esa-sceva/satcom-chunk-collection huggingface dataset:
# MAC Address Anonymization for Crowd Counting
## 2 Results
### Requirement 4: A Collision Rate of Less Than \(10^{-9}\) for \(10^{7}\) MAC Addresses
#### 2.4.4 Concentration Inequality for the Collision Rate  
While it is interesting to find an upper bound for the expectation of the collision rate, \(Y^{(n,m)}/n\), finding an upper bound for the probability that it exceeds some threshold is also a worthy endeavor. We propose such a (coarse) inequality. Because \(Y^{(n,m)}/n\geq 0\), we can apply [PERSON]'s inequality:  
\[\mathbb{P}\left[\frac{Y^{(n,m)}}{n}\geq a\right]\leq\frac{\mathbb{E}\left[Y^{ (n,m)}/n\right]}{a}. \tag{8}\]  
Figure 4: Level sets of the approximation (3) of the collision rate as a function of the number of inserts \(n\) and the number of buckets \(m\).  
Using Theorem 2 with \(K=2\), we only know that \(\mathbb{E}\left[Y^{(n,m)}/n\right]=\alpha/2+\delta(\alpha,n)+R_{1}(\alpha)\) where \(\delta(\alpha,n)\leq 0\) and \(R_{1}(\alpha)\leq\alpha^{2}/6\). Therefore, we can only use the slightly more pessimistic concentration inequality that is  
\[\mathbb{P}\left[\frac{Y^{(n,m)}}{n}\geq a\right]\leq\frac{\alpha/2+\delta( \alpha,n)+R_{1}(\alpha)}{a}\leq\frac{\alpha/2+\alpha^{2}/6}{a}, \tag{9}\]  
where the term \(\alpha^{2}/6\) is negligible in comparison to \(\alpha/2\) for \(\alpha\) sufficiently low (e.g., \(\alpha\leq 10^{-3}\)).  
For example, let us consider again the previous calculation of Section 2.4.3 (with \(n=10^{7}\) MAC addresses, \(m=26^{4}\) and \(\alpha=10^{-12}\)), which yielded \(\mathbb{E}[Y^{(n,m)}/n]=\alpha/2+\delta(\alpha,n)+R_{1}(\alpha)\simeq 10^{-1 2.5}\). Owing to \(\alpha^{2}/6\ll\alpha/2\) and with \(a=10^{-9}\),  
\[\mathbb{P}\left[\frac{Y^{(n,m)}}{n}\geq 10^{-9}\right]\leq\frac{10^{-12.5}}{10^ {-9}}=10^{-3.5}\simeq 3.16\times 10^{-4}, \tag{10}\]  
which shows that, with probability 99.968%, the collision rate of our counting system does not exceed \(10^{-9}\).  
[PERSON]'s inequality is coarse (and it may be possible to improve our result using a more sophisticated inequality) but, within the context of finding an upper bound for the collision rate of our crowd counting system for large crowds, that inequality is sufficient to prove its collision rate does not exceed \(10^{-9}\) with high probability for large crowds (\(10^{7}\) MAC addresses per minute).
## 11 Conclusions and Future Perspectives  
Addressing tropical diseases, such as Leishmanasis and trypanosomiases, requires cross-sectoral approaches for drug discovery that span from phenotypic-based screening to target-based screening. However, information pertaining to the molecular pathogenesis of trypanosomatids is still controversial. In-depth understanding of the pathogenic mechanisms of action of _Leishmana_ and _Trypanosoma_ parasites might unravel difficulties in discovering new vaccines and treatments against leishmaniasis and trypanosomiasis. In fact, two key enzymes that are involved in the biosynthesis of cholesterol in humans and ergosterol in trypanosomatids include farnesyl diphosphate synthase and squalene synthase. In fact, these enzymes are involved in the isoprenoid pathway and sterol biosynthesis, respectively, to generate cholesterol in humans and ergosterol in trypanosomatids. Notably, the latter compound (ergosterol) is also essential for parasite survival. Thus, inhibitors of these enzymes (farnesyl diphosphate synthase and squalene synthase), which contribute to ergosterol biosynthesis, can be used as potential targets for the development of anti-trypanosomatid drugs.  
In fact, the present study aimed to summarize recent developments in anti-_Trypanosomatidae_ hit compounds using a target-based approach using farnesyl diphosphate synthase and squalene synthase as the target enzymes. Validation of the anti-trypanosomatid activity of farnesyl diphosphate synthase and squalene synthase inhibitors using phenotypic screening against trypanosomatids is also highlighted. Thus, there is no denying that inhibitors of farnesyl diphosphate and squalene synthases can afford potentially active compounds against _Leishmana_ and _Trypanosoma_ species. However, more in vitro and in vivo phenotypic screenings are needed to verify the potential activity of these inhibitors against _Leishmana_ and _Trypanosoma_ species. More toxicity studies and pharmacokinetics and structural modifications are recommended to identify the most active inhibitors as starting points for the discovery of new anti-trypanosomatid drugs.  
Conceptualization, B.P.K. and F.F.B.; methodology, B.P.K. and F.F.B.; software, B.P.K.; validation, B.P.K. and F.F.B.; formal analysis, B.P.K.; investigation, B.P.K.; resources, B.P.K.; data curation, B.P.K.; writing--original draft preparation, B.P.K.; writing--review and editing, B.P.K. and F.F.B.; visualization, B.P.K.; supervision, F.F.B.; project administration, F.F.B.; funding acquisition, B.P.K. and F.F.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# MAC-Based Compression Ratio Improvement for CAN Security
## 2 Existing CAN Data Authentication Methods
### Triple ID Algorithm  
Unlike the Mini-MAC or modified MAC algorithms, the Triple ID algorithm always transmits a MAC of at least 4 bytes to ensure sufficient authentication performance of CAN frames [14]. The Triple ID algorithm uses a CAS map-based data compression technique similar to the ICANDR algorithm.  
Figure 3 shows the MAC generation procedure of the Triple ID algorithm. The generated MAC including the authentication key \(k\) is  
\[MAC_{TI}=T\big{[}s,H_{k}(k\|C_{C_{n}}\|CTR_{ECU_{k}}\|C_{C_{n-1}}\|\cdots C_{C_ {n-\lambda}})\big{]}, \tag{5}\]  
where \(C_{C_{n}}\) is the current encrypted compressed message, and \(\big{\{}C_{C_{n-1}},\cdots C_{C_{n-\lambda}}\big{\}}\) are the previous encrypted compressed messages. Therefore, malicious attackers attempting to intrude into the CAN system should collect previous valid messages in addition to the authentication key. (Equations (5) and (6) of [14] may be referred to in order to calculate a compressed ciphertext and retrieve the compressed data from the compressed ciphertext.)  
In the Triple ID algorithm, when the compressed data length is less than 4 bytes, the length of the MAC is calculated as  
\[L(MAC_{TI})=8-L(M_{c}). \tag{6}\]  
Figure 3: MAC generation procedure of Triple ID algorithm.  
Then, the compressed data and the MAC are transmitted as one frame using ID \(i\) as shown in Table 4.  
If the compressed data length is greater than 4 bytes, one frame cannot contain both compressed data and MAC since the length of MAC should be at least 4 bytes. If the compressed data length is greater than 4 bytes and less than 7 bytes, the compressed data is transmitted with ID \(i+1\) and the 8-byte MAC is transmitted with ID \(i+2\). If the compressed data length is greater than 7 bytes, the uncompressed CAN data is transmitted using ID \(i+1\), and the 8-byte MAC is transmitted using ID \(i+2\).  
The Triple ID algorithm uses 6 header bits. As in the ICANDR algorithm, the 3 header bits of HA, HB and HC indicate the presence of Sig A, Sig B and Sig C, respectively. The other three bits LC[2], LC[1], and LC[0] indicate the compressed data length in bytes and the length of the MAC in the data field can be determined using this information.  
The Triple ID algorithm does not require any changes to the CAN protocol to provide authentication. In addition, the Triple ID algorithm always transmits a MAC of at least 4 bytes to provide sufficient authentication performance for CAN frames. However, two frames must be transmitted if the compressed data length is larger than 4 bytes. Therefore, it is important to compress CAN data to 4 bytes or less for communication efficiency such as low bus load and short latency.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 2 Experimental Setup and Methodology
### Data Pre-Processing  
The localisation algorithms used in the comparison to the Direct method, made use of the received power, time delay and NLOS/LOS classification, for each path. For each BS-MS link, the first arriving ray or path, is the one whose parameters are used. The assumption is that the first arriving ray represents a LOS path (or a ground reflected path in some cases). This increases the chance of having a data set with LOS rays. On selecting the multipath components (MPCs) for localization, a prioritization approach is adopted, where LOS paths are chosen. If an MS sees multiple LOS paths to multiple BSs (case for TDOA), then those with the shortest delay are selected. If, for example, in TDOA, which requires 3 BSs, an MS has not enough LOS paths, the ground reflected rays are used. Our empirical observations indicated that choosing rays with least time delay produced better localization performance than selecting the rays based on received power level. For the 3 BS TDOA algorithms, each MS selects 3 BSs within its proximity with the least time delays and use those rays for localization. Ground reflected rays are given preference over other NLOS paths because usually ground reflected paths interfere with direct LOS paths. This means that the ground reflected multipath component may be irresolvable from the LOS path since they generally exceed the temporal and spatial resolution capabilities of most measurement systems. The severity of this issue depends on the antenna patterns, the BS/MS heights and how far the mobile station is from the base station [14]. Range error produced by ground reflected (also rooftop diffracted) paths may be smaller than other NLOS scenarios, in most cases. Figure 3, below, demonstrates why ground reflected rays may have time delays comparable to LOS rays.  
From the ray-tracing data, ground reflections or rooftop diffractions are determined by rays that exhibit a LOS matching azimuth AOA and AOD but different elevation angles. The total data sets that are selected to be used in these localization algorithms are the same data that was the subjected to the proposed Direct method.
# Towards Incidence Management in 5G Based on Situal Awareness
## 4 Information Security Architecture for 5G
### Virtual Infrastructure and Sensors  
The main purpose of this layer is the abstraction of the different hardware/software elements running in the mobile infrastructure and enabling the monitoring of low-level metrics related to the network behavior/status. Its developments includes the innovative designs principles of 5G networks: decoupling  
Figure 2: Endsley model for Situational Awareness.  
of data and control planes, virtualization of mobile functions, and a complete integration with cloud computing environments. In this way, the SDN architecture promotes the separation between data and control planes in mobile infrastructure (base stations, links, servers, gateways, Deep Packet Inspection (DPI), among others). The network administrator is not limited by the traditional private/closed hardware/software system and, consequently, the data forwarding engine can be customized. Similarly, the virtualization layer enables the dynamic allocation of virtual resources based on the user requirements. For its part, the NFV approach proposes the implementation of the different services (e.g., firewall, DPI, Quality of Service optimizer, load balancer) as virtual software functions that can be instantiated in different points of the virtualized infrastructure. As a result, the architecture considers two software applications types: SDN-Apps and NFV-Apps. SDN-Apps is executed in software programs to control the data plane in network devices and the NFV-Apps are virtual functions that can be instantiated in virtualized elements to develop a particular service. In the proposed architecture, the sensors are specialized NFV-Apps capable of monitoring different metrics on the system. Traffic analyzers, QoS analyzers, and anomalies/botnet/Distributed Denial of Service (DDoS) attack detectors are examples of sensors. These sensors (NFV-Apps) can be instantiated in different locations of the virtual infrastructure and reconfigured depending on the requirements of upper layers. Consequently, the system is able to increase the surveillance in suspected hazardous areas and establish quarantine regions.
## 4 Interactive Compression
### Interactive compression of books. A possible application of interactive data compression is in the transmission of natural language textual data, for example electronic newspapers or books, which can be sent from a remote source to a client destination. In this case we can improve the transmission/compression process by taking into account the common rules of the specific natural language (_i.e._, the set of common words of the natural language, the syntax, _etc._). All these rules might be considered as shared knowledge between the Sender (that sends the book) and the Receiver (that receives the data). For example, when the Receiver receives the book it can use a standard on line dictionary of the language in which the book is written to decode the Sender's messages. The Sender does not know which static dictionary the Receiver is using. It might try to send the words as pointers that the Receiver could decode by using its dictionary. We have experimented with this dictionary based approach by digitizing ten books: five of them are in the Italian language and the other five are in English. The Sender sends a book to the Receiver a word at a time. If the word is long enough, instead of the raw word, the Sender sends a token including the initial character of that word, the length of the word and a hash value (for example a Karp and Rabin hash) for that word. The Receiver will receive the token and will try to decode the word by using its local dictionary. If the token sent by the Sender cannot be decoded because for the hash value that the Sender has sent there is a collision in the Receiver's dictionary, then the Receiver asks the Sender to send a new token for the same word but with a different letter (may be the middle letter of the word) and a different hash. Figure 2: Sender and Receiver communicate a book. If the Receiver finds a unique word that matches the token in the dictionary then it decodes the word and eventually acknowledges the Sender. If the Receiver does not find any word that matches the token in the dictionary, then it sends a request to have the word (entropy coded and) sent directly as raw data to the Sender. Figure 2 shows the communication line between Sender and Receiver. This communication protocol could introduce errors, _i.e._, situations in which the Receiver believes it can decode correctly the word but the word that it finds in its dictionary is not the correct one. If the probability of an error is very low, as shown in subsection 4.2, then the Receiver might accept to have a few words incorrectly decoded: it will be still possible to read and understand the book. If we assume that the dictionary used by the Receiver is the correct dictionary for that specific book language, then the probability of an error depends on the choice and length of the hash value. In Table 1 we show the results obtained by compressing ten books by using, Karp and Rabin, hashes of length 12 bits. The results obtained are compared with the \"off of the shelves\" standard dictionary based compressors Zip and Gzip.
## 3 Results
### Results and Evaluation of Attitude Estimation
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Distance [cm]**} & \multicolumn{3}{c}{**Devices**} \\ \cline{2-4}  & **Device Affecting the Geomagnetic Field (iPhone)** & **Device Affecting the Geomagnetic Field (AppleWatch)** \\ \cline{2-4}  & **Measured Value [\(\mu\)T]** & **Absolute Error [\(\mu\)T]** & **Measured Value [\(\mu\)T]** & **Absolute Error [\(\mu\)T]** \\ \hline
40 & 45.3 & 0.05 & 45.2 & 0.11 \\
30 & 45.3 & 0.04 & 45.3 & 0.03 \\
20 & 45.8 & 0.45 & 45.2 & 0.15 \\
10 & 55.2 & 9.85 & 48.6 & 3.22 \\
0 & 2395.3 & 2349.95 & 215.2 & 169.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Geomagnetic readings when other devices are close by. Figure 7: Estimation results after the sensor becomes stationary when \(\beta\) is varied (**Left:**\(\beta=0.01\); **Right:**\(\beta=0.2\)). Table 9 shows that the error is within 2 degrees when \(\beta\) is changed dynamically, while the error is more than about 100 degrees when \(\beta\) is fixed. Thus, the dynamic change of \(\beta\) is very important to cope with the disturbance. Although the error is a little larger than in the case without disturbance (Table 8), if \(c_{1}\) is set such that the geomagnetic field is not taken into account when there is even a small deviation from the normal geomagnetic field value, then even a small change in value due to changes in the surrounding environment will cause the geomagnetic field to not be considered. Therefore, we believe that the value set in this study is appropriate. If the error between the geomagnetic field and the normal geomagnetic field value becomes small, then the geomagnetic field value is referred to again to correct the correct attitude; thus, the error in Table 9 is not considered to be a problem. Various estimations were performed using the complementary filters obtained from the above results.
## 1 Introduction  
Infrared sensors have been employed in many applications such as military, industrial and civil fields. Moreover, the cost reduction is dramatic due to technological advancements. Thus, infrared sensors are expected to be employed in more fields. In fact, the infrared image reflects thermal radiation of the object in the scene which is not influenced by its surface features and illumination conditions. Specifically, much attention has been paid to long-wave infrared (LWIR) light in the range of 8-12 nm which can transmit in the conditions of smoke, fog, haze, _etc._[1].  
Target tracking is a key component in many vision-based tasks and many algorithms have been proposed. For an infrared image, its intensity denotes temperature and radiated heat; thus, very limited information can be employed to represent the target. Besides, infrared images are usually characterized by low signal to noise ratio (SNR), poor target visibility, and time-varying target appearances [2]. All factors mentioned above make target tracking in infrared image still a challenging problem.  
Sparse representation has drawn much attention in visual tracking. In L1 tracker [3], the tracking problem is formulated to find a sparse representation of the target candidate using templates. A set of trivial templates is introduced to deal with occlusion, noise, and other challenging issues seamlessly. Similarity measurement is performed between each candidate and the target templates. Thus, hundreds of \(\ell_{1}\) norm minimization problems must be solved in each frame, which is equal to the number of target candidates. Finally, the target candidate with the smallest reconstruction error is treated as the optimal one.  
In this paper, a parallel search strategy based on sparse representation is proposed to track target in infrared images. In the particle filter framework, all target candidates are treated as whole to compose an overcomplete dictionary. When the target templates are represented linearly with such a dictionary, the entries of the coefficient denote the weights of target candidates. Nonnegative sparse constraints are imposed to choose the efficient target states; thus, the efficient states with associate weights are selected simultaneously from all the particles. Finally, the optimal target state can be estimated by the weighted combination of such efficient states.  
The rest of this paper is organized as follows. Section 2 will review the related work about L1 tracker. In Section 3, we propose a parallel search strategy based on sparse representation in the particle filter framework. Experimental results and analysis for infrared images are shown in Section 4, and the conclusion is given in Section 5.
# An AC/DC LED Driver with Unity Power Factor and Soft Switching
###### Abstract  
Traditional light-emitting diode (LED) drivers with pulse-width modulation (PWM)-type converters suffer the problem of hard switching, leading to low circuit efficiency and low reliability. LED drivers supplied by alternating current (AC) line source generally require using an additional power-factor correction (PFC) stage to satisfy the regulations on power factor (PF) and total current harmonic distortion (THDi). It results in more circuit losses, especially when the active switch of the PFC stage operates at hard switching. This paper presents an alternating current-to-direct current (AC/DC) converter for driving high-brightness LEDs with the features of soft switching and high PF. The proposed single-stage circuit is formed by integrating a buck-boost converter and a buck converter. By elaborately rearranging the wirings between the circuit components of both converters, the power MOSFETs can be switched on at zero voltage. The operating modes at steady-state are analyzed and the mathematical equations for deriving circuit parameters are conducted. Finally, a prototype circuit for driving 60-W LEDs was built and measured. Based on the experimental results, the feasibility and satisfactory performance of the proposed LED driver are proved.  
hard switching; light-emitting diode (LED); power-factor correction; single stage; soft switching +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### UV-Visible Spectra  
To confirm the formation of pDA, we first performed the UV/visible spectra of the pDA solution during the hydrothermal reaction from 300 nm to 800 nm. As shown in Figure 4, a characteristic peak appeared at 450 nm, which could be attributed to the polymerization of dopamine [19]. The results confirm that the pDA could successfully be formed during the hydrothermal process. Moreover, with the amount of dopamine rising, the absorbance of the characteristic peak gradually increased from 0.5 to 0.9, suggesting that more pDA formed during the hydrothermal process. With more dopamine in the solution, the pDA could form a polymer with higher molecular weight, and thus, the absorbance of the characteristic peak enhanced. The peak intensity of the curve of 70 mg dopamine dramatically enhanced, while it decreased at 80 mg dopamine, implying that 70 mg of dopamine should be the most efficient amount in our study.  
Figure 3: (**a**) The H\({}_{2}\)O\({}_{2}\) production (mg/L) of carbon felt, ACF, ao-pDA/ACF and ht-pDA/ACF cathode (voltage: 2.0 V, electrolyte: 0.05 mol/L H\({}_{2}\)SO\({}_{4}\) solution, anode: 1 cm\(\times\)1 cm platinum plate, distance between two electrode: 1 cm). (**b**) The H\({}_{2}\)O\({}_{2}\) production of the ht-pDA/ACF cathode under different voltages from 2.0 V to 2.4 V.