esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Vibration Reduction Performance of Gear Pairs Made by a High-Strength Fe-Mn Damping Alloy
## 2 Preparation of High-Strength Fe-Mn Alloy
### Phase Transformation Point Test of Fe-Mn Alloy  
Differential scanning calorimetry (DSC) was adopted to test the phase transition point of the alloy, and the temperature rise and fall speed was 10 \({}^{\circ}\)C/min. As shown in Figure 1, there is an endothermic peak in the heating process of Fe-Mn alloy; the start point and the endpoint are the austenite transformation start temperature (\(A_{s}\)) and the austenite transformation end temperature (\(A_{p}\)), respectively. There is also an endothermic peak during the cooling process from high to low temperatures. Its start point and endpoint are the martensite transformation start temperature (\(M_{s}\)) and martensite transformation end temperature (\(M_{f}\)). Since the transformation of \(\gamma\)\(\rightarrow\)\(\varepsilon\) gradually proceeded with the decrease in temperature after heat treatment, the DSC curves of some samples did not obtain a clear \(M_{f}\) temperature.  
Due to the segregation of C atoms in the stacking fault zone, the damping performance of the Fe-Mn-based damping alloy could be greatly reduced after long-term aging. [PERSON] et al. [29] proposed adding strong carbide elements, such as Nb and Co, to pin the diffusion of C atoms to inhibit the deterioration of damping properties of the Fe-Mn alloy during aging. The damping performances of the Fe-22 Mn-12 Cr-4 Co-0.02C and Fe-22 Mn-12 Cr-4 Co alloys were studied experimentally.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Alloy**} & \multicolumn{6}{c}{**Chemical Compound (wt.\%)**} \\ \cline{2-7}  & **Mn** & **Cr** & **Co** & **Nb** & **C** \\ \hline FeMnCrCo & 22 & 12 & 4 & 0 & 0.02 \\ FeMnCrCoNb & 22 & 12 & 4 & 0.2 & 0.02 \\ \hline \end{tabular}
\end{table}
Table 1: The chemical compound of the alloy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multicolumn{2}{c}{\(\gamma\)**Austenite (f.c.c.)**} & \multicolumn{6}{c}{\(\varepsilon\)**Martensite (h.c.p.)**} & \multicolumn{6}{c}{\(\alpha^{\prime}\)**Martensite (b.c.c.)**} \\ \multicolumn{2}{c}{**a = 3.60 Å**} & \multicolumn{6}{c}{**a = 2.54 Å, c = 4.14 Å**} & \multicolumn{6}{c}{**a = 2.866 Å**} \\ \hline
**hkl** & **d (Å)** & **20 (\({}^{\circ}\))** & **hkl** & **d (Å)** & **20 (\({}^{\circ}\))** & **hkl** & **d (Å)** & **20 (\({}^{\circ}\))** \\ \hline
111 & 2.08 & 50.85 & 100 & 2.195 & 48.14 & 110 & 2.027 & 52.19 \\
200 & 1.8 & 58.72 & 002 & 2.071 & 51.07 & 200 & 1.433 & 72.15 \\
220 & 1.27 & 78.53 & 101 & 1.94 & 54.54 & 211
## 3 Discussion
In the EXAFS (Extended X-ray Absorption Fine Structure) region, data were collected up to k = 12 A\({}^{-1}\). The operando XAS spectra were first globally analysed by a statistical tool named Principal Component Analysis (PCA), employing the Singular Value Decomposition (SVD) algorithm with the computer program Matlab. The number of principal components determined in this way was used as the basis for Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis, which allows the stepwise reconstruction of the spectral components which are necessary for interpreting the whole multiset of operando spectra. The whole procedure is described in detail in ref. [19]. The MCR-ALS analysis was performed with the following constraints: (i) 100 non-negativity of the intensity and of the concentration of the components, (ii) no unimodality and (iii) closure (sum of the components always equal to 100 % of the intensity). The reconstructed pure spectral components were extracted and fitted using the IEEFFIT software package [27]. Fourier transform of EXAFS oscillations with different \(k\) weights was carried out in \(k\)-range from 2.5 to 11.0 A\({}^{-1}\). Fitting was performed in R-range from 1.4 to 3.65 A using \(k^{2}\) and \(k^{3}\) weights. EXAFS amplitudes and phase-shifts were calculated by FEFF starting from the calculated lattice parameters of TiO\({}_{2}\). Interatomic distances (R) and the Debye-Waller factors (\(\sigma^{2}\)) were calculated for all paths included in the fits. Conceptualization, N.L. and L.S. ; methodology, A.Z. and L.S. ; validation, B.B., N.L. and L.S. ; formal analysis, M.F. and L.S. ; investigation, A.Z., A.H. and L.S. ; resources, B.B., N.L. and L.S. ; data curation, M.F. and L.S. ; writing--original draft preparation, M.F. ; writing--review and editing, all authors; visualization, all authors; supervision, L.S. ; project administration, B.B. and N.L. ; funding acquisition, B.B., N.L. and L.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Synchrotron SOLEIL is acknowledged for providing beamtime at beamline SAMBA (proposal number 20151085). Spanish Ministerio de Ciencia e Innovacion is acknowledged for its support through the project ION-SELF (ref. PID2019-106519 RB-I00). [PERSON] (RS2E) is gratefully acknowledged for technical and logistic help in the preparation of the measurement session at the synchrotron. Last but not least, the authors are in debt to [PERSON] (ICGM) for continuously providing valuable professional input and fruitful discussion throughout the length of this study. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} EOC & End of Charge \\ EOD & End of Discharge \\ EXAFS & Extended X-ray Absorption Fine Structure \\ MCR-ALS & Multivariate Curve Resolution - Alternating Least Squares \\ PCA & Principal Component Analysis \\ PDF & Pair Distribution Function \\ XANES & X-ray Absorption Near-Edge Structure \\ XAS & X-ray Absorption Spectroscopy \\ XPS & X-ray Photoelectron Spectroscopy \\ XRD & X-ray Diffraction \\ \end{tabular}
# Key Concepts, Weakness and Benchmark on Hash Table Data Structures
## 1 Introduction
### Concepts and Classification of Hash Tables
Note that assuming this design implies that the hash function, \(h\), is _external_ while the reduction function, \(h_{r}\), is _internal_ to the data structure and should be considered as part of the algorithm. This is the design in all the programming languages in our study (and it is also present in others). The alternative solutions for solving qualified collisions induce the classification of hash table data structures. Most implementations follow one of these two alternative strategies [9]:  
* **Open Addressing. ** When a _qualified collision_ occurs another place is searched following a probing strategy, for instance: linear probing, double hashing, etc. The important feature is that at most one pair \(\langle k,v\rangle\) is stored in the array (in the _bucket_). * **Chaining**. When a qualified collision occurs, the pair is stored anyway in that array slot. That means that _buckets_ do not store pairs \(\langle k,v\rangle\) directly, but some other data structure capable of storing several pairs, usually linked lists, but also other data structures such as binary trees. Even if most implementations could be classified either as open or chaining, some of them have their own particularities, for instance, cuckoo hashing follows an open address strategy, but it uses two hash tables instead of one as in the other alternatives [10].
# Effect of Thermo-Mechanical Processing on the Corrosion Behavior of Fe-30 Mn-5 Al-0.5C TWIP Steel
###### Abstract  
Electrochemical corrosion of thermo-mechanically processed (TMP) and recrystallized Fe-30 Mn-5 Al-0.5C twinning-induced plasticity (TWIP) steels containing 30 wt.% Mn was studied in a 1.0 wt.% NaCl electrolyte solution. The alkaline nature of the corrosion products containing manganese oxide (MnO) increases the dissolution kinetics of the TWIP steel in acid media, obtaining Mn\({}^{2+}\) ions in solution, and producing the hydrogen evolution reaction (HER). X-ray photoelectron spectroscopy (XPS) surface analysis revealed an increased Al\({}_{2}\)O\({}_{3}\) content of 91% in the passive layer of the recrystallized TWIP steel specimen, while in contrast only a 43% Al\({}_{2}\)O\({}_{3}\) was found on the TMP specimen. Additionally, the chemical composition of the surface oxide layer as well as the TWIP alloy microstructure was analyzed by optical microscopy (OM) and scanning electron microscopy (SEM). The results indicate an enhanced corrosion attack for the TMP high-Mn TWIP steel.  
thermo-mechanical processing; dislocation glide; TWIP steel; corrosion; cyclic potentiodynamic polarization; XPS +
Footnote †: journal: Applied
## 2 Materials and Methods  
### PID Control  
Over the years, several PID controller structures and tuning rules have been proposed. Three such structures that are commonly applied in the industry are given in Equations (1)-(3):  
\[PID_{1}=K_{c}\bigg{(}1+\frac{1}{\tau_{I}s}\bigg{)}\bigg{(}\frac{\tau_{D}s+1}{ \pi\tau_{D}s+1}\bigg{)} \tag{1}\]  
\[PID_{2}=K_{c}\bigg{(}1+\frac{1}{\tau_{I}s}+\frac{\tau_{D}s}{\pi\tau_{D}s+1} \bigg{)} \tag{2}\]  
\[PID_{3}=K_{c}\bigg{(}1+\frac{1}{\tau_{I}s}+\frac{\tau_{D}s}{\pi\tau_{D}s+1} \bigg{)}\bigg{(}\frac{1}{\tau_{I}s+1}\bigg{)} \tag{3}\]  
where \(K_{c}\) represents the proportional gain; \(\tau_{I}\) is the integral time; \(\tau_{D}\) is the derivative time; and \(\tau_{f}\) is the PID filter time constant. The variable alpha is a constant with a reasonable value of \(\alpha=0.1\). In this work, we adopt the process model defined by Equation (4):  
\[G(s)=\frac{Ke^{-\theta s}}{\tau^{2}s^{2}+2\xi\tau s+1} \tag{4}\]  
where \(K\) is the process gain; \(\tau\) is the time constant; \(\xi\) is the damping ratio; and \(\theta\) represents the dead time. For overdamped systems, let \(-\tau_{P1}\) and \(-\tau_{P2}\) represent the system poles such that \(\tau_{P1}>\tau_{P2}\). Some examples of well-known second-order systems include armature-controlled DC motors, Servo Systems, Automotive Suspension Systems, pneumatic pressure Systems, Liquid level models, thermal wells and other heating systems, and hydraulic systems.  
Details of the three PID controllers adopted in this study are given in [7]. However, we present a summary of the three PID controllers for completeness.
# Global IoT Mobility: A Path Based Forwarding Approach
## 4 Mobility over Path Forwarding Architectures
#### Macro Mobility Support
Upon receiving NAP A and B's publication and subscription messages, the Rendezvous updates its mobility binding with MN A and B's EUI48 and corresponding IP address that are extracted from the subscription messages. It also publishes scope /M/EUI48-B/IP-B 1 on behalf of MN A, and scope /M/EUI48-A/IP-A 1 on behalf of MN B, and then matches the corresponding publications and subscriptions for those scopes. The Rendezvous then triggers the Topology Manager (TM) to create new FID's both ways from MN A to MN B and vise versa. The TM creates and forwards the new FID's to the MNs, which will then be able to stream data packets between each other. Figure 6: Mobility namespace. Figure 8 shows the sequence of messages when [PERSON] changes its point of attachment to a new network domain (domain 2). When [PERSON] de-attaches from NAP A, NAP A then un-publishes scope (/M/IP-B 1) with implicit un-subscription from (/M/EUI48-A/IP-A 1) on behalf of [PERSON]. At this point, the RV keeps the entry in its binding list that has IP-A 1 as the last known IP address for [PERSON]. Supposing [PERSON] then re-attaches to NAP C in domain 2, NAP C publishes Scope (/M/IP-B 1) on behalf of MN A with implicit Subscription to (/M/EUI48-A/IP-A 2), which includes its new IP address (IP-A 2) allocated by the new domain. At this point, the RV updates its binding entry for MN A to include IP-A 2 as the latest IP address and also keeps IP-A 1 in the list in case it receives any publication request towards [PERSON]'s previous address. It also instructs the TM to update the FID's of MN A and MN B accordingly. This way, session continuity is maintained between the two mobile nodes even when IP addresses change. It is worth noting that, if an IP address that was previously added to the RV binding list is reassigned to a new MN, then it would be removed from the old MN entry and added as a new IP for the new MN, keeping only one instance of the IP address on the binding list at any time. Figure 7: Sequence diagram of session establishment with global mobility support in IP-over-ICN.
## 3 Materials and Methods
### Radio Access Network (RAN)
The electromagnetic radio simulator provides a graphical representation of the radio coverage in a selected area with real-world physical data. In addition, the simulated radio coverage values can be exported as a comma-separated value (.CSV) file. The file contains the space coordinates and power values (expressed in dBm). This work focuses on the 5G NR specification provided by the local MNO, Chunghwa Telecom, the largest telecommunications company in Taiwan. Using Altair, WinProp conducted the radio coverage analysis in a warehouse environment. By simulating the physical characteristics of the space, it is possible to estimate the maximum range of coverage of the radio signal. This helps to speed up the validation time of 5G use cases.
# Time-Universal Data Compression +
## Appendix A  
Proof of Theorem 3.1.: Let \(\lambda_{i}=\lim_{t\to\infty}|\varphi_{i}(x_{1}...x_{t})|/t\) and \(\varphi_{min}\) be such a data compressor that \(\lambda_{min}=\min_{i}\)\(\lambda_{i}\). Having taken into account that the set of data compressors \(F\) is finite, we can see that for any \(\epsilon>0\) there exists such \(t_{1}\) that for all \(\varphi_{i}\in F\) and \(t>t_{1}\)  
\[(\mid\varphi_{i}(x_{1}...x_{t})\mid-\log\omega_{i})/t-\lambda_{i}\mid<\epsilon. \tag{13}\]  
From (ii) we obtain that there exists such \(t_{2}\) that \(\tau_{i}(t_{2})>t_{1}\) for all \(i=1,...,m\). Let \(n\geq t_{2}\) and \(\hat{\Phi}\) be applied to \(x_{1}x_{2}\ldots x_{n}\). Suppose that a data-compressor \(\varphi_{s}\) was chosen, when \(\hat{\Phi}\) was applied. Hence,  
\[(-\log\omega_{s}+|\varphi_{s}(x_{1}...x_{\tau_{s}(n)}|)/\tau_{s}(n)\leq(-\log \omega_{min}+|\varphi_{min}(x_{1}...x_{\tau_{min}(n)}|)/\tau_{min}(n). \tag{14}\]  
From (13) we can see that  
\[(-\log\omega_{s}+|\varphi_{s}(x_{1}...x_{\tau_{s}(n)}|)/\tau_{s}(n)\geq \lambda_{s}-\epsilon \tag{15}\]  
and  
\[(-\log\omega_{min}+|\varphi_{min}(x_{1}...x_{\tau_{min}(n)}|)/\tau_{min}(n) \leq\lambda_{min}+\epsilon. \tag{16}\]From the inequalities (A2)-(A4) we obtain \(\lambda_{s}\leq\lambda_{min}+2\epsilon\). Taking into account, that, by definition, \(\lambda_{min}\leq\lambda_{s}\) we get  
\[\lambda_{min}\leq\lambda_{s}\leq\lambda_{min}+2\epsilon.\] (A5)  
Let us estimate \(\hat{\Phi}(x_{1}...x_{n})/n\). When \(\hat{\Phi}(x_{1}...x_{n})\) was applied, the data compressor \(\varphi_{s}\) was chosen. Hence, from (A1) we get  
\[\lambda_{s}-\epsilon\leq\hat{\Phi}(x_{1}...x_{n})/n\leq\lambda_{s}+\epsilon.\]  
From those inequalities and (A5) we can see that  
\[\lambda_{min}-\epsilon\leq\hat{\Phi}(x_{1}...x_{n})/n\leq\lambda_{min}+3\epsilon.\]  
It is true for any \(\epsilon>0\), hence, \(\lim_{n\to\infty}\hat{\Phi}(x_{1}...x_{n})/n=\lambda_{min}\). The theorem is proven.  
Proof of Theorem 2.: It is known in Information Theory [22] that \(h_{r}(\mu)\geq h_{r+1}(\mu)\geq h_{\infty}(\mu)\) for any \(r\) and (by definition) \(\lim_{r\to\infty}h_{r}(\mu)=h_{\infty}(\mu)\). Let \(\epsilon>0\) and \(r\) be such an integer that \(h_{r}-h_{\infty}<\epsilon\). From (11) we can see that there exists such \(t_{1}\) that \(m(t)\geq r\) if \(t\geq t_{1}\). Taking into account (8) and (11), we can see that there exists \(t_{2}\) for which a.s. \(||\psi_{r}(x_{1}...x_{t})|/t-h_{r}(\mu)|<\epsilon\) if \(t>t_{2}\). From the description of \(\Psi^{\delta}\) (the step 3) we can see that there exists such \(t_{3}>\max\{t_{1},t_{2}\}\) for which a.s.  
\[||\psi_{r}(x_{1}...x_{t})|/t-h_{\infty}(\mu)|\leq||\psi_{r}(x_{1}...x_{t})|/t -h_{r}(\mu)|\]  
\[+(h_{r}(\mu)-h_{\infty}(\mu))<2\epsilon\,,\]  
if \(t>t_{3}\). By definition,  
\[|\Psi^{\delta}(x_{1}...x_{t})|/t\leq(|\psi_{r}(x_{1}...x_{t})|-\log\omega_{r+1 })/t.\]  
Having taken into account that \(\epsilon\) is an arbitrary number and two latest inequalities as well as the fact that a.s. \(\inf_{i=0,1,...}\lim_{t\to\infty}|\psi_{r}(x_{1}...x_{t})|/t=h_{\infty}(\mu)\), we obtain (12). The theorem is proven.
## 3 Building Load Type
### Classify Based on Time Span
#### 3.1.2 Short Term Load Forecasting  
The short-term prediction of building energy consumption is closely linked to the daily operations of various service systems [46; 47], playing a crucial role in reducing the uncertainty associated with future building operations [48]. This not only assists building managers in developing more cost-effective energy-saving strategies but also enables the optimization of building energy management and the enhancement of environmental sustainability [49]. Additionally, short-term building energy consumption prediction plays a pivotal role in driving the development of model-based predictive control, fault detection, and diagnostic methods. Currently, the prediction of short-term heating loads [50; 51], cooling loads [52; 53; 54], and power loads [55; 56; 57] is a particularly hot topic of research. As illustrated in Figure 3, a stacking ensemble framework for short-term load forecasting is proposed [58]. The framework encompasses feature engineering, object determination, forecasting, and an evolution stage. It utilizes a stacking strategy to predict short-term dynamic changes in building loads.  
Data-driven modeling is a commonly used approach in short-term load prediction, which adjusts and optimizes model parameters based on actual building operational data to improve prediction accuracy [59]. With the continuous advancement of deep learning technology, it has provided a more powerful tool for short-term building load prediction. Leveraging this technology, we can further enhance prediction precision, ensuring the reliability and economic efficiency of building energy management systems [60].
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 3 Ship Ad Hoc Network Construction
### Networking and Network Management
#### 3.3.3 Network Mode Switching  
The condition for us to initialize and select the network mode is the water area to navigate. As mentioned above, the ship has chosen a network mode according to the navigation waters at that time when establishing the network. However, the number of other ships in both navigation waters and waters is dynamic, so it is necessary to design two switches to switch the network mode.  
Switch from Intercommunication Mode to Cluster Mode  
When navigating from open waters to heavy traffic waters or special waters, you can selectively switch modes. However, if the number of nodes in the network increases sharply or continuously, we need to switch to cluster mode.  
During the switching process, the ship nodes with certain transmission and computing capabilities are first sorted to select the cluster head. After the cluster head node is determined, initialization information is broadcasted. Surrounding ship nodes send a request to enter the network after receiving the initialization message. After receiving the network entry message, the cluster head node determines the network entry conditions, replies with a confirmation message to join, and numbers them in the group. This completes the model switching.  
Switch from Cluster Mode to Intercommunication Mode  
Ships sailing out of heavy traffic waters and special waters and into open waters are often accompanied by a reduction in the number of ships around. At this time, we choose to switch to the intercommunication mode to realize the direct full connection of all nodes in the network.  
During the switching process, the cluster head node first sends a cluster exit message to all nodes within the cluster. After receiving the cluster exit message, the ship node exits the cluster communication mode. All ship nodes publish network initialization information within their perception range, and establish node communication tables for all nodes within their perception range, completing the construction of intercommunication mode.
# Dynamic Depth Learning in Stacked AutoEncoders
## 4 Dynamic Depth for Stacked AutoEncoders
### Layer Relevance Weight Learning Component  
Learning the relevance of each layer, \(l\), at the end of each trained batch, \(b\), \(RL(l,\ b)\) is done through the optimization of a new cost function \(J\) independent from the loss function of the AE. As such, \(J\) measures the conservation of the inner product between the input, \(x(i)\), and its mapping of layer \(l\) at the end of \(b\), \(x^{\prime}(i,l,b)\). It is defined as in (2).  
\[J=\sum_{l=1}^{NL(b)}RL(l,b)^{q}\sum_{b}^{NB(b)}\sum_{i\in b}\sum_{j\in b}\bigl{(} x(i).x(j)-x^{\prime}(l,i,b).x^{\prime}(l,j,b)\bigr{)}^{2} \tag{2}\]  
subject to:  
\[\sum_{l=1}^{NL(b)}RL(l,b)=1 \tag{3}\]  
In Equations (2) and (3), \(NL(b)\) and \(NB(b)\) are the number of layers and the number of batches trained so far at the end of batch, \(b\), respectively. In addition, \(x(i).x(j)\) is the inner product between the two inputs \(x_{i}\) and \(x_{j}\), and \(x^{\prime}(l,i,b).x^{\prime}(l,j,b)\) is the inner product between the mapped vectors of layer \(l\) at the end of \(b\). Moreover, \(q\) is an exponent that controls the fuzziness of the relevance weights \(RL(l,\ b)\), which is usually set to 2. The distance \(\bigl{(}x(i).x(j)-x^{\prime}(l,i,b).x^{\prime}(l,j,b)\bigr{)}^{2}\) measures the difference in terms of pairwise distance and angle between the input and mapped vectors. Minimizing the cost function \(J\) with respect to \(RL(l,b)\) aims at learning the optimum relevance layer weights that allow preserving the moment and the argument of the input instances. Nevertheless, these weights are different from the AE model weights. In fact, they are learned from a cost function different from the loss function of the network and intend to segregate irrelevant layers.  
In order to find the optimum relevance layer weights \(RL(l,b)\) that minimize the cost function in Equation (2), we derive Equation (2) with respect to \(RL(l,b)\) subject to  
Figure 3: DDSAE training process illustration.  
Equation (3). Then, we set the obtained derivative to zero. This gives the update equation of \(RL(l,b)\) as expressed in Equation (4).  
\[RL(l,b)=\frac{\frac{1}{Q(l,b)}}{\sum_{p=1}^{NL(b)}\frac{1}{Q(l,\pi)}\frac{1}{q^{- 1}}} \tag{4}\]  
where  
\[Q(l,b)=\sum_{b}^{NB(b)}\sum_{i\in b}\sum_{j\in b}\left(x(i).x(j)-x^{\prime}(l,i,b).x^{\prime}(l,j,b)\right)^{2} \tag{5}\]  
These relevance layer weights give an interpretation of the contribution of each layer. In other words, they give insight on the importance of each layer. They are not necessarily increasing from left to right. In this regard, they will be used to prune irrelevant layers. Algorithm 1 portrays the steps of learning the relevance weights for each layer using the DDSAE approach.