esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards the Quantification of 5f Delocalization
## 3 Results and Discussion
### A Revisitation of XAS and the Branching Ratio  
Let us begin by first considering the processes of X-ray Absorption Spectroscopy. Of course, [PERSON] and [PERSON] dealt with this in their earlier, ground-breaking work, including not just electric dipole transitions but also the intricacies of angular momentum for the three cases of Russell-Saunders or LS coupling, jj coupling and the Intermediate coupling case [22]. The goals here are more modest: to (1) better understand the underlying cause of the n = 2 localized/n = 3 delocalized dilemma and (2) to prepare to address the XES question.  
The foundational single electron equations are below. A photon is absorbed and an electron moves from the 4d state into an empty 5f state. In Equation (1), there is only the 5f\({}_{5/2}\) final state. Under electric dipole selection rules, the transition into the 5f\({}_{7/2}\) state is forbidden. This selection rule, combined with preferentially filling the 5f\({}_{5/2}\) states, is the driving force behind the reduction in the 4d\({}_{3/2}\) peak in Pu, as seen in Figure 1[10, 11].  
\[4\mathrm{d}_{3/2}+\mathrm{h}\mathrm{v}\to 5\mathrm{f}_{5/2} \tag{1}\]  
\[4\mathrm{d}_{5/2}+\mathrm{h}\mathrm{v}\to 5\mathrm{f}_{5/2}\text{ and }5 \mathrm{f}_{7/2} \tag{2}\]  
If one applies the electric dipole operator to transitions between the states in the 4d manifolds [j =3/  
\[I_{\frac{3}{2}}=\frac{N_{5/2}}{6}\bigg{(}\frac{56}{15}\bigg{)} \tag{4}\] \[BR=\frac{I_{\frac{5}{2}}}{I_{\frac{5}{2}}+I_{\frac{3}{2}}}=\bigg{(} \frac{1}{15}\bigg{)}\bigg{(}\frac{N_{5/2}}{N}\bigg{)}+\bigg{(}\frac{N_{7/2}}{N} \bigg{)}=1-\bigg{(}\frac{14}{15}\bigg{)}\bigg{(}\frac{N_{5/2}}{N}\bigg{)} \tag{5}\]  
where \(N\) is the total number of 5f holes, \(N_{5/2}\) is the number of holes in the 5f\({}_{5/2}\) manifold and \(N_{7/2}\) is the number of holes in the 5f\({}_{7/2}\) manifold. Obviously, \(N=N_{5/2}+N_{7/2}\) and n = \(14-N\), n\({}_{5/2}=6-N_{5/2}\) and n\({}_{7/2}\)\(=8-N_{7/2}\), where n\({}_{\rm x}\) is the corresponding number of electrons in each 5f manifold. If the appropriate filling pattern is used for each case, Equation (5) can generate the correct branching ratio for every case and equation in Table 1 in Reference [10], including the statistical, jj and intermediate cases as well as the experimental values. This then brings the discussion back to the degeneracy of the n = 2 localized case and the n = 3 delocalized case. While the two cases have significantly different occupations, they both have the same percentage un-occupations in the 5/2 and 7/2 manifolds of the 5f states. (Table 2) This suggests that a technique that was dependent upon occupation, not un-occupations, would be able to differentiate the two cases. Such techniques would include X-ray Emission Spectroscopy between the 5f and 4d or 3d manifolds. That will be considered next.  
Values extracted from [2; 10] and the equations above.
## 2 Dynamic Load Testing  
Dynamic load tests are commonly used to estimate the bearing capacity of piles on-site. Developed in 1964 at Case Western Reserve University in the United States, this method is based on the wave equation theory related to the impact wave applied to piles [15]. The Pile Driving Analyzer (PDA) system is specifically designed for dynamic load testing and drive monitoring.  
Dynamic monitoring involves installing two sets of sensors at the pile head, which include two strain sensors and two acceleration sensors placed on opposite sides of the pile above ground level. When connected to the pile head and the PDA system, these sensors capture data on the relationship between the pile top force and velocity over time during vertical hammering of the pile head. Figure 1 shows the acquisition of physical properties on-site using a PDA (Pile Driving Analyzer), manufactured by Pile Dynamics, Inc., located in Cleveland, OH, USA. The PDA analyzer records various parameters, such as estimated driving resistance, estimated static resistance, and energy transmitted through the sensors [8].  
The acquired data are analyzed using the Case Pile Wave Analysis Program (CAPWAP: software version: 2006-2). This method involves calculating waveforms from force and velocity data and comparing them with measured waveforms under varying boundary conditions [16]. The CAPWAP program employs wave equations to analyze the pile driving parameters, which help calculate pile movement and stress during the pile driving process. By establishing a pile-soil model, the program adjusts the pile and soil resistance models until the calculated parameters match the actual measured parameters [17]. Once a good match is achieved, the dynamic model of the pile can be used to determine the distribution of pile parameters along its length and predict the load and settlement of the pile.  
Using CAPWAP analysis, the bearing capacity, load-settlement relationship, and other critical pile performance metrics can be determined. This comprehensive analysis allows for a more accurate assessment of pile behavior under load, ensuring the reliability and safety of pile foundations in various geotechnical conditions.
## 2 Isolation and Characterization of the Sub-6 GHz Multi-Port MIMO Antenna
### ECC Derivation
#### 2.2.2 ECC Based on Radiation Patterns  
The radiation-pattern-based ECC is derived from the 3D, complex far-field radiation characteristics of antenna elements. This method accurately captures the spatial, polarization, and phase relationships between antenna and is considered the most rigorous approach. The correlation is derived by integrating the normalized complex radiation fields over the entire sphere, as described in (8).  
\[\rho_{e}=\frac{\left|\int_{0}^{2\pi}\int_{0}^{\pi}\left[\stackrel{{ \rightarrow}}{{E}}_{1}(\theta,\varnothing)\cdot\stackrel{{ \rightarrow}}{{E}}_{2}^{\ast}(\theta,\varnothing)\right]^{2}\sin\theta\;d \theta\;d\varnothing\right|^{2}}{\left[\int_{0}^{2\pi}\int_{0}^{\pi}\left| \stackrel{{\rightarrow}}{{E}}_{1}^{\ast}(\theta,\varnothing) \right|^{2}\sin\theta\;d\theta\;d\varnothing\right]\cdot\left[\int_{0}^{2\pi} \int_{0}^{\pi}\left|\stackrel{{\rightarrow}}{{E}}_{2}^{\ast}( \theta,\varnothing)\right|^{2}\sin\theta\;d\theta\;d\varnothing\right]} \tag{8}\]  
* \(\stackrel{{\rightarrow}}{{E}}_{1}^{\ast}(\theta,\varnothing)\) and \(\stackrel{{\rightarrow}}{{E}}_{2}^{\ast}(\theta,\varnothing)\) are the complex far-field radiation patterns of antenna 1 and 2, respectively;
* \(\theta\) and \(\varnothing\) are the spherical coordinate angles;
* \(\cdot\) denotes the dot product and \(\ast\cdot\) the complex conjugate.  
This yields a precise measure of the ECC but requires complete 3D radiation pattern data, which are often unavailable during early-stage design or from initial measurements [14].  
This method is very accurate because it considers the spatial diversity and polarization mismatches between antenna elements. Neither impedance matching nor system losses are of concern, rendering the method well-suited for final-stage validation and comprehensive performance assessment. However, complete 3D radiation pattern data are required. This is often challenging if a device is compact or the design thereof is at an early stage. Additionally, the computational burden associated with spherical integration limits the practicality thereof in terms of iterative optimization or rapid prototyping.
## 2 High Diffraction Efficiency and Damage-Resistance Threshold Gratings
### Multilayer-Dielectric-Film Grating
#### 2.2.2 Multilayer Dielectric Film Transmission Grating
Laser-damage thresholds can be greatly improved by using transmission gratings made of fused silica. Depending on the designer's vision of the grating structure, the grating layer material can have either a high refractive index or a low refractive index, commonly combining HfO\({}_{2}\), Ta\({}_{2}\)O\({}_{5}\), SiO\({}_{2}\), and other materials. This can not only lessen the difficulties of the manufacturing process but also increase the grating's damage resistance, allowing the transmission grating to be used in a high-power laser system. Furthermore, the anti-laser-damage performance is not only connected to the material but can also be considerably enhanced and improved throughout the manufacturing process. Vacuum step annealing, for example, is used in the process of depositing a multilayer dielectric film to relieve the internal stress of the film layer, enhance the growth structure of the film layer, and increase the element's damage resistance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Years** & **Units** & **Structures/Contributions** & **Performance** \\ \hline
1983 & [PERSON] and [PERSON] [47] & Period 330 nm, & \(\theta_{1}\) = 42\({}^{\circ}\), \\  & & groove depth 370 nm, & DE \(>\) 88\% @632.8 nm \\ \hline
1997 & [PERSON], & Period 350 nm, & \(\theta_{\text{Littrow}}\) = 30\({}^{\circ}\), TE mode, \\  & [PERSON], et al. [48] & duty cycle is 0.5, & \(-\)1 st DE = 98\%, \\  & & groove depth 600 nm & LIDT = 13.2 J/cm\({}^{2}\) @3 ns \\ \hline  & & Quartz substrate, & \\
2003 & [PERSON], & period 800 nm, & \(\Lambda\) = 1064 nm, \(\theta_{\text{Littrow}}\) = 41.5\({}^{\circ}\) \\  & [PERSON], et al. [49] & groove depth 1540 nm, & \(\text{DE}_{\text{max}}\) = 97\%, \\  & & duty cycle 0.45 & LIDT = 2400 J/cm\({}^{2}\) @170 ns \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of multilayer dielectric transmission gratings in recent years. Figure 35: Schematic diagram of the three dielectric layers with a period of 1270 nm and a duty cycle of 0.58 at an incident wavelength of 1550 nm under Littrow incidence.
## 2 Related Work and Theoretical Background
### Life Cycle Assessment (LCA) for Evaluating Environmental Burdens  
Life Cycle Assessment (LCA) is the internationally recognized standard for evaluating environmental impacts associated with a product, process, or service throughout its entire life cycle from raw material extraction to manufacturing, use, and end-of-life disposal [12]. In manufacturing, LCA is crucial to identify environmental hotspots within the production chain, such as stages or activities that contribute the most significantly to greenhouse gas emissions, energy demand, water consumption, resource depletion, or waste generation [13; 14]. It also supports eco-design by guiding product and process design decisions toward more sustainable alternatives, considering material selection, energy efficiency measures, and waste minimization strategies [4]. It provides quantitative metrics for environmental performance, essential for reporting, benchmarking, and tracking progress towards sustainability goals [2; 3].  
Limitation: Traditional LCA methodologies often rely on static aggregated data, frequently obtained from generic databases or averaged historical plant performance. This approach presents significant limitations for real-time operational management. First, static LCA typically provides a high-level view and struggles to capture the environmental impact variations associated with specific production orders, machine settings, or real-time process deviations. Second, LCA studies are often conducted retrospectively, making them unsuitable for immediate feedback or dynamic control adjustments on the factory floor. Third, linking static LCA models directly with the dynamic event data captured by manufacturing execution systems (MES) or process mining tools is non-trivial. There is a disconnect between the operational event stream and the environmental assessment framework.
# Spectrum Slicing for Multiple Access Channels with Heterogeneous Services
## 3 System Model
\[\text{Mult}(\mathbf{k};n,\mathbf{p})=\frac{n!\prod_{i=1}^{|\mathbf{p}|}p_{i}^{k _{i}}(1-\sum_{i=1}^{|\mathbf{p}|}p_{i})^{n-\sum_{i=1}^{|\mathbf{p}|}k_{i}}}{(n -\sum_{i=1}^{|\mathbf{p}|}k_{i})!\prod_{i=1}^{|\mathbf{p}|}k_{i}!}, \tag{1}\]  
where \(|\mathbf{p}|\) is the length of vector \(\mathbf{p}\). The binomial function \(\text{Bin}(k;n,p)\) is the special case in which \(|\mathbf{k}|=|\mathbf{p}|=1\). We also define the modulo function, which behaves as expected from integer arithmetic. \[\text{mod}(m,n)=m-\left\lfloor\frac{m}{n}\right\rfloor \tag{2}\]  
for \(m,n\in\mathbb{Z}^{+}\). \(\mathbb{Z}^{+}\) is the set of non-negative integers.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 4 Adversarial Attack Generation Methods for IoT Networks
### Exploratory Attack Methods
#### 4.1.8 Zeroth-Order Optimization  
Zeroth-order optimization (ZOO) is a type of adversarial attack that targets ML models where the adversary has only partial knowledge about the targeted model and cannot access its internal parameters or gradients. The attacker's capability is limited to querying the model's output by providing inputs and observing the corresponding predictions. This type of attacks is also known as black-box optimization attacks.  
Proposed by [PERSON] et al. [55], the ZOO technique estimates the gradient of the classifier without accessing it ML model by using the symmetric difference quotient approach.  
Based on the C&W attack method idea, [PERSON] et al., in contrast, want to design black-box attacks. Therefore, they used the probability distribution instead of using the logit layer representation of a targeted model and they estimated the gradients of the targeted model by finite differences. Then, the optimization problem is formulated by Expressions (10)-(13).  
\[\underset{X^{\prime}}{min}\big{\|}X^{\prime}-X\big{\|}_{2}+c.f\big{(}X^{\prime}, t\big{)}\quad s.t.\ X^{\prime}\in\ [0,1]^{p} \tag{10}\]  
where, \(p\) is a dimensional column vector and \(c>0\) is a regularization parameter. For \(X\) is the original input (i.e., image) affiliated with the specified label \(l\), \(X^{\prime}\) is the adversarial sample affiliated with the specified label \(t\) (i.e., \(f(X^{\prime})=t\
eq f(X)=l\), where \(f(X^{\prime}+t)\) is the loss function defined by below Expression (11):  
\[f\big{(}X^{\prime},t\big{)}=max\Big{\{}\underset{l\
eq t}{max}log\big{[}F\big{(} X^{\prime}\big{)}\big{]}_{l}-log\big{[}F\big{(}X^{\prime}\big{)}\big{]}_{t}-k \Big{\}} \tag{11}\]  
where \(F(X^{\prime})\in\mathbb{R}^{K}\) is the probability distribution of the back-box output, \(K\) is the number of classes and \(k\geq 0\) serves as a tuning parameter to enhance attack transferability.  
The approximated gradients, defined as \(\hat{g_{i}}\), are computed using the finite differences method called also symmetric difference quotient as per the Expression (12).  
\[\hat{g_{i}}:=\frac{\partial f(x)}{\partial x_{i}}\approx\frac{f(x+he_{i})-f(x- he_{i})}{2h} \tag{12}\]  
with \(h\) being a small constant and \(e_{i}\) represents the \(i\)-th component of the standard basis vector. ZOO can be used in [PERSON]'s method with Hessian estimate \(\hat{h_{i}}\) as per the following Expression (13).  
\[\hat{h_{i}}:=\frac{\partial^{2}f(x)}{\partial x_{i}^{2}}\approx\frac{f(x+he_{ i})+2f(x)-f(x-he_{i})}{h^{2}} \tag{13}\]  
Although this method has proven its efficacy in estimating the gradient and Hessian while resulting in a similar performance to the C&W attack, without the requirement of training substitute models or information on the target classifier; however, it necessitates a considerable number of queries to the model, which can add to significant computational costs and time requirements and may cause detection of the attacker in real scenarios.
# Design and Analysis of an Efficient Energy Algorithm in Wireless Social Sensor Networks
## 6 Checkpointing and Recovery Algorithms
### Algorithm for Checkpoints in Different Clusters  
The DC is triggered by the message that is sent from a process in a different cluster. The processing of DC is relatively simple. The following steps are its detailed procedures:  
1. When a process sends a DM to MHs in different clusters, it adds the current checkpoint to the message.
2. After received the DM, the CH updates \(S\_InterCDG\) according to the dependence relation between checkpoints of the sender and receiver process and then transmits the DM.
3. The CH of the cluster to which the receiver process of the DM belongs updates the \(R\_InterCDG\) also after received the DM, and then transmits the DM to the receiver process.
4. By the time the process received the DM, if it has taken DC for the cluster, it receives the DM directly. Otherwise, it checks whether it receives or sends any message during the current checkpoint interval. If it has sent or received any message, it stores a \(OC\). Or else, it turns the current checkpoint to \(OC\).  
Let DM be a message sent by \(P_{i}\) (belongs to \(C_{s}\)) and received by \(P_{j}\) (belongs to \(C_{t}\)). \(CKP_{i}\) and \(DC_{j}\) are respectively the checkpoints of \(P_{i}\) and \(P_{j}\) related to DM. The algorithm for checkpoints in different clusters is described as Algorithm 3.  
```
\(\text{SEND\_message}(P_{i},DM)\); // \(P_{i}\) sends \(DM\) \(\text{TRANSMIT\_message}(CH_{s},DM)\); // \(CH_{s}\) transmits \(DM\) \(\text{UPDATE\_S\_InterCDG}(P_{i},P_{j})\); // update \(S\_InterCDG\) \(\text{TRANSMIT\_message}(CH_{t},DM)\); \(\text{UPDATE\_R\_InterCDG}(P_{i},P_{j})\); \(\text{RECEIVE\_message}(P_{j},DM)\); // \(P_{j}\) receives \(DM\) if(\(P_{j}\) has taken \(DC_{j}\)) then \(\text{RECEIVE}(DM)\); else if(\(\
exists\)\(M\)(\(\text{Send}(M)>>CKP_{k}\)) \(|\) (\(\text{Receive}(M)>>CKP_{k}\))) then \(\text{TURN\_CKP}(CKP_{k},DC_{j})\); // \(CKP_{k}\) turns to \(DC_{j}\) else \(\text{STORE\_CKP}(DC_{j})\);
```  
**Algorithm 3 DC_Processing()**
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 6 Cluster Head Selection Mechanism
### Utility Optimal Cluster Head Selection Algorithm  
The D2D multicast utility metric, an evaluation metric for D2D multicast transmission performance, reflects certain information such as D2D multicast transmission rate and system throughput. From Equation (22), the value of D2D multicast utility metric is mainly determined by the strength of social relationships and channel quality between the cluster head and other members. Due to the different social relationships among users, each user shows different degrees of social activity, namely some users have poor social relationships with other members, while others have strong social relationships. Obviously, the latter are more suitable as a cluster head user in D2D multicast cluster. What's more, due to the differences in user's relative distance and channel characteristics, the selection of cluster head affects the change of the worst signal to interference and noise ratio (\(SINR_{i}^{min}\)), which in turn leads to different D2D transmission rates and system throughput. In summary, to better D2D multicast communication performance, selecting a suitable user as the cluster head is the key to data distribution.  
In this paper, the channel model follows path fading and Rayleigh fading, each channel obeys one-mean Rayleigh fading independently, and assuming that the system knows the distance between all users, user's status information, and history viewing information, etc. Therefore, the signal to interference and noise ratio of any two users can be calculated according to Equation (18), and then the channel quality matrix \(\Gamma_{sint}\) can be expressed. Likewise, the social relationship strength of any two users can be calculated according to Equation (16), and then the social relationship strength matrix \(\mathbf{S}\) can be obtained. As a result, by traversing the user set \(U_{C}\), we can calculate the value of D2D multicast utility metric when each user acts as cluster head according to Equation (22), further sorting out the cluster head that maximizes the multicast utility value.  
By the above analysis, this paper proposes a cluster head selection algorithm to maximize utility value, in which the physical information of links and the social relationship information among users are considered. The details are shown in Algorithm 2.
## 6 Experimental Results
### G.726: Adaptive Differential Pulse Code Modulation
each G.726 bit rate for the sentence \"We were away a year ago\" and for the sentence \"A lathe is a big tool\", and list these values in Tables 1 and 2, respectively, along with the corresponding signal-to-noise ratios in dB and the PESQ-MOS values [23]. A few observations are possible from the data in Tables 1 and 2. From Table 1, we see that for 24 Kbps, the PESQ-MOS indicates a noticeable loss in perceptual performance, even though the log likelihood ratio has values above the perceptually significant threshold only 25% of the time. Although not as substantial as in Table 1, the PESQ-MOS at 24 Kbps in Table 2 shows that there is a noticeable loss in quality even though the log likelihood ratio exceeds the perceptually significant threshold only 9% of the time. However, it is notable that at 24 Kbps in Table 1, \(D>24\) for 75% of the frames, and in Table 2, for more than 50% of the frames. This leads to the needed interpretation of the log likelihood ratio as a comparison of distributional properties rather than just the logarithm of the ratio of mean squared prediction errors (_MSPEs_) or as a measure of spectral match. Considering the log likelihood as a difference between differential entropies, we can conclude that the differential entropy of the coded signal when passed through a linear prediction filter based on the coefficients computed on the original speech is substantially different from the differential entropy when passing the coded signal through a linear prediction filter based on the coefficients calculated on the coded speech signal. Further, in terms of the difference between two mutual informations, the mutual information between the coded speech signal and the signal passed through a linear filter with coefficients calculated based on the coded speech is substantially greater than the mutual information between the coded speech and the coded speech passed through a linear prediction filter with coefficients calculated on the original speech. Notice also that, for both the differential entropy and the mutual information interpretations, as the coded speech signal approaches that of the original signal, that is, as the rate for G.726 is increased or the quantizer step size is reduced, the difference in differential entropies and the difference in mutual informations each approach zero.
## 2 Spatial Domain Chaotic Watermarking
### Chaotic Logistic Map Watermark Generator  
The chaotic system has characteristics of ergodicity, which is also sensitive to initials. The chaotic logistic map was chosen to generate the chaotic sequence in order to substitute the classical pseudorandom sequence. The chaotic sequence (key) was generated by a typical chaotic logistic map. The logistic difference equation is simply formulated as (1).  
\[x_{n+1}=\mu x_{n}(1-x_{n}) \tag{1}\]  
where \(n\) is the index; \(x_{n}\) is between 0 and 1; 0 \(<\mu\leq 4\) is the driving parameter that could exhibit bifurcation behaviors and lead to oscillation between two values when \(\mu\) = 3.0. A chaotic Boolean vector is generated when a number of the logistic map iterations are completed. It serves as the private key or chaotic watermark.