esa-sceva/satcom-chunk-collection huggingface dataset:
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 3 Overview of BBR
### BBR Algorithm
However, unlike traditional slow-start, BBR's StartUp phase incorporates three consecutive rounds of multiplicative rate increases and transitions to the Drain phase only when bandwidth growth is deemed ineffective, ensuring no premature exit from the probing phase. By this point, the system's operating state has already shifted to the right side of Kleinrock's optimal operating point (indicating persistent queue buildup). Drain phase: In this phase, the sender dynamically adjusts the transmission rate by applying the multiplicative inverse of the startup parameter (set to ln2/2) to systematically drain potential queues created during the StartUp phase. This rate-limiting mechanism persists until the inflight data volume falls below or equals the estimated bandwidth-delay product (1*BDP), at which point the algorithm transitions into the ProbeBw phase for bandwidth utilization balancing. ProbeBw phase: This operational logic constitutes the core of BBR's congestion control mechanism. The algorithm cyclically modulates transmission rates using a deterministic pacing gain sequence [1.25, 0.75, 1, 1, 1, 1, 1, 1, 1] for bandwidth probing. In the upward probing phase (_pacing_gain_ = 1.25), BBR aggressively increases transmission rates to detect unused bandwidth. The subsequent RTT triggers a downward adjustment phase (_pacing_gain_ = 0.75), explicitly designed to drain excess queues accumulated during upward probing. For the next six RTT intervals, _pacing_gain_ is held at 1 to stabilize transmission rates, with the concurrent congestion window gain (_cvnd_gain_) fixed at 2 throughout these phases to maintain sufficient inflight data for reliable network state inference. A critical timeout mechanism governs persistent congestion detection: if the minimum RTT sampling process fails to update the RTprop (propagation delay estimate) within 10 s, BBR infers sustained path congestion and transitions to the ProbeRTT phase, initiating aggressive buffer drainage by reducing the congestion window to a single packet. ProbeRTT phase: In this phase, the congestion window size (CWND) is initialized to 4\"MSS with rigorous pacing control, limiting transmissions to four packets per round within a 200 ms timeout threshold to aggressively drain existing network queues while probing for the minimum RTT. Upon timeout expiration, the algorithm evaluates the current network load metrics (e.g., sustained bandwidth/RTT ratios) to determine whether to restart bandwidth probing via the StartUp phase or transition to steady-state bandwidth balancing in the ProbeBw phase, ensuring operational adaptability under dynamic network conditions.
# Intelligent System for Railway Wheelset Press-Fit Inspection Using Deep Learning
## 3 Intelligent Railway Wheelset Inspection System
### Deep Learning Architecture Used in This Study  
The layout of the used SNN architecture, which is mainly based on the well-known ResNet-50 [40], is shown in Figure 6. It receives a pair of images with an image size of 400 \(\times\) 602 pixels as input in the first layer. Each image is then processed through ResNet-50 network architecture. While referring to the network architecture of ResNet-50, we will extract 2048 feature maps after applying 2048 filters at the last convolutional layer. To obtain the length-2048 difference vector, we first obtain 2048 \(\times\) 1 feature vectors from each twin by applying the global-max pooling operation to the output feature maps from the previous layer, and then calculate Euclidian distance between the twin difference feature vectors. Finally, the neurons in the distance layer are fully connected with one unit and passed to the sigmoid function to measure the degree of similarity between the two input images of press-fit curves.  
Figure 5: The query page of inspection results for the proposed system.  
Figure 6: The architecture of the SNN is used in this study. The two images from the same class are called a positive pair, and those from the different classes are called a negative pair. This network is mainly based on the well-known ResNet-50.  
In general, the loss function may suffer from receiving lots of easily classified samples during the training stage. Whether positive or negative, a sample is called an easy sample when the model distinguishes it as successfully dominated by its high prior probability. This means that the model tends to be dominated by easy samples that contribute little to gradient computing, leading to the model finally being failed by the imbalance [42]. To suppress the impact of the above problem, [PERSON] et al. [43] presented focal loss (FL). FS has proved effective in related research; therefore, this study adopted FS as a loss evaluation function.  
To address the class imbalance, a modulating factor \(\big{(}1-p\big{(}x_{i},x_{j}\big{)}\big{)}^{\gamma}\) was added to the cross-entropy loss function, as defined in Formula (1), with a tunable focusing parameter \(\gamma\geq 0\). Easily classified samples will be down-weighted due to the low values of the modulating factor [42]. The FL can be defined as [43]:  
\[\text{FL}=-\alpha\big{(}1-p\big{(}x_{i},x_{j}\big{)}\big{)}^{\gamma}\big{(}y \big{(}x_{i},x_{j}\big{)}\log\big{(}p\big{(}x_{i},x_{j}\big{)}\big{)}+\big{(}1 -y\big{(}x_{i},x_{j}\big{)}\big{)}\log\big{(}1-p\big{(}x_{i},\ x_{j}\big{)} \big{)}\big{)}, \tag{2}\]  
where \(\alpha\in[0,1]\) is a weighting factor. To achieve the best experimental results, we set the parameters \(\alpha\) and \(\gamma\) to 0.25 and 2, respectively. In addition, we set the learning rate and batch size to 0.00006 and 8, respectively, in the hyperparameter tuning setting during the training stage.
# Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator
## 3 System Control
### Hex-rotor Control
#### 3.1.2 Hoo Controller
** The Lyapunov function of the closed-loop system is defined as follow:  
\[V(x)=x^{T}Px \tag{18}\]  
Thus we have,  
\[ \begin{array}{ll}\dot{V}(x)&=x^{T}((A+BK)^{T}P+P(A+BK))x+\Delta^{T}D^{T}Px+x^ {T}PD\Delta+\delta^{T}(e_{\Phi_{b}})E^{T}Px+x^{T}PE\delta(e_{\Phi_{b}})\\ &=x^{T}((A+BK)^{T}P+P(A+BK))x+\frac{1}{\gamma^{2}}x^{T}PDD^{T}Px+\frac{1}{ \lambda}x^{T}PEE^{T}Px\\ &+x^{T}C^{T}Cx+\lambda\|\delta(e_{\Phi_{b}})\|^{2}-\gamma^{2}\|\Delta-\frac{1 }{\gamma^{2}}D^{T}px\|^{2}-\lambda\|\delta(e_{\Phi_{b}})-\frac{1}{\lambda}E^{ T}Px\|^{2}+\gamma^{2}\|\Delta\|^{2}-\|y\|^{2}\end{array}  \tag{19}\]  
Combining Equation (16) with Equation (19), yields,  
\[ \begin{array}{ll}\dot{V}(x)&\leq x^{T}((A+BK)^{T}P+P(A+BK))x+\frac{1}{\gamma ^{2}}x^{T}PDD^{T}Px+\frac{1}{\lambda}x^{T}PEE^{T}Px+x^{T}C^{T}Cx\\ &+\lambda\|\delta(e_{\Phi_{b}})\|^{2}+\gamma^{2}\|\Delta\|^{2}-\|y\|^{2}\\ &\leq x^{T}((A+BK)^{T}P+P(A+BK))x+\frac{1}{\gamma^{2}}x^{T}PDD^{T}Px+\frac{1}{ \lambda}x^{T}PEE^{T}Px+x^{T}C^{T}Cx\\ &+\lambda x^{T}F^{T}Ex+\gamma^{2}\|\Delta\|^{2}-\|y\|^{2}\end{array}  \tag{20}\]  
If Equation (17) is satisfied, we have,  
\[\dot{V}(x)\leq\gamma^{2}\|\Delta\|^{2}-\|y\|^{2} \tag{21}\]  
This means that, for any T\(>\)0,  
\[ \begin{array}{ll}\int_{0}^{T}(\|y(t)\|^{2}-\gamma^{2}\|\Delta(t)\|^{2})dt\\ =\int_{0}^{T}(\|y(t)\|^{2}-\gamma^{2}\|\Delta(t)\|^{2}+\dot{V}(x(t)))dt+V(x(T)) \\ \leq V(x(T))\end{array}  \tag{22}\]  
That is,  
\[\int_{0}^{T}\|y(t)\|^{2}dt\leq\int_{0}^{T}\gamma^{2}\|\Delta(t)\|^{2}dt+V(x(T)) \tag{23}\]  
This means that the system is finite gain L\({}_{2}\)-stable from disturbance \(\Delta\) to outputs \(y\) and the L\({}_{2}\) gain is less than or equal to \(\gamma\). The gain matrix \(K\) can be got through the following LMI:  
\[\left[ \begin{array}{ccccc}AX+BW+(AX+BW)^{T}&\frac{1}{\gamma}D&\frac{1}{ \sqrt{\lambda}}E&(CX)^{T}&\sqrt{\lambda}(FX)^{T}\\ \frac{1}{\gamma}D^{T}&-I&O&O&O\\ \frac{1}{\sqrt{\lambda}}E^{T}&O&-I&O&O\\ CX&O&O&-I&O\\ \sqrt{\lambda}(FX)&O&O&O&-I\\ \end{array} \right]\leq 0 \tag{24}\]where \(X\) is a positive definite matrix. Once \(W\) and \(X\) are obtained, the control law is as follow:  
\[u=Kx=WX^{-1}x \tag{25}\]  
Then the closed-loop system will become asymptotically stable and the \(H\infty\) norm from disturbance \(\Delta\) to output y is less than or equal to \(\gamma\). So, the virtual inputs, \(\
u_{1}\) and \(\
u_{2}\), are as follows:  
\[\left[ \begin{array}{c}\
u_{1}\\ \
u_{2}\end{array} \right]=Kx+\left[ \begin{array}{c}\ddot{p}_{b,d}\\ \ddot{\Phi}_{b,d}\end{array} \right] \tag{26}\]  
Combining Equation (12) and Equation (13), the \(F_{t}\) and \(\tau\) needed for the hex-rotor can be found. It is the \(H\infty\) controller of the hex-rotor that can ensure system BIBO stability and compensate partial disturbance of the robotic arm.
# The Effect of Energy Loss Straggling on SEUs Induced by Low-Energy Protons in 28 nm FDSOI SRAMs
## 1 Introduction  
As an important type of space application integrated circuits, SRAM has attracted much attention about improving its irradiation resistance, in which single event upsets (SEUs) are the main issue referring to a storage state changed by one single ionizing particle striking a sensitive node. Silicon on insulator (SOI) technologies can effectively suppress short channel effect and have intrinsic hardness towards single event effects (SEEs) [1; 2], therefore it may be a preferable choice compared with bulk technologies for sub-28 nm process nodes.  
Space is a proton rich environment [3] and proton-induced SEU and were conventionally dominated by secondary ions generated from nuclear reactions between high-energy protons and other atoms in devices [4]. However, SRAMs have scaled to the point that even protons with low-energy can deposit sufficient energy in the sensitive volume to cause upsets via direct ionization [5]. For example, it was reported that the low-energy proton cross-section was more than 1000 times larger at 0.65 MeV than at 200 MeV for 90 nm bulk SRAMs [6]. Meanwhile, the feature size of integrated circuits further shrinking may bring a new challenge. Previous studies reported that energy deposited in thin silicon layers by light ions such as protons should be treated as a broadened energy spectrum instead of one single value [7; 8]. The phenomenon is known as energy loss straggling, contradicting the accuracy of soft error rates evaluation method based on LET. SOI devices have much smaller sensitive volume than bulk ones. For instance, the body region and the active region of a transistor manufactured with ST28 ultra-thin buried oxide and body fully depleted SOI (UTBB FDSOI) technology are merely 7 nm and 22 nm thick [9], respectively. This means that energy loss straggling should not be ignored any more in the future.  
Prior researchers have contributed to the issue. For example, a 22 nm SOI SRAM cell in Geant4 was established to explore the influence of energy loss straggling on high-energy proton-induced SEUsthrough direct ionization [10]. By contrast, direct ionization is the dominant mechanism of low-energy proton-induced upsets, as mentioned above. It may be more valuable to study the impact of energy loss straggling on SEUs when SRAMs are irradiated by low-energy protons.  
The present article performed irradiation experiments by using a SRAM fabricated with the ST28 FDSOI technology with sub-8 MeV protons and constructed a corresponding memory cell model in Geant4 to calculate deposited energy in the sensitive volume. Section 2 gives details about the method of experiments and Geant4 simulation. According to results presented in Section 3, the SEUs caused by proton direct ionization can be ascribed to energy loss straggling. Finally, Section 4 makes a thorough discussion about the reason that energy loss straggling cannot be ignored for modern SOI circuits and provides some advice of SEU evaluation methods.
## 2 Problems with CSI Feedback and Overview of IBF
### Ibf  
Various CSI compression methods have been proposed, with the aim of reducing the overhead incurred by the transmission efficiency as a result of the CSI feedback [14; 15]. However, even if CSI compression is employed, the CSI feedback continues to incur a significant overhead in cases for which a large number of antennas are located at the BS (e.g., for massive MIMO [11; 12]) and/or when user scheduling is assumed.  
To overcome this problem, beamforming without CSI feedback, or IBF, has been proposed [17]. Figure 2 shows the frame format for a MU-MIMO system when IBF is employed. It is apparent from this figure that the BS obtains the CSI directly from multiple UTs during period D, by utilizing the channel reciprocity between the transmission and reception in the TDD mode [17]. Moreover, because the number of UTs \(K\) is significantly less than the number of antennas at the BS \(M\) (Figure 1), the overhead during period B can be decreased using the frame format shown in Figure 1.  
Originally, an adaptive array utilizing the channel reciprocity was proposed in order to avoid interference through use of the uplink channel [29; 30]. Channel reciprocity means that the uplink and downlink share the same frequency band in TDD systems and that the receive weight created by the uplink channel can be utilized for the transmit weight [30]. In order to realize IBF, a calibration technique for the transmitters and receivers is essential [22; 23; 24].
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 5 Results
### Dataset 5: Sealing Group  
In this case study, the AAD + ADP with pretraining has been used. Similar to the case study shown in Section 5.2, the three fault conditions correspond to three distinct improper working conditions. Hence, the algorithm is applied separately for each condition. The pretraining consists of selecting the most relevant features using 70% of the observations corresponding to the nominal condition and the condition fault 1. These data are also used to initialize the local parameters of the clusters with the ADP algorithm. Then, the remaining observations are used to demonstrate that the algorithm assigns the points belonging to the nominal condition and the condition fault 1 to the corresponding existing clusters and creates new clusters for faults 2 and 3.  
Features selected through the Pearson correlation analysis are shown in Figure 13, while the local parameters of the clusters obtained during the training phase are summarized in Table 8.  
Figure 12: Results of the AAD + ADP algorithm (extruder-M2).  
Figure 13: Selected features (sealing group).  
Finally, the algorithm is applied to all observations of each condition. The performances of the algorithm applied to the features selected through the Pearson correlation analysis are summarized in Table 9. In the first case, when the nominal condition and fault 1 are considered, fault 1 has been detected with a latency of 10 cycles (8 s). In addition, no cluster is created, and the points are correctly assigned to cluster 1 and cluster 2. Similarly, fault 2 is detected after 8 s, and the points corresponding to the nominal condition are correctly assigned to cluster 1. Furthermore, a new cluster is created when fault 2 occurs. Finally, fault 3 is not detected by the algorithm. Figure 14 shows the results of the AAD + ADP algorithm for each condition
## 4 Fatigue Stress Calculation
### Fatigue Damage  
This section describes the process of calculating the fatigue damage based on the probability distribution of the stress amplitude obtained from Method 1 and Method 2. Fatigue damage is calculated according to [PERSON]'s linear cumulative damage law as expressed in Equation (22).  
\[D=\sum_{j=1}^{k}\frac{n_{j}}{N_{j}} \tag{22}\]  
where \(k\) is number of stress amplitudes, \(N_{j}\) is the number of cycles until failure at a specific stress amplitude \(S_{j}\) on the S-N diagram, and \(n_{j}\) is the number of actual stress cycles of the amplitude \(S_{j}\).  
To determine \(n_{j}\), the number of stress cycles, the stress range is discretized into appropriate interval \(\Delta S\) to obtain probability \(P_{j}\) using the probability density function \(f_{i}(S_{j})\). The calculation of \(P_{j}\) is as follows:  
\[P_{j}=f_{i}\big{(}S_{j}\big{)}\Delta S \tag{23}\]  
From a statistical point of view,  
\[P_{j}=\frac{n_{j}}{N_{0,i}} \tag{24}\]  
where \(N_{0,i}\) is the number of total stress peaks that occur under condition i.  
From Equations (23) and (24), the number of actual stress cycles of the amplitude \(S_{j}\) is arranged as follows:  
\[n_{j}=N_{0,i}f_{i}\big{(}S_{j}\big{)}\Delta S \tag{25}\]  
The S-N diagram is given in Equation (26), and the allowable number of stress cycles \(N_{j}\) is expressed as Equation (27)  
\[\log N=\log K-m\log\Delta S \tag{26}\]\[N_{j}=K\,\Delta S_{j}^{-m} \tag{27}\]  
where \(N\) is the predicted number of cycles to failure for stress range \(\Delta S\), \(m\) is the negative inverse slope of S-N curve, and \(\log K\) is the intercept of the \(\log N\) axis on the S-N curve.  
Finally, we can calculate the fatigue damage for each condition using Equation (28).  
\[D_{j}=\frac{N_{0,j}}{K}\sum_{j=1}^{k}S_{j}^{m}f_{j}\big{(}S_{j}\big{)}\Delta S \tag{28}\]
# Toward Blockchain-Enabled Supply Chain Anti-Counterfeiting and Traceability
## 6 Discussion of Research Results
### Summary of Vulnerabilities on Centralized System Architecture  
Among NAS and other existing anti-counterfeiting alternatives with centralized architecture, utilizing wireless tag communication technologies, there could be at least three common probable counterfeit attacks applied to these anti-counterfeiting solutions. These attacks manipulating threats listed under the _physical NFC tag threats_ and _system threats_ according to the threat analyses performed are (1) modification of product records stored in tags, such as fabricating product identifiers or vintages of any product; (2) cloning of metadata stored in tags such as those genuine product records to any counterfeit product tag; and (3) removal of a legitimate tag from a genuine product and its reapplication to any other counterfeit products.  
It has come to a point that even though the implementation of NAS itself is already more effective and secured than most of its typical supply chain anti-counterfeiting and traceability counterparts, with original product records being validated at any node along the supply chain, the centralized architecture of NAS could still pose risks in data integrity and product authenticity as any node, not only winemakers, along the supply chain have full control of product records stored in their own database architectures. In case different nodes along the supply chain are untrusting to each other, there could still be possibilities that a product record being duplicated adversely leading to a situation that product consumers could still purchase a product counterfeit at retail points, with fabricated wine records retrieved from NAS or its counterparts implemented in specific supply chain industries.  
The typical architecture of centralized supply chains creates several concerns. First, there is a tremendous processing burden on servers, as significant numbers of products processed by multiple supply chain nodes. Second, substantial storage is required to store authentication records for every single processed product. Third, with centralized systems, traditional supply chains inherently have the problem of single-point failure and so potential service downtime and data loss could be expected. All in all, centralized product anti-counterfeiting and traceability systems, such as NAS, rely on a centralized authority to combat counterfeit products which would result in _single-point processing, storage, and failure_ and those potential attacks via manipulating the security threats identified in threat analysis performed against NAS.
# Compositional Analysis of Chalcopyrite Using Calibration-Free Laser-Induced Breakdown Spectroscopy
### Experimental Arrangement  
Details of the experimental arrangement can be found elsewhere [25]. Briefly, a Q switched Nd:YAG laser ([PERSON], Quantel, Les Ulis CEDEX, France) with 532 nm wavelength, 5 ns pulse width and repetition rate of 10 Hz was employed as a source of plasma excitation. This laser system was capable of delivering energy up to 200 mJ. The chalcopyrite pellet was mounted on a x,y,z translational stage and position of laser spot on target was varied in order to avoid crater formation and to reduce the effect of sample heterogeneity. With the help of a quartz lens (f = 20 cm), the laser beam was perpendicularly focused on smooth surface of the chalopyrite target. The diameter of the laser spot on the target surface was 0.55 mm as measured with the help of an optical microscope. Laser pulse energy of 120 mJ corresponding to laser irradiance of \(1.0\times 10^{10}\) W/cm\({}^{2}\) was used in plasma generation from the target. Thje energy of the laser pulse was measured with the help of a joule-meter (Nova Quantel P/niz01507).  
The optical signal from chalcopyrite plasma was registered by collecting radiation from the plasma with the help of an optical fiber of high-OH, core diameter of 600 \(\upmu\)m, and collimating lens of 0-45\({}^{\circ}\) field of view, and coupling it with the LIBS2000+ (Ocean Optics. Inc., Largo, FL, USA) system comprised of five spectrometers (slit width = 5 \(\upmu\)m) which cover the 200 to 720 nm spectral range.  
Each spectrometer installed in the LIBS2000+ detection system was outfitted with 2048 element linear charge coupled device CCD array with a spectral resolution of \(\approx\) 0.06 nm. Detection system was synchronized with Q-switch of the Nd:YAG laser with the help of digital delay generator (SRS DG 535). Delay time between the firing of the laser pulse and the detection system opening was set at 3 \(\upmu\)s to eliminate the contribution of continuum radiation to the desired emission spectrum. Plasma was found to be sufficiently homogeneous for a time window of greater than 2 \(\upmu\)s. Data was acquired using single laser shot mode and integration time for detector was 2.1 ms. For the purpose of minimizing statistical errors, output data was averaged for 20 laser shots. The detection system was calibrated in wavelength by recording the well-known emission lines of mercury, argon and neon in the spectral range of 200-720 nm. Spectrometers installed in the LIBS2000+ system were manufacturer-calibrated in efficiency using the DH-2000-CAL standard light source. Data was acquired by all five spectrometers simultaneously and stored by computer with the help of OOILIBS software. The dark signal of the detector was subtracted from the recorded spectrum. The experimental arrangement is shown with the help of a schematic diagram in Figure 1.
## 2 Materials and Methods
### The Scientific Analyses
#### 2.2.2 Hyperspectral Imaging  
Hyperspectral imaging was performed using a Specim IQ spectral camera in the 400-1000 nm range. It employs the push broom method [21; 22] measurements of a scene made by scanning line-by-line across a spectral range of 400-1000 nm, thereby capturing the spectral information for each line of the scene across that spectral range. Across that spectral range, there are 204 binnable bands, partially overlapping, at 7 nm resolution. It can produce a square image of 512 \(\times\) 512 px, each 17.58 \(\upmu\)m \(\times\) 17.58 \(\upmu\)m in size. Two halogen lamps were used for the lighting (dimmable 150 W, with a light condenser to reduce surface heating).