esa-sceva/satcom-chunk-collection huggingface dataset:
# L-rCBF: Learning-Based Key-Value Data Structure for Dynamic Data Processing
## 4 Theoretical Analysis for _r_CBF
### Search Failure Probability of rCBF  
The search failures of an rCBF include both false positives (FPs) and indeterminables (INDETs). The search failure probability (\(P(F_{s})\)) of the rCBF is formulated as follows, based on [20]:  
\[P(F_{s}) =P(I)+P(F_{P})\] \[=\Big{(}P(S)P(I|S)+P(S^{c})P(I|S^{c})\Big{)}+P(S^{c})P(F_{P}|S^{c})\] \[=P(S)P(I|S)+P(S^{c})\Big{(}P(I|S^{c})+P(F_{P}|S^{c})\Big{)}, \tag{4}\]  
where \(P(I)\) is the probability of an INDET for input \(y\) in set \(S\) or \(S^{c}\), and \(P(F_{P})\) is the probability of an FP for input \(y\) in \(S^{c}\).  
\(P(I|S)\) is the probability that every counter in the \(k\) cells for \(y\) included in \(S\) is two or more (i.e., \(c\geq 2\)). Hence,  
\[P(I|S)=(p_{ci})^{k}, \tag{5}\]  
where \(p_{ci}\) is the probability that, for a specific cell pointed to by at least one of the \(k\) indexes for \(y\) in \(S\), the cell is selected by at least one of the \(k(n-1)\) indexes. Thus,  
\[p_{ci}=1-(1-\frac{1}{m})^{k(n-1)}. \tag{6}\]  
\(P(I|S^{c})\) is the probability that every counter in the \(k\) cells for \(y\) not in \(S\) (i.e., in \(S^{c}\)) is two or more (i.e., \(c\geq 2\)). Hence,  
\[P(I|S^{c})=(p_{cn})^{k}, \tag{7}\]where \(p_{cn}\) is the probability that a specific cell is selected by at least two of the \(kn\) indexes of the \(n\) elements in \(S\). Thus,  
\[p_{cn}=1-(1-\frac{1}{m})^{kn}-\frac{kn}{m}\cdot(1-\frac{1}{m})^{kn-1}, \tag{8}\]  
where \((1-\frac{1}{m})^{kn}\) is the probability that the cell is not selected by any of the \(kn\) indexes, and \(\frac{kn}{m}\cdot(1-\frac{1}{m})^{kn-1}\) is the probability that the cell is selected by one of them.  
\(P(F_{P}|S^{c})\) is the probability that the rCBF returns a value for \(y\) not in \(S\) (i.e., in \(S^{c}\)). Let \(Q\) be the number of return values; we assume that the values are uniformly distributed. Then,  
\[P(F_{P}|S^{c})=Q\sum_{i=1}^{k}\binom{k}{i}\cdot(p_{pn})^{i}\cdot(p_{cn})^{k-i}, \tag{9}\]  
where \(p_{pn}\) is the probability that a specific cell has one of the \(Q\) values with \(c=1\). Hence,  
\[p_{pn}=\frac{1}{Q}\cdot\frac{kn}{m}\cdot(1-\frac{1}{m})^{kn-1}. \tag{10}\]  
The \(P(F_{P}|S^{c})\) of the rCBF in (9) seems to be the same as that of the FBF for static data [20]; however, \(p_{pn}\) and \(p_{cn}\) in (10) and (8) are not the same as those of the FBF for static data because the FBF does not support the deletion operation. Thus, the \(P(F_{P}|S^{c})\) of the rCBF is not the same as that of the FBF for static data.  
If an FBF supporting the deletion operation is constructed, the search failure probability of the FBF is the same as that of the rCBF. In addition, the undeletable probability of the FBF (\(P(U_{f})\)) is the same as \(P(I|S)\) in (5). When using the same \(m\), \(n\), and \(k\) for the rCBF and FBF, \(P(U_{f})<P(U_{f})\) under the constraint \(c_{max}\geq 3\) (i.e., \(R\geq 2\)). Hence, the L-rCBF using an rCBF is more suitable for dynamic data processing than the L-FBF using an FBF.
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 4 Evaluations
### Number of Duplicated ACKs  
The cwnd decrease mechanism of S-TCP unnecessarily repeated the slowstart phase, resulting in many duplicated ACKs. To examine by how much the number of duplicated ACKs is reduced by the modified cwnd increase/decrease mechanism of mmS-TCP, we performed the single TCP experiments of Scenarios 1-3. The total number of packets and the number of duplicated ACKs in each TCP connection were confirmed using Wireshark.  
Figure 9a shows the number of duplicated ACKs generated by S-TCP and mmS-TCP divided by the total number of packets. In each scenario, duplicated ACK packets from S-TCP account for about 10-11% of the total packets. However, mmS-TCP is only about 5-6% of all packets and is reduced by about half compared with S-TCP.  
To confirm the performance change due to the modification of the cwnd decrease mechanism, we checked the number of duplicated ACKs that occurred during the slowstart phase of each connection. A single S-TCP enters the congestion avoidance phase after performing a slowstart twice. The time when the slowstart phases end is about 3 s. Therefore, the number of duplicated ACKs generated in the S-TCP connection for 0 to 3 s was summed. In the case of mmS-TCP, because slowstart is performed only once, the number of duplicated ACKs that occurred between 0 s and 1 s was counted.  
Figure 9b shows that the proportion of duplicated ACKs occurring in the S-TCP flow during the slowstart phase of 3 s in the entire 15-s simulation experiment is approximately 7%. That is, about 7% of all packets of S-TCP are duplicated ACK packets transmitted during the slowstart phase. In mmS-TCP, about 2% of all packets were found to be duplicated ACKs generated during a relatively short slowstart phase of 0 s to 1 s. As a result of comparing the number of duplicated ACKs for the same 3 s as S-TCP, mmS-TCP showed a significantly lower rate of approximately 1.8%, 2.3%, and 2.6% in each scenario.  
Therefore, with the redesign of the cwnd increase/decrease mechanism, mmS-TCP halved the total number of duplicated ACKs compared with S-TCP, and it reduced the number of duplicated ACKs by approximately 1/4 during the slowstart phase.  
Table 2 compares the rate of duplicated ACKs to total packets generated by one TCP flow for each CCAs. CUBIC has a significantly lower rate of duplicated ACKs compared to S-TCP and mmS-TCP. However, in Scenario 2 and 3, except for Scenario 1, CUBIC has approximately 40% (for Scenario 2) and 60% (for Scenario 3) lower throughput than mmS-TCP because CUBIC does not fully utilize the available bandwidth. Therefore, mmS-TCP, which aims to provide higher throughput than CUBIC in mmWave networks, shows advantages in terms of throughput improvement.  
Figure 9: Comparisons of duplicated ACKs generated by S-TCP and mmS-TCP: (**a**) total duplicated ACKs/total packets; (**b**) duplicated ACKs during slowstart phase/total packets.
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
## 3 Proposed Solution
### Software Architecture  
Figure 3 illustrates the power meter's software block diagram. As one can see, after each startup, the devices execute an initialization procedure, in which all the General-Purpose Input/Output (GPIO) pins are configured. Also, the Universal Asynchronous  
Figure 2: Device PCBs 3D view.  
Receiver-Transmitter (UART) interface and Serial-Peripheral Interface (SPI) are configured and initialized to be used for the ADC, micro-SD card slot, debug/programming, and RS485 interfaces. After checking if a card is present, the device tries to read a standard configuration file from it, containing information about the Wi-Fi network (SSID and password) and other setting values such as time server information and communication protocol parameters. If the data are available and valid, they are further used by the algorithm; if not, a set of pre-programmed values are used instead.  
If the Wi-Fi network connection is successful, the current timestamp is requested from a remote time server using the Network Time Protocol (NTP). If the response is valid, the current timestamp is used to name a new file that is created on the micro-SD card to store the signal samples for further analysis. If either the Wi-Fi or NTP fails, a generic name is used for the log file. After this step, a dedicated task running a Modbus protocol server is launched and used to serve information to the connected clients. The selection between the Modbus TCP and Modbus RTU protocols, used over the Wi-Fi or RS485 interface, is performed using a parameter stored in the configuration file read from the micro-SD card.  
In the next steps, the ADC is initialized, and the acquisition completion (data ready) interrupt routine is activated, which is used by the microcontroller to read each data sample.  
Figure 3: Device software block diagram.  
After gathering the required number of data samples, the interrupt routine is deactivated temporarily until the values are processed. The algorithm computes the grid voltage and current RMS (root mean square) values and other grid parameters such as frequency and active and apparent power. A Fast Fourier Transform (FFT) algorithm is applied to the sampled data to extract the harmonics amplitude and calculate the total harmonic distortion (THD) values of the grid voltage and current. After this step, the Modbus protocol registers are updated with the new values, and if the configuration file dictates, the data are also transmitted via the Message Queuing Telemetry Transport (MQTT) protocol to a dedicated server for storage and further analysis. Before reactivating the ADC data ready interrupt routine, the data samples are written on the micro-SD card.
# Synchronised TeraHertz Radiation and Soft X-rays Produced in a FEL Oscillator
## 5 The Inverse Compton Scattering Process: Numerical Results
The spectrum is indeed fixed at the zero order by the collimation system and could depend in a finer way on the emittance and energy spread of the electrons, while the length of the pulse is determined by the length of the electron beam. Jitters of the radiation follow those of the electron beam. Figure 13 shows the spectra of the X-ray radiation at 3 nm (window (a)) and 0.5 nm (window (b)) collimated at a bandwidth of 10% for the five reference cases. Figure 14 presents the effect of the presence of a collision angle. The case with \(\lambda_{\text{THz}}=15\)\(\upmu\)m and cavity losses \(L_{c}=L_{ext}+L_{mirr}+L_{lens}\) = 2%, 4% and 6% was analysed as a function of the interaction  
angle \(\alpha\) (\(\alpha=0\) for head-to-head collisions). The data show a contained decrease in the photon number that remains quite negligible for angles below \(5^{\circ}\). The Rayleigh length of the X radiation is of the order of tens of meters and its size on the front mirror much smaller than the dimension of the hole, allowing the extraction of the X-rays from the cavity without problems. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{\gamma}\) & **e-Beam** & & **55.5** & **81.5** & **65** & **55.5** & **86** \\ \hline \(\lambda_{THz}\) & THz & \(\mu\)m & 36 & 13.2 & 51.6 & 36 & 15 \\ \(E_{IC}\) & THz & mJ & 3 & 3.38 & 1 & 2.1 & 2.8 \\ N/shot & IC THz & \(\times 10^{17}\) & 5.5 & 2.6 & 2.5 & 3.8 & 2.1 \\ N/s & IC THz & \(\times 10^{25}\) & 2.75 & 1.3 & 1.25 & 1.9 & 1.05 \\ N/shot & EC THz & \(\times 10^{15}\) & 11 & 5.2 & 5 & 7.6 & 4.2 \\ N/s & EC THz & \(\times 10^{23}\) & 5.5 & 2.6 & 2.5 & 3.8 & 2.1 \\ size in IP & THz & \(\mu\)m & 180 & 65 & 250 & 180 & 75 \\ \hline \(\lambda_{X}\) & X-ray & nm & 3 & 0.5 & 3 & 3 & 0.5 \\ N\({}_{X}\)/shot & X-ray & \(\times 10^{5}\) & 1.7 & 1.05 & 1.27 & 1.19 & 0.96 \\ N\({}_{X}\)/s & X-ray & \(\times 10^{12}\) & 8.5 & 5.3 & 6.3 & 6 & 4.8 \\ N\({}_{X,coll}\)/shot & X-ray & \(\times 10^{5}\) & 0.41 & 0.42 & 0.23 & 0.29 & 0.37 \\ N\({}_{X,coll}\)/s & X-ray & \(\times 10^{12}\) & 2.2 & 2.25 & 1.15 & 1.45 & 1.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: X-ray radiation: electron energy, pump characteristics, results. Repetition rate of the source \(5\times 10^{7}\) Hz. IC: intra-cavity, EC: extra-cavity.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## 5 Limitations of This Work  
The state-of-the-art solutions guard the AES block cipher against DPA attacks. However, these solutions have a few limitations, such as area, security of linear and non-linear functions simultaneously, instantaneous power, etc. The techniques proposed in [36; 37; 43] tackle the DPA attack and its vulnerabilities. In contrast with our technique, we focused on area and security, simultaneously. The proposed security method protects both linear and non-linear functions of the AES algorithm. Moreover, the power leakage is approximately small as compared to the aforementioned solutions. Concerning our solution, our design could be implemented on FPGA and it could also be implemented as an embedded design, e.g., SoC (system on chip). This could be implemented on any SoC device, such as Zybo, Zedboard, Intel Arria, etc. This requires a C/C++ code, which will be executed on the processor to a special register inside the FPGA. The real-time control of the cipher/decipher is possible, which is controlled from a PC. However, this requires an external module that is integrated into the design for serializing the transmission, e.g., UART (universal asynchronous receiver-transmitter). These two limitations are user oriented, and we will address them in our future work for RFID (radio frequency identification)-based applications. Moreover, we mainly focused on the security side of the AES, which was also a primary goal of this work.
# Visible-Light CDMA Communications Using Inverted Spread Sequences
## 2 Optical CDMA with MUI Cancellation
### Conventional EWO Scheme  
In the conventional EWO scheme [19], \(N_{\text{max}}\) is given by  
\[N_{\text{max}}=q\lfloor q/2\rfloor, \tag{3}\]  
where \(\lfloor x\rfloor\) is the maximum integer that is not greater than the positive real number \(x\). For example, the maximum number of users \(N_{\text{max}}\) is eight for an MPSC with \(q=4\). Because the EWO scheme employs code-shift keying signaling, this scheme is also referred to as CSK scheme [22; 23]. Two codewords, \(\mathbf{w}_{k,0}\) and \(\mathbf{w}_{k,1}\), in the same group are assigned to the \(k\)th user as spread sequences (\(k=0,\ 1,\ \cdots,\ N_{\text{max}}-1\)). When the \(k\)th user is active, its encoder selects the codeword \(\mathbf{w}_{k,1}\) for transmission of data \(1=1\) and \(\mathbf{w}_{k,0}\) for \(I=0\). An example of the codeword assignment is shown in Table 2. Figure 2 illustrates a block diagram of the \(k\)th user's encoder. We note that when the \(k\)th user is not active, an all-zero sequence is transmitted constantly in the conventional EWO, Shalaby's, and Liu's schemes. Figure 3 illustrates examples of signal sequences corresponding to MPSC codewords and a multiplexed signal sequence for \(q=4,N_{\text{L}}=8\), and \(N=4\). In this case, the code length of the MPSC is 16. A _slot_ represents a time period during which one spread sequence
## 5 Results and Discussion
### Bit Error Rate  
To plot the ECMA-368 BER curves, we used the UWB multipath channel based on the Saleh and Valenzula model proposed by IEEE 802.15.3a [25; 26; 27; 28]. In this channel, the multipath components, denoted as rays (paths), arrive at the receiver in groups of clusters. A double Poisson process can represent this phenomenon. The IEEE 802.15.3.a considers four Channel Models (CM1 to CM4) used in this paper and configured as shown in Table 3.  
To analyze the BER by simulation, we use the UWB channel model and Additive white Gaussian noise (AWGN) is added at the Rx. These simulations adopt three different data rates 53.3, 200 and 480 Mbps, and the Time Frequency Codes TFC1 of the band group number 1 [19]. This will allow us to test the proposed solutions according to the modulations imposed by the standard (QPSK and DCM) taking into account the full range of possible rates. It should be noted that, besides the real implementation on FPGA (Xillinx or Altera), some simulations have also been carried out in order to check and validate the implementations. Figures 18 and 19 show, in comparison with the simulations, that the hardware implementations have very little impact on the BER. In fact, this slight degradation of the BER can be justified by the truncation error caused by a fixed-point calculation.
## 3 Results and Discussion
### Chemical Composition

Table 2: Chemical composition, total polyphenols, and fatty acid profile of Caciocavallo cheeses produced from milk of cows in the CTR group (without ECO) and cows in the ECO group (with the integration of 7% of ECO in the concentrate). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Milk**} \\ \hline  & \multicolumn{2}{c}{**Groups**} & \multicolumn{2}{c}{**SEM 1**} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \cline{2-5}
**Item** & **CTR** & **ECO** & & \\ \hline C18:1 trans9 & 0.68 & 0.78 & 0.09 & 0.05 \\ C18:1 cis11 * & 3.08 & 3.64 & 0.11 & 0.07 \\ C18:1 trans11 * & 0.25 & 0.31 & 0.17 & 0.13 \\ C20:1 n-11 & 0.06 & 0.06 & 0.08 & 0.98 \\ C22:1 n-9 & 0.01 & 0.01 & 0.36 & 0.94 \\ C24:1 n-9 & 0.03 & 0.04 & 0.05 & 0.11 \\ MUFA & 23.14 & 26.42 & 0.07 & 0.05 \\ C16:3 n-4 * & 0.04 & 0.07 & 0.30 & 0.06 \\ C18:2 cis9 cis12 & 1.38 & 1.60 & 0.21 & 0.09 \\ C18:2 trans9 trans12 & 0.67 & 0.66 & 0.09 & 0.98 \\ C18:3 cis6 cis9 cis12 & 0.05 & 0.05 & 0.03 & 0.19 \\ C18:3 cis9 cis12 cis15 & 0.71 & 1.06 & 0.24 & 0.05 \\ C20:2 n-6 & 0.05 & 0.06 & 0.15 & 0.09 \\ C20:3 n-6 & 0.04 & 0.03 & 0.16 & 0.45 \\ C20:3 n-3 & 0.01 & 0.01 & 0.28 & 0.32 \\ C20:4 n-6 & 0.04 & 0.05 & 0.19 & 0.09 \\ C20:5 n-3 & 0.06 & 0.07 & 0.12 & 0.09 \\ C22:2 & 0.10 & 0.14 & 0.24 & 0.06 \\ C22:6 n-3 & 0.02 & 0.04 & 0.31 & 0.07 \\ PUFA & 3.15 & 3.85 & 0.13 & 0.05 \\ n3 & 0.80 & 1.18 & 0.23 & 0.05 \\ n6 & 2.22 & 2.45 & 0.14 & 0.51 \\ n6/n3 & 2.82 & 2.10 & 0.25 & 0.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The fatty acid profiles of milk and cheese are summarized in Tables 1 and 2, respectively. The analysis reveals a consistent pattern in the fatty acid profiles of both milk and cheese. Notably, compared with CTR samples, the ECO samples exhibited an increase in monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA), alongside a decrease in saturated fatty acids (SFA). Specifically, there was an increase in fatty acids such as C16:1, C18:1 cis9, and C18:3 cis6 cis9 cis12, while a decrease was noted in C12:0, C14:0, C17:0, and C18:0. Among the MUFAs, the levels of C16:1, C18:1 cis9, and C18:1 trans9 showed statistically significant differences between diet groups (\(p\leq 0.05\)), with C18:1 trans9 being notably higher in the CTR samples compared with ECO samples. Conversely, C18:3 cis6 cis9 cis12 was the only PUFA that demonstrated a significant difference, being higher in the ECO samples compared with CTR samples. In terms of fatty acid composition, the addition of olive cake led to a reduction in both short- and medium-chain
# Management Platforms and Protocols for Internet of Things: A Survey
## 9 Performance Evaluation, Discussion, and Open Issues
#### Open Issues on IoT Management  
Considering the previous discussion regarding the available management protocols and platforms for IoT, this section identifies open research issues on IoT resources management. They are presented as follows:  
1. Performance evaluation metrics: other performance metrics may be considered or proposed for performance comparison of the available solutions. Thus, a comparative study using error probability, mean response time, and latency may be considered to evaluate the performance of a given solution.
2. Energy saving: extending the lifespan of IoT applications is a matter that may be considered. Connected devices have limited capabilities and should not be overloaded with high throughput.  
Figure 30: Percentage of resources used in IoT management protocols and platforms.  
Thus, there is a need to transmit simpler and smaller packets while not overlooking device security.
3. Security: it is a key issue to favor the advancement of IoT. However, due to devices heterogeneity, each company uses different security protocols. Therefore, standardization is an important challenge. Moreover, as the importance and popularity of IoT devices increases and people provide more information on the topic, scammers and the most experienced cyber-criminals continue to search new ways to attack and compromise devices. Consequently, research related to this topic is on the rise in the research community.
4. Real-time management: various types of IoT application domains require high network availability. Health applications, for example, have a critical degree of availability and, therefore, require real-time management. However, limited device resources and power savings are related features for a management solution that should not be overhead in its communication within the network.
5. Interoperability: There are currently several types of devices, protocols, and communication technologies that determine the heterogeneity of IoT networks. These devices must communicate and inter-operate to provide a network service to users. Some research studies assume that new gateways must support several protocols. Furthermore, there is still research on low power device networks as shown in [120].
6. Scalability: The number of devices connected to the network increases exponentially and, thus, the scalability of IoT networks is a critical requirement. The solutions found in literature do not address features regarding scalability. With this, IoT Network Management must support scalability due to the great evolution of IoT devices and related technologies.
# A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection
## 3 Problem Statement  
Our feature selection problem takes a sensor data stream as the input feature sequence, and makes the feature selection decision at real time. As a matter of fact, the input sensor data are heterogeneous and multisource, especially in an industrial environment. However, we do not want to consider the ETL (extract transform load) problems, which are nontrival problems. Herein, in our online feature selection problem, we assume all sensor data streams arrive synchronously.  
Let \(x_{1},x_{2},\ldots,x_{t},\ldots\) denote the sequence of features, where \(x_{t}\)\((t=1,2,\ldots)\) represents a \(d\)-dimentional data, noted as \((a^{(1)},a^{(2)},\ldots,a^{(d)})\), arriving at time \(t\). Here, d is the quantity of the involved sensors, and also refers to the size of the feature space.  
Our online feature selection problem aims at selecting a subset of the feature space, which includes the features with evident correlation changes. Formally, at each time \(t\), we generate a feature subset \(y_{t}\) which includes features like,  
\[cor_{t-1}\Big{(}a^{(i)},a^{(j)}\Big{)}-cor_{t}\Big{(}a^{(i)},a^{(j)}\Big{)}> \varepsilon,1\leq i,j\leq d \tag{1}\]  
Here \(cor_{t}\Big{(}a^{(i)},a^{(j)}\Big{)}\) denotes the correlation metric between features \(a^{(i)}\) and \(a^{(j)}\) at time \(t\), and \(\varepsilon\) is a predefined threshold for correlation changes.  
Apparently, to monitor the correlation changes for each pair of features is a bad idea, especially when the feature space is on a large scale. Since we merely want to consider the correlation changes for the pair of features whose correlation reflect the equipment's intrinsic mechanism. We just need to focus on the feature pairs which are always highly correlated according to the historical data, noted as correlated features. Herein, in Equation (1), \(cor_{t-1}\Big{(}a^{(i)},a^{(j)}\Big{)}\) should be considered as close to 1, and we just need to make sure that \(cor_{t}\Big{(}a^{(i)},a^{(j)}\Big{)}\) is small enough when selecting features \(i\) and \(j\), i.e., our problem is to monitor the correlations between correlated features pairs, and select the features pairs whose correlation is small enough, as shown in Equation (2).  
\[cor_{t}\Big{(}a^{(i)},\ a^{(j)}\Big{)}<\varepsilon,1\leq i\leq d \tag{2}\]