22]. Although the approach is not practically feasible, it provides a concrete formal model for minimal \(k\)-anonymization. Work in [9] searches for \(k\)-anonymizations using a binary search. Since binary search is a blind search, computational cost can still pose a problem when searching for all possible \(k\)-generalizations as discussed in [15]. Other approaches focus on efficiency rather than minimal k-anonymizations [19; 20]. Unlike the approaches that search for minimal generalizations blindly or focusing on efficiency rather than minimal anonymization, our proposed approach [15] aims to efficiently search for anonymized data that strike a balance between satisfying \(k\)-anonymity requirements and maximizing retention of the original data.
## 4 Experiment Arrangement and Results
### Performance Metrics
#### 4.1.3 Grey Relational Analysis  
The Grey Relational Degree (GRD) is utilized to evaluate the correlation between the prediction outcomes of various methods and the actual data. A GRD value closer to 1 indicates a stronger correlation between the predicted and actual curves. The procedure is outlined as follows [42].  
The contrast time series is  
\[X_{i}=(X_{i}(1),\ X_{i}(2),\cdot\cdot\cdot X_{i}(n)) \tag{7}\]  
The reference time series is  
\[X_{0}=(X_{0}(1),\ X_{0}(2),\cdot\cdot\cdot X_{0}(n)) \tag{8}\]  
The two time-series are standardized by  
\[x_{i}(t)=\Bigg{(}X_{i}(t)-\frac{1}{n}\sum\limits_{t=1}^{n}X_{i}(t)\Bigg{)}/ \sqrt{\frac{1}{n-1}\sum\limits_{t=1}^{n}\Bigg{(}X_{i}(t)-\frac{1}{n}\sum\limits _{t=1}^{n}X_{i}(t)\Bigg{)}} \tag{9}\]  
The correlation coefficient between \(x_{0}\) and \(x_{i}\) is  
\[\begin{array}{ll}\xi(k)=&(\min_{i}\min_{k}|x_{0}(k)-x_{i}(k)|+\rho\max_{i} \max_{k}|x_{0}(k)-x_{i}(k)|)/\\ &(|x_{0}(k)-x_{i}(k)|+\rho\max_{i}\max_{k}|x_{0}(k)-x_{i}(k)|),\rho\in(0,\infty )\end{array} \tag{10}\]  
The GRD value is  
\[r_{i}=\frac{1}{n}\sum\limits_{k=1}^{n}\xi_{i}(k) \tag{11}\]
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 5 Performance Analysis
### Economic Issue  
In order to build a cost-effective hybrid supply system, the results of the HOMER optimization software are used in this section to examine various expenses connected with the hybrid solar PV/BG system. Critical studies of these costs have been conducted, considering the dynamic character of renewable energy sources in various network topologies.  
The nominal cash flow summary of the proposed system under a 10 MHz system bandwidth is shown in Figures 17-20 for macro-, micro-, pico-, and femto-base stations, respectively. The results were obtained by modeling the system in HOMER using the average solar radiation profile and biomass availability in the selected location. It is seen that, as compared to other types of components, the battery bank has a greater cost since the battery's lifetime is shorter than the project's term. It should be noted that the grid system has a negative NPC value because a larger volume of excess energy is sold to the grid.  
Figure 17: Cash flow summary of the grid-tied hybrid solar PV/BG system under macro-BS.  
Figure 18: Cash flow summary of the grid-tied hybrid solar PV/BG system under micro-BS.  
Figure 21 depicts a comparison of NPC for the various network topologies. With the escalation of system bandwidth, all NPC curves are decreased linearly to meet the increased energy requirement. In addition, the NPC curve of the macro-BS system has a lower value as compared to the other system, which indicates that higher system bandwidth and energy trading strategy may decrease capital costs considerably. In this research on maximizing the use of renewable energy sources and increasing system dependability, the energy trading strategy for the grid is thoroughly studied.  
The quantitative comparison of the per-unit capital cost of power generation in different network topologies of the proposed system is illustrated in Figure 22. Note that, in line with the theory, a reduced energy generation price value is achieved for the greater system bandwidth. In addition, the costs of generating electricity for the smaller system are greater than those of the larger size system because, as indicated previously, NPC is more involved in the larger base station.  
Figure 19: Cash flow summary of the grid-tied solar PV system under pico-BS.  
Figure 20: Cash flow summary of the grid-tied solar PV system under femto-BS.
# The Effect of Processing Methods on Phytochemical Composition in Bergamot Juice
## Materials and Methods
### In Vitro Antioxidant Activity  
The free radical scavenging activity (RSA) of the bergamot juice was evaluated by 2,2'-diphenyl-1-picrylhydrazyl (DPPH) assay according to the procedure of [PERSON] [26]. Prior of spectrophotometrically measurement, thawed juices were centrifuged at 10,000 rpm in an 5804R Eppendorf centrifuge (Eppendorf srl, Milano, Italy) for 5 min at room temperature and diluted with analytical-grade ultrapure H\({}_{2}\)O with a concentration ranging from 5 to 20 uL/mL.  
Briefly, 150 uL of bergamot diluted juices were mixed with 1.35 mL of 60 uM DPPH methanolic solution. The absorbance reduction at 517 nm of the DPPH was determined continuously for 40 min. The RSA was calculated as a percentage of DPPH discoloration, using the following equation:  
\[\%\;\mathrm{RSA}=\;\left[\frac{(\mathrm{A}_{\mathrm{DPPH}}\;-\;\mathrm{A}_{ \mathrm{S}})}{\mathrm{A}_{\mathrm{DPPH}}}\right]\times\;100 \tag{1}\]  
where \(\mathrm{A}_{\mathrm{S}}\) is the absorbance of the solution when the extract was added and \(\mathrm{A}_{\mathrm{DPPH}}\) is the absorbance of the DPPH solution. The extract concentration (EC) necessary to achieve a 50% of radical DPPH inhibition (EC\({}_{50}\)) was obtained by plotting the RSA percentage as a function of juice concentrations and was expressed as uL/mL.  
Ascorbic acid was used as a reference standard (positive control) and dissolved in ultrapure H\({}_{2}\)O to prepare a 100 ug/mL stock solution. The antioxidant activity of standard ascorbic was tested at various concentrations (10, 15, 25, 50, 60 ug/mL).  
To standardize DPPH results, the antioxidant activity index (AAI), proposed by [PERSON] and [PERSON] [27], was calculated as follows:  
\[\mathrm{AAI}=\mathrm{DPPH}\;\mathrm{concentration}\;\mathrm{in}\;\mathrm{reaction}\; \mathrm{mixture}/\mathrm{EC}_{50}. \tag{2}\]  
Samples were classified as showing poor antioxidant activity (AAI \(<\) 0.5), moderate (0.5 \(<\) AAI \(<\) 1.0), strong (1.0 \(<\) AAI \(<\) 2.0), and very strong (AAI \(>\) 2.0) as reported by [PERSON] and [PERSON] [27].
## 5 Results
### Performance  
All performance measurements were conducted with the above-mentioned data set, consisting of one million static and ten million dynamic signatures. For time reasons, only configurations with 512 bit width, which are most relevant for the application under consideration, could be included. Using a threshold value of 205 about 0.00025 % (24.75 million) of all results passed the filter and were written to the DDR memory. Per iteration (streaming the dynamic signatures through all available PE) each static signature produced approximately 25 valid results. The calculation times were measured including all data transfers from and to the host, but without validation of the results, as this validation is not required during normal operation in the target application.  
The achieved performance for different configurations are displayed in Table 4 and in Figure 6.  
The results show that the performance of the architecture scales linearly in terms of the number of processing elements and the clock frequency. For all configurations the measured performance is within 98.2% to 99.99% very close to the maximum theoretically achievable throughput, which is determined by \(T_{max}=clock\_frequency*\#PE\). The highest throughput in terms of compared bits per second was achieved using 384 PE at 200 MHz with 38.63 Tbit/s.  
Figure 6: Graphical representation of Table 4.
## 3 Model and Background
### Fully Homomorphic Encryption  
FHE is a powerful technique that allows aggregators to aggregate received data directly without decryption [16; 17; 18]. At a high-level, the essence of FHE is simple: given ciphertexts that encrypt \(\pi_{1}\),..., \(\pi_{t}\), FHE should allow anyone (not just the key-holder) to output a ciphertext that encrypts _f_(\(\pi_{1}\),..., \(\pi_{t}\)) for any desired function \(f\), as long as that function can be efficiently computed.  
The first construction of an FHE was conducted in several steps by [PERSON] [17]. First, one constructs a somewhat homomorphic encryption (SWHE) scheme, which only supports a limited number of multiplications: ciphertexts contain some noise that becomes larger with successive homomorphic multiplications, and only ciphertexts whose noise size remains below a certain threshold can be decrypted correctly. The second step is to squash the decryption procedure associated with an arbitrary ciphertext so that it can be expressed as a low degree polynomial in the secret key bits. Then, [PERSON]'s key idea, called bootstrapping, consists in homomorphically evaluating this decryption polynomial on encryptions of the secret key bits, resulting in a different ciphertext associated with the same plaintext, but with possibly reduced noise. This refreshed ciphertext can then be used in subsequent homomorphic operations. By repeatedly refreshing ciphertexts, the number of homomorphic operations becomes unlimited, resulting in a FHE scheme.  
In [18], [PERSON] using the elementary modular arithmetic to construct a simple and secure SWHE scheme, based on the idea of [PERSON] in [17], to convert it to an FHE scheme over the integers, called DGHV scheme. This scheme merely uses addition and multiplication over the integers and its concepts are simple. An FHE scheme has four algorithms: KeyGen, Encrypt, Decrypt, and an additional algorithm Evaluate. The algorithm Evaluate takes as input a public key _pk_, a circuit \(C\), a tuple of ciphertexts \(\mathbf{c}<c_{1}\),..., \(c_{d}\)\(>\) (one for every input bit of _C_), and outputs another ciphertext \(c\). In the DGHV scheme, because computing the decryption seems to require Boolean circuits that are deeper (by a constant factor) than what the SWHE scheme can handle, the \"squash the decryption circuit\" is used for transformation. In this transformation, some extra information about the secret key are added to the public key, and then used to \"postprocess\" the ciphertext. The post-processed ciphertext can be decrypted more efficiently than the original ciphertext, thus making the scheme bootstrapping. In this paper, we apply the DGHV scheme to protect end-to-end data confidentiality.
## 4 Discussion
This tool could also be useful for monitoring daily motor responses to L-Dopa and other antiparkinsonian drugs in order to optimize pharmacological therapy and tailor the best treatment for a patient. The ultimate goal is certainly to have a tool that is as early, precise and as faithful as possible to the patient's motor alterations in order to investigate the degree of motor disturbance as best as possible, with relevant diagnostic and therapeutic implications. The main limitation of the approach considered in this study concerns the information acquisition device and the methodology for obtaining an estimate of the depth of the scene. In the first case, the vision system is clearly ineffective in its blind spots. An accurate installation and the eventual use of several video cameras could solve this issue if installed in large rooms or with an irregular layout. For the second point, we fall back into the classic drawbacks concerning artificial stereoscopic vision, where the main problem lies in the fact that for some pixels in one image there may be non-univocal correspondence with pixels in the other image, or those pixels in the other may not even exist. This occurs, for example, when some parts of the scene are visible to one camera only and occluded from the other. In case of no correspondence, the direct calculation of the disparity for those pixels is impossible and a 3D reconstruction of the portion of the scene that those pixels represent cannot be made. One of the most widely applied solutions for solving this problem, at least in part, takes into consideration the possibility of obtaining, when possible, an estimate of the target's position based on its previous history. Additionally, the enlargement of the number of subjects involved in future clinical tests could also facilitate statistical analysis that could further orient the next development of the proposed system. Further aspects of gait should be deepened, not only from a kinematic but also a cognitive point of view; at a more strictly speculative level, it would be interesting to find ways to deepen the relationship between intention and gait in PD. Future developments of the proposed system will consist of assessing additional MDS-UPDRS items for other manifestations of PD. Another prospective implication of our tool concerns the possibility of implementing it as a telemedicine tool. The pandemic phase we are experiencing has aroused more interest in tools that can provide parameters on a patient's disease status from a distance. Our instrument is conducive to the future and these new needs, giving the possibility of using a high-definition camera in the future, perhaps even a smartphone, to measure the degree of motor deficit in PD patients at a distance, with the possibility of being able to intervene on the therapeutic level.
## 5 Discussion
### Shortcomings of the Study and Future Directions  
1. The digital twin learning system developed in this study still has deficiencies in providing an immersive experience. Students may not fully immerse themselves in the virtual environment, which could negatively affect their learning experience and outcomes. The lack of immersion in the current system may be due to technical limitations or design flaws. In the future, more advanced virtual reality (VR) and augmented reality (AR) technologies could be applied to enhance the system's immersion. System optimization based on user feedback, improving interactive design and operational processes, will make the system more seamless and intuitive for students.
2. This study did not adequately consider that learning modes may vary by age group and gender, overlooking the different needs and responses of students of different genders and ages when using the digital twin learning system. Future research should examine the experiences and outcomes of individuals from various gender and age groups when using the digital twin learning system.
3. The sample size of this study was relatively small, consisting of only 70 students from a single vocational high school. This limitation may affect the representativeness and generalizability of the findings. Future research should aim to expand the sample size to include more students from various disciplines and institutions to enhance the representativeness of the results. Conducting large-scale empirical studies will help verify the broad applicability of the system.
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 6 Experimental Setup
### Implementing Details for Sub-Band BadNets Attack  
To develop the sub-band BadNets backdoor attack, we first trained a clean deep learning model. We then employed the Score-CAM model [52], as described in Section 4, to identify important and less important bands for classifying images into the river class. We embedded the square trigger into important and less important bands, respectively, to test which approach is more effective. Figure 2b shows the saliency maps of the 13 bands in thedataset, where red regions indicate the focused areas when classifying the input image to the river class. We used a threshold to decide whether or not a band is important.
## 7 Performance Evaluation
### Simulation Results and Discussions
#### 7.3.3 DRRF Extensions  
We show that DRRF can support three other nrtPS QoS parameters. With a minimum reserved traffic rate extension, the result shows DRRF can support the minimum reserved traffic rate. Figure 6b shows the throughput for all five MSs. In general, \(MS_{I}\) receives the minimum bandwidth at 2 Mbps, and the left-over bandwidth is distributed fairly among \(MS_{I}\) and other mobile stations. Observing that to meet the 2 Mbps rate guaranteed, BS also needs to consider two main overheads: a 6-byte MAC header and a 2-byte fragmentation subheader.  
For DRRF with a maximum sustained traffic rate extension, Figure 7a shows that the average throughput of \(MS_{I}\) is 492 kbps, and the left-over bandwidth is distributed fairly among others (fairness indexes are all 1, each flow throughput except \(MS_{I}\) is around 1.2 Mbps.)  
Finally, for DRRF with a traffic priority extension, Figure 7b shows both flows of priority 2 receive equal throughput, _i.e._, 1.5 Mbps, while the three flows of priority 1 receive a fair share of the left-over capacity, _i.e._, 0.7 Mbps.
# Enabling Horizontal Collaboration in Logistics Through Secure Multi-Party Computation
## 2 Preliminaries
### Extended Motivation and Background  
As already mentioned earlier, collaboration in logistics may be beneficial on various levels. In the following, we briefly introduce three main dimensions in which such collaborative efforts may generate impact.
## 6 Conclusions and Future Work  
In this article, we have analyzed the high-speed, enhanced transmission capacity OAM-OFDM-SMM FSO system, which combines the advantages of OAM multiplexing and OFDM techniques with MIMO spatial mode diversity schemes in the existing OAM and OFDM, MIMO FSO communication system to investigate the performance of the system under turbulence conditions. Furthermore, the proposed OAM-OFDM-SMM based MIMO-FSO system is a multi-transmission optical link system that exploits the spatial dimensions of laser beams to design a reliable crosstalk FSO model under the impact of atmospheric turbulence to increase the spectral efficiency and diversity gain. Moreover, the numerical results obtained from the simulations illustrate that the proposed OAM-OFDM FSO system, based on spatial mode diversity, has effectively improved the performance of the FSO communication link using spatial mode diversity in various atmospheric turbulence conditions. The BER and the capacity performances of the proposed system have been compared for various parameters such as number of OAM states, link distance, OSNR, and RIS. Although the proposed system can also be investigated for parameters such as power penalty calculation for intermodal crosstalk, received power vs. angular receiving aperture, and outage probability for transmitted power. The link distance needs to be increased while keeping the speed and the channel capacity constant.  
We can also improve the performance of the existing work and propose a new OAM-OFDM-SMM, MIMO FSO communication system in the future by incorporating different channel coding techniques, multiplexing techniques such as dense wavelength division multiplexing (DWDM), various turbulence compensation techniques, and the CSI model to improve the channel capacity enhancement of the proposed FSO system. Also, we can exploit the performance of the proposed system for different OFDM subcarriers' under various refractive index structure constants to analyze the capacity of the FSO system.  
Methodology, A.A., M.S., M.A. and K.A.; Writing--original draft, S.S.; Writing--review & editing, C.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
The authors declare no conflict of interest.  
Figure 9: Channel capacity vs. OSNR for the conventional OAM and the proposed system.
## 3 Methodology
### Model Selection  
The problem of predicting the class label of an essential oil sample is a classification problem. This section discusses building three widely-used classification models: \(k\)-Nearest Neighbours, Logistic Regression, and Random Forest. For each one of these models, this section discusses the selection of the various model parameters. All these algorithms were implemented using Python's scikit-learn library [45; 53; 54].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Testing (70:15:15)** & **Testing (50:25:25)** & **Testing (50:50)** & **Mean CV (70:15:15)** & **Mean CV (50:25:25)** & **Mean LOOCV** \\ \hline \hline \(k\)NN & 36.88 & 35.72 & **78.03** & 32.42 & 30.5 & **59.92** \\ \hline Logistic Regression & 60.42 & 40.27 & **86.11** & 33.47 & 33.5 & **64.68** \\ \hline Random Forest & 38.03 & 39.29 & **70.45** & 39.26 & 39.29 & **67.06** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average accuracy for each algorithm using different model evaluation techniques.
# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 4 Experimental Results and Evaluation
### Evaluation Metrics  
It has been observed from the literature review that Machine Learning and Deep Learning approaches are evaluated using four evaluation metrics [41]. These are: accuracy, precision, recall, specificity, and F1-score [42]. It has been further observed that recall and sensitivity have been used interchangeably [43]. These evaluation metrics are defined by Equations (29), (30), (31), (32), and (33), respectively.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{29}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{30}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{31}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{32}\]  
\[\text{F1 Score}=\frac{2\times(\text{Precision}\times\text{Recall}))}{\text{ Precision}+\text{Recall}} \tag{33}\]The accuracy, precision, recall, specificity, and F1-score calculations are dependent on the values of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) [44]. A confusion matrix has been generated by using the test dataset on the BiLSTM network. The TP, TN, FP, and FN have been calculated from the confusion matrix [45].