esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Spectral Identification in the THz Band with Reflection Spectroscopy in an Open Atmosphere
## 2 Materials and Methods
### Reflection Setup  
To reconfigure the setup for reflection configuration, a 3\" flat mirror was placed after the polarizers. With this mirror, the THz beam was directed onto the specially designed holder with the sample. The signal reflected from the sample hit the 8\" flat mirror and then was directed to the 8\" parabolic mirror, which focused the beam on the detector, as shown in Figure 2. The optical alignment was designed in a way that allowed for the sample holder to be placed at distances from 0.5 to 5 m. The sample holder was placed on the optical pedestal, between 3\" and 8\" flat mirrors, about 10 cm below the optical table level. For practical reasons, holder distances from the mirrors of 1, 3, and 5 m were selected for the experiments. The THz radiation angles of incidence were 14\({}^{\circ}\), 5\({}^{\circ}\), and 3\({}^{\circ}\), respectively. This is a reconfigured and optimized version of the setup from [44].  
Since the size of the beam directed onto the sample was wider than the sample, a special holder with rounded surfaces was built to eliminate the signal backscatter noise from the sides, as shown in Figure 3. That solution assured that no unwanted THz interference was directed to the detector. This is a slightly upgraded version of the holder in [44]. This solution has no back wall behind the sample, thus preventing reflections. The sample holder was moved from the optical table and placed on a separate pedestal, as the reflections from the metal elements of the holder tend to find their way to the detector by bouncing from the table. Additionally, absorbing metal foam was added as a cover of exposed parts of the holder. Samples used in the research were flat but not with a mirror quality, rather with a rough surface which resulted in radiation bounced from the sample being more scattered in nature than reflected, and making obtained signals very weak. For the reference measurements in this setup, a flat 30 mm polished metal disc was used.  
Figure 1: Measurement setup in the transmission configuration.
## 3 Instrument Development
### Embedded System  
Figure 3a shows the complete embedded system scheme of the AMULSE. The main electronic board of the sensor is a single National Instruments (NI, Nanterre, France) RIO-9636 board. The system is powered from a single +8 VDC Li-ion battery (rechargeable, 7800 mAh) dispatched through regulators that are well adapted to the different parts and components. The total power consumption for the electronics, excluding the mirrors heaters, is equal to 8 W. The digital lock-in/data acquisition system is integrated into the FPGA unit on the NI sbRIO-9636. It generates the laser modulation waveform and applies it to the home-made current/temperature laser driver. The modulated signal from the photodetector is digitized into 8192 sample points using a 16-bit digitizer from the NI single board. After processing by the digital lock-in FPGA system, WMS-2\(f\), WMS-1\(f\) and direct signals are stored onboard into binary files in a SDHC memory card.  
Figure 3: (**a**) Full schematic diagram of the developed AMULSE in the embedded system configuration; (**b**) 3D drawing of the final version of the AMULSE. The dimensions of this instrument are 290 \(\times\) 175 \(\times\) 227 mm; (**c**) Photography of the AMULSE taken after the measurement campaign.  
The atmospheric pressure and temperature are monitored by iMET-1-RS radiosondes (International Met Systems, Grand Rapids, MI, USA) equipped with RS232 interfaces. Temperature accuracy given by the head thermistor is equal to 0.2 \({}^{\circ}\)C while the pressure accuracy given by the piezo-resistive sensor is equal to 0.5 mbar. The location of the sensor in flight is obtained from the iMET-1 onboard GPS. Sensor location, monitoring parameters, WMS-2\(f\) and WMS-1\(f\) signal amplitudes and atmospheric pressure and temperature are transmitted to the ground through a 2-way satellite communication system (Iridium).  
The sharp (ON/OFF button) plug-and-play spectrometer weighs 1.8 kg in flight-ready condition, including cabling, thermal insulation and power supplies to be autonomous for 8-9 h according to ground tests. The instrument is compact (290 \(\times\) 175 \(\times\) 227 mm) as can be shown on Figure 3b,c.
# Backdoor Attacks on Deep Neural Networks via Transfer Learning from Natural Images
## 2 Materials and Methods
### Performance of Backdoor Attacks  
Following previous studies [10; 13; 15], we used error rates (ERs) and attack success rates (ASRs) to evaluate the performance of the backdoor attacks on the testing datasets. The ER was defined as 1--accuracy on clean images (i.e., images without triggers): \(\text{ER}=|\mathbf{X}|^{-1}\sum_{\mathbf{x}\in\mathbf{X}}\mathbb{I}(C(\mathbf{x})\
eq y_{\mathbf{x}})\), where \(C(\mathbf{x})\) and \(y_{\mathbf{x}}\) are the outputs (label or class) of the DNN model and the true label for an input image \(\mathbf{x}\) in set \(\mathbf{X}\), respectively. \(\mathbb{I}(A)\) takes 1 if condition \(A\) is true and 0 otherwise. A small ER indicates that the DNN model correctly predicted clean images. ASR was defined as the ratio of images with the trigger classified as class \(K\) to all images in set \(\mathbf{X}\): \(\text{ASR}=|\mathbf{X}|^{-1}\sum_{\mathbf{x}\in\mathbf{X}}\mathbb{I}(C(\mathbf{x}(\mathbf{x}))=K)\). We selected the class \(K\) for the ASR that was highest for the backdoored models, including the fine-tuned models. A high ASR indicates that all-to-one attacks against the class \(K\) were successful because of the backdoor trigger. Notably, ASR has a baseline (i.e., an ASR computed from clean images). Except for the aerial image dataset, the baseline ASR was \(\sim\)50% because the datasets were balanced between binary classes. For the aerial image dataset, however, the baseline of the ASR against the \"normal\" class was \(\sim\)70%, given the class composition of the image data.
# A Millimeter-Wave Radar Tunnel Obstacle Detection Method Based on Invalid Target Filtering
## 3 Test and Result Analysis
### Comparison of the Filtering Rate of Invalid Targets in Different States  
In order to visually analyze the filtration effect in different states, the obtained filtration rates and average values are shown in Table 6.  
The experimental results show that the average filtering rate of the algorithm is more than 87.82% in the static state. When the speed is 5 km/h, the average filter rate is 87.70% for smooth driving and 86.83% for uneven road surfaces. When the speed is 7 km/h, the smooth driving rate is 87.54%, and the uneven road surface is 86.56%. When the speed is 10 km/h, the smooth driving rate is 86.50%, and the uneven road surface is 86.44%. Although the filter rate decreases with the increase of speed or vehicle vibration in the running state, the average filter rate of the proposed algorithm can reach more than 86% under all conditions.  
With the increase in speed, the filtering effect of the algorithm decreases to a certain extent no matter whether the vehicle is driving in a straight line on a flat road surface or on an uneven road surface because the vibration of the vehicle will be more obvious with the increase of the vehicle speed. This is because as the speed of the vehicle increases, the vibration of the vehicle becomes more obvious. When the vehicle is running in a straight line under the smooth road surface, compared with the uneven road surface, the vibration of the vehicle will be more obvious as the vehicle speed increases. At the same speed, the filter rate decreases because when the vehicle is driving on an uneven road surface, the vibration of the vehicle is also intensified due to the increase of the work done by the motor.
## 3 Case Study
### PV Power Plant Configuration  
As was previously described in Section 2.2, the rate power of the PV installation can be estimated by considering the depth of the aquifer level, the averaged crop water demand and the aggregated crop area. Under these requirements, the specific PV power plant configuration is not in line with usual individual installations, mainly promoted in the agricultural sector and based on isolated pumping systems. In our case, we propose an aggregated PV pumping solution without accumulation, directly connected to the grid and excluding any water reservoir facility. The proposed pumping solution thus requires more power, but lower annual maintenance. Therefore, PV power plant solutions with PV modules based on mono-silicon PV technology in fixed installations is considered for the analysis, being the rate PV power estimated to cover the average daily demand according to a specific crop water need. From the aquifer characteristics, ranges to be considered for the study can be then summarized as follows: groups from 1 to 2000 ha, groundwater pumping levels between 10 and 55 m of aquifer depth; and two representative crop water need: 1500 m\({}^{3}\)/ha and 3000 m\({}^{3}\)/ha--discussed in Section 3.2. Different PV power plant solutions are determined based on the different configurations assumed in the case study. In this way, Figure 7 summarizes the PV solutions (in kWp power capacity) for the different scenarios. In all cases, PV power plant is determined to supply the averaged power demand according to the crop water need, the aggregated area (ha) and groundwater pumping level (m). Therefore, the PV power plants to be installed (in kWp) would provide power enough to supply the corresponding pumping groundwater requirements.
# Performance Comparison of NB-Fi, Sigfox, and LoRaWAN
## 3 An Overview of NB-Fi, Sigfox, and LoRaWAN
### Frame Format  
#### 3.2.1 NB-Fi  
The uplink frame structure in NB-Fi is shown in Figure 2. It starts with a four-byte long preamble that serves for synchronization. It is fixed and equals 0x97157A6F in hexadecimal form.  
The preamble is followed by data encoded with an error correction code, which is either a convolutional code or a non-systematic polar core [29] with the code rate of \(\frac{5}{8}\).  
The encoder input is 20-byte long and consists of the following fields:  
Figure 2: Uplink frame structure in NB-Fi.  
Modem_ID: a 32-bit sensor identifier.  
Crypto Iter: 8 least significant bits of the cryptoiterator, which is a 32-bit counter stored at the device and incremented by one for every transmitted frame. NB-Fi devices use the Magma [30] symmetric key block cipher to produce security keys and encrypt the payload. When Crypto Iter makes a full turn, devices update the security keys. Devices also use the cryptoiterator to produce the start value for payload encryption.  
Payload: a 9-byte field with encrypted payload. It contains a transport layer packet, which consists of a header (1 byte) and the upper layer payload (8 bytes). The header contains four fields:  
* SYS: a flag that identifies service frames;
* ACK: a flag that informs that the data frame requires acknowledgment;
* MULTI: a flag that is needed for block transmissions and informs that the frame is not the last in the block;
* ITER: a 5-bit field that is used for numerating frames in a block and can take values from 0 to 31.  
MICO_7: 3 least significant bytes of the message authentication code (MAC) which is also calculated using the Magma algorithm in Encrypt-then-MAC mode; MIC0_7 is used to make sure that the payload has not been changed. In addition, as described further, MIC0_7 is used to calculate the central frequency of the transmission.  
Packet CRC: Three least significant bytes of the CRC32 checksum calculated from Modem_ID, Crypto Iter, and Payload.  
Figure 3 shows the downlink frame structure in NB-Fi. As opposed to the uplink frame, the downlink frame does not contain Modem_ID, and the preamble is not fixed but is calculated for every transmission according to an algorithm based on the iterative pseudorandom number generator, where the receiver's Modem_ID is the starting position of the generator. The preamble is followed by the Crypto Iter, Payload, Data Authentication Code, and Packet CRC, which are similar to the fields in the uplink frame. These four fields are used as input to the Zigzag error correction code [31] with the code rate of \(\frac{1}{2}\). The downlink frame ends with the control bits produced by the Zigzag encoder.
###### Abstract  
To avoid signal interference in L1 frequency and provide various services, GPS has updated a modern signal, called L1C, which has been tested with three QZSS satellites launched in 2017. In December 2018, the first GPS III satellite was launched, which implies improved joint positioning using GPS and QZSS L1C signal. The L1C signal offers a series of advanced designs in signal modulation, message structure and coding. We present complete methodologies for joint L1C signal receiving and processing. For the transmitted signals, we present a methodology and results from collecting and assessing Binary Offset Carrier (BOC) modulation and time-multiplexed BOC (TMBOC) modulation used in the L1C signal. Using the same omnidirectional antenna and test equipment, we collected the L1C signal in Xi'an and Sanya, China, respectively. The experiments in Xi'an verify the joint positioning method to complement the GPS III and QZSS satellite constellations. Our methodology evaluates the ranging difference and positioning error of BOC and TMBOC modulation under the same environment and satellite constellation configuration in Sanya. It is also verified that the joint positioning error is less than the QZSS-only positioning due to the optimization of the satellite constellation.  
L1C; GPS III; QZSS; software-defined receiver; joint positioning +
Footnote †: journal:  
_et electronics_
## 3 Theoretical Framework and Hypotheses Development  
The proposed model in Figure 1 is based on the information adoption model (IAM). The IAM, as stated by [32], \"can explain how individuals adopt information and thus change their intentions and behaviors within the computer-mediated communication platforms\". The IAM is based on both the TAM and the dual-process model of informational influence, i.e., the elaboration likelihood model (ELM). According to the same source, while quoting others, the TAM's explanatory power is constrained and concentrates both information systems and the individual usage of a computer while neglecting the social processes.  
The ELM, on the other hand, can be used to describe the attitude change from and articulate the process underlying the effectiveness of persuasive communication since the ELM is used to explain how the recipients are affected by message information [33]. Hence, [PERSON] and [PERSON] in 2003 developed the IAM, which was published in [34]. The original IAM is composed of two independent variables, argument quality and source credibility, one intermediate variable, information usefulness, and one dependent variable, information adoption, as can be seen in [34]. Argument quality is \"the persuasiveness of arguments within an informational message\". [35] and has four dimensions: accuracy, comprehensiveness, relevance, and timeliness [35]. According to [35], source credibility is \"the extent to which a message source is perceived to be believable, competent and trustworthy by the recipients\". \"Argument quality and source credibility are the two most cited central and peripheral signals\" [35].  
The proposed model in Figure 1 is composed of independent, intermediate, dependent, and moderating variables. The independent variables are source credibility (SC), information language (IL), intrinsic information quality (IIQ), contextual information quality (CIQ), and accessibility information quality (AIQ). The intermediate variables are information usefulness (IU), information quality (IQ), and information adoption (IA). The dependent variable is academic achievement (AA). The moderating variables are age, gender, education level, and previous experience.  
Furthermore, the original IAM model was expanded to include IL, IIQ, CIQ, AIQ, AA, and the moderating variables. The IL was adopted based on the work of [35; 36; 37; 38; 39]. The IQ was expanded with IIQ, CIQ, and AIQ based on the work of [34; 35; 40]. The variable AA  
Figure 1: Proposed model based on expanded IAM.  
was adopted based on the work of [8; 9; 10; 34; 42; 43; 41]. In addition, the model was expanded with moderating variables adopted from [6; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53]. Furthermore, the model was analyzed, validated, and verified using structural equation modeling (SEM) and confirmatory factor analysis (CFA).
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Controller Functions
#### 5.4.4 Traffic Engineering  
Traffic engineering involves optimizing the flow of traffic to improve network performance and reliability. The control plane can monitor traffic in real-time and detect areas of the network that become congested. The control plane can distribute network traffic evenly across multiple paths in the network, which will help avoid congestion on specific links and improve performance. In [390], according to bandwidth utilization, traffic is redirected from congested links by the centralized controller to enhance the efficiency of link usage in terms of throughput, jitter, and packet loss rate. The controller can also allocate resources,  
Figure 8: Network policy life-cycle in a KDN.  
such as bandwidth, in affected areas of congestion to reduce the congestion [391]. Furthermore, the control plane can enforce QoS policies to prioritize certain traffic over others. For instance, video traffic can be given higher priority than file transfer traffic, as video traffic is time-critical, in order to increase the overall quality of experience of the user [392]. The control plane should ensure that traffic is rerouted in the event of a network failure. It can use protocols such as Multi Protocol Label Switching (MPLS) for fast and reliable rerouting of traffic, which will help minimize downtime and ensure network reliability. Specifically, a protection scheme using pre-computed backup paths by the controller to use MPLS routing, which guarantees instantaneous recovery time and a zero packet loss rate after failure detection, can be employed [393]. Finally, dynamic policies can be used to adjust routing policies to improve the efficiency of network traffic in response to network change events. For instance, if the controller detects that network devices' resource utilization is high, it can enforce a policy to use a routing protocol that uses less device resources [394].
# Evolution Mechanism of Interlayer Properties of CRTS III Slab Track during Construction
## 3 Results and Discussion
### Influence of Key Factors on the Evolution of Interlayer Damage
#### 3.3.1 Interlayer Bonding Property  
After the cast-in-place of SCC, the bonding performance at the interface between the track slab and SCC gradually develops. To investigate the influence of interlayer bonding performance between the track slab and SCC on the evolution of interlayer damage, four different working conditions were constructed with interlayer bonding performance reduction coefficients of 1/3, 1/2, 2/3, and 1. Among them, a smaller reduction coefficient represents better interlayer bonding performance, and when the reduction coefficient is 1, it indicates that no reduction has been performed. A comparison of \(D_{I}\) and vertical displacement time-history curves at different reduction coefficients for the corner of the slab is shown in Figure 17.  
Figure 16: Mises stress distribution of the slab at different moments with and without damage was considered. (**a**) Without damage; (**b**) with damage.  
From Figure 17a, it can be observed that there is a significant difference in the development rate of \(D_{I}\) under different reduction coefficients, and it notably decreases with the growth of the interlayer bonding performance. This is because \(D_{I}\) is mainly in the ascending segment of the CZM, and the development rate of this segment is primarily controlled by interfacial strength, and the interfacial strength exhibits a linear increase trend with the growth of the reduction coefficient, which results in a reduction in the development rate of \(D_{I}\). From Figure 17b, it can be seen that the shape of the vertical displacement curve of the corner of the slab remains relatively consistent under different bonding properties. With the increase in the reduction coefficient, the abrupt displacement time of the corner exhibits a nonlinear growth trend. When the reduction coefficient is \(1/3\), the corner experiences an abrupt displacement at 146.8 h, while for the reduction coefficient of 1, the corner experiences an abrupt displacement at 354.5 h. The magnitude of the abrupt vertical displacement also increases, with a value of 2.11 mm when the interfacial age reduction coefficient is \(1/3\) and 4.42 mm when it is 1. Overall, the values and times of abrupt displacement at the corner of the slab tend to exhibit nonlinear growth as the reduction coefficient increases. The rate of their development gradually decreases. This is because, with the increase in the reduction coefficient, the interlayer damage development rate at the corner of the slab continuously decreases. The equivalent critical shrinkage corresponding to extensive interlayer debonding also increases continuously, making interlayer debonding less likely to occur.
## 3 Results
#### 3.2.2 Crystal Structure  
Figure 8 presents the X-ray diffraction patterns of the as-cast, hydrided, and desorbed Ti\({}_{1.26}\)V\({}_{0.315}\)(FeV)\({}_{0.315}\)Cr\({}_{1.11}\) + 4% Zr\({}_{7}\)Ni\({}_{10}\) alloy. Table 4 presents the crystal structure parameters, as determined by Rietveld refinements.  
The as-cast alloy crystallizes with a BCC structure, as reported for its 100 at% vanadium homologous alloy and has a slightly smaller lattice parameter (0.3062 nm as compared to 0.3074 nm for the Ti\({}_{1.26}\)V\({}_{0.63}\)Cr\({}_{1.11}\) alloy) [20]. From the value of lattice parameter of Table 4, the volume of the formula unit was found to be 14.34 A\({}^{3}\). The diffraction pattern of the hydride sample clearly shows two phases. One of these phases is the dihydride FCC phase. The other phase is more difficult to determine, and two possibilities were investigated: (1) a BCC phase with hydrogen in solid solution  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sample** & **Crystal Structure** & **Lattice Parameter (nm)** & **Crystallite Size (nm)** & **Micro-Strain (\%)** \\ \hline As-cast & BCC & 0.3062 (2) & 13 (1) & 0.6 (1) \\ Hydrided & FCC & 0.4296 (3) & 16 (2) & 1.1 (1) \\ Desorbed & BCC & 0.3059 (4) & 10 (1) & 1.2 (2) \\ \hline \end{tabular}
\end{table}
Table 4: Crystal parameters of the co-melt Ti\({}_{1.26}\)V\({}_{0.315}\)(FeV)\({}_{0.315}\)Cr\({}_{1.11}\) + 4% Zr\({}_{7}\)Ni\({}_{10}\). Uncertainties on last significant digit are indicated in parentheses.  
and (2) a monohydride BCT (body centered tetragonal) phase. The Rietveld refinement using both structures gave similar Goodness-of-fit and R-values. It should be noted that the formula unit volume was exactly the same (15.76 A\({}^{3}\)) for both BCT and BCC phases, thus corresponding to an expansion of 1.42 A\({}^{3}\). It is very close to one fourth of the expansion of the FCC phase. Therefore, even if we could not precisely determine the crystal structure of this second phase, we are confident that it contains about one hydrogen atom for two formula units.