esa-sceva/satcom-chunk-collection huggingface dataset:
# Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks
## Appendix A Analytical Model
### Probability of a Flow with Malicious Devices to Be Correctly Detected (General Formula)  
This appendix develops a general formula for the probability of flow being detected as malicious provided that it contains malicious devices. It has been used for validating the results provided by the simulations.  
Let us define the following list of events to be used in the formulas:  
* \(A\): A flow is detected as malicious.
* \(B\): A flow contains malicious devices.
* \(E(x)\): A flow generates \(x\) excess throughput due to malicious devices.  
\[P(A\mid B)=\sum_{\forall x=(t_{r}-1)\times\sum_{i=0}^{3}\left(n_{i}\times r(i) \right)\mid\ n_{i}\in\{0,\dots,n\},\ \sum_{i=0}^{3}n_{i}\leq n}P\left((A\cap E(x))\mid B\right) \tag{13}\]  
where this holds for the law of total probability.  
\[\begin{split} P((A\cap E(x))\mid B)=&\frac{P(A\cap E (x))\times P(B\mid(A\cap E(x)))}{P(B)}=\frac{P(A\cap E(x))}{P(B)}\\ =&\frac{P(A\mid E(x))\times P(E(x))}{P(B)}\end{split} \tag{14}\]  
where the first step holds due to [PERSON] theorem, the second step holds because \(P\left(B\mid(A\cap E(x))\right)=1\) since flows detected as malicious always contain malicious devices (i.e., flows without malicious devices are never detected as malicious).  
\[P(B)=P\left(\text{B}(n,p_{m})>0\right)=1-(1-p_{m})^{n} \tag{15}\]\[\begin{split} P(E(x))&=\sum_{n_{0}=0}^{n}\sum_{n_{1}=0}^{n- n_{0}}\sum_{n_{2}=0}^{n-n_{0}-n_{1}}\binom{n}{n_{0}}\binom{n-n_{0}}{n_{1}}\binom{n-n_{0} -n_{1}}{n_{2}}\\ &\times(p_{t}\left(0\right))^{n_{0}}\times(p_{t}\left(1\right))^{ n_{1}}\times(p_{t}\left(2\right))^{n_{2}}\times(p_{t}\left(3\right))^{n-n_{0}-n_{1}-n _{2}}\\ &\times[(n_{0}\times r(0)+n_{1}\times r(1)+n_{2}\times r(2)+(n-n_ {0}-n_{1}-n_{2})\times r(3))\times(t_{r}-1)=x]\end{split} \tag{10}\]  
\[\begin{split} P(A\mid E(x))&=\sum_{n_{0}=0}^{n}\sum_{ n_{1}=0}^{n-n_{0}}\sum_{n_{2}=0}^{n-n_{0}-n_{1}}\sum_{n_{3}=0}^{n-n_{1}-n_{2}} \binom{n}{n_{0}}\binom{n-n_{0}}{n_{1}}\binom{n-n_{0}-n_{1}}{n_{2}}\\ &\times\binom{n-n_{0}-n_{1}-n_{2}}{n_{3}}\times p_{m}^{n_{0}+n_ {1}+n_{2}+n_{3}}\times(1-p_{m})^{n-n_{0}-n_{1}-n_{2}-n_{3}}\\ &\times(p_{t}\left(0\right))^{n_{0}}\times(p_{t}\left(1\right))^ {n_{1}}\times(p_{t}\left(2\right))^{n_{2}}\times(p_{t}\left(3\right))^{n_{3}}\\ &\times[(n_{0}\times r(0)+n_{1}\times r(1)+n_{2}\times r(2)+n_{3 }\times r(3))\times(f_{m}-1)>x]\end{split} \tag{11}\]  
where \([\text{expression}]\) notation refers to Iverson brackets.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 4 Security Issues of UWSNs
### Security Mechanisms  
To achieve the objectives of the security requirements mentioned above, a set of security mechanisms and technologies should be utilized to enforce these security requirements and prevent UWSNs from attacks.  
To provide a comprehensive solution, according to the open system interconnection (OSI) network, the security issues of UWSNs are logically divided into separate secure components. As shown in Figure 10, the security architecture of UWSNs can be divided into four layers: physical layer, link layer, transport network layer, and application layer.  
The security issues that are applied in UWSNs include: key management, intrusion detection, trust management, secure localization, secure synchronization, secure MAC, and secure routing.
## 4 Semantic Representation: EUREKA Schema Proposal  
To enable intervention-aware understanding of emergency telemedicine dialogue, we propose the EUREKA schema (Emergency Understanding Representation for Evidence-based Knowledge and Action). EUREKA is a structured, interpretable semantic representation that captures clinical relevance from spontaneous, multilingual, and multi-role conversations. It integrates utterance-level intent, patient-level context, and triage-level urgency into a unified format suitable for downstream reasoning and intervention recommendation.
## 3 RT-IoT System Model
At times, for some input tasks, the hyper-period gets enormously high and so too the computational complexity of the algorithm. Since these algorithms are needed to be run on IoT devices which have constrained capabilities concerning power and computation, so this becomes more catastrophic if hyper-period increases above a certain threshold. For instance, if a task set \(\Gamma_{1}=\{12,11,19,23,17\}\) is supplied as an input to Equation (12), the result produced of 980,628 is enormously high and no embedded device can have sufficient resources to fulfill the scheduling requirement of this task set. In contrast, if the same number of tasks with different periods as in \(\Gamma_{2}=\{24,15,15,12,3\}\) is given as an input to Equation (12), the result 120 is produced, which is well within the system available resource. This can be described in \[E=PxT, \tag{2}\]3  
The left part of Figure 2 shows that the task set has a high hyper-period and thus it consumes more CPU cycles. In contrast, the task set on the right side shows a considerably lower value of the hyper-period and thus consumes fewer clock cycles. The strip below each task set represents that, after the hyper-period amount of clock cycles, the same scheduling pattern will be repeated. It is formally called a period optimisation problem and the period is recognised as the optimisation parameter. Let us assume that \(J_{i}(P_{i})\) is a monotonously increasing function of period \(P_{i}\) when \(P_{i}\geq 0\), the loss index (U) of the system is found by Equation (13):  
\[E=PxT, \tag{2}\]4  
where \(w_{i}\) is a constant defined by a user representing the priority of the ith task. For example, in a typical IoT context, if a _readSensor_ task has \(w_{i}=1\) and \(turnOnLED\) has \(w_{i}=0.5\), it would mean that the former task is more important than the latter in terms of power-consumption. Real-time scheduling and controlling modules in general real-time systems receive the constraints imposed on selecting the period of input tasks, and it is generated in a bounded interval \((P_{i}^{min},P_{i}^{max}\) according to these constraints. The constraints can be power-efficiency, response time and steady-state accuracy. One crucial constraint to consider is a multi-core utilization bound, which plays a significant role in guaranteeing schedulability. For the RM algorithm, total system utilization and individual task utilization need to be within the computed bound as in Equations (7)-(9). On the basis of the above claims, the period optimisation problem is categorised as a nonlinear constraint optimiser as in Equation (14):  
\[minJ=\sum_{\tau_{i}\in\Gamma}w_{i}I_{i}(P_{i}), \begin{cases}wcet_{i}\leq P_{i} \\ U\leq U_{bound},\\ P_{i}^{min}\leq P_{i}\leq P_{i}^{max}.\end{cases}  \tag{14}\]  
Figure 2: Hyper-period comparison of different task sets and its effect on CPU clock cycles.
## 2 Materials and Methods
When designing the camera configuration, we considered the following factors: a minimum triple coverage of the face; the angle between adjacent camera below 30\({}^{\circ}\) to improve image alignment and subsequent orientation; and a camera-to-subject distance of 1.5 m or less to meet the space constraints of the RIS investigator. The most basic camera configuration that meets these guidelines is a single row of eight cameras arranged in a semicircle with a radius of 1.5 m, covering a 180\({}^{\circ}\) arc. The angle between adjacent cameras is 25.74\({}^{\circ}\), the distance between the projection centres of adjacent cameras is 40.39 cm and a field of view of 20\({}^{\circ}\) ensures triple coverage. Starting from this basic configuration, we gradually increased the number of cameras to achieve additional facial coverage. This involved adding cameras above and below the initial row, creating both side and lateral overlap. The final configuration consisted of 24 cameras, through the addition of two more rows of 8 cameras positioned half a metre above and below the first row, resulting in a total cluster of 24 cameras. The cameras in the additional rows were tilted 18\({}^{\circ}\) along the x-axis, downwards for the 8 cameras in the row above the first and upwards for the 8 cameras in the row below. Figure 6 shows the most basic camera configuration composed by 8 cameras and the complete 24-camera configuration. We used the software Agisoft Metashape ver.1.7 [24] to solve the image orientation and image matching step. At first Metashape detects correspondences across the photos, then it applies a greedy algorithm to find approximate camera location and refines them later using a bundle adjustment algorithm to obtain an accurate camera position, orientation, and distortion parameter. The image alignment is followed by the dense matching (reconstruction stage) whose outcome is the point cloud of the acquired object. All reconstruction errors were calculated using the Cloud-to-Mesh Distance plugin in the open-source software Cloud Compare [25]. Figure 6: Camera Network Configuration: from 8 cameras to 24 cameras.
## 3 Proposed Algorithm
### Classic Proportional Fair Criterion  
The ratio of a user's instantaneous data rate to his or her average data rate is indicated by the standard PF utility function. This utility function is important, because in the context of the PF scheduling process the user with the highest utility value is selected. This function can be formally expressed by [27]  
\[k^{*}=\underset{k}{\text{argmax}}\frac{R_{k}}{\overline{R}_{k}} \tag{1}\]  
where \(R_{k}\) is the instantaneous transmittable data rate at the current time instant of user \(k\) and \(\overline{R}_{k}\) is the average data rate of the previous time of user \(k\). According to [PERSON] et al. [27], the idea behind Equation (1) is to ensure a balance between the maximization of system throughput and the maintenance of fairness among all users.  
Meanwhile, [PERSON] et al. [30] have noted that the PF scheme possesses certain advantages for NRT traffic; specifically, it is capable of achieving substantially greater system throughput than other packet scheduling schemes, such as the round-robin (RR) scheme. In the same study, [PERSON] et al. [30] also proved that the PF scheme can provide, at least on average, the same level of fairness as the RR scheme.
## 3 Brief Introduction to the Algorithm
### Algorithm  
The algorithm first detects a position that is close to the left motif boundary in a sequence. It finds such a position via sampling and collision between two sequences. After the rough left boundary of a sequence is found, it is used to find the rough boundaries of the rest of the sequences. Similarly, we find those right boundaries of the motif among the input sequences. The exact left boundary of each motif region will be detected in the next phase via voting. Each character of the motif is recovered by voting among all the characters at the same positions in the motif regions of input sequences. For a sequence, \(S\), a sample point is a random position, \(i\), in \(S\). For two sequences, \(S\) and \(S^{\prime}\), with two sample points, \(i\) and \(j\), respectively, a rough motif boundary is detected by the similarity of \(S[i,i+l]\) and \(S^{\prime}[i,i+l]\) for some reasonably large parameter, \(l\).  
**Descriptions of the Algorithm**  
Input: \(Z=Z_{1}\cup Z_{2}\), where \(Z_{1}=\{S^{\prime}_{1},\cdots,S^{\prime}_{2k_{1}}\}\) and \(Z_{2}=\{S^{\prime\prime}_{1},\cdots,S^{\prime\prime}_{k_{2}}\}\) are two sets of input sequences.  
Output: planted motif in each sequence and consensus string. **Start:**  
Randomly select sample points from each sequence, both in \(Z_{1}\) and \(Z_{2}\).  
For each pair of sequences selected from \(Z_{1}\) and \(Z_{2}\),  
find the rough left and rough right boundaries via the matching at sample points.  
Improve the rough boundaries.  
If the motif boundaries of each sequence in \(Z_{2}\) are not empty,  
use the Voting algorithm to get the planted motifs.  
**End of Algorithm.**
# Doherty Power Amplifier Design via Differential Combining
## 3 DPA Design
The gain at saturation consistently exceeds 11 dB, which is crucial for maintaining signal integrity and amplification quality. The output power surpasses 36.5 dBm, equivalent to over 4.47 W, reflecting the amplifier's capability to deliver substantial power levels for high-demand applications. Figure 3: Drain efficiency and load modulation simulation using ideal components. For the load modulation: main devices (red) and peak devices (blue). Figure 4: Schematic of the designed DPA. Figure 5: Layout of the designed DPA (4.6 \(\times\) 3.6 mm). The efficiency and gain profiles as functions of output power are shown in Figures 6a and 6b, respectively. These figures illustrate how the amplifier's performance evolves with varying output power, providing insight into its operational efficiency and amplification characteristics. Figure 7 presents the results across the entire frequency range, highlighting the amplifier's ability to maintain performance consistency over its operational bandwidth. In addition, Figure 8 provides a detailed view of the gain and PAE profiles specifically at 32 GHz, which is the center frequency of the design. This figure also includes fundamental load modulation performance, demonstrating how well the amplifier handles load modulation and maintains efficiency. The detailed analysis confirms that the proposed design not only achieves high performance, but also provides effective load modulation, making it well-suited for practical high-frequency applications. The performance of the newly designed mmW DPA was tested using the Keysight ADS 2024 DPD Explorer simulation tool. The simulation involved a 100 MHz 5G NR signal  
Figure 8: Simulated PAE and load modulation at 32 GHz for the designed DPA. Figure 6: Simulated PAE and transducer gain versus output power. (**a**) PAE profiles. (**b**) Gain profiles. Figure 7: Simulated PAE, transducer gain and output power as a function of frequency. with a carrier frequency of 32 GHz, a Peak-to-Average Power Ratio (PAPR) of 9 dB, and an available power of 12 dBm. Results, as depicted in Figure 9, compare the power spectral density of the PA's output with and without the application of Digital Predistortion (DPD). Incorporating DPD, using a Generalized Memory Polinomial (GPM) model with a memory depth of 3 and order 7, significantly improved the Adjacent Channel Leakage Ratio (ACLR), raising it from 28.5 dBc to 46.3 dBc. The Error Vector Magnitude (EVM) was improved from \(-22\) dB to \(-44.6\) dB. The average output power simulated was 27.4 dBm, and the PAE averaged 21.9%. Overall, the simulation results validate the effectiveness of the proposed design, showing that the amplifier maintains excellent performance metrics across a wide bandwidth and operational conditions. This robustness in efficiency, gain, and power output underscores the amplifier's suitability for advanced high-frequency applications, including those in the mmW range.
###### Abstract  
Chemical fractionation and speciation of metals species in natural waters and its relation with bioavailability have received increased attention in recent years. A simple liquid membranes method, based on coupled liquid extraction and re-extraction processes, is proposed to separate and quantify the species of nickel present in water samples. A simplex optimization of chemical variables, such as carrier concentration in the organic solution and nitric acid concentration in the receiving solution, was performed and, under optimized conditions, the extraction system was applied to determine nickel species in water samples at natural level concentrations. A linear relationship was established between extraction efficacy and the concentration of dissolved organic carbon in the samples, allowing the separation and determination of labile and non-labile nickel fractions, since the latter was not transported through the organic solution acting as liquid membrane. When the total and labile concentrations of metals were analyzed in real samples with different salinities, no significant differences were found between the results obtained and those from well-established methods. An average relative error of 1.50 and 2.37 was obtained for total Ni concentration and labile fraction, respectively. Finally, a comparison with the theoretical speciation data calculated with the software WinHumic V was successfully performed. Thus, the proposed method allows the simultaneous determination of labile and non-labile nickel fractions, presented as a simple alternative to nickel fractionation in natural waters.  
Keywords:natural waters; extraction; re-extraction; liquid membrane; metals; nickel; speciation; fractionation; labile; non-labile Article
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 2 Related Work
### Sketch Structure  
With the increasing prevalence of high-speed and non-uniformly distributed network traffic, accurately measuring network properties has become a challenging task. This is due to the sheer volume of network traffic data, which can result in decreased throughput if the statistical measurement method employed has a certain degree of latency.  
To address this issue, the field of network traffic statistical analysis has resorted to using sketch structures. This technique involves constructing a hash function to count and compress the network traffic. A sketch structure typically consists of two components: (1) hash function, which calculates the hash value corresponding to the key in the data stream as an index for data compression, aggregation, and storage, and (2) hash bucket, which the sketch structure creator sets to a specific number for storing the required feature values of the data stream.  
Sketch structures have found extensive applications in a variety of areas, including traffic analysis, stream size change measurement, large stream detection, and persistent stream measurement. It has been demonstrated that the use of sketch structures can significantly improve the statistical accuracy of network traffic data while simultaneously compressing, aggregating, and storing it [38].  
The classical CM sketch, introduced by [PERSON] in [39], has been widely utilized in various studies pertaining to network traffic statistics and analysis. This sketch structure is shown in Figure 2. It serves as the foundation for many of its variants and consists of n hash functions and n\(*\)m two-dimensional arrays for compressing and counting network traffic.  
When using the CM sketch for network traffic analysis, it is crucial to designate a feature of the traffic, such as the DNS domain name or the Source IP, as the key and the rest of the features as the values to be counted. The location of the network is calculated using the n hash functions as specified in Equation (1).  
\[loc_{i}=H_{i}(key), \tag{1}\]  
Each network traffic is mapped to n hash buckets, and then the value in the network is added to the value in the hash bucket.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Methods** & **Advantages** & **Disadvantages** \\ \hline Detection of unknown malicious traffic [33] & The unknown malicious samples showed high accuracy & Lack of real dataset \\ Detection of certain APT group identification [12] & Two features of original detection are proposed & The detection accuracy is not high \\ PCA or PCC combined with machine learning [32; 34] & It can better extract the characteristics of APT attack & The number of datasets is small and the model training time is long \\ DNS traffic analysis [35; 36; 37] & High accuracy of model recognition & DNS characteristics of APT attacks need to be accurately determined \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing APT detection methods.  
Figure 2: CM sketch structure.
# Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
## 2 Methods
### Parameter Estimation Based on Expectation-Maximization (EM)
A large portion of the existing literature on the EM algorithm has proven that the algorithm is not only simple in calculation but can also guarantee convergence. With the increase in iteration times, the likelihood function will also increase, so that the result will improve under the maximum likelihood function. Because Y\({}_{0.\text{k}}\) is obtained with the continuous measurement of processing, the algorithm can be used to estimate the model parameters at any time after obtaining the degradation data. Furthermore, with the increase in available data, the estimated model parameters will be more accurate.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Software Security  
Software security is an idea implemented to protect software against malicious attacks and other hacking risks. For CPSs, researchers have designed security architectures to enhance security in IoT software. These architectures consider the balance of performance and safety.  
After being first proposed in 2013 at USENIX, Sancus [155] is a security architecture that supports security extensibility in third-party software for an IoT network with a hardware-only trusted computing base (TCB). In recent years, many types of research have been conducted with Sancus to guarantee the security of IoT devices. Furthermore, Sancus 2.0 [107] with an updated design and implementation was proposed and supports private deployment and more efficient cryptography. The authors developed and evaluated a prototype FPGA implementation to evaluate this scheme in proximal, colocated, and remote settings under surveillance, and the method was proven to defend against acoustic side-channel attacks.  
Soteria [127] is a lightweight open-source solution for offline software protection. It is implemented based on Sancus [155], which provides the isolation, remote attestation, and secure linking of software. In addition, it ensures the confidentiality and integrity of software with hardware-supported integrity checks, which use loader modules to decrypt a protected software module only when the integrity of both is not compromised. After it was evaluated on an openMSP430 extension, the results showed that Soteria effectively supports confidential loading of code with only small load-time overhead.