esa-sceva/satcom-chunk-collection huggingface dataset:
# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
## 5 To Cache and Replace Transient Contents in VNDN
### Cache Consistency  
In principle, multiple versions of the same content may exist in VNDN networks. The work in Reference [41] provides a detailed analysis of cache consistency in VNDN and shows that maintaining only the FreshnessPeriod in the Data packet, as foreseen in vanilla NDN, is not enough to ensure consistency in the CS. Indeed, the FreshnessPeriod does not indicate when the content has been generated, but only the time period it can remain in the CS since it has been cached. The value is also immutable and the cacher(s) cannot update it. In vehicular environments, especially when mobile nodes act as data mules, this behavior can create some major inconsistency effects, as demonstrated in the following example from Reference [41]. Let us consider a simple scenario where an RSU monitors the average speed on a certain road and produces Data packets named \(avgSpeed\) with FreshnessPeriod\(=60\) s. An NDN car A passing nearby requests the Data at time \(t=0\) s and it is allowed to cache it for \(60\) s. Then, when \(t=50\) s another vehicle B asks for the same packet and receives it from A. By reading the FreshnessPeriod, the packet could be stored in the CS of node B for \(60\) s, but the actual residual lifetime of the packet is only \(10\) s. If, at \(t=70\) s, a vehicle C receives the same packet from B, the information is actually stale but C does not have any means to understand this. Therefore, to avoid cache inconsistency and correctly compute the residual lifetime, a timestamp should be added in the Data packet header. If adding additional timing information is not possible, e.g., when data are provided by IoV sensors implementing low-rate and low-power protocols and there are limitations in the packet size, consumers can apply forecasting methods, like the one considered in Reference [50], to estimate the residual lifetime.
## 2 Materials and Methods  
### Experimental Setup for Laser Transmission Welding  
For welding, a thulium fiber laser (\(\lambda\) = 1940 nm, TLR-120-WC-Y12, IPG Laser GmbH, Burbach, Germany) with 120 W (cw) power was used. Figure 1 shows the experimental setup. The fixed-focus objective with an adjustable, comparably high NA of 0.4 to 0.6 was mounted on an optical rail with slides. The NA was set to a value resulting in a Rayleigh length of 0.3 mm inside the material. A fine-threaded spindle moved the rail, enabling the variation of the distance between the optics and the specimen and, thus, the adjustment of the laser focus position. A measurement system with 0.01 mm resolution controlled the rail's position. A clamping device with a conical slit hole fixed the 2 specimens (50 \(\times\) 15 \(\times\) 1.05 mm\({}^{3}\) each) in an overlapping position. It is moved by a 2-axis linear system enabling feed rates \(v\) of up to 300 mm/s. We have already presented a detailed description of the experimental setup, the processing parameters and the simulation-based process layout [14]. The samples are made of semi-crystalline polyamide 6 (Ultramid B3S) provided by BASF SE (Ludwigshafen am Rhein, Germany) and molded by Gerresheimer Regensburg GmbH (Regensburg, Germany) with a coefficient of absorption \(\alpha\) of 0.8 1/mm at a wavelength of 1940 nm.  
The welds were processed at 3 different laser focus positions, \(z_{focus}\), of 1.0 mm, 1.1 mm, and 1.2 mm at a 200 mm/s feed rate between 0.14 J/mm and 0.46 J/mm energy per unit length (\(E\)). The value of \(z_{focus}\) = 1.0 mm was set to the joining zone by processing and evaluating little seams. For every parameter setting, one sample containing at least six weld seams was manufactured.
# Analysis of the Energy Efficiency of a Pneumatic Booster Regulator with Energy Recovery
## 4 Simulation and Experiment on VBA-R
### Energy Efficiency Test  
To assess the energy efficiency of the booster regulator, an air power metre (APM) [24] was used to measure the energy input and output. The core element of an APM is its quick laminar flow rate sensor, which can convert the pressure and flow rate into air power based on Equation (2). The real-time energy consumption can also be monitored by integrating the air power.  
Figure 4: Flow chart of simulation programme.  
The experiment circuit was configured as shown in Figure 5. It consisted of a precision regulator (IR3020-02A, SMC, Tokyo, Japan), a booster regulator (VBA20A-03, SMC, Tokyo, Japan; VBA-R), two APMs (APM-L-800s, TOKYO METER, Tokyo, Japan), an air tank (VBAT10A1-15, SMC, Tokyo, Japan), a pressure sensor (AP-13S, KEYENCE, Osaka, Japan), a throttle valve (AS300, SMC, Tokyo, Japan), and a data acquisition card (TNS6810, INTERFACE, Hiroshima, Japan). The accuracy of the APM and pressure sensor were \(\pm\)2% F.S. and \(\pm\)0.5% F.S., respectively.  
To verify the energy recovery performance considering the cost and period, we used an alternative method rather than manufacture a new VBA-R. The experimental apparatuses used to verify the energy recovery of the VBA-R are shown in Figure 6. According to the structure shown in Figure 2, a double-acting, double-rod cylinder (CG1 WLN, SMC, Tokyo, Japan) and two double-acting, single-rod cylinders (CDG1 YL, SMC, Tokyo, Japan) were assembled together. The 7/2 solenoid valve was replaced by two 5/2 solenoid valves (SV3200R, SMC, Tokyo, Japan), which were connected in parallel and controlled by a programmable logic controller (PLC).  
The details of experiment apparatuses are listed in Table 1.  
Figure 5: Schematic diagram of booster regulator test. 1: air source, 2: regulator, 3: air power metre (APM), 4: booster regulator, 5: air tank, 6: APM, 7: throttle valve, 8: silencer, 9: pressure sensor.  
Figure 6: Schematic diagram of VBA-R. 1,2,3,4: check valve, 5,6: auto switch, 7,9: single-rod cylinder, 8: double-rod cylinder, 10,11: 5/2 solenoid valve.  
With this equipment, the configuration of the experiments corresponding to Figures 5 and 6 are shown in Figure 7.  
In the test circuit, the APM was mounted at the outlet of tank instead of the booster regulator. As we know, the cheque valve opens only when the pressure in the boost chamber is larger than that in the tank. In that situation, the cheque valve opened intermittently, and air was exhausted in an unsteady flow. The flow rate during unsteady flow is difficult to measure accurately. However, the flow rate output from the tank was almost steady for the constant load, which was easy to measure.
# Higher-Order-Mode Triple Band Circularly Polarized Rectangular Dielectric Resonator Antenna
## 2 Materials and Methods
### Double Stub Feed DR Antenna (DRA2)  
The antenna in Figure 2 is improved by adding 5.27 mm \(\times\) 1 mm stub located on top of a single stub-shaped monopole (see Figure 5). It is improved by adding a 5.27 mm \(\times\) 1 mm stub located on top of a single stub-shaped monopole (see Figure 5). This resulted from an improvement in the antenna operating frequency and impedance bandwidth. The antenna resonates at 2.82 GHz (13.13%), 3.8 GHz (18.4%), and 5.72 GHz (1.75%), as shown in Figure 6a. In contrast, the antenna's AR occurs at 3.1 GHz (5.3%) and 5.2 GHz (12.15%), with minimum AR at 0.3 dB. The configuration exhibits fundamental mode at 3 GHz and higher-order mode TE\({}_{223}^{Y}\) and TE\({}_{145}^{Y}\) at 4.9 GHz and 5.4 GHz respectively, shown in Figure 7. Introducing double stubs in the design improved the S\({}_{11}\) at 3 GHz but reduced the matching at 5 GHz. Additionally, the AR BW at 5 GHz was enhanced, as can be seen from the result shown in Figure 6b. Thus, further optimization on the design was carried on in the later section.  
Figure 3: Result for a single stub DR antenna (DRA1). (**a**) Reflection coefficient and (**b**) axial ratio.
## 4 Results
### Dataset  
This study used two publicly available datasets for an evaluation of the proposed system. The first dataset used in this study was the University of Rzeszow Fall Detection (URFD) dataset [80], created at the University of Rzeszow, and comprises 70 sequences at a resolution of 640 \(\times\) 480, including 30 falls and 40 activities of daily living, such as sitting, crouching, picking up objects, lying on the floor, and lying on a bed or couch. The dataset features a total of 13,000 images--3000 dedicated to fall sequences and 10,000 to daily activities. They are categorized into three classes: non-falling activities, post-fall scenarios (not used for classification in this study), and active falls (used for classification).  
The other dataset was curated by the \"Laboratoire Electronique, Informatique et Image\" (LE2I) [81] and contains 191 annotated videos from various elderly living environments, including home (60 videos), coffee room (70 videos), office (33 videos), and lecture room environments (28 videos), 130 out of which show falls and the rest of which show daily activities. This dataset includes a range of actions, such as walking, sitting on a chair, sitting on a sofa, and bending over, providing a comprehensive view of typical elderly movements. These activities are particularly valuable as they enable the evaluation of fall detection models to determine whether they can accurately distinguish actions like sitting and bending from actual falls. This dataset was captured using a single camera with a resolution of 320 \(\times\) 240 and a frame rate of 25 frames per second. However, in this study, these videos were resized to 640 \(\times\) 480 resolution to match the input resolution of YOLOv8.
## 3 Proposed Scheme
### Obstacle Detection Notification  
When a CV detects an obstacle using its sensors (e.g., cameras or LiDAR), it broadcasts an obstacle detection notification message, which includes the obstacle's position and the index of the closed lane, to the vehicles in all lanes behind it. In this paper, we assume the index starts from the right edge lane. The index of the right edge lane of a three-lane road is 0, and that of the center lane is 1. We assume that the message is disseminated to vehicles on the same road within a few kilometers range using a flooding protocol with appropriate optimization. A vehicle that detects an obstacle will continue to broadcast the same message periodically until it passes the obstacle. Each obstacle detection notification message is valid during a given period of validity, e.g., 60 s. If the obstacle is cleared, no vehicle broadcasts obstacle detection notification messages, and thus the following vehicles are not notified. Therefore, vehicles can know the obstacle has been cleared indirectly by not receiving obstacle detection notification messages. Then traffic flow returns to normal. However, the obstacle avoidance operation of each vehicle continues until the end of the validity period of the last received obstacle detection notification message.
## 2 Vortex Acoustic Signal Feature Extraction
### TRM Extracts Vortex Acoustic Signal Features  
TRM technology, as a current research hotspot in underwater acoustics, has been applied to the extraction of vortex acoustic signal characteristics. Acoustic waves are affected by vortex fields, and the changes in sound speed are associated with the propagation directions \(\mathbf{n}\) and \(\mathbf{m}\). The time-reversal invariance of acoustic waves can be disrupted, preventing the effective compensation for phase changes, and instead, wavefront deformation may be further amplified. As shown in Figure 1a, when TRM1 emits plane waves into a vortex field, acoustic waves passing through the upper part of the vortex are accelerated due to flow velocity, and reach TRM2 earlier, while those passing through the lower part are delayed due to speed reduction, resulting in phase shifts.  
As shown in Figure 1b, TRM2 first emits the delayed acoustic waves. The waves that initially arrived are further accelerated in the lower part of the vortex field, while the delayed waves are further decelerated in the vortex field. After mutual transmission between TRM1 and TRM2, the received acoustic waves experience additional acceleration or deceleration, which enhances the wavefront deformation.  
Numerical simulations of the received signals using TRM technology in both vortex and non-vortex conditions provide the phase difference in vortex acoustic signals under different iterations, offering a basic approach for extracting the phase characteristics of vortex acoustic signals.
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
## 2 Related Work  
### Optimizing SGX Performance  
Several studies have been reported to reduce the overhead of using SGX. In particular, they have focused on improving the SGX performance based on system-level optimization by modifying SGX runtime or kernel module [16; 21; 22; 24]. However, the system-level optimization cannot precisely reflect the application's characteristics (e.g., threading model), so it does not always work perfectly for legacy SGX applications. Our proposed methodology achieves application-level optimization by measuring the efficiency of leveraging switchless calls affected by the application-specific characteristics without modifying the legacy codebase.  
SCONE [24] suggests the container-based SGX framework that enables the execution of legacy applications without modification by providing its library. Based on user-level threading and asynchronous system call mechanism, SCONE avoids thread blocks due to synchronization.  
[PERSON] et al. [22] design SGXKernel that leverages library OS to execute unmodified binaries to make it more practical, similar to the SCONE framework. The proposed system includes asynchronous communication primitives between enclaves, called delegated calls. With delegated calls, an SGX enclave does not trigger any enclave switches when communicating with other enclaves.  
Similar to switchless calls that we further explain in Section 3.2, Eleos [21] propose exitless paging mechanism and exitless system calls to reduce both main factors of SGX overhead, EPC paging, and enclave transition. In particular, they implement an exitless system call service routine in a Remote Procedure Call (RPC) fashion, and the RPC-based implementation can be transparently integrated with a vanilla SGX SDK.  
[PERSON] et al. [16] explore the performance implications of running real-world applications on SGX hardware to achieve performance optimization. Based on the preliminary evaluations with various micro-benchmarks, they build a new application-enclave interaction framework called HotCalls. The core idea of HotCalls is utilizing shared variables located in the untrusted part of the SGX application and spin-lock synchronously. The evaluation shows that HotCalls dramatically improves both latency and throughput of popular macro-benchmarks, such as memcached, openVPN, and lighthttpd.  
Meanwhile, [PERSON] et al. [17] extend existing transport layer security (TLS) libraries to protect sensitive data, such as session contexts and private keys. While our methodology optimizes the performance of legacy codebases, it ports and redesigns an existing TLS library to reduce enclave transition overhead. To make it become SGX-friendly, they modify the implementation of enclave entry-calls and out-calls. For this, it uses an in-enclave memory pool and thread locks implementation to handle operations that raise enclave transitions internally within an enclave.
## 2 Brief Overview of Geographic CA Modeling and Tools
### Previous General-Purpose CA Modeling Tools  
Currently, there are few general-purpose, explainable CA modeling tools usable for representing various geospatial phenomena. One example is _SpaSim_[57], a spatial modeling tool developed to overcome requirements of programming skills and the specialization of CA tooling already observed thirty years ago. Since then, the _Dinamica Geoprocessing Objects (EGO)_ software [45] was presented as an openly available geosimulation modeling solution with phenomena-agnostic functionality. However, the introductory documentation, expansive and jargon-filled functor list, and interface design pose difficulties for users unacquainted with GIS analysis terminology or other GIS software. CA model-buildingworkflows are also possible within the TerrSet 2020 GIS software [58] using the _Macro Modeler_, _CELLATOM_ module, and the experimental (and problematic) _CA-Markov_ module [59]. For example, the _Macro Modeler_ supported CA model implementation of landslides [49] and insect infestation scenarios [60]. Still, the TerrSet GIS software is challenging for users who are unfamiliar with performing raster GIS operations, preparing data for use in different GIS software environments, resolving ambiguous and often undocumented errors, and navigating the legacy UI design. Furthermore, initial hands-on model-building experiences are often negatively impacted by the disconnection of rote implementation and theory of geographic CA. When scientific procedures are predominately composed of memorized, unintuitive procedures, beginners avoid experimenting beyond the guiderails they were taught [61]. Hence, geographic CA modeling tools directly linked to key terminology and integrated within existing GIS software environments would better support users of all skill levels.
## 4 Security Assumptions
### Unforgeability  
In this scheme, we define the secure digital signature scheme in the above. First, we define an attacker \(\mathcal{F}^{*}\), whose ability is to forge a signature that can be verified successfully through the \(\mathit{Ver}(\cdot)\) verification function with non-negligible probability \(\varepsilon^{\prime}\). We also define a simulator \(\mathcal{D}\) that adopts \(\mathcal{F}^{*}\)'s ability to break the underlying hard problem (such as the RSA factoring problem) in the above secure signature scheme. After \(\mathcal{D}\) is given the environment parameters \(G(\cdot)\), it can start the protocol simulation with \(\mathcal{F}^{*}\). \(\mathcal{F}^{*}\) can make the signature queries to the \(\mathcal{D}\). \(\mathcal{D}\) will also output the signature back according to the received input \(m\) from \(\mathcal{F}^{*}\) on some user \(i\). After this simulation, if \(\mathcal{F}^{*}\) generates a forged signature \(S^{\prime}_{i,j+1}\), the verification result of \(S^{\prime}_{i,j}\) is valid. We then have  
\[\mathit{Pr}[\mathcal{D}^{\mathcal{F}^{*}\longrightarrow S^{\prime}_{i,j+1}}| \text{Use }S^{\prime}_{i,j+1}\text{ to solve the RSA factoring problem}]\geq \tag{2}\] \[\mathit{Pr}[S^{\prime}_{i,j+1}\longleftarrow\mathcal{F}^{*}( \mathit{Sig}(sk_{i},\cdot),\mathit{Ver}(pk_{i},\cdot),i\in\{U,V,HCA\})| \mathit{Ver}(pk_{i},S^{\prime}_{i,j+1})=1]\geq\varepsilon^{\prime}.\]  
In fact, if there is no attack \(\mathcal{F}^{*}\) that can make a forged signature pass the verification successfully with non-negligible probability \(\varepsilon\), then we cannot use \(\mathcal{F}^{*}\) to solve the RSA factoring problem with non-negligible \(\varepsilon^{\prime}\) probability.  
**Lemma 1** (Unforgeability).: _First, we define Sig, which is a secure digital signature function and equips two secure hash functions, \(H_{1}\) and \(H_{2}\), which can be replaced with two random oracles functions \(\mathit{RO}_{1}\) and \(\mathit{RO}_{2}\). In our proposed EHR scheme, we also define our proposed EHR scheme with unforgeability (Unf), which satisfies the following situations. In other words, if Sig is \((t^{\prime},\varepsilon^{\prime})\) and unforgeable, then_  
\[\mathit{Adv}^{\mathit{Unf}}_{\mathcal{F}^{*},\mathit{Sig}^{H_{1},H_{2},\mathit {RO}_{1},\mathit{RO}_{2}}}(\theta,t^{\prime})\leq\frac{1}{2\cdot I^{3}\cdot q_ {s}}+\varepsilon^{\prime}, \tag{3}\]  
_where \(t^{\prime}\) is the maximum total experiment time, including an adversary execution time, 1 is an upper bound on the number of parties, at most signature oracle \(q_{s}\), and \(\varepsilon^{\prime}\) is taken over the coin flip of our EHR scheme._
# Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
## 3 Method
### Network Architecture  
CNN-SA is a multi-modal data fusion network that can simultaneously handle various types of input information from different sensor systems. The overall architecture of CNN-SA is illustrated in Figure 5 and mainly comprises the point cloud feature extraction module, image feature extraction module, self-attention mechanism, and multi-layer perceptron. The self-attention mechanism, among these components, serves as the feature fusion module.  
Figure 5: The overall architecture of the CNN-SA.  
The design and layer selection of the point cloud feature extraction module were primarily based on the characteristics of 3D point clouds and the advantages of CNNs. By employing three 1D convolutional layers, diverse features of 3D point clouds were extracted and downsampled while incorporating batch normalization operations and non-linear transformations through activation functions to effectively enhance the representation capacity of 3D point clouds, thus boosting the feature extraction capability of the entire network.  
The image feature extraction module provides an effective and reliable approach to extracting image features from a network model. This module utilizes 2D convolutional and max-pooling layers to progressively compress input images and extract a series of features. By mapping these features to a vector space through fully connected layers, the module retains the original image information while effectively reducing feature dimensionality, thereby improving the computational efficiency and generalization ability of the model.  
The self-attention mechanism can adaptively weigh the information at different positions in the input sequence to enable more effective utilization of the information in the input sequence during the input processing of the network, as well as to improve the network performance and accuracy. In addition, the self-attention mechanism can filter irrelevant data in the input sequence such that the network is more robust to noise and redundant information in the input sequence.  
The multi-layer perceptron layer applies a series of linear and nonlinear transformations to the input features, and the outputs of this layer are mapped to the desired output dimension. By learning more complex and abstract representations in the feature space of the input data, the network achieved a better predictive performance.  
The CNN-SA employs a point cloud feature module and an image feature module to process multi-source and multi-modal data, extracting their features. Additionally, it introduces a self-attention mechanism to weigh and sum features of different modalities. Finally, it uses a multilayer perceptron to map the outputs that pass through the layer to the desired output size, adaptively focusing on the importance of different modal data. This enables mutual support, complementarity, and correction among the multi-modal data.