esa-sceva/satcom-chunk-collection huggingface dataset:
# An FPGA-Oriented Baseband Modulator Architecture for 4G/5G Communication Scenarios
## 1 Introduction
The baseband modulators for the selected 5G waveforms are implemented on FPGA reconfigurable regions. The functionality of these regions can be customized at run-time through DPR. Although only two modes of operation are considered for each waveform, new modes or even waveforms can be added in the future by generating partial bitstreams for the corresponding reconfigurable regions. In our system, three independent and reconfigurable baseband processors can be simultaneously used to process different component carriers in CA, multiple waveforms in coexistence scenarios, or different access modes in C-RANs. Besides DPR, the proposed architecture employs Dynamic Frequency Scaling (DFS) [7] in order to adapt throughput and power consumption to the instant communication requirements. The results highlight the resource efficiency achieved with DPR at the cost of a tolerable reconfiguration latency that is below 1 ms per baseband processor; and the power efficiency of DFS with negligible latency and resource overhead. This paper's contributions are: (1) a flexible, reconfigurable, and adaptable FPGA-oriented architecture for baseband transmission in 5G communication scenarios combining DPR with DFS; (2) a qualitative and quantitative evaluation of the impact of DPR and DFS in this type of application regarding adaptation latency and resource overhead; and (3) a low-latency UFMC modulator scheme with multiplier-less FIR filtering to reduce embedded DSP block utilization. After discussing baseband processing in 5G scenarios and reviewing reconfigurable baseband processor implementation in Section 2, the proposed baseband modulator architecture is described (Section 3) and evaluated (Section 4). The obtained results are discussed and conclusions are finally presented in Section 5.
# Enhanced Linearity in Intracranial Pressure Monitoring System Through Sample Isolation Bridge ROIC
###### Abstract  
This study presents a sample isolation bridge readout integrated circuit (ROIC) specifically designed for intracranial pressure (ICP) monitoring systems. The ROIC consists of an instrumentation amplifier (IA) and a successive approximation register (SAR) analog-to-digital converter (ADC). Additionally, the output of the IA is isolated to protect against output spikes that could compromise the linearity and stability of the ROIC. Both traditional and proposed ROIC circuits are fabricated using 0.18 \(\upmu\)m complementary metal-oxide-semiconductor (CMOS) technology. The peak signal-to-noise ratio (SNR) for the traditional ROIC is 40.9 dB, while the peak signal-to-noise and distortion ratio (SNDR) is measured at 40.1 dB. In contrast, the proposed ROIC, which incorporates the SAR ADC, achieves a peak SNR of 54.6 dB and a peak SNDR of 51.8 dB, demonstrating a significant improvement in linearity. The new ROIC consumes 39.5 \(\upmu\)A of current from a 1.8 V power supply and occupies a chip core area of only 0.27 mm\({}^{2}\).  
bridge readout IC; energy efficient; instrumentation amplifier; analog-to-digital converter; successive approximation register; intracranial pressure; sampling isolation +
Footnote †: journal: Applied sciences  
Article
# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
###### Abstract  
Landslide-generated seismic waves (landslide-quakes), exhibiting distinctive waveforms and frequency characteristics, can be recorded by nearby seismometers. Implementing an automatic classifier for landslide-quakes could help provide objective and accurate initiation times of landslides with efficiency. This study collected and analyzed the time information of 214 landslide seismic records due to 33 documented landslide events, from the Broadband Array in Taiwan for Seismology (BATS). In addition, equal numbers of earthquake and noise signals were also incorporated. The 642 seismic signals and time information were carefully examined using the random forest algorithm to create an automatic landslide-quake classifier. By validating the signal attributes of the landslide, earthquake, and noise events, specifically in the time and frequency domains, it was shown that the proposed classifier can reach an accuracy (the proportion of all correctly classified events to the total number of events) of 91.3%. To further evaluate the applicability of the automatic classifier, landslide-quakes generated during the devastating Typhoon Morakot (2009) and Typhoon Soudelor (2015) were also verified, showing that the sensitivity of the classifier is higher than 98%.  
Keywords:landslide-quake; broadband array in Taiwan for seismology; machine learning +
Footnote †: journal: Applied Sciences
# A Study on Cyber Target Importance Quantification and Ranking Algorithm
## 4 Design of Experimental Scenario and Experiment Results  
### Design of Experimental Scenario  
#### 4.1.1 Analysis of STUXNET Case  
The STUXNET attack, which destroyed and incapacitated the SCADA system of an Iranian nuclear power plant, was an incident in which a centrifuge in the Iranian Natanz nuclear facility was damaged as a result of a series of cyberattacks between 2007 and 2012 [11]. The Iran nuclear facility target system consists of the Natanz nuclear facility and Arack; the centrifuge, power supply system, and SCADA system can be viewed as target components. The identified cyber targets were the PLC H/W, PLC logic control S/W, etc., considering the three layers of cyberspace [11]. The SCADA system is a major piece of equipment that collects and controls data in real time. It is mainly installed in computer-controlled core infrastructure such as power plants, oil and gas pipelines, refineries, and water supply facilities, and programmable logic controllers (PLCs) H/W. The PLCs at the Natanz nuclear facility that STUXNET attacked were Simatic Step 7 controllers from Siemens, Germany. STUXNET infected this system to tamper with the PLC code blocks, hide the tampered code, and execute the hidden tampered code block when Step 7 was called. The final targets of STUXNET's attack were two central processing units, S7-315 and S7-417. In this case, certificates from trusted companies were used to prevent exposure by automatic detection systems. In order to break into the computers in the facility (Air-Gap),  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rank**} & **Target** & **Target** & **Target** & **Target** & \multirow{2}{*}{**TIR**} \\  & **(Host)** & **Exposure** & **Criticality** & **Connectivity** & & \\ \hline  & & & & & \\
2 & C & 0.6 & 0.44 & 5 & 34.3 \\
3 & C & 0.4 & 0.53 & 1 & 8.1 \\
4 & D & 0.2 & 0.11 & 2 & 3.9 \\ \hline \hline \end{tabular}
\end{table}
Table 11: PageRank-based Target Connectivity scoring parameters.  
Figure 9: Examples of logical network topologies for deriving Target Importance priorities.  
we used vulnerabilities that existed between computers or between computers and USB. An analysis of the above text shows that the Natanz nuclear facility is a production and enrichment facility among Iranian nuclear facilities, and can be classified as a component of a target system. As a target, it can be classified as a centrifuge, a power supply system, and a SCADA system. For the selection of the target, the experiment was performed by selecting the SCADA system and PLCs as dual components and selecting the access path to connect the internal worker PC via USB.
# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 4 Experiments
### Experimental Setup
#### 4.1.3 Dimension 3: ML Model  
We are interested in explaining the predictions of process instance outcomes. This type of prediction task constitutes a binary classification task. Here, we employ two predictive models, XGBoost and Logistic regression (LR). Our target is to study the way explainability outcomes reflect the sensitivities of the predictive model being explained. Our choice of these models was based on two reasons:  
* Both models provide mechanisms to highlight the most important features used in generating their predictions. In LR, the weights of the features may serve this purpose, whereas in XGBoost, a built-in capability can be used to retrieve feature sets and rank them by their importance to the model. Criteria used to rank features include _gain_, _weight_, and _cover_, according to the XGBoost API documentation [27]. Two complementary importance criteria are available through the scikit-learn implementation of XGBoost. These criteria are _total_gain_ and _total_cover_.
* XGBoost is the best performing model according to the findings reported in [2], and is one of the best performing according to [12]. In order to build on the results and findings of these benchmarks with respect to different prediction tasks in predictive process monitoring, especially outcome predictions, we decided to adopt XGBoost.  
Table 7 reports the performance of both models after training them in three iterations and using them to generate predictions for all the event logs. Note that both predictive models do not perform well on _Sepsis1_, although these results can be justified when considering the imbalance between class labels indicated in Table 4.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 4 Discussion
### Legal and Epistemological Considerations on 3D Cheloscopy  
#### 4.6.1 A Technological and Epistemological Paradigm Shift  
The introduction of 3D technology in cheloscopy represents a significant step forward compared to traditional methodologies [13]. Three-dimensional scans offer unprecedented precision, reducing the operator's subjective influence and ensuring more reproducible results [30]. This not only improves the accuracy of the analyses, but also redefines the concept of forensic \"evidence\" [26]. 3D technology allows us to obtain detailed and objective data, which overcomes the limits linked to analog methods, where the operator's experience and the quality of the materials used could significantly influence the reliability of the evidence [28].
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 4 Analysis of Test Results
### Calculation of Internal Force and Safety Factor
#### 4.2.2 Calculation Method of the Safety Factor  
The strength safety factor of each section of the secondary lining needs to be calculated in two cases large eccentric compression and small eccentric compression.  
According to the \"Code for Design of Highway Tunnels\" (JTG D70-2004) [27], the calculation equation of the section safety factor of the large eccentric compression member with reinforced concrete rectangular section is as follows:  
\[K_{1}=\frac{R_{w}bx+R_{g}(A_{s}^{\prime}-A_{s})}{N} \tag{7}\]  
\[K_{2}=\frac{R_{w}bx(h_{0}-x/2)+R_{g}A_{s}^{\prime}(h_{0}-a_{s}^{\prime})}{Ne} \tag{8}\]  
According to the \"Code for Design of Highway Tunnels\" (JTG D70-2004) [27], the equation for calculating the section safety factor of small eccentric compression members with reinforced concrete rectangular sections is as follows:  
\[K_{3}=\frac{0.5R_{a}bh_{0}^{2}+R_{g}A_{s}^{\prime}(h_{0}-a_{s}^{\prime})}{Ne} \tag{9}\]  
When the axial force \(N\) acts between the center of gravity of the reinforcement \(A_{s}\) and the center of gravity of the reinforcement \(A_{s}^{\prime}\), the following requirements shall be met:  
\[K_{4}=\frac{0.5R_{a}bh_{0}^{2}+R_{g}A_{s}(h_{0}-a)}{Ne^{\prime}} \tag{10}\]  
where \(K_{1}\), \(K_{2}\) is the safety factor of the large eccentric compression member; \(K_{3}\) is the safety factor of the small eccentric compression member; \(K_{4}\) is the safety factor of the member when the axial force acts on the centroid of the reinforcement and the centroid of the reinforcement; \(M\) is the bending moment of the cross-section; \(N\) is the axial force of the cross-section; \(R_{w}\) is the standard value of the concrete bending compressive ultimate strength, and the \(R_{w}\) of C35 concrete value is 28.1 MPa; \(R_{g}\) is the standard value of the tensile or compressive strength of the steel bar, and the longitudinal steel adopted HRB335, the standard value of its tensile and compressive calculation strength \(R_{g}=R_{g}^{\prime}=335\) MPa; \(R_{a}\) is the compressive ultimate strength of concrete or masonry, and the value of C35 concrete is 22.5 MPa; \(A_{s}\) is the cross-sectional area of the steel bar in the tension zone; \(A_{s}^{\prime}\) is the cross-sectional area of the steel bar in the compression zone; \(h\) is the total height of the section; \(h_{0}\) is the effective height of the section, \(h_{0}=h-a_{s}\); \(x\) is the height of the compression zone of the section, \(x=ax_{c}\), \(\alpha\) takes 0.8; \(b\) is the section width; \(e,e^{\prime}\) is the distance from the center of gravity of reinforcement \(A_{s}\) and \(A_{s}^{\prime}\) to the point of application of the axial force.
## 3 An Expert System to Estimate RIF
### Fuzzy Logic Inference  
Experts' judgments are uncertain as they cannot determine a crisp value of RIF for each criterion, and some studies have used fuzzy theory to obtain experts' judgments [42; 43; 44]. A fuzzy judgment is determined by experts' knowledge and experience of a product compared to a similar product. For example, for the criteria of technical maturity, they may choose variables such as negligible, low or high.  
Suppose that a membership function \(u_{A}(x)\in[0,1]\) corresponds to any fuzzy set \(A\) in the universe of discourse \(U\). \(u_{A}(x)\) is the membership of \(A\) to \(U\). \(u_{A}(x)=1\) describes that \(x\) is the full member of \(A\), while \(u_{A}(x)=0\) means non-membership. The most common fuzzy number is the triangular fuzzy number, and the membership function of triangular fuzzy number \(\bar{A}=(a_{1},a_{2},a_{3})\) can be described as \[u_{\tilde{A}}(x)=\left\{\begin{array}{c}\frac{x-a_{1}}{a_{2}-a_{1}},a_{1}\leq x \leq a_{2}\\ \frac{a_{3}-x}{a_{3}-a_{2}},a_{2}\leq x\leq a_{3}\\ 0,\text{ other wise}\end{array}\right. \tag{6}\]  
Because the RIF is determined by the fuzzy number, we need to diffuse the fuzzy number to obtain a crisp number. The centroid of the area is the most frequent method used for defuzzification [45]. The center of gravity of the triangular fuzzy number is also its mean value, which is defined as  
\[X=\frac{\int xu(x)dx}{\int u(x)dx} \tag{7}\]  
where \(X\) is the crisp number of defuzzification, \(u(x)\) is the aggregated membership function and \(x\) is variable.  
A group of experts give their judgments about the RIF for each criterion. As experts' judgments are described by fuzzy logic, the similarity aggregation method is used to aggregate the opinions of a homogeneous/heterogeneous group of experts. Finally, the aggregated experts' judgments for the RIF of the \(i\)th criterion \(\lambda_{i}^{agg}\) can be calculated as  
\[\lambda_{i}^{agg}=CC\left(E_{1}\right)\times\tilde{R}_{1}+CC\left(E_{2} \right)\times\tilde{R}_{2}+\cdots+CC\left(E_{n}\right)\times\tilde{R}_{n} \tag{8}\]  
where \(CC\left(E_{i}\right)\) is the consensus coefficient degree of expert \(i\) and \(\tilde{R}_{i}\) is the judgment of expert \(i\).
## 4 Discussion  
This study was the next step in ongoing work to demonstrate the feasibility of obtaining direct measurements of the vocal fold collision pressure during phonation in an individual that was specially selected to reduce the challenges of performing the procedure in a typical individual with bilaterally vibrating vocal folds. The synchronized collection of estimates of subglottal air pressure below the vocal folds was necessary to effectively interpret the impact of aerodynamic versus tissue collision forces on the pressure measurements being obtained at the vocal fold level. The simultaneous recording of high-speed videoundoscopy and non-invasive recordings of neck-surface accelerometry, acoustics, and electroglottography allow for the cross-correlation of these measures with the intra-glottal/subglottal pressure signals of the ISP probe. Such data are much more valid and valuable if they can be reliably acquired in vivo as opposed to the excised situation where there is no neural innervation of associated flacid muscles and no perfusion (blood supply) to the muscles and phonatory mucosa. These data are also critical for improving our ability to use and validate computer models of vocal fold phonatory function that will provide  
Figure 6: Bayesian estimation of physiological parameters of interest using the subject-specific modeling approach of [PERSON] et al. (2016). Shown are the (**a**) observed (Obs.) and estimated (Est.) glottal area waveforms with estimated posterior glottal opening (PGO), (**b**) estimated muscle activation levels of the cricothyroid (\(a_{CT}\)) and thyroartytenoid muscles (\(a_{TA}\)), (**c**) measured vocal fold displacement for the upper (\(x_{u}\)), lower (\(x_{l}\)), and body (\(x_{b}\)) masses of the left (vibrating) vocal fold, (**d**) subglottal pressure as estimated by the model (Est.) and directly measured by the ISP probe’s subglottal pressure sensor, (**e**) measured vocal fold velocity for upper (\(x_{u}\)), lower (\(x_{l}\)), and body (\(x_{b}\)) masses of the left (vibrating) vocal fold, and (**f**) vocal fold collision pressure as estimated by the model (Est.) and directly measured by the ISP probe’s intraglottal pressure sensor. Shaded areas correspond to 95% confidence intervals around the model-estimated parameters.  
better insight into the underlying biomechanics associated with normal and pathological voice production, and ultimately help guide the design of improved prevention, diagnostic, and treatment approaches.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 4 Discussion
### Bandwidth Utilization  
Figure 8 shows the bandwidth used by our AC agent compared to the optimal policy in different contexts. To make good use of the bandwidth, the agent must determine appropriately when to use replicas to compensate for losses without wasting bandwidth. As mentioned earlier, too much redundancy can waste bandwidth, while too little redundancy means less protection. The results show that the performance of the AC agent was similar to that of the optimal policy in terms of bandwidth utilization. Both policies traded the bandwidth to overcome high loss rates in high-loss contexts (contracts 2 and 3) and use little bandwidth in contexts with relatively favorable conditions in terms of loss rate (contracts 1, 4, 5 and 6). On average, both had a repetition factor of 1.4, which corresponded to an average redundancy of 40% (i.e., 40% of the traffic was used to protect information traffic). The repetition factor of 1.4 corresponded to an average throughput of 2.1 Mbps, with 1.5 Mbps as the average source rate. On the other hand, the RR system consumed the least bandwidth, with an average bandwidth of 1.5 Mbps (omitted in the figure because it always transmits with one bit without repetitions), but it did not reach the E2E loss threshold (see Figure 7) because it did not use redundancy.
## 3 Numerical Simulation of Shockwave Soil Loosening  
To evaluate the behavior and efficiency of the proposed shockwave-loosening system, a numerical simulation was conducted using computational fluid dynamics (CFD) techniques. The purpose of the simulation was to analyze the propagation of shockwaves in soil, quantify their impact on soil porosity and compaction and optimize the system's design parameters for effective soil loosening.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 5 Analyzing the Challenges and Advantages of Protocols in Underwater Communication
### MAC Layer Protocols
#### 5.1.1 Contension-Free Based MAC Protocol Design  
In 2009, a CDMA-B was developed whose main purpose was to save energy. Near-far signal is one of the major problems affecting performance levels underwater [8]. In 2011, POCA-CDMAMAC was developed, where the round-robin methods in it are used to receive packets from neighboring nodes. All nodes sending the packet in the same interval of time are the disadvantage [9]. In [10], the PLAN-MAC protocol was developed for long latency access networks. In this approach the contention free algorithm is used to spread the codes, i.e., each node will get a unique code. The staggered TDMA Underwater MAC Protocol (STUMP) [11] was developed in 2009, where there is no need of synchronization between nodes in order to avoid overlap during transmissions. The disadvantages of STUMP are the usage of a number of time slots. ER-MAC [12] was developed in 2008. This is an energy efficient protocol, but it is not suitable for multi-hop communications. WA-TDMA [13] was developed in 2009; the sleep-awake method is used for each node to avoid energy wastage. In WA-TDMA, slot allocation is one of the major issues. ACMENet [14] was developed in 2009; the sleep-awake method is used for each node to avoid energy wastage. Slot allocation is the main issue in this protocol. Dynamic Slot Scheduling Strategy (DSSS) [15] was developed in 2011 for the best usage of channels in underwater communication. The transmission pairs are increased to avoid collisions and parallel data transmissions. Synchronization is the essential requirement in this approach. UW-FLASHR [16] was developed in 2008; tight synchronization is not required in its use. There is a time gap during the transmission of data. ST-MAC [17] was developed in 2009; it describes the delays between the transmission links openly and modelled the ST-MAC as a new technique for solving vertex coloring problem. OFDMA [18] was developed in 2009; in it, topology is centralized. This protocol is used to solve the hidden terminal problem, but many users can affect the performance of the system. UW-OFDMAFC [19] was developed in 2009; in it, the topology is centralized. This protocol is used to solve the hidden terminal problem. Many users can affect the performance of the system.