esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Tunnel Bending Moment  
The bending moments measured at transverse sections of left and right tunnels after four excavation stages are plotted in Figure 11. The positive values of tunnel bending moments denote the outer side compressive moment and inner side tensile moment, while the negative values denote the opposite condition. It is demonstrated that the tunnel crowns and inverts are mainly under tension, while the side walls are mainly under compression. The tunnel bending moment increases with the increasing excavation depth of the foundation pit, and the variations are more pronounced at the crowns and inverts. The maximum positive bending moments of the left and right tunnels are 52.49 kN\(\cdot\)m and 53.56 kN\(\cdot\)m, respectively, and both of them are located at the tunnel inverts, i.e., the L9 and R9 regions. The maximum negative bending moments are recorded at the L5 and R13 regions of the left and right tunnels, and these two values are \(-\)51.31 kN\(\cdot\)m and \(-\)46.76 kN\(\cdot\)m, respectively. In addition, the increasing depth of the pit excavation leads to an increasing tunnel bending moment, at an increased rate. However, the increasing trend of the bending moments of the tunnel near the foundation pit side is slightly more pronounced than that far from the foundation pit. The last two excavation stages result in a more significant increase in the bending moments than the first two excavation stages, indicating that the disturbance effect on the tunnel increased as the excavation advances. Therefore, the excavation and corresponding unloading of the foundation pit cause significant disturbance to the tunnels, especially at the measurement points near the pit side, where the bending moment varies more dramatically.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Monitoring Points**} & \multicolumn{3}{c}{**Value**} \\ \cline{3-5}  & & **Before** & **After** & **Ratio (\%)** \\ \hline \multirow{4}{*}{Vertical} & L1 & 182.41 & 58.27 & 31.94 \\  & L9 & 229.91 & 83.47 & 36.31 \\  & R1 & 185.41 & 55.21 & 29.78 \\  & R9 & 226.20 & 91.75 & 40.56 \\  & L5 & 107.17 & 52.51 & 48.99 \\ \multirow{2}{*}{Horizontal} & L13 & 106.84 & 53.86 & 50.41 \\  & R5 & 99.81 & 48.03 & 48.12 \\ \multirow{2}{*}{Vertical} & R13 & 100.24 & 44.57 & 44.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variations of earth pressure around tunnel before and after excavation (unit: kPa).  
Figure 11: Variation of bending moment with excavation stage (unit: kN\(\cdot\)m).
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Modality Translation:
Edges in a medical image contain principal details of anatomy such as tissue, organ, and abrasion details. However, the images produced by a normal GAN have blurred boundaries. A flexible, gradient-prior integrated, encoder-decoder-based adversarial learning network (FGEAN) is an end-to-end framework of multiple inputs and multiple outputs that uses gradient-prior to retain tissue composition type of high-frequency details [72]. Edge-aware GAN (Ea-GAN) is a 3D method that extracts voxel-wise intensity and image structure information to overcome slice discontinuity and blurriness problems. The Sobel operator is used to extract the edge details. The Sobel filter assigns higher weights to its nearer neighbors and lower weights to the farther neighbors, which is impossible with direct image gradient application [73]. CycleGAN-based unified forward generative adversarial network transforms any T2-FLAIR images in different groups into a single reference one [74]. In [75], WGAN generates multi-sequence brain MR images with the advantage of stable learning. The Earth Mover (EM) distance (a.k.a. the Wasserstein-1 metrics) of WGAN allows minor mode collapse. In [76], sample-adaptive GAN imitates each sample by learning its correlation with its neighboring training samples and applying the target-modality features as auxiliary information for synthesis. The self-attention GAN (SAGAN) of [77] attends to various organ anatomical structures via attention maps which showcase spatial semantic details with the help of an attention module. In [78], the GAN framework learns shared content encoding and domain-specific style encoding across multiple domains. CGAN in image modality translation (IMT) network employs nonlinear atlas-based registration to register a moving image to the fixed image. The PatchGAN classifier with no constraints on each patch's size acts as the discriminator generating acute results with lesser parameters and a low running time [15]. In [79], GAN provides a solution for the detection of Small Vessel Disease (SVD) by estimating the advancement of White Matter Hyper-intensities (WMH) during a year. Disease Evolution Predictor (DEP) model notices WMH in T2-weighted and T2- FLAIR MRIs. DEP-GAN (Disease Evolution Predictor GAN), an extension of visual attribution GAN (VA-GAN), uses an irregularity map (IM) or probability map (PM) for both input and output modalities to represent WMH. This generated image is called Disease Evolution Map (DEM), which classifies brain tissue voxel among progressing, regressing, or stable WMH groups.
# On the Need for a General REST-Security Framework
## 5 Related Work Analysis
### HTTP Basic and HTTP Digest Authentication  
HTTP basic [33] and HTTP digest authentication [34] have been the two first standards for authenticating HTTP requests. Both schemes require a username and a password for the authentication process. If a client tries to access a resource, which is protected by one of these mechanisms, the server returns an error response message including the WWW-authenticate header containing the name of the mechanism, i.e., basic or digest, and a realm which is a description of the secured resource. In case of basic authentication, the client must authenticate itself by sending the former request with an Authorization header, which includes the base64-encoded username and the password. The, i.e., plain-text transfer of username and password transfer is the main downside of this approach. To protect this sensitive information in transit, TLS must be applied additionally. If transport-oriented security is not available, the hole message remains unprotected and password eavesdropping as well as any kind of request manipulations including the ones in Table 1 are feasible.  
HTTP Digest Authentication provides a slightly improved approach, as it does not transfer the credentials in clear-text. Here, the username and the password are hashed. Besides the username and password the hash computation includes the URL path, the HTTP method, a client- as well as a server-generated nonce, a sequence number and optionally a quality of protection description. Since this scheme considers the HTTP method and the URL path in the hash calculation, a manipulation of these request message elements is not feasible. However, an attacker can still perform malicious changes of other message entities such as distinct headers and the body.  
The other main drawback of both authentication mechanisms is that the request can be authenticated only. Servers are not able to authenticate their responses opening the door for MITM attacks.
# Discussion on the Design of Sprayed Eco-Protection for Near-Slope Roads Along Multi-Level Slopes
## 7 Original Design Scheme
### Design Comparison  
In compliance with the Technical Code for Building Slope Engineering, the steep western slope of Hill No. 1 was reconstructed using a benched excavation-filling approach involving 87,254.25 m\({}^{3}\) of cut material and 7823.45 m\({}^{3}\) of fill, with 10-m high benches connected by 5-m wide berms at an approximate slope angle of 30\({}^{\circ}\) (Figure 6). Following slope trimming, exposed cut surfaces received hydroseeding with imported soil matrix, while berms were vegetated with topsoil and grass seeding, supported by a newly installed sprinkler irrigation system throughout the 3-year maintenance period.  
As illustrated in Figure 7, the yellow zones represent soil spray-seeding areas, blue linear sections denote berms, blank areas indicate non-greened regions, red lines mark near-slope roads, and blue lines signify drainage ditches. Key design parameters include:  
Figure 5: Field Geomorphology Photos of Mountain No. 1 Area.  
Figure 6: Original Design Cross Section.  
Figure 7: Original Design Planar Layout Diagram of Multi-Level Slopes.  
Access ramps: Designed with a width of 5 m, gradient of 12%, and curve radii of R1 = 30 m, R2 = 25 m, R3 = 35 m, R4 = 21 m, R5 = 12 m, R6 = 4 m.  
Drainage ditches: Feature internal dimensions of 0.6 m \(\times\) 0.5 m, masonry thickness of 0.3 m, and double-sided masonry construction in baseplate zones, adhering to the <<Code for Design of Irrigation and Drainage Engineering>>.  
This configuration ensures precise alignment with ecological, structural, and hydraulic requirements while maintaining operational efficiency.  
In the original design scheme, the curve radii of some berms and access ramps were inadequately designed, only accommodating the movement of excavation equipment, as shown in Figure 8c,d. During the spray-seeding phase, due to these suboptimal radii, the sprayer could only operate from the third-level slope platform for upward seeding. To maintain efficiency, extended spray pipes and additional labor were required, increasing spraying costs, as illustrated in Figure 8e,f. For post-construction maintenance, despite excavation and widening of the ramp curves to allow water trucks to park at the first-level slope entrance, the vehicles remained unable to freely access the berms, necessitating manual hose connections for watering. This reduced watering efficiency, raised labor costs, and slowed construction progress, as depicted in Figure 8a,b.
# A Photon Imaging Detector Model with High Resolution and High Counting Rate
## 5 Conclusions  
This paper proposes a new electronic signal processing scheme, which uses a digital trapezoidal shape filter instead of a Gaussian shaping amplifier. For the superimposed waveform, the influence of shaping parameters on the shaping error and the influence of the photon-counting rate and noise on the spatial resolution are studied. An adaptive digital trapezoidal filter that automatically selects the shaping parameters according to the pulse interval in the sampling window is proposed, and the detector imaging model is established to test the resolution. The imaging simulation results show that the detector with this scheme has a high dynamic range, and can still achieve a spatial resolution of <50 \(\upmu\)m at a photon-counting rate of >6 MHz for a noise of 1000 e RMS. The new detector will have great applications in the large dynamic range of photon intensity scenes, such as 3D situation awareness or aurora detection.  
Conceptualization, Z.J.; Software, Z.J.; Validation, Z.J.; Data curation, Z.J.; Writing--original draft, Z.J.; Writing--review & editing, Z.J.; Supervision, Q.N.; Project administration, Q.N.; Funding acquisition, Q.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under grant no. 62135015.  
**Institutional Review Board Statement:** Not applicable.  
Figure 10: Variation in FWHM with photon-counting rates for different noise RMS.  
Figure 9: The photon position distribution obtained by detector imaging at the 5 M photon count rate and 500 e RMS noise.
# Precise Photon Correlation Measurement of a Chaotic Laser
## 3 Experiment Setup  
The experimental setup is shown in Figure 1, which can be used to determine time and frequency domain of the laser characteristics and measure photon pair time interval distribution. A 1550 nm laser was generated by a distributed feedback laser diode (DFB-LD), and a thermoelectric temperature controller (TTC, ILX-Lightwave LDT-5412) was used to stabilize the temperature with an accuracy of 0.01 K. A precision current source controller (CSC, ILX-Lightwave LDC-3412) controlled the output intensity of the DFB-LD laser nearly 1.5 times the threshold current with a value of 15.9 mA. The output laser passed through a polarization controller (PC) which maintained the polarization of the feedback beam paralleling to that of the output laser. With the help of an optical circulator (OC), the optical feedback loop was realized. The output of the OC was connected to a 20:80 fiber coupler (FC). A total of 80% of the output light passed through the variable optical fiber attenuator (VA1) and went back to the OC. Another output was connected with a 50:50 FC, and one port of output was detected by a high-speed photodetector (PD, FINISAR XPDV2120 RA). Signal time series were recorded by an oscilloscope (OSC, Lecroy LabMaster10-36 Zi) and the frequency spectrum was obtained by a frequency spectrum analyzer (Agilent N9020A). On the same output port of the 50:50 FC, the optical spectrum was also measured by an optical spectrum analyzer (Yokogawa AQ6370C). The other output port of this 50:50 FC was connected with another attenuator (VA2) followed by an HBT system, which was based on a 50:50 beam splitter (BS) with a dual channel single photon detector (SPD,urea Technology LYNXEA. NTR. M2). When the photons impinged on the SPD, the SPD delivered pulses to a time to digital converter (TDC). An internal clock triggered two channel gates simultaneously. Then precise time information (i.e., the time between photons arrival at different channels) was extracted via a subtractor and an integrator. Each photon pair time interval was placed in the one-time bin. The histogram of the photon pair time interval distribution was obtained through cumulative measurement. The data were read out to a laptop computer (LC) via universal serial bus (USB) connection. When the laser beam passed from the fiber to space or space to fiber, the fiber lens collimators were required. In Figure 1, L1, L2, L3 represents the aspheric lens collimators, and F is an optical filter used to filter out the background noise. The chaotic laser was divided into two equal intensity beams whose intensity were measured by the detectors SPD1 and SPD2. One could adjust the mean photon intensity of the light through the VA2. After the above steps, the photon pair time interval distribution was attained.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 1 Introduction
### Further Operating Environment Details  
We consider broadcast networks in which a single communication medium is shared by all participants, direct communication between any pair of nodes is possible, as well as direct multicasting to all nodes within range (a single-hop network). However, we remark that many of our results may be generalized to apply to multi-hop networks as well.  
The shared communication channel is assumed to have very low bandwidth, precluding the use of many standard mechanisms. To motivate our threat model, we also assume the channel to have large latency, giving rise to attack vectors exploiting the notable differences between reception times of a broadcast message at different nodes.  
A slow and unreliable communication channel creates new opportunities for adversaries. First, missing packet delivery or delay is no longer a symptom of an attack. It might frequently be the case that one node receives a multicast message and another does not. In certain cases, such as the underwater acoustic channel scenario, it may be possible for an adversary to use two colluding nodes connected via an efficient and reliable out-of-band channel (e.g., a cable or a radio connection above the surface) to launch wormhole and sinkhole attacks [11].  
Unreliability of the channel further prevents the use of protocols based on delayed disclosure of hash pre-images, such as \(\mu\)Tesla [12], as an adversary may learn the authenticating piece of information (the pre-image) ahead of the nodes in their vicinity and then successfully impersonate the sender.
## 4 Simulation Verification and Results  
In this section, we verify the proposed RL based creep control method for autonomous operation of mining electric locomotives through simulation experiments. A dynamic model of the CTY1.5/6 mining electric locomotive is built through Simpack. And the algorithm framework based on the Python language environment is designed. MATLAB is used as the intermediate carrier for connecting the algorithm and model, mainly responsible for the collection of dynamic model data and the output of algorithm operation results. Therefore, in this paper, the validation of the algorithm will be carried out through a co-simulation platform built by Simpack, MATLAB, and Python. The simulation platform is used to verify the effectiveness of the creep control method for autonomous operation of mining electric locomotives. The creep control method is compared with traditional \(\varepsilon\)-greedy algorithms to highlight the advantages of the improved \(\varepsilon\)-greedy algorithm in terms of learning efficiency.
## 5 Performance Evaluation
### Performance Matrices
#### 5.2.3 Average Waiting Time  
The waiting time of a request is the time count from its arrival to the request queue until it is assigned to a BBU in the BBU pool. The smaller the average waiting time of the incoming requests, the more quickly users will get a response from the system, which indicates greater user satisfaction. Let \(\varpi_{r}^{a}\) and \(\varpi_{r}^{l}\) denote the arrival time to the queue and leaving time from the queue of a request, respectively. The average waiting time of each request is defined as:  
\[\overline{\varpi_{r}}=\frac{1}{|R|}\sum_{r\in R}\left(\varpi_{r}^{a}-\varpi_{ r}^{l}\right), \tag{19}\]  
where \(R\) is the set of all requests arrived during the whole simulation period. A lower \(\overline{\varpi_{r}}\) value corresponds to better system performance.
# A Transfer Learning Method for Pneumonia Classification and Visualization
## 5 Results
### Test Results  
As mentioned in Section 3.1, the Pneumonia dataset contains its own test set. After performing several experiments, we selected the model that performed best in the validation set taking into account the value of loss of the cost function, and then evaluate the full test set.  
To highlight the advantages of the proposed model, we compared with some of the state-of-the-art baseline models used for CV task and medical images applications as VGG-16 [13], ResNet50v2 [14], DenseNet121 [15] and the LZNet2019 [37] that was the top result until the development of this research.VGG-16 and DenseNet121 use images of dimensions \(224\times 224\) as input. Moreover, the LZNet2019 uses \(150\times 150\) as input. On the other hand, our model uses \(299\times 299\) as the input shape. We need to mention that VGG-16, ResNet50v2, and DenseNet121 were fed with the same images as the proposed model. The CM and performance measures of classification results for all models are shown Table 4. Best results for each measure are in bold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Best** & \multirow{2}{*}{**Validation**} & **Average** & **Average** & **Convergence** \\  & & & **Training Time** & **Training Time** & **Time (Best** \\  & **Epoch** & **Loss** & **(Epoch)** & **(Example)** & **Model)** \\ \hline VGG-16 & 97 & 0.1928 & 44 s & 0.0202 s & 4268 s \\ ResNet50-v2 & 93 & 0.0514 & **43 s** & **0.0198 s** & 3999 s \\ DenseNet121 & 98 & 0.0496 & 51 s & 0.0234 s & 4998 s \\ Our model & **14** & **0.0453** & 53 s & 0.0244 s & **742 s** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of validation loss and training time for all models.  
Figure 10: Validation loss of all single CNN models.  
In order to offer a more precise analysis of the proposed method compared with the baseline models, we show the ROC curves with its respective AUC, and the precision-recall curves with its own AUC in Figure 11.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 2 Hardware Accelerated Crypto-Algorithms
### Symmetric Cryptography
A pair-wise key pre-distribution scheme presented in [35] improves network resilience at the cost of additional computational requirements. LEAP proposed in [24] is a key management protocol designed to support multiple keying mechanisms and in-network processing. It uses RC5 block cipher to ensure the data encryption and encrypted authentication. The work presented in [36] proposes a security framework based on a triple-key management scheme developed for cluster-based network topologies. The scheme consists of three keys: two pre-deployed keys in all nodes and an in-network generated cluster key for a cluster to address the hierarchical nature of the network. The framework also defines the routing and localization algorithms and a mechanism for malicious node detection. The presented approach promises low packet overhead and low transmission latency. In [37], the authors propose a key management scheme based on dynamic key reconfiguration where already deployed nodes refresh their keys prior to another deployment phase. A scalable key management scheme that uses the unital design theory is presented in [38]. The presented algorithm relies on the mapping of basic unitals to the key pre-distribution scheme providing high network scalability and secure connectivity.
# Antenna Design for Microwave and Millimeter Wave Applications: Latest Advances and Prospects
## 7 Software-Defined Radio Beamforming System for 5G/Radar Applications  
Based on the flexibility of software-defined radio (SDR) techniques applied to an array of antennas, this paper [6] presented a beamforming architecture designed to operate in millimeter-wave bands (28 GHz), with possible applications in radar and 5G systems. The system structure, including its main constituents such as the radio frequency (RF) front-end modules, the radiating elements as well as the baseband processing on the host computer, were widely described. Beamforming was achieved by digitally controlling the signals that fed the antennas. The experimental measurements performed in an anechoic chamber validated the proposed approach.  
Design of a Monopulse System Using a Single Patch Radiator with a Simple Multi-Mode Substrate Integrated Waveguide Feeding Network  
[PERSON] et al. [7] proposed the design of a single radiator monopulse antenna with a substrate integrated waveguide (SIW) multi-mode feeding system to reduce the aperture size and simplify the feeding structure. The proposed antenna can effectively reduce the overall system size by using a single patch radiator, compared to conventional systems that require multiple radiators. In addition, to simplify the structure of the pattern comparator, an SIW multi-mode feeding system with TE\({}_{10}\) and TE\({}_{20}\) modes was used. To verify the feasibility, the patch antenna with the SIW multi-mode feed was fabricated, and its antenna characteristics of reflection coefficients, mutual coupling, and radiation patterns were measured in a full anechoic chamber. The accuracy of the direction of arrival (DOA) estimation was further improved by applying the derived calibration factor to the resulting sum and difference ratio. The results demonstrated that the proposed antenna is suitable for compact and lightweight monopulse systems with the single radiator and the simple multi-mode feeding network.