esa-sceva/satcom-chunk-collection huggingface dataset:
# From Classic to Cutting-Edge: A Near-Perfect Global Thresholding Approach with Machine Learning
## 2 Materials and Methods
### Tools  
ML.NET [30] is a framework that allows users to train and evaluate a plethora of machine-learning models. Its AutoML component allows users to automatically explore various hyperparameter configurations for said models using one of the several search strategies, such as random search, grid search, and cost frugal tuner (CFT) [31]. There are several model types available in AutoML for regression, but LightGbm [32] always scored best in our tests. LightGbm is a gradient boosting decision tree that uses gradient-based one-sided sampling to exclude a significant proportion of data instances with small gradients, using only the rest to estimate the information gain, and exclusive feature bundling to bundle mutually exclusive features to reduce the number of features.  
MLNET does not allow for custom metrics. The metrics available for regression are the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R-squared (R2), also known as the coefficient of determination [33]. For the first three metrics, values range from 0 to infinity, with 0 indicating a perfect fit. For R2 values range from negative infinity to 1, with 1 indicating a perfect fit. Because MSE and RMSE square the errors, they are more sensitive to larger errors than MAE.  
Training a model on a dataset might cause overfitting, i.e., maximizing the performance of the model for the training dataset at the cost of lower performance on new datasets. Resampling the dataset can alleviate or even eliminate overfitting. According to [PERSON] [34], 10-fold cross-validation is one of the most widely used data resampling methods. k-fold cross-validation splits a dataset into k equally sized disjunct partitions and forms k training sets and k test sets. Each test set consists of one of the k partitions, and its corresponding training set consists of the other k-1 partitions. For each training set, the model is fitted on it and evaluated on its corresponding test set, resulting in k performance metrics, one for each test set, which can be averaged to obtain the performance metric of the model.  
[PERSON] et al. [35] introduced nested cross-validation and proved that the method is more robust than cross-validation in many examples. The idea behind nested cross-validation is splitting the dataset into train/test sets twice, fitting the model on the inner train sets and evaluating them on the inner test sets, fitting them on the outer train sets and then using all the obtained metric on all the test sets to compute a robust approximand for the metric consisting of an average value and an MSE.
## 4 Materials and Methods
### Nb Purification  
For batch-mode Nb purification, BL21(DE3) cells producing the 1.29, C11, and K9 Nbs were pelleted and resuspended in binding buffer (20 mM sodium phosphate, 500 mM NaCl, 10 mM imidazole, 0.5% \(v/v\) Triton-X100, pH 8), sonicated on ice using Sonifier(r) SFX150 cell disruptors and homogenizers (Branson) 3 times at 30 s intervals with 45% amplitude, 50% duty cycle. The samples were then centrifuged and loaded onto Econo-column(r) chromatography columns (Bio-Rad) packed with Ni-NTA beads (Macherey-Nagel, Duren, Germany) that were equilibrated by washing 3 times with 10 mL of His-flow buffer prior to sample loading. Binding was allowed to continue at 4 \({}^{\circ}\)C for 1 h; subsequently, the lysate was allowed to flow through via gravity. The column was washed 3 times with 20 mL washing buffer (His-flow buffer with 20 mM imidazole). Purified Nbs were eluted using elution buffer (His-flow buffer with 120 mM imidazole). After purification, the elutions were pooled, buffer-exchanged with PBS, partially purified to remove high molecular weight impurities, and concentrated using 30 kDa and 3 kDa MWCO columns (Amicon Ultra-0.5 centrifugal filter unit, Merck Millipore, Burlington, MA, USA). The concentration of each concentrated and purified Nb was measured using a Bradford assay (Quick Start Bradford, Bio-Rad). To assess protein purity, 5 \(\upmu\)L of elutions and 2 \(\upmu\)L of concentrates of each sample were mixed with 6\(\times\) loading dye containing a reducing agent (\(\beta\)-mercaptoethanol) and incubated at 100 \({}^{\circ}\)C for 10 min to denature the proteins. The protein samples were loaded onto 10% polyacrylamide gels (TGX FastCast Acrylamide Solutions, Bio-Rad) and subjected to gel electrophoresis followed by staining with InstantBlue(r) Coomassieie Protein Stain (Abcam, Cambridge, UK) for 10 min. Gel imaging was conducted using the Gel Doc EZ Gel Documentation System (Bio-Rad).
## 3 Results
### Results on the Case Study with Real Data
Table 6 also reports the discrimination performance of the developed models. All the models achieved similar C-index values, both on the 5-fold cross-validation and on the test set. The C-index values obtained are in line with those of literature models using similar feature sets [3]. Interestingly, in this case study, some variables related to the psychologic and economic wellbeing, not present in most of existing T2D models, were selected by the models. These variables are: the CESD depression scale, the perceived health status, and the self-reported level of economic deprivation.
# An Overview of the Historical Retrofitting Interventions on Churches in Central Italy
## 4 Assessment of the Seismic Interventions
### Central Italy Seismic Sequence  
The area mostly affected by the 2016-2017 earthquakes involves the territories of Abruzzo, Lazio, Umbria and Marche. Reliable earthquake characterization is important  
Figure 22: Usability of the sample of over 600 churches in Central Italy after the 2016 earthquake according to the A-DC form (source: Da.Do database). The colored areas identify the distances of the churches from the epicenters of 24 August and 30 October 2016, as shown in the legend.  
to better understand the seismic effects on the territory [90]. Therefore, a summary of the most important features of the main shocks of the Central Italy seismic sequence is given below (data retrieved from INGV, [[http://terremoti.ingv.it](http://terremoti.ingv.it)]([http://terremoti.ingv.it](http://terremoti.ingv.it)), accessed on 26 November 2022):  
- 1 st shock--24 August 2016: epicenter in Amatrice, Mw = 6.0;  
- 2 nd shock--26 October 2016: epicenter in Castelsantangelo sul Nera, Mw = 5.4  
- 3 rd shock--26 October 2016: epicenter in Vismso, Mw = 5.9  
- 4 th shock--30 October 2016: epicenter in Norcia, Mw = 6.5  
- 5 th shock on 18 January 2017: epicenter in Capitigiliano, Mw = 5.5  
One month after the first shock in August, numerous groups from different Italian universities, through an agreement of DPC, MiBACT and ReLUIS, carried out an assessment of the usability and damage of churches through the A-DC form. In this work, the maximum intensity measure registered during the whole seismic sequence before the inspection date of the specific church was considered. Because most of the surveys used for the database by the authors were conducted after the first and fourth shocks, these were chosen as the reference earthquakes for the statistical analyses, presented next. In this regard, Figure 23 shows the peak ground shaking provided by the Da.Do web gis platform, which merges the new implementation of ShakeMap at INGV [91] and the damage data collection [92].  
The effects of the 2016 earthquakes on the investigated sample of more than 600 churches were carefully analyzed on the basis of the forms completed during onsite inspections and the photographic documentation acquired during the aforementioned reconnaissance activities, allowing a critical assessment of the strengthening and repairing interventions executed in the years preceding the event, whenever these were visible. The outcome of this evaluation process is detailed in the following, in relation to the major interventions observed in the sample of churches: ties rods and ring beams. In fact, among the types of interventions discussed in Section 3, these were the most prevalent, thereby allowing a reliable statistical evaluation of their effectiveness.
## 3 Scenario and System Model
### Channel Model  
We assume a massive multiple-input multiple-output (MIMO) system for presenting CIR, where each BS is equipped with \(N_{t}\) transmission antennas (Tx), and each UE has \(N_{r}\) receiving antennas (Rx). In a different environment, the mobile radio propagation link between BS and UE could be LOS or NLOS. To establish CIR, we use 3D channel models to  
Figure 1: Schematic diagram of indoor positioning scenario.  
express the channel coefficients between each Tx and Rx according to 3 GPP TR 38.901 [47]. For each link, let there be \(Z\) clusters, each with \(M\) rays. We use \(m\in\{1,2,\ldots,M\}\) to denote the index of a specific ray. For the NLOS case, when cluster \(\zeta\) belongs to the \(Z-2\) weakest clusters represented as \(\zeta\in\{3,4,\ldots,Z\}\), the power of different rays in each cluster is equal. The channel coefficient of ray \(m\) in cluster \(\zeta\) for the receiver and transmitter antenna element pair \(u\), \(s\) can be expressed as  
\[\begin{split} H_{u,s,\zeta,m}^{NLOS}(t)=\sqrt{\frac{P_{\zeta}}{ M}}\begin{bmatrix}F_{rx,u,\theta}\\ F_{rx,u,\phi}\end{bmatrix}^{T}\begin{bmatrix}e^{i\phi_{\zeta,m}^{\phi}}& \sqrt{\kappa_{\zeta,m}^{-1}}e^{i\phi_{\zeta,m}^{\phi_{\zeta,m}^{\phi_{\zeta,m} }}}\\ \sqrt{\kappa_{\zeta,m}^{-1}}e^{i\phi_{\zeta,m}^{\phi_{\zeta,m}^{\phi,m}}}&e^{i \phi_{\zeta,m}^{\phi,m}}\end{bmatrix}\\ \begin{bmatrix}F_{rx,s,\theta}\\ F_{rx,s,\phi}\end{bmatrix}\text{exp}(\frac{j2\pi(\hat{r}_{rx,\zeta,m}^{T} \bar{d}_{rx,u}+\hat{r}_{rx,\zeta,m}^{T}\bar{d}_{rx,s}+\hat{r}_{rx,\zeta,m}^{T} \bar{u}t)}{\lambda_{0}}),\end{split} \tag{1}\]  
where \(P_{\zeta}\) stands for the power of the cluster, \(F_{rx,u,\theta}\), and \(F_{rx,u,\phi}\) is the field pattern of \(u\) in the direction of the spherical basis vectors, \(\theta\) and \(\phi\), respectively. \(F_{rx,s,\theta}\) and \(F_{rx,s,\phi}\) are the field patterns of \(s\) in the direction of \(\theta\) and \(\phi\), respectively. \(\hat{r}_{rx,\zeta,m}^{T}\) is the spherical unit vector with an azimuth arrival angle and elevation arrival angle, and \(\hat{r}_{rx,\zeta,m}^{T}\) is the spherical unit vector with an azimuth departure angle and elevation departure angle. \((\bar{d}_{rx,u},\bar{d}_{rx,s})\) is the location vector of \(u\) and \(s\), \(\kappa_{\zeta,m}\) is the cross-polarization power ratio in a linear scale, and \(\lambda_{0}\) is the wavelength of the carrier frequency. \(\Phi\) represents the random initial phase for different polarization combinations, and \(\bar{\sigma}\) is the velocity vector of the UE.  
For the LOS case, the channel coefficient is given by:  
\[\begin{split} H_{u,s,1}^{Lo\zeta}(t)=\begin{bmatrix}F_{rx,u, \theta}\\ F_{rx,u,\phi}\end{bmatrix}^{T}\begin{bmatrix}1&0\\ 0&-1\end{bmatrix}\begin{bmatrix}F_{rx,s,\theta}\\ F_{rx,s,\phi}\end{bmatrix}\text{exp}(-j2\pi\frac{d_{3D}}{\lambda_{0}})\\ \text{exp}(\frac{-j2\pi(\hat{r}_{rx,LoS}^{T}\bar{d}_{rx,u}+\hat{r}_{rx,LoS}^{T} \bar{d}_{tx,s}+\hat{r}_{rx,LoS}^{T}\bar{u}t)}{\lambda_{0}}),\end{split} \tag{2}\]  
where \(d_{3D}\) represents the 3D distance between Tx and Rx.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 4 Performance Evaluation
### Experimental Results
#### 4.4.6 The Impact of Router Failures and Traffic Variations  
Figure 8 illustrates the impact of router failures on the performance of our approach, RECIF+PIF [41] and EEGPR [31]. This simulation adopts PoP-level SPRINT topology [7; 33]. To simulate the failures of routers, several routers are randomly selected to crash after running half of the simulations. Furthermore, the centrality threshold and retransmission threshold of RECIF+PIF has been set to 9 and 5, respectively. All results are the average of five simulations. As demonstrated in Figure 8a,b, as the number of router failures increases, so does network delay and packet loss. In particular, the delays of RECIF+PIF and EEGPR are 5.4 ms and 9.9 ms longer than that of the _PIT probe_, respectively. EEGPR only improves the routing table for data packets while RECIF+PIF focuses on decreasing the number of interests instead of improving the routing table to speed up routing.  
Figure 9 illustrates the impact of traffic variations of our approach, RECIF+PIF and EEGPR. The simulating parameters follow the settings of the router failure experiment. The traffic variation was simulated by increasing the frequency of interest packets sent by consumers. The frequency was set from 1 to 30 interests per second. The bandwidth was set to 1024 bits/s. As shown in Figure 9, traffic variations are inversely proportional to delay and packet loss for all the approaches. In particular, comparing the delays of 1 and 30 router failures, the increments of other approaches are 1.07 ms and 3.95 ms longer than the _PIT probe_, respectively. That is because, with the increase of interest packet frequency, more probes are used to update FIB so the accuracy and expiration of FIB are improved.  
Figure 8: Impact of router failures. (**a**) Delay; (**b**) packet loss.  
To summarize, the results demonstrate that our probe-based approach performs better than RECIF+PIF and EEGPR. Owing to speedup FIB updating by probes, our approach provides more reliable interest packet routing when the router failures.
## 2 Experiments
### Underwater Laser Cutting  
To verify the effect of the aforementioned cutting-process parameters, actual laser-cutting experiments were performed underwater, as shown in Figure 4. Liquid nitrogen was selected as an assist gas and an evaporator was used in order to maintain a high gas pressure. The IPC 20 kW fiber laser (YLS-20000) was used for the heat source of the thermal cutting. The shape of the cut specimen is shown in Figure 5. Stainless steel 304, with a dimension of 50 \(\times\) 50 \(\times\) 100 mm\({}^{3}\), was used as a specimen. The SOD was set at 1 mm. Pre-flow gas was set to 1 bar in order to prevent the water flowing back into the nozzle while the laser-cutting head moved into the water pool (z-direction). The nozzle was positioned at the top edge of the specimen, the assist gas pressure was set, and the laser emission was initiated for cutting. The moving stage made the laser-cutting head move in the cutting direction (x-direction). To make an initial cut surface from the edge to 15 mm, the feed rate was set to 5 mm/min and, afterward, it was changed to a 30 mm/min feed rate.  
Three types of nozzles were used: conical, conical-flat, and supersonic. To adjust the top cut kerf width, the focus position of the laser was selected as a process parameter. The relationship between focus position and cut kerf width is plotted in Figure 6. The top kerf width can be formed if the focus position of the laser is located on the top surface of the specimen. As it became located deeper into the depth direction (defocus) of the specimen, the top kerf became wider.  
Figure 4: Laser-cutting experiment setup.  
Figure 5: Dimension and shape of cut specimen.
# Research on the Mechanism and Dynamic Characteristics of Intermitt Failure of Electrical Connectors
## 2 Analysis of the Electrical Connector Intermittent Failure Mechanism
### Electrical Contact Model Considering the Plowing Effect  
In this paper, the \"\(a\)-spot\" is assumed to be a contact area that is formed by extrusion of elastic asperity and a rigid plate. Initially, because of the interior or external stress, the asperity would slide on the rigid plate with a micro-amplitude. The relative motion of contact surface leads to the softer metal surface being plowed. On the one hand, the plowing causes oxidation, wear and debris accumulation; on the other hand, the plowing could destroy the original \"quasi-metallic\" contact film and form a more stable metallic contact.  
The region formed by plowing is called the plowing region. Because the \"quasi-metallic\" film in the plowing region could be destroyed, it can be assumed that the plowing region is the \"entire-metallic\" contact region. For investigating the influence of the oxidized film, it is assumed that the plowing region is circular and only exists in the rigid plate, and the oxidized film covers the rigid plate only.  
The established electrical contact model considering the plowing effect is shown in Figure 3.  
In Figure 3a, \(L_{1}\) denotes the plowing radius at time \(t\), and \(L_{2}\) denotes the plowing radius at \(t+\Delta t\). The gray part represents the increment area of the oxidized film during the time interval (\(t\),\(t+\Delta t\)). The white portion within the circle represents the metal contact surface of the rigid plate, and the yellow circle represents the \"a-spot \". With an increasing amount of oxidized film, the metal area of the rigid plate decreases. When the oxidized film entirely covers the \"\(a\)-spot \", as shown in Figure 3b, the initial \"\(a\)-spot \" is replaced by a \"metal-film\" contact, which manifests as electrical contact failure. The degradation rate could be expressed as \(\d  
Figure 3: Electrical contact model considering the plowing effect.  
Figure 2: The degradation process of contact surface.
## 3 Background on DoS/DDoS Attacks and Defense Mechanisms
#### Taxonomy of DDoS Defense Mechanisms  
In [51], different types of DDoS defense methods proposed in the literature were analyzed in a methodical approach and a total number of twelve distinct mitigation measures against DDoS attacks were identified as follows:  
* Resource accounting: privilege and behavior-based access to resources.
* Resource multiplication: increasing the bandwidth of links, deploying load balancers (a costly method).
* Resource pricing: adding a cost to resources on a computational basis.
* DoS-aware algorithms: implementing operating system (OS)-based basic algorithms that can do periodical scans.
* Traffic flow monitoring: utilizing tools to monitor network traffic to identify traffic characteristics.
* Traffic volume monitoring: sample-and-hold method and multistage filter methods.
* Source IP address monitoring: based on the fact that the source IP address is known for legitimate traffic but unknown for DDoS attack traffic.
* Monitoring other features: content and IP header filtering.
* IP hopping: detecting the change of IP address without a change in the physical location.
* Load balancing: balancing the load on servers in a multiple-server architecture for improved performance.
* TCP-migrate: migrating active TCP connections to establish a secure connection by using a cryptographic cookie and sending a new SYN request.
* Mutable service: relocation of services through a secure Domain Name System (DNS) server.  
Among the above-listed general DDoS mitigation measures, we incorporated the following three in our SIP DRDoS defense mechanism: source IP address monitoring, traffic flow monitoring, and traffic volume monitoring. The main pillar of our proposed defense mechanism is traffic volume monitoring. Traffic volume monitoring is about monitoring sudden growths in the network traffic volume which is essential for mitigating DDoS attacks. We used the sample-and-hold method in our defense mechanism which is described in detail in Section 5.1.
# Exploration of Floral Volatile Organic Compounds in Six Typical _Lycoris_ taxa by GC-MS
## 2 Results  
### Identification of VOCs  
The floral VOCs of the six _Lycoris_ taxa were analyzed by GC-MS, and the representative total ion chromatogram is shown in Figure 1. Thirty-two floral VOCs were identified, including terpenoids, alcohols, esters, aldehydes, ketones, and phenols (Table S1). The aroma profiles and total volatile content of the six taxa varied. For example, among the identified terpenoids, \(\beta\)-ocimene and its trans isomer, _trans_-\(\beta\)-ocimene, were not detected in _L. sprengeri_ and _L. radiata_, but had a high relative content in _L. longituba_, _L. longituba_ var. _flava_, _L. aureus_, and _L. chinensis_. Moreover, cryophyllene was only found in _L. longituba_, \(\alpha\)-farnesene was only found in _L. aureus_, and _trans_-\(\beta\)-farnesene was only detected in _L. sprengeri_ (Table S1). Interestingly, no terpenoid was discovered in _L. radiata_ (Table S1).  
Different floral VOCs were identified in the six _Lycoris_ taxa. _L. longituba_ had a relatively high content of terpenoids and esters, which were the main VOCs. Moreover, the relative content of esters was much larger than that of terpenoids (Figure 2). _L. longituba_ var. _flava_ contained similar floral VOCs as _L. longituba_, mainly consisting of terpenoids, esters, and a small amount of phenols. However, there were also differences in the VOC content between _L. longituba_ var. _flava_ and _L. longituba_. Specifically, the terpenoid and ester content were similar, and a small amount of alcohols and aldehydes were also detected, which made the aroma of _L. longituba_ var. _flava_ more complex in its sensory experience than _L. longituba_ (Figure 2). In comparison, _L. sprengeri_ mainly contained esters and alcohols, and the content of esters accounted for more than 88% of the total VOC content of _L. sprengeri_ (Figure 2). In addition, the floral VOCs in _L. aurea_ had the highest content of terpenoids, followed by aldehydes, as well as a small number of ketones. These compounds have a strong citrus aroma, a distinguishing characteristic that is easy to identify, which mainly defined the aroma of _L. aurea_ (Figure 2). The VOC content in _L. chinensis_ and _L. radiata_ differed greatly from other _Lycoris_ taxa. _L. chinensis_ and _L. radiata_ are essentially non-fragrant, with only a small amount of terpenoids, esters, and aldehydes detected in _L. chinensis_. Similarly, only a small number of aldehydes was detected in _L. radiate_ (Figure 2).  
Figure 1: GC-MS total ion chromatograms of volatile organic compounds in six _Lycoris_ taxa. (**A**) _L. sprengeri_, (**B**) _L. longituba_; (**C**) _L. longituba_ var. _flava_; (**D**) _L. aurea_; (**E**) _L. chinensis_; (**F**) _L. radiate._  
Figure 2: The content of various classes of aromatic compounds in six _Lycoris_ taxa. Aromatic compounds mainly include terpenoids, alcohols, esters, aldehydes, ketones, and phenols.