esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Reinforcement Mechanism and Reinforcement Effect of Saturated Soil with a Weak Layer by DC
## 3 Validation of the Numerical Method  
### Pore Water Pressure and Lateral Displacement  
The proposed Abaqus Dual-Phase Coupling Method with the cap hardening soil model was validated by [PERSON] et al. [17; 31] using some field measurement data (pore pressure and soil displacement) [34]. It is not repeated here for brevity, but interested readers can refer to [PERSON] et al. [17; 31]. The section mainly presents important data. Figure 2 shows the numerical computation of excess pore water pressure at 3 m and 7 m below ground surface during the first DC blow. The comparison results of lateral movement and excess pore water pressure at different depths for 1500 kN\(\cdot\)m and 2500 kN\(\cdot\)m were measured after the first blow, as presented in Figures 3 and 4. Although there are some differences between numerical model and measuring result are observed, the changing laws of the numerical result coincide basically with those of measurements. In general, it is pretty reliable to use the Dual-Phase Coupling Method to predict the responses of saturated soils (stress, displacement, and pore pressure) during DC.
## 2 Numerical Study
### Design of braces for Parametric Study  
Continuing with the validated FE modeling approach, a series of square and circular brace dimensions were chosen to investigate their cyclic behavior and compressive resistance. In this study, the slenderness ratio (\(\lambda\) = L/r) is defined as the length of the specimen to the radius of gyration, and its range for the selected braces was maintained at 50-150 with an increment of 10, as per the requirements of ANSI/AISC 341-16 and GB 50011-2010 [21; 22]. Correspondingly, the B/t and D/t were altered from 8 to 20 with an increment of 4, and the width of the square braces for calculation was taken as B-3t. To ensure this study was consistent with realistic engineering applications, an identical length of all braces equal to 3500 mm was adopted for buildings with the same floor height. The values of width/diameter-to-thickness ratio were varied to satisfy the required slenderness ratio, as presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**B (mm)** & **t (mm)** & **B/t** & **SR** & **D (mm)** & **t (mm)** & **D/t** & **SR** \\ \hline
187 & 16.5 & 8 & 50 & 227 & 29 & 8 & 50 \\
182 & 12.5 & 12 & & 220 & 19.2 & 12 & \\
179 & 9.5 & 16 & & 215 & 13.9 & 16 & \\
177 & 7.7 & 20 & & 212 & 11 & 20 & \\
161 & 15 & 8 & 60 & 191 & 25.5 & 8 & 60 \\
157 & 11 & 12 & & 182 & 16 & 12 & \\
154 & 8.5 & 16 & & 178 & 11.3 & 16 & \\
152 & 6.8 & 20 & & 176 & 9 & 20 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric details of braces (B = width; t = thickness; B/t = width-to-thickness ratio; D = diameter; D/t = diameter-to-thickness ratio; SR = slenderness ratio).  
Figure 7: Validation of experiment results and current finite element study (**a**) 60 \(\times\) 60 \(\times\) 3 \(\times\) 2050 square specimen; (**b**) 48.3 \(\times\) 3.2 \(\times\) 1500 circular specimen under axial cyclic loading with fixed–fixed end conditions.
# Dynamic Line Rating--An Effective Method to Increase the Safety of Power Lines
## 1 Introduction  
One of the critical issues for high-voltage power lines is to increase the transmission capacity of existing infrastructure without compromising the level of operation safety, reliability and security of supply [1; 2; 3]. Dynamic line rating (DLR) is a smart method to utilize the maximum ampere capacity (ampacity) of transmission lines cost-effectively [4; 5]. Most commonly, the so-called static line rating (SLR), a transfer capacity calculated from the worst-case combination of the environmental parameters, is used. While the significant advantage of SLR is its simplicity--one ampacity limit value describes the power line for the entire year--it does not fully utilize the operating infrastructure. DLR is based on online monitoring of the temperature, load, and environmental parameters in the power line's vicinity with sensors and weather stations. Its application ensures the current limit to be adjusted according to changes in weather parameters so that the transmission line can always operate close to its maximum temperature state [4; 5; 6]. This way, for a dedicated line, almost 20-30% extra capacity can be reached during most of its operating time [5; 6]. The conductor temperature can be measured with line monitoring sensors installed on the conductor and calculated based on different physical models [7; 8]. Any malfunction in the sensors' operation can lead to insecure operational states, especially when the safety reserve for clearance is minimal [9]. In such cases, the application of DLR may result in critical ground clearance values which, in addition to breaching the legal and safety requirements, may result in the electric field exceeding its limit value around the phase conductors [8]. To avoid this and ensure precise operation, the conductor's inclination measurement is vital after installing the DLR sensors.  
The low-frequency electric and magnetic fields around the overhead lines (OHL)s' phase conductors are strictly regulated parameters at the national and European Union levels [10; 11]. The electric and magnetic fields' origin is different, the former being caused by high field strength, while the latter results from high current density. At a regular operation frequency (50-60 Hz), these fields' effect can be examined separately, and different limit values are in force for each parameter [10; 11]. It is essential to mention that in the case of radiations under 100 kHz, the phenomena are called non-ionizing radiations [10]. This means that for an electric and magnetic field, the defined limit values need to be provided around the OHL parts all the time.
# Photoconductive Semiconductor Switches: Materials, Physics, and Applications
## 8 Application Case Study: A Medium-Voltage PCSS Breaker
### SSCB Topology  
The system showcased in Figure 15 is the solid-state circuit breaker (SSCB) that has been proposed, designed, and demonstrated by Sandia National Laboratories [88]. This system is characterized by a high-voltage switch (HV switch), which is assembled using a series of low-voltage, normally-on SiC junction field-effect transistors (JFETs). It also incorporates an absorbing branch, composed of a capacitor (\(C\)) in series with the GaN PCSS devices described in Section 7 (forming the normally-off branch), and a metal oxide varistor (MOV) for overvoltage protection. During standard operation, the HV switch acts as the main pathway for conducting the load current (the normally-on branch). When a fault occurs, the PCSS is triggered, effectively rerouting a portion of the fault current away from the HV Switch. This strategic diversion facilitates a safe transition of the HV switch to an off-state, significantly mitigating the potential thermal stress associated with disrupting the full fault current.  
Figure 16 conceptually illustrates the operational behavior of the SSCB during a fault event. At the initial moment of the fault (\(t_{0}\)), there is an increase in the current flowing through both the normally-on branch and the load. Upon fault detection at \(t_{1}\), the cascaded JFET circuit is deactivated. This results in a rise in the voltage across the cascaded JFETs and the PCSS, while the voltage across the capacitor remains at 0 V, due to the absence of current flow through the normally-off branch. Once the voltage across the PCSS escalates beyond its threshold voltage level (\(V_{th}\)), the laser is triggered at \(t_{2}\), causing the PCSS to enter a high-gain mode and conduct the fault current. The interval from \(t_{2}\) to \(t_{3}\) is marked by a simultaneous increase in both the capacitor voltage and the HV switch voltage, until the PCSS reaches a cut-off threshold. The PCSS ceases to conduct current, effectively concluding the turn-off transient phase.
# An Implementation of the HDBSCAN* Clustering Algorithm
## 4 Results and Discussion
### Performance  
The results from comparing the model training times are best visualized. Figure 6 shows a clustered column chart comparing the model training times for the three datasets. Tribuo Hdbscan performs better than the reference implementation. Even though there is some overhead from the Tribuo framework, and some extra steps to compute the cluster exemplars, the improved training times are a direct result of parallelizing the core distance calculations. On the test hardware, instantiating the HdbscanTrainer class with the variable numThreads set to eight produced the best results, as can be seen in the relevant notebooks [16].  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & **Tribuo Hdbscan** & **Python Hdbscan** \\ \hline
4 Centroids, 3 Features, 20 points & 0.87 & 0.87 \\
3 Centroids, 4 Features, 1000 points & 1.0 & 1.0 \\
5 Centroids, 4 Features, 1000 points & 1.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The adjusted mutual information scores for the predicted cluster assignments.  
Figure 6: A clustered column chart comparing the model training times for the three datasets.  
The Python module hdbscan is faster than Tribuo Hdbscan by an order of magnitude in the time taken to train the models. This implementation takes advantage of scikit-learn's KDTree and BallTree nearest neighbor algorithms to compute the core distances. It also uses numpy extensively and leverages its optimized numerical array operations.  
The results from comparing the model prediction times are also best visualized. Figure 7 shows a clustered column chart comparing the model prediction times for the two datasets.  
The novel prediction technique provided with Tribuo Hdbscan is able to make predictions much faster than the Python module hdbscan. This technique, which uses computed cluster exemplars to estimate cluster assignments, has an asymptotic complexity of \(O(\sqrt{n})\) as discussed above. The Python module hdbscan closely follows the HDBSCAN* algorithm to make predictions which has a theoretical complexity of \(O(n^{2})\). In practice, the hdbscan module leverages pre-computed structures stored in memory, so it should be somewhat faster, but it is still slower than the technique proposed in this work.
## 6 Sensitivity Analysis
### Results of Sensitivity Analysis  
Table 14 and Figure 12 reveal how much each parameter is changed for release time T. From Table 14, b is the most sensitive parameter and \(\mathrm{T_{0}}\) is the most insensitive parameter. Moreover, the values in the sensitivity analysis of parameters \(\alpha\) and \(\beta\) are almost equal because the software reliability function has the same amount of variation when parameters \(\alpha\) and \(\beta\) vary. In brief, the optimal release time (\(\mathrm{T^{*}}\)) increases with the decrease in a and b. Furthermore, \(\mathrm{T^{*}}\) decreases with the increase in \(\alpha\), \(\beta\), \(\mathrm{T_{0}}\), and N.  
If the software is released too early, more resources will be required, such as risk cost, update cost, and human resources from the users and the development company. On the contrary, if the software is released too late, it will be necessary to assume higher cost of development. Therefore, the overestimation of \(\alpha\), \(\beta\), \(\mathrm{T_{0}}\), and N and the underestimation of a and b, which can lead to misestimations, such as underestimation of the optimal release time (\(\mathrm{T^{*}}\)), should be avoided.
# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 4 Experiment
### Experimental Evaluation Index  
The mean average precision (mAP) value was used in this experiment to assess the accuracy of the model. We chose mAP as the core evaluation metric for this object detection experiment because it takes into account both the classification and localization performance of the model. Specifically, for each category, we first calculated its recall (\(R\)) and precision (\(P\)):  
\[R=\frac{TP}{FN+TP},\quad P=\frac{TP}{FP+TP} \tag{10}\]  
True negative, false negative, true positive, and false positive are denoted as \(TN\), \(FN\), \(TP\) and \(FP\), respectively. Precision (\(P\)) is the proportion of correctly identified targets among all detected targets. Recall (R) is the proportion of instances that the model is able to identify. While higher values of both precision (\(P\)) and recall (\(R\)) are desirable, there is an inherent trade-off between them; enhancing precision may affect recall, and vice versa. Thus, achieving a balance between precision and recall is essential. By examining the relationship between precision and recall at various classification thresholds and plotting their corresponding points, a precision-recall (PR) curve can be generated to assess the model's classification performance. This curve represents the equilibrium between the classifier's precision in identifying positive instances and its comprehensiveness in capturing positive instances. The area under the curve (AP value) indicates the model's discriminative power for a certain class, with a higher AP value signifying superior performance. The AP value is calculated by integrating the area under the curve, using the following equation specifically:  
\[AP=\int_{0}^{1}P(R)\,dR \tag{11}\]The mAP is calculated by constructing precision-recall (PR) curves for each category, determining the area under these curves for different categories, and then averaging all the average precision (AP) values to assess the performance of a multi-class classification model. The specific definition is as follows:  
\[mAP=\frac{1}{N_{c}}\sum_{i=1}^{N_{c}}AP_{i} \tag{12}\]  
where \(N_{c}\) is the number of classes in the dataset, and \(AP_{i}\) is the AP value for class \(i\).  
Additionally, this experiment takes into account the number of floating-point operations (FLOPs) and the number of model parameters (Params) as metrics for evaluation. FLOPs are employed to gauge the computational complexity of the model, specifically referring to the total count of FLOPs executed during the forward pass of the network. A lower FLOP count indicates higher computational efficiency for the model. Meanwhile, Params is used to measure the size of the storage space the network requires.
## 3 Encapsulation Protocol
### Dns  
**Intro**  
In a DNS tunnel, the DNS server checks its database for the address to be resolved when it receives a DNS request. If no records are found in the database, the server sends the request to the specified domain. According to known analysis of the DNS protocol, the QNAME field in the query area and the RDATA field in the response area are the highest frequency areas where features are embedded. In terms of analyzing \(\|A\circ B\|_{\rm S}\) from DNStraffic, extracting resource records such as TXT, A, AAAA, and MX and observing the access count of resource records are common methods.  
The parser depends on the number of resource records specified in the header to determine the parsed data, and the last record is considered to have reached the end. As a result, DNS packets can be tipped with any amount of data, and RawUDP is where the information is embedded at high frequency. The statistical distribution of DNS tunnel traffic is very different from that of normal domain. The former obeys random distribution, while the latter obeys Zipf distribution. A number of studies have been conducted on detecting DNS tunnels by analyzing the statistical information in DNS packets, such as domain name length and entropy. Tools such as NSTX, DNSCat, and Iodine have been used to detect DNS tunnels.
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 2 Related Works
### Migration Strategy  
While the above strategies can address the problem of buffer overflow, it decreases the message delivery ratio due to the loss of the message. The most critical is that none of them consider migrating messages to other nodes. Therefore, the authors [25] proposed a message migration replacement storage strategy, which is a Storage Routing (SR) algorithm and determines which messages are migrated to which nodes. The policy defines a migration cost function as the migration standard, where the migration costs consist of storage cost and transmission cost, and it avoids dropping messages by migrating messages to neighbors to resolve node buffer congestion problem. [PERSON] et al. [26] propose the size-aware drop (SAD) strategy, which aims to improve buffer utilization and avoid unnecessary loss of information. Similarly, [PERSON] et al. [27] apply the concept of revenue management, and employ dynamic programming to develop a congestion management strategy for DTN. The author dynamically determines whether to change the state of the message in the node by calculating the maximum expected return over a period of time. However, this migration method does not take into account the available buffer size of the neighbor node and directly forwards the message to the neighbor node. Therefore, we use the dynamic programming method and apply the revenue management concept to develop a reasonable transfer strategy, and consider the storage capacity of the neighbor node to avoid the node buffer congestion after receiving the message.
# An Uplink Channel Estimator Using a Dedicated Instruction Set for 5G Small Cells
###### Abstract
Compared with the general vector processor instruction set, it can complete the channel estimation with fewer instructions. The main tasks involved in this study are: at the transmitting end, the demodulation reference signal (DMRS) is mapped to the OFDM resource block according to the 3 GPP standard; at the receiving end, the channel estimation algorithm of the pilot point is completed by designing a simple LS estimation algorithm; a dedicated instruction set is designed to use the VP for channel estimation of pilot points and interpolation estimation of non-pilot points; the instructions in the subject loop of the channel estimation module are merged and accelerated, which speeds up the calculation of the processor; finally, the instruction overhead estimates of linear, second-order and Wiener filter interpolation in the time-frequency domain are summarized. The rest of the paper is organized as follows. Section 2 introduces the basic model of 5G small cells, including the process of signal sending and receiving and the standard of pilot design. Section 3 introduces the main algorithms used and the methodology of designing a dedicated instruction set and acceleration for the algorithm. Section 4 shows the pseudo code of the algorithm, the assembly code of the instruction and the optimization effect after acceleration. Section 5 contains the conclusion and the significance of the research.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Membrane Properties and Module Configurations
#### 5.1.5 Fiber Length  
Simulation studies have indicated that longer fibers lead to a higher lumen-side axial pressure drop and consequently an uneven permeation flux distribution along the fibers, which can result in the particle deposition being more severe near the suction ends relative to the closed end of the fibers due to a higher local flux [99]. Experimental evidence is in agreement that this non-uniform pattern of particle deposition along the fibers is more apparent for longer fibers (lengths tested were in the range of 0.3-1 m) [100; 195; 196; 197]. On the other hand, fouling control is facilitated by the greater mobility of longer fibers [98], which can be enhanced by bubbling [15].
## 4 Our Proposed Framework
### Phase 1: Encoding Instances Using Restricted Boltzmann Machine  
In the first step, we use RBM to learn the hidden layer's representation (latent representation) of the input data. We refer to these representations of the input data as \"encodings\" because they encode the essential features contained in the input instances, as shown in Figure 3.  
Figure 3: Encoding instances using Restricted Boltzmann Machine.  
Figure 2: Privacy-preserving deep learning framework based on Restricted Boltzmann Machines and Instance Reduction Algorithms.  
The reason we do not utilize other generative models instead of RBM to generate the encodings is that, in contrast to conventional autoencoders that provide deterministic representations of the data in the hidden layer, Restricted Boltzmann Machines produce stochastic representations of the data since they learn the joint probability distribution of the visible and hidden units [25]. This stochasticity works in favor of privacy since it produces harmless noise during alternating Boltzmann sampling, making it difficult to trace the generated encodings back to the actual data points [23]. Therefore, the hidden layer learns the essential properties without being overly constrained by the samples from the training data. Furthermore, as far as we know, no work has used the mentioned method of generating the encodings of the inputs to preserve privacy.
# FANT: Flexible Attention-Shifting Network Telemetry
## 3 FANT System
### Control Plane  
The architecture of the FANT control plane is shown in Figure 2. The INT controller, based on the topology and the mounting locations of the probe collector/generator, invokes an existing probe generation algorithm to create a set of probe traces that cover the entire network. These probe traces for measurement are assigned a number ranging from 1 to the maximum probe number and recorded in the INT controller. Then, the INT controller also adds a record of each probe's type. Initially, all probes are set for basic measurement, but in subsequent cycles, some are adjusted to detailed measurement based on changing requirements. Based on the record of probe traces and their types, the Probe Configure Module configures which probe generator/collector needs to send which numbered probe. It also specifies the particular SR protocol stack and INT instructions that need to be added to each probe.  
Once these instructions are issued, the probe generator/collector's Probe Construction Module is configured to generate packets containing the specified SR protocol stack and INT instructions. Based on the configuration, the probe generator/collector calls the Probe Generation Module to generate the probe packets containing the specified SR protocol stack and INT instructions. When time is \(t\), these packets are injected into the data plane to fetch the network status.  
When the packets are forwarded back to the probe generator/collector by the data plane, they first pass through a Parsing Module that extracts the network measurement information carried by the probes. This information is then written locally via Redis for suspicious metrics detection and transmitted to a distributed database via a Kafka Producer for long-term storage (after which, the data can be consumed by network applications). For each metric, the Result Analysis Module retrieves data from Redis to assess both its absolute value and its variation over time. If the absolute values or their changes exceed predefined thresholds, these metrics are flagged as suspicious. The links associated with these suspicious metrics are then updated to a notification table within Redis.  
The Requirement Collection Model subscribes to this table and is notified when updates occur. Based on the notification, the model updates a record for links which require more comprehensive measurement. After a specified waiting period, the INT controller invokes an attention-switching method based on the suspicious links record and probe traces record, subsequently updating the record of each probe's type. Utilizing the record of the updated probe traces and each probe's type, the Probe Configure Module reconfigures the generator/collector to construct the SR stack and INT instructions for the probe packets for the \(t+1*\delta t\) measurement cycle. When time reaches \(t+1*\delta t\), the probe generator/collector dispatches these probe packets.