esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Two Detector Magnetic Systems for the Future Circular Hadron-Hadron Collider
## 1 Introduction  
The conceptual design report for the Future Circular Collider (FCC) [1] with a center-of-mass energy of the order of 100 TeV, assumed to be constructed at CERN in a new tunnel of 80-100 km circumference, was presented in 2019 [2]. As part of this study, the concept of an FCC-hh detector for hadron-hadron physics was developed [3]. Apart from the particle sub-detectors, the FCC-hh detector will comprise an iron-free magnetic system consisting of three superconducting solenoids: the main coil with a central magnetic flux density of 4 T and two auxiliary forward solenoid coils with a central magnetic flux density of 3.2 T each [4].  
Following the alternative study of the magnetic system for the FCC-hh detector based on the superconducting solenoid with a minimal steel yoke [5], the possibility of using the steel yoke in the same baseline layout of the coils and the particle sub-detectors is considered, and the main parameters of both magnetic systems are compared here. For these comparisons, both the baseline FCC-hh detector magnetic system [4], and the magnetic system with the minimal steel yoke, are calculated with the program TOSCA from Cobham CTS Limited [6].  
A part of this study was presented in 2018 at 6 th International Conference on Superconductivity and Magnetism--ISCM2018 in Antalya, Turkey. At the time, the minimal steelyoke option included exact shapes of the radiation protection shields around the forward coils in correspondence with the baseline magnetic system. In the baseline option, these shields are assumed to be constructed from a nonmagnetic material. In the minimal steel yoke option, the shields are the parts of the steel flux return yoke. The original shapes of these shields include the large cones propagated inside the main coil, that in the minimal steel yoke design creates the enormous magnetic flux density inside the cones and, as a result, the large axial electromagnetic forces towards the center of the main coil. In this study, these cones are assumed to be from a nonmagnetic material to reduce the axial forces and the perturbation of the magnetic flux density in the endcap parts of the yoke.  
To study the quality of the magnetic field inside the tracking system volume, the magnetic field double integral method [7] is used instead of an investigation of the magnetic field bending power for the charged particles.  
The paper is organized in a way as follows: Section 2 contains a description of both options of the FCC-hh detector magnetic systems; Section 3 includes general comparisons of both options and a detailed investigation of the magnetic field quality in terms of the magnetic field double integral method; Section 4 represents a small discussion of the obtained results; and finally, conclusions are presented in Section 5.
## 2 System and Channel Model
### RIS-Assisted Wireless Channel  
We need to model two types of dynamic wireless channels in the system: one is the channel from the base station to the RIS relay, denoted as \(\mathbf{H}_{BR}(t)\), and the other is the channel from the RIS relay to individual vehicle users (VUs), denoted as \(\mathbf{h}_{RV,k}(t)\). The base station to the RIS channel model and the RIS to vehicle user channel model can be shown as:  
_Base station to RIS relay channel model:_  
\[\mathbf{H}_{BR}(t)=\sqrt{\beta_{BR}(t)}\times\mathbf{a}(\phi_{R},\theta_{R},t )\times\mathbf{a}^{H}(\phi_{BS},\theta_{BS},t) \tag{3}\]  
Here, \(\sqrt{\beta_{BR}(t)}\) represents the time-varying channel gain from the base station to the RIS relay. For the transmission data process from the BS to the RIS relay, the presentation of the array response vectors for multi-RIS units is denoted as \(\mathbf{a}(\phi_{BS},\theta_{BS},t)\) and \(\mathbf{a}(\phi_{R},\theta_{R},t)\). Specifically, \(\mathbf{a}(\phi_{BS},\theta_{BS},t)=[a_{1}(\phi_{BS},\theta_{BS},t),\dots,a_{N }(\phi_{BS},\theta_{BS},t)]^{T}\in\mathbb{C}^{N\times 1}\) and \(\mathbf{a}(\phi_{RIS},\theta_{R},t)=[a_{1}(\phi_{R},\theta_{R},t),\dots,a_{M}( \phi_{R},\theta_{R},t)]^{T}\in\mathbb{C}^{M\times 1}\). Next, the wireless channel model from the RIS relay to the user equipment \((VU_{k})\) is described as follows:  
\[\mathbf{h}_{RV,k}(t)=\sqrt{\beta_{RV,k}(t)}\times\mathbf{a}^{H}(\phi_{RV,k}, \theta_{RV,k},t) \tag{4}\]  
Here, \(\sqrt{\beta_{RV,k}(t)}\) characterizes the time-varying channel gain from the RIS relay to vehicle user \(k\) at time \(t\) (\(k\in[1,\dots,K]\)), and \(\mathbf{a}(\phi_{RV,k},\theta_{RV,k},t)\) represents the multi-antenna array response vector from the RIS relay to vehicle user \(k\), defined as \(\mathbf{a}(\phi_{RV,k},\theta_{RV,k},t)=[a_{1}(\phi_{RV,k},\theta_{RV,k},t), \dots,a_{M}(\phi_{RV,k},\theta_{RV,k},t)]^{T}\in\mathbb{C}^{M\times 1}\).  
Figure 1: RIS-assisted wireless electric vehicle charging network.  
In the context of the non-line-of-sight (NLOS) situation of the communication systems, the time-varying signal-to-interference-plus-noise ratio (SINR) for user \(k\) (where \(k\in(1,\ldots,K)\)) can be obtained as follows:  
\[\gamma_{k}(t)=\frac{p_{k}(t)|(\mathbf{h}_{RV,k}^{H}(t)\Phi_{k}(t)\mathbf{H}_{BR,k}(t))\mathbf{q}_{k}(t)|^{2}}{\sum_{j\
eq k}^{K}p_{j}(t)|\mathbf{h}_{RV,k}^{H} (t)\Phi_{k}(t)\mathbf{H}_{BR,k}(t))\mathbf{q}_{j}(t)|^{2}+\sigma_{k}^{2}}, \tag{5}\]  
Moreover, the spectral efficiency (SE) of the real-time system, measured in \(\text{bps}/\text{Hz}\), can be expressed as:  
\[\mathcal{R}(t)=\sum_{k=1}^{K}log_{2}(1+\gamma_{k}(t)), \tag{6}\]
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 5 Architectures for Real-Time Home Services
The main advantage of this architecture is that it decouples the customer HAN from the network used for providing real-time services, thus making real-time services easier to provision and manage, especially remotely. The main disadvantage is that technology choices on the SM are made by the DSO (who is responsible for the deployment of the SM), while the consumer has a limited choice of WAN technology and, consequently, of the designated third party. As a consequence, the DSO is legally responsible for guaranteeing that the third party will make the data available to the end-user. According to Directive 2012/27/EU [31], the SM must make the measurements available to the customer without any charge, implying that it should support at least one of the first two architectures, which do not require the involvement of a third party. Figure 4: Architecture 2: the SM makes the data available to the consumer Home Area Network (HAN). Figure 5: Architecture 3: The SM sends data only to an authorized third party. The SM is expected to stay in place several years. During this time the communications needs of the consumers, and the consumers themselves or even the DSO, could change. Therefore, it is important that the DSOs deploy SMs that support multiple architectures and are able to cope with evolving communications technologies, both in the home network domain and in the WAN domain. Additionally, many DSOs have concerns regarding the increase of the attack surface of the SM resulting from two-way communications, especially if connections to public networks are envisaged for cyber-security reasons. A more flexible and secure solution can be achieved with a two-tier approach with Architecture 4, shown in Figure 6, in which the SM supports only Architecture 1 and only sends data over a one-way communications bus. Architectures 2 and 3 are achieved by means of gateway nodes (IHD-GW) deployed in the household premises and connected to the same one-way bus. This approach also has the advantage of enabling two-way communications from the IHDs to the IHD-GW, which can provide additional services such as past metering data, maintain the state of the system, carry measurements from multiple SMs (electric but also for gas or water), or even enable access to the services exposed by the DSO, which, in turn, can count on a two-way-like communications with the SM through the AMI infrastructure. Nevertheless, SM vulnerabilities would be minimized thanks to one-way communication along the branch from SM to IHD-GW.
###### Abstract  
The effect of thermal pasteurization (TP, 62.5 \({}^{\circ}\)C/30 min--conditions similar to those used in milk banks/hospitals, known as Holder pasteurization) and high-pressure pasteurization (HPPP: 400-625 MPa/2-5-30 min) was studied on immunoglobulin (IgG, IgM and IgA) content, lysozyme activity and microbial load of donkey colostrum (in this case, after 40 days at 4 \({}^{\circ}\)C). IgG level remained unchanged with HPP at 400 MPa, increased up to 4-fold at 625 MPa/10 min and decreased 90% with TP, while IgM decreased progressively with pressure treatment intensity increment to below the detection limit at 625 MPa and decreased 20% with TP. IgA decreased to below the detection limit after TP and all HPP treatments. Lysozyme activity presented overall a higher decrease after TP (37%) compared to HPP (decreasing from 20% at 400 MPa to about 40% at 600 MPa/10 and 30 min). Furthermore, both total aerobic mesophiles and Enterobacteriaceae are remained below detection limits after 40 days of refrigerated storage for both TP and HPP. So, HPP can be considered a potential alternative to the conventional TP to preserve donkey colostrum, with overall equal to better retention, particularly for IgG and lysozyme activity. As far as the authors are aware, this is the first study evaluating the effects of HPP on donkey colostrum, and research in this field should be pursued.  
donkey colostrum; high-pressure; thermal pasteurization; microbial load; immunoglobulins; IgG; IgM; IgA 2
## 2 Materials and Methods
### Statistical Analysis
#### 2.6.2 Partial Correlation Analysis  
Simple linear regression has been widely used for correlation analysis assuming dependent variables are independent of each other. However, climate factors are often found to be correlated with each other [2]. In order to detect how cropland productivity is driven by each climate factor at the temporal scale, a partial correlation analysis between climate variables and cropland productivity was performed. This analyzes the linear relationship between crop productivity and each climate factor after excluding the effect of other factors. A partial correlation coefficient (\(r\)) was calculated using Equation (3) to Equation (5) [57]. The value of \(r\) varies between \(-1\) and \(1\) and shows the strength of the relationship between cropland productivity and a climate variable.  
\[r_{xy}=\sum_{i=1}^{n}\left[\left(x_{i}-\overline{x}\right)\left(y_{i}-\overline {y}\right)\right]/\sqrt{\sum_{i=1}^{n}\left[\left(x_{i}-\overline{x}\right)^{2 }\left(y_{i}-\overline{y}\right)^{2}\right]} \tag{3}\]  
\[r_{xy,z}=\left(r_{xy}-r_{xz}r_{yz}\right)/\sqrt{\left(1-r_{xz}^{2}\right) \left(1-r_{yz}^{2}\right)} \tag{4}\]  
\[r_{xy,zq}=\left(r_{xy,z}-r_{xzq,z}r_{yq,z}\right)/\sqrt{\left(1-r_{xzq,z}^{2} \right)\left(1-r_{yq,z}^{2}\right)} \tag{5}\]  
\[t=r_{xy,za}/\sqrt{\left(1-r_{xy,za}^{2}\right)/\left(n-m-1\right)} \tag{6}\]  
where, \(x\), \(y\), \(z\), and \(a\) are the independent factors; \(r_{xy}\) is the zero order coefficient calculated using a simple linear regression between two variables (\(x\) and \(y\)); \(r_{xy,z}\) is the first order coefficient of the linear relationship between \(x\) and \(y\) after excluding the effect of \(z\) on \(x\) and \(y\); \(r_{xy,zq}\) is the second order co-efficient of the linear relationship between \(x\) and \(y\) after excluding the effect of \(z\) and \(q\) on \(x\) and \(y\), respectively.  
The significance of the partial correlation coefficient was also evaluated with the Student's \(t\)-test statistic (Equation (4)). A correlation was presented at three significance levels 0.01 (\(p\)-value, \(p\) = 0.01), 0.05 (\(p\) = 0.05) and 0.1 (\(p\) = 0.1), with decreasing strength of correlation, respectively.
# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 3 The Proposed Method
### Attention Enhancement Feature Fusion Network  
The attention enhancement feature fusion network is designed to fuse the \(F_{t}\) and \(F_{S}\) features. Firstly, the channel dimensions of \(F_{t}\) and \(F_{S}\) features are reduced through a \(1\times 1\) convolution, resulting in two low-dimension feature maps: \(F_{T0}\in R^{H_{T}\times W_{T}\times d}\) and \(F_{S0}\in R^{H_{S}\times W_{S}\times d}\), where \(d\) is set to be 256. Then, \(F_{T0}\) and \(F_{S0}\) are flattened along the spatial dimensions to obtain \(F_{T1}\in R^{H_{T}W_{T}\times d}\) and \(F_{S1}\in R^{H_{S}W_{S}\times d}\), which can be viewed as sets of feature vectors with a length of \(d\). As shown in Figure 2, \(F_{T1}\) and \(F_{S1}\) are taken as inputs for the template branch and search region branch in attention-enhancement fusion networks, respectively. Two ESA modules are used to enhance input features by making these features adaptively focus on useful information through multi-head fast self-attention. Two ECA modules receive these feature maps simultaneously and fuse these feature maps with multi-head ECA. The attention-enhanced fusion layer is formed by these two FSA and ECA modules, as shown by the dashed box in Figure 2. The attention-enhanced fusion layer is repeated \(N\) times, and then an additional ECA module is used to fuse these feature maps from the two branches, resulting in the fused feature map, \(F\in R^{d\times H_{S}W_{S}}\). \(N\) is set to be 4 in this paper.
# Seismic Response Mitigation of Base-Isolated Buildings
## 3 Mathematical Model of Isolator  
In this study, a lead rubber bearing (N-Z) isolator is considered. [PERSON]'s model (Wen, [38]) is used for characterization of hysteretic behavior of the bearing. Figure 2a shows schematic representation of lead rubber bearings. The restoring force developed in the isolation is given by  
\[{\rm F_{b}}=c_{\rm b}\dot{x}_{\rm b}+\alpha k_{\rm b}x_{\rm b}+(1-\alpha)\ {\rm F_{y}}Z \tag{5}\]  
where the yield strength of the bearing is denoted by \({\rm F_{y}}\), initial stiffness of the bearing is denoted by \({\rm k_{b}}\), and viscous damping of the bearing is denoted by \(c_{\rm b}\); \(\alpha\) signifies the ratio of post- to pre-yielding stiffness. A non-dimensional hysteretic displacement component is denoted by \(Z\), which satisfies the following non-linear first order differential equation  
\[q\dot{Z}=A\dot{x}_{b}+\beta\dot{x}_{b}\|Z|Z|^{n_{k}-1}+\tau\dot{x}_{b}|Z|^{n_{ k}} \tag{6}\]  
where the yield displacement is denoted by \(q;A,\ \beta,\tau\), and \(n_{\rm k}\) are dimensionless model parameters, often calibrated from experimental tests. The integer parameter \(n_{\rm k}\) controls the smoothness of transition from elastic to plastic deformation. The isolation period (\(T_{\rm b}\)), damping ratio (\(\xi_{b}\)), and normalized yield strength characterizes the N-Z isolation system. Using the post-yielding stiffness (\(k_{\rm p}\)) of the bearing, the isolation period (\(T_{\rm b}\)) and damping ratio (\(\xi_{b}\)) are computed by Equations (7) and (8), respectively.  
\[T_{b}=2\pi\sqrt{\frac{M}{k_{p}}} \tag{7}\]  
\[\xi_{b}=\frac{c_{b}}{2M\omega_{b}} \tag{8}\]  
where \(\omega_{b}\) is the isolator frequency and \(W=Mg\) is the total weight of the building plus isolator and TMDs, and \(g\) is the acceleration due to gravity. Other parameters are maintained constant with \(q=2.5\) cm, \(\beta=\tau=0.5\), \(A=1\), and \(n=2\).
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 5 Experiments and Results
### Experimental Setup
#### 5.2.2 Acoustic Features and SNR Settings  
In this paper, D = 90 dimensional features, with 30 CQCCs, 30 \(\Delta\) (delta coefficients) and 30 \(\Delta\Delta\) (delta-delta coefficients), were utilized. Each frame of an utterance was processed by a 25 ms Hamming window with a 10 ms shift. The number of bins per octave was set to 96. The highest and lowest frequencies were set to 8000 Hz and 16 Hz, respectively. For the experiments in noisy environments, four different kinds of noises were added to the original ASVspoof 2017 Version 2.0 dataset [20]. These four kinds of noises were white, babble, f16, and leopard. The experiments were conducted in four different SNR conditions: \(-\)5 dB, 0 dB, 5 dB, and 10 dB.
## 2 Basic Elements
### FPGA  
An FPGA is an integrated circuit designed to be configured after the manufacture, assembly, and even deployment of the product it is part of. FPGAs contain programmable logic blocks (capable of performing a range of operations from simple logic gates to complex functions) and memory blocks. These blocks can then be connected using a hierarchy of reconfigurable interconnects. The combination of these elements improves the performance of the FPGA in many varied applications.  
Some advantages of FPGAs are:  
1. FPGAs are more flexible than complex programmable logic devices as they generally have a greater number of both logic blocks and programmable interconnects.
2. FPGAs have a lower development cost than application-specific integrated circuits (ASICs). Although an ASIC can perform the same operations as an FPGA and is specific to the application, it cannot be reprogrammed.
3. FPGAs have a faster time-to-market and lower non-recurring engineering cost than ASICs.  
Three of the main characteristics of FPGAs, which are related to the speed of the process, are:Throughput: the amount of data that are processed per clock cycle (bits/second).
2. Latency: the time between data input and processed data output (clock cycles).
3. Timing: the logic delays between sequential elements (frequency).
# An Overview of Quantum Machine Learning Research in China
## 5 Challenges and Prospects
### Prospects
#### 5.2.1 Practical Applications  
At present, most QML studies are theoretical analysis of algorithms, and the numerical experiments are only conducted on a few standard datasets (e.g., MNIST, IRIS, etc.). However, there is hardly research that tests the performance of the QML algorithms on complicated real-world datasets. In order to enhance the practical value of QML algorithms, we need to make improvements in both hardware and software aspects. As for hardware, it is necessary to address the technical issues of how to increase effective qubits in the system and reduce noise interference. Regarding software, it is important to design better core algorithms and/or quantum circuit structures to alleviate problems such as barren plateaus, catastrophic forgetting, and so on.
## 3 Calibration of the Optoelectronic FDS-System
### Linearity Calibration  
Amplitude linearity calibration refers to testing that the measured signal amplitude is linear with the incident signal. This is necessary because many types of instrumental effects can result in non-linear amplitude measurements. Two types of amplitude nonlinearity are most frequently observed: (i) amplitude saturation at high signal levels; and (ii) amplitude tail at low signal levels when approaching the noise floor. Amplitude linearity can be tested by employing a series of attenuators of known power, or combinations of attenuators [32,33]. In order to test broadband systems, such as TDS and FDS, attenuators must have frequency-independent attenuation that is accurately known.  
Amplitude calibration was performed using a set of high-resistivity Si plates [32]. The normal-incidence amplitude transmission through a Si plate is 0.7; and a stack of \(N\) plates will have multiplicative transmission of 0.7\({}^{N}\). Thus, a stack of Si plates can provide a variable, controlled, and known attenuation, such as that required for linearity testing. However, it should be noted that Si plates must be tested to verify their refractive index and transparency.  
The T-Sweeper had its linearity tested using this method. Figure 5a plots the transmitted amplitude as a function of the number of Si plates placed in the beam path, on a semi-log scale. If the amplitude response is linear, the amplitude would be expected to decrease linearly; the black dashed lines demonstrate the expected slope of 0.7. Figure 5b shows the fitted slopes over the entire spectrum.  
Figure 4: **(a)** Frequency of the peak maxima plotted versus peak order, with the linear fit for all three devices. For clarity, values of the TeraScan and TeraFlash pro have been offset. The slopes of linear regression are shown in the bottom right inset. Since this is the distance between the successive minima and maxima, this must be doubled for the calculation of FSR (cf. above). **(b)** Frequency residuals of extreme values, measured with T-Sweeper, TeraFlash and TeraScan. The dashed lines represent half the frequency resolution.  
Figure 5: **(a)** Measured transmitted amplitude as a function of the number of Si plates, for different frequencies. Dashed lines show the expected slope. **(b)** The fitted slopes for all frequencies up to 2.7 THz. For clarity, only every 20 th datapoint is shown.  
It is observed that at all frequencies between 0.1-2.2 THz the slopes are close to the expected value of 0.7, with variations within the measurement uncertainty, and the mean value of \(0.695\pm 0.021\). This linearity calibration method therefore reveals the operational frequency range of the instrument (using the present settings). At higher frequencies, the slopes deviate from linearity when the number of plates exceeds 7, revealing the operational dynamic range limits at these frequencies. Above 2.2 THz the signal is dominated by noise, especially for a high number of silicon plates.
# Microbes as Medicines: Harnessing the Power of Bacteria in Advancing Cancer Treatment
## 2 Mechanism of Tumor Suppression
### Bacterial Cell Death Inducing Agents
#### 2.1.3 Bacteriocins  
Bacteria produce a broad spectrum of ribosomal synthesized bacteriocins and antimicrobial peptides (AMP) which prevent the invasion of other bacterial strains in its own niche. The bacterial peptides can also inhibit the growth of tumor cells. Bacteriocins have an anti-cancer effect due to the cationic charge and amphiphilic nature [38]. This characteristic enables the bacteriocin to act as \"membrane active\" by interacting with the negatively charged cell membrane. The electrostatic interaction between cationic bacteriocins and cancer cells play an important role in cytotoxic effect. The cancer cells also have low cholesterol levels and with the cancer progression, the membrane fluidity increases making the cancer cells more susceptible to the action of AMPs [38]. Bacteriocins can destroy the membrane integrity and trigger the apoptosis of the cancer cells. Other mechanism involves the inhibition of angiogenesis and hence, impeding progression of cancer [39]. For instance, Nisin--a 34 amino acid residue produced by _Lactococcus lactis_ subspecies _lactis_ has antimicrobial activity against majority of the Gram-negative bacteria and has also demonstrated to be cytotoxic to MCF-7 cells (human breast adenocarcinoma cell line) [40]. _Brevibacillus_ species produce defensin like peptide Laterosporulin 10 which was found to be cytotoxic against cancer cell lines like MCF-7 (human breast adenocarcinoma cell line), HeLa cells (uterine cervix cancer cells), HEK293T cells (human embryonic kidney), and HT1080 (fibrosarcoma) cell line [41].