esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Analysis of the Main Pier during the Construction Period of HLCR Bridges
## 2 Theoretical Foundation  
### Checkpoint Method (JC Method)  
Let the function of the structure be \(Z=g(x_{1},x_{2},\cdots,x_{n})\), assuming point \(P\big{(}x_{1}^{*},x_{2}^{*},\cdots x_{n}^{*}\big{)}\) is the design checkpoint on the limit state surface, and satisfying [29]  
\[Z=g(x_{1}^{*},x_{2}^{*},\cdots x_{n}^{*})=0. \tag{1}\]  
Here, the underlying variables are assumed to obey a normal distribution where the mean is \(\mu=(\mu_{x_{1}},\mu_{x_{2}},\cdots,\mu_{x_{n}})\), and the standard deviation is  
As \(\left.\frac{\partial x}{\partial x_{i}}\right|_{x^{*}}\
eq 0\), there must be \(\mu_{x_{i}}-x_{i}^{*}-\beta\alpha_{i}\sigma_{x_{i}}=0(i=1,2,\cdots,n)\), i.e.,  
\[x_{i}^{*}=\mu_{x_{i}}-\beta\alpha_{i}\sigma_{x_{i}} \tag{10}\]  
\[g(x_{1}^{*},x_{2}^{*},\cdots x_{n}^{*})=0 \tag{11}\]  
In a special case, when the random variables are independent of each other, then Equation (6) can be changed to:  
\[\sigma_{Z}=\sqrt{\sum\limits_{i=1}^{n}\left(\sigma_{x_{i}}\left.\frac{\partial g }{\partial x_{i}}\right|_{x^{*}}\right)^{2}}=\sum\limits_{i=1}^{n}\alpha_{i}^ {\prime}\sigma_{x_{i}}\left.\frac{\partial g}{\partial x_{i}}\right|_{x^{*}}, \tag{12}\]  
\[\alpha^{\prime}=\frac{\sigma_{x_{j}}\left.\frac{\partial g}{\partial x_{j}} \right|_{x^{*}}}{\sqrt{\sum\limits_{i=1}^{n}\left(\sigma_{x_{i}}\left.\frac{ \partial g}{\partial x_{i}}\right|_{x^{*}}\right)^{2}}} \tag{13}\]  
The iterative method is commonly used to solve the design verification point \(P^{*}\left(x_{1}^{*},x_{2}^{*},\cdots x_{n}^{*}\right)\) and the corresponding reliability index \(\beta\). The iterative procedure is as follows:  
1. Assume that the design checkpoint \(P^{*}\left(x_{1}^{*},x_{2}^{*},\cdots x_{n}^{*}\right)\) is initially assigned (generally taken as the mean value point);
2. Carry out equivalent normalization of the non-normal variable \(x_{i}\). The statistical parameters \(\mu_{y_{i}}\) and \(\sigma_{y_{i}}\) of its equivalent normal distribution \(y_{i}\) are calculated and used in place of \(\mu_{x_{i}}\) and \(\sigma_{x_{i}}\). This is recorded as \(\mu_{x_{i}}=\mu_{y_{i}}\),\(\sigma_{x_{i}}=\sigma_{y_{i}}(i=1,\cdots,n)\);
3. From Equation (7), calculate the value of \(\alpha_{i}\), including \(\left.\frac{\partial g}{\partial x_{i}}\right|_{x^{*}}\) and \(\left.\frac{\partial g}{\partial x_{j}}\right|_{x^{*}}\);
4. Using Equation (8), calculate the value of \(\beta\);
5. Using Equation (10), calculate the new value of \(x_{i}^{*}\);
6. Repeat (2)-(5) with this \(x_{i}^{*}\) until the difference between the first and second \(\|x^{*}\|\) or the absolute value of \(\beta\) is less than the allowable error \(\varepsilon\).
## 1 Introduction
In this case, however, it will not necessarily fulfill the optimization criteria as formulated in [PERSON] and [PERSON] [3] that create a faithful audio reproduction. The state-of-the-art true stereo systems combine two microphones with a characteristic directivity pattern, placed at different distances and under different angles relative to one another. Depending on these parameters, a deviating spatial rendering of the distributed sources can be observed [6]. Despite this, for use in the method proposed by [PERSON] and [PERSON] [3], these systems have some disadvantages that make them unsuitable to be implemented in this specific sound reproduction system because there is a high percentage of recorded reverberant sound, which should be avoided in the system of [3]. Figure 1: Recording and playback configuration with a processing stage in between to maintain the acoustical perception of a recording room. The microphone \((C)\) records the direct sound, which is played later by two conventional loudspeakers, whereas the two microphones \((B^{l})\) and \((B^{r})\) record the reverberant sound field, which is played later by two dipole loudspeakers. Figure reproduced with permission from [3], Copyright IEEE, 2015. We overcome these disadvantages with the development of a new method of a true stereo microphone array, using a superdirective beamforming algorithm that is applied on two logarithmically-spaced microphone arrays. Correct, frequency-dependent interchannel level differences are captured by optimizing the shape of the two main lobes of the arrays. Together, they create the proper interchannel level difference required for an accurate spatial reproduction of the sound field while ensuring that no interchannel phase differences occur that can result in unintended changes in the perceived location of sound sources. Additionally, an optimal side lobe suppression is applied to reduce the influence of the reverberant sound field on the recording of the direct sound. This proposed stereo microphone array is compared to the state-of-the-art stereo microphone configurations mentioned earlier that shows a clearly reduced level of the reverberant sound field.
## 2 WSN-Based ITS Applications
### Requirements  
The success or failure of a WSN-based ITS application is decided by many factors. As regards the design of the application, there are some of them which, to a greater or lesser extent, must be satisfied. Some of these are as follows:  
* **Low cost**. In order to be an attractive alternative to other technologies, a WSN-based system must be cost-effective. This implies reducing the costs of the deployment and maintenance by using as few devices as possible and assigning them the minimum set of functionalities required, reserving more costly functions to a reduced group of nodes.
* **Lifetime**. The deployment of the system infrastructure carries an associated investment. Its benefit will depend on the period of time during which the system is exploited. Given the power restrictions of WSN, this period will be determined by the ability of the network to reduce power consumption and by the use of additional power supply sources.  
* **Flexibility and scalability**. A system must be able to adapt to the different situations which are expected under its normal operation. This includes variable traffic conditions and the penetration ratios of smart vehicles in the system. Similarly, it is desirable that a system be versatile enough as not to be restricted to a fixed scenario, making it reusable for different purposes and in different locations. On the other hand, the system must be allowed to grow, either in number of users or in the size of the area it covers. To this extent, growth must be facilitated, making necessary to provide the system with self-organizing capabilities.
* **Robustness and fault tolerance**. Sensor nodes deployed on the road are subject to adverse situations such as the passing of vehicles over them and unfavorable atmospheric conditions which may provoke either the failure of the node itself or its malfunction. This imposes the necessity for means of physical protection. In addition, in case of breakdown of single nodes due to harsh conditions or battery depletion, the overall operation of the network must not be affected, which implies providing the network with redundancy or alternative mechanisms to guarantee connectivity.
* **Appropriate Quality of Service (QoS) provision**. According to the function of the system different QoS parameters must be satisfied including reliability (referring to the correct reception of delivered data at the destination), delay and, in infrequent cases, bandwidth. Safety applications are the most demanding when it comes to reliability and delay, since it is crucial to report any imminent danger well in advance. Other applications are less strict as sporadic losses or delays neither provoke accidents nor probably the malfunction of the system.
## 3 Results and Discussions
### Order Picking Performance
#### 3.2.4 Sensitivity Analysis of the Effect of Error on the Order Picking Performance  
We evaluated the effect of the stochastic behavior of the picking errors on order picking (see Table 7). The probability of error is set to vary around the mean with standard deviation of respectively 0.01, 0.02, 0.03, 0.04, and 0.05. Not many changes were observed when the errors probability changes over the experimented samples compared to previous results where error probability is fixed. Midpoint and traversal routings performance, in terms of subset presence, remain equally the same; except when the standard deviation of error probability is equal to 0.02. In that case, the midpoint performs the best. However, very small reductions were observed in the efficiency level. As previously found, at least 9 out of 10 subsets from each scenario contain voice picking technology. Likewise, warehouse 1 and 3 perform the best as previously found. In sum, the performance of routing policies and solution technologies is robust against error deviation.
# Optimization of Processing Parameters to Increase Thermal Conductivity of Rice Straw Fiber Film
## 3 Results and Discussion
### Optimization Analysis  
The BMF with desired wet tensile strength, dry tensile strength, and thermal conductivity was obtained by optimized formulations and processing conditions. Due to the design model, the restrictions as previously required, and graphic optimization with the DX 6, the optimum conditions are basis weight 66 to 70 g/m\({}^{2}\), wet strength agent content 1.4% to 1.6%, neutral sizing agent content 0.5%, charcoal addition ratio 15%, and beating degree 55 \({}^{\circ}\)SR (Figure 5). At the optimum condition, dry and wet tensile strengths are larger than 32 N and 11 N, and thermal conductivity is larger than 0.0700 W/m\(\cdot\)K.  
Figure 4: Effects of five variables on thermal conductivity (**a**) Basis weight (**b**) Neutral sizing agent content (**c**) Charcoal addition ratio (**d**) Wet strength agent content (**e**) Beating degree.
# The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey
## 5 Fog Computing Security Challenges
### Data Center Level Security Challenges  
The fog is equipped with resources that can be used for storing big data collected from IoT devices temporarily. This makes the data readily available to be frequently accessed by users, as well as helpful in maintaining and updating the data in a well-organized and flexible way. This temporary storage on fog nodes significantly reduces the delay in communication between the fog and the cloud and, hence, cuts down the response time to access and update the data. Fog data centers act as the intermediate devices in networks taking up the responsibility of performing different communication functions such as data collection and assembling, packet transfers and routing. The fog data centers are also capable of simple processing of the received data and can select the appropriate audience for data distribution. Fog data centers serve the purpose of implementing a multi-tenant virtualization setup. These systems work under the control of infrastructure providers. These data centers are used by every user including consumers and infrastructure providers. These edge data centers work autonomously while cooperating with other connected devices, but they are also interconnected with traditional cloud centers. Thus, there is a possibility to implement multi-tiered architecture using network infrastructures for network formation. For the implementation of this system, we have to evaluate the existing support infrastructure systems such as mobile core networks and centralized cloud services. The fog-based systems are heterogeneous in nature, which allows high speed wireless connections and technologies to work together [16,102]. Large-scale data collection and distribution is expressively optimized because of the involvement of fog nodes. Fog nodes and IoT maintain temporary data as a transient storage to reduce big data management complexity. It is also very crucial to safeguard data distribution and dissemination. However, some security and privacy issues seriously affect data privacy. There are several challenges and proposed solutions found in the literature; some are surveyed in the following subsections and are summarized in Table 4.
## 3 PPK2 Enzymology
### Class III PPK2 Enzymology  
Class III is the most recently discovered PPK2 subfamily, capable of phosphorylating either nucleoside mono- or diphosphates. Based on their phylogenetic prediction of this new class of enzymes, [PERSON] and colleagues first validated PPK2s from _M. ruber_, _Meiothermus silvanus_, Deinococcus geothermalis, Thermosynechococcus elongatus, and _D. radiodurans_ as capable of polyP-driven ATP synthesis from AMP [50]. [PERSON] et al. added two more class II enzymes to the list: _A. aurescens_ (_Aa_PPK2) and _C. hutchinsonii_ (_Ch_PPK2). _Ch_PPK2, _Dr_PPK2, and _Aa_PPK2 all showed significant activity with Mg\({}^{2+}\), Mn\({}^{2+}\), Ca\({}^{2+}\), Co\({}^{2+}\), and Ni\({}^{2+}\)[46], indicating that class III PPK2s appear to accept a broader range of metal ion cofactors relative to class I and II enzymes. As expected, the polyP synthesis activity of _Aa_PPK2, _Ch_PPK2, and _Dr_PPK2 was at least three orders of magnitude lower than polyP-dependent phosphorylation of NMPs or NDPs. While _M. ruber_ PPK2 was capable of using both purine and pyrimidine bases [50], _Aa_PPK2, _Ch_PPK2, and _Dr_PPK2 showed no activity with pyrimidines. However, the latter three enzymes showed significant activity with dAMP and dADP, suggesting that these PPK2s may contribute to the synthesis of DNA precursors. Interestingly, these three class III PPK2s in addition to the class I SMc0218 and class II PA3455 showed weak adenylate kinase activity (conversion of ADP to AMP and ATP) [46]. An outstanding question surrounding class III PPK2s is whether the conversion of nucleoside monophosphates to nucleoside triphosphates involves a direct (pyroph phosphorylation) or sequential transfer of phosphate groups. A recent study by [PERSON] et al. concluded that a significant amount (30-80%, depending on the PPK2 tested) of ATP produced from AMP occurs via pyroph phosphorylation, and the rest occurs via step-by-step monophosphorylation by separate P\({}_{1}\) molecules [51]. While the authors of this study concede that their methodology could not exclude the possibility of some sequential monophosphorylation being mistaken for pyroph phosphorylation, previous reports that PPK2 polyP utilization is processive [34; 35; 69]--i.e., the polyP chain is not released by the enzyme between reactions--support the possibility that multiple P\({}_{1}\) units can be transferred from polyP at once. Regardless of the reaction mechanism, class III enzymes offer a streamlined means of recycling both AMP and ADP and are thus beginning to supplant class I enzymes for use in biotechnological ATP regeneration systems [63].
## 4 Results  
### Overall Study Population and Impact on ECOG PS Completeness  
As of 30 June 2021, in the overall database across all tumor types (N = 480,825 patient lines, N = 229,257 patients), structured ECOG PS scores were available for 290,343 patient lines (60.4%). After applying NLP-extraction, the availability of ECOG PS scores increased to 73.2% of patient lines. For certain treatment lines, the improvement was greater than 20% (Table S2). For example, for AML patients at 3L, our baseline completeness for ECOG PS was 43.4%, and after adding NLP ECOG PS, our completeness increased to 63.9%. Additionally, across many diseases, completeness of ECOG PS improved to a greater extent in later lines. For example, in the metastatic breast cohort, the baseline completeness of ECOG PS at first line therapy was 65.5% compared to 78.7% in fourth line (Table S2). This is perhaps attributable to clinicians being more likely to explicitly document ECOG PS in patients who have progressed through multiple treatments and are therefore likely to be sicker.
# A 60 GS/s Two-Stage Sampler with a Linearity Calibration Loop for PAM-8 Receivers
## 2 Track-And-Hold Circuit
### Buffer Distortion Control  
An easy way to understand how the distortion of the buffer varies for various values of \(v_{corr}\) is to plot, as shown in Figure 4, the relative error (\(\varepsilon_{r}\)) between the small signal gain for high-DC differential input \(V_{S}\) and the one for zero-DC differential input in different bias conditions:  
\[\varepsilon_{r}=\frac{V_{outd}(V_{S})-G_{AC}V_{ind}(V_{S}=0)}{G_{AC}V_{ind}(V_{ S}=0)} \tag{1}\]  
with \(G_{AC}\) being the small-signal DC gain, \(V_{outd}\) the differential small signal output, and \(V_{ind}\) the input one. By tuning \(v_{corr}\), the input-output characteristic of the buffer can compensate not just for the buffer distortion but also for the static nonlinearities of the previous stages and AFE as well. This is represented in Figure 3, where the input-output characteristic of the AFE plus the first-stage sampler is schematized with the typical compressing behavior, and where the second-stage buffer with behavior that compensates for this compression is introduced. Unfortunately, the optimal \(v_{corr}\) for different PVT conditions may have considerable variations, thus a calibration that tracks the nonlinearities of the circuit becomes mandatory.  
A further benefit of the calibration approach is that it may be able to identify the distortions of all the AFE and TH and maximize the THD performance of the system.  
Figure 3: Second-stage TH buffer in blue with its distortion contribution compensating for the previous stages non-linear block in green [8].
## 4 Discussion
### Effect Analysis  
In this research, the AOI technique is used for quality control in the surface-mount technology production to detect the defects in semi-finished/finished products and is widely used for the defect detection of printed circuit boards (PCB) in electronic industries where surface mount technology (SMT) is applied. In this order, we considered a strict threshold value for AOI to prevent 'escapes', but this can lead to serious unwanted noise. Therefore, they tend to set up a Noise-Removal procedure after AOI, which increases the computational cost. By applying deep learning to circuit images of the ceramic substrates in AOI, this study aimed to construct an automatic defect detection system that could also identify the categories as well as the locations of defects. Therefore, we proposed and evaluated three models with integrated structures: ResNeXt+YOLO v3, Inception v3+YOLO v3, and YOLO v3. The outcomes indicated that the defect detection system built on ResNeXt+YOLO v3 could most effectively detect standard images that had been misidentified as defects by AOI, categorize genuine defects, and find their location. The testing results showed that the ResNeXt+YOLO v3 integrated model was the best among all three. When it was introduced to the subject company of this study to replace the manual re-examination after AOI, 10 people were needed for manual re-examination before the introduction, and the accuracy rate and false alarm rate of AOI were 85% and 75%, respectively, whereas only one person was needed after the implementation as the person can operate this flaw detection system to conduct an automatic re-examination, and the accuracy rate and false alarm rate can reach 100% and 0%, respectively.
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 2 Related Work
### Protocol-Specific Attacks in RPL-Based IoT  
Protocol-specific attacks are named based on an RPL feature or mechanism that an attacker aims to target. For example, in a PS-R attack, the attacker manipulates the rank value, rank type, and its routing operation in an IoT network. Since this attack is capable of modifying or altering RPL features, impacting the network performance, forming routing loops, and disrupting the topology [15], it is considered one of the highly damaging attacks. A malicious node manipulates the rank value and objective function through control messages, severely affecting the routing topology [16]. To increase the attack impact, it broadcasts a fake, lower rank value (decreased rank attack) and shortest distance toward the root node in the network. Consequently, the child IoT nodes add the attacker node to its preferred parent list and, in other cases, directly select it as a parent. This causes the RPL to rebuild the directed acyclic graph (DAG). Moreover, the resources of victim nodes are wasted in the attack process. The rank attack causes loop formation, which results in node isolation at the individual or cluster level where a group of victim nodes are isolated and obstructed from communicating in the network. If the rank rule in RPL is compromised, it can cause control overhead, delay, and packet collisions [17]. When this type of attack is conceptualized in a critical IoT-enabled scenario, such as healthcare, the impact is severely detrimental and may cause irreversible damage to the system and associated bodies.  
There are three popular variants of rank attacks: (1) decreased rank attack, (2) increased rank attack, and (3) worst parent attack [10]. In the decreased rank attack, the malicious nodes illegitimately broadcast lower rank to other nodes and attract network traffic. As a result, most of the normal nodes choose the illegitimate node as their preferred parent [10]. This attack is similar to the SN-based sinkhole attack in some regards. In contrast, adversaries in an increased rank attack advertise a higher rank value illegitimately to deteriorate the nearest parent selection process. Consequently, the routing topology is disrupted, node communications are delayed, and latency is introduced in the overall RPL network by forcing the nodes to select other nodes as parents that might be farther away from the root node [18]. In the worst parent selection, the victims simply select a parent with poor communication or routing capabilities because of rank manipulation by the attacker. Overall, the important consequences of rank attacks are communication delay resulting from disruption in packet transmission, end-to-end delay, latency, weak or worsened routing path, loop formation, and decreased packet delivery ratio.