esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances
## 3 Proposed Scheme
### Login Phase  
Once \(U_{i}\) was registered, \(U_{i}\) can connect to any server of the cloud by initiating the login phase. The details are shown in Figure 4.  
Step 1: In order to start the authentication phase, \(U_{i}\) must first complete the login phase. Firstly, \(U_{i}\) inserts \(SC_{i}\) and keys his/her \(ID_{i}^{*}\) and \(PW_{i}^{*}\).  
Step 2: Then, \(SC_{i}\) computes and checks:  
\[C_{4}^{*}=h\left(ID_{i}^{*}\parallel PW_{i}^{*}\parallel h\left(n_{U}\right) \right)?=C_{4} \tag{10}\]  
Equation (10) is used to verify the legitimacy of \(U_{i}\) by \(SC_{i}\) for getting access to security parameters provided by \(S_{j}\).  
Figure 3: Server registration sub-phase.  
Step 3: If the verification process is correct, \(SC_{i}\) generates a random nonce \(n_{U}^{new}\), obtains the current timestamp value \(T_{U}^{new}\) and computes:  
\[D_{1}=C_{2}\oplus ID_{i} \tag{11}\]  
\[D_{2}=C_{3}\oplus h\Big{(}T_{U}^{new}\parallel ID_{i}\Big{)}\oplus h(n_{u}^{new}) \tag{12}\]  
\[U_{i}\to S_{j}:M_{5}=\Big{\{}T_{U}^{new},D_{1},PID_{i},D_{2}\Big{\}}\]  
Equations (11) and (12) contain \(U_{i}\)'s information which will be used by \(CS\) to verify its legitimacy. Finally, \(U_{i}\) sends the user authentication request message \(M_{5}\) to \(S_{j}\) through an open communication channel.  
Step 4: After receiving the user authentication request message \(M_{5}\), \(S_{j}\) generates a random nonce \(n_{S}^{new}\), obtains the current timestamp value \(T_{S}^{new}\) and computes:  
\[D_{3}=B_{2}\oplus SID_{j} \tag{13}\]  
\[D_{4}=B_{3}\oplus h\Big{(}T_{S}^{new}\parallel SID_{j}\Big{)}\oplus h\Big{(}n_ {S}^{new}\Big{)} \tag{14}\]  
\[D_{5}=h\Big{(}PID_{i}\parallel T_{U}^{new}\parallel SID_{j}\parallel PSID_{j} \parallel T_{S}^{new}\Big{)} \tag{15}\]  
\[S_{j}\to CS:M_{6}=\Big{\{}T_{U}^{new},D_{1},PID_{i},D_{2},T_{S}^{new},D_{3}, PSID_{j},D_{4},D_{5}\Big{\}}\]  
Equations (13) and (14) contain \(S_{j}\)'s information which will be used by \(CS\) to verify its legitimacy. Equation (15) contains information about \(U_{i}\) and \(S_{j}\) as evidence of its connection attempt. Finally, \(S_{j}\) sends the authentication request message \(M_{6}\) to \(CS\) through an open communication channel.
## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Planning and Data Collection
#### 3.1.2 Maximizing Canopy Penetration
The resulting points were classified into ground returns and non-ground returns using the same classification parameters for the lines corresponding to each site (different sites use different classification parameters), and then relevant statistics from the classified returns were computed. From the results several general conclusions can be reached: lower PRFs and wider beam divergences in general produce more returns, including more ground returns per LASER shot, and in some cases higher ground return densities than acquisitions using higher PRFs. Everyone involved in ALS survey commissioning (or working with an existing dataset) needs to be aware of the fact that, in forested environments, higher shot densities produced by higher PRFs do not necessarily translate into higher ground return densities. It is not the number of shots that matter as much as the energy contained in each laser pulse, and how much of the surface is illuminated. In some cases, when there are thick canopies rather than a simple side overlap of 50% where the surface is illuminated from two different angles, an orthogonal flight grid might be planned, in which the surface is illuminated from four different angles, increasing the chance of finding gaps in the vegetation cover. The pulses from lower PRFs have another advantage over higher PRF pulses with regard to canopy penetration, and it has to do with the fact that lower PRF in general will have slightly shorter pulse widths (shorter pulse lengths), which improves the ALS range resolution. This translates into a better ability to detect ground returns under short vegetation. In this situations full waveform recording ALS systems [34], especially the ones whose data can be post-processed using specialized algorithms, can produce better ground products than discrete systems. It is not that the intrinsic physical limitation imposed by the range resolution is overcome, but specialized algorithms can be used to account for the pulse broadening of a mixed vegetation/ground signal and the ground level might be estimated from a return pulse that originated a few decimeters above ground level.
# Bearing Capacities and Failure Behaviors of F-Type Socket Joint in Rectangular Pipe Jacking Tunnel
## 2 Full-Scale Experiments  
### Experiments Background  
At present, with the development of urban rail transit, China's rectangular pipe jacking engineering practice has achieved rapid development. Longitudinally connected with F-type socket joints is used in most rectangular pipe jacking tunnels. This type of joint is generally not equipped with bolts, which mainly play a waterproofing role and belong to flexible joints. When the uneven settlement of the foundation occurs, the bending deformation of the joint opening is very easy to occur at the joint location. The excessive longitudinal uneven deformation leads to the local failure of the joint, which greatly reduces the overall waterproof effect, resulting in leakage, water dripping, even sand and mud leakage and other diseases affecting normal use, endangering structural safety, and even causing engineering disasters. Therefore, this paper carries out relevant research on the bending resistance of the longitudinal joint of rectangular pipe jacking tunnel, explores the mode and characteristics of the joint bending failure, as well as the relationship between the joint stress and the joint opening. Through the indoor bending test of the rectangular pipe jacking tunnel F-type socket joint, the joint moment-relative rotation relationship is studied, and the joint moment-relative rotation model is established.
# A Lyapunov-Optimized Dynamic Task Offloading Strategy for Satellite Edge Computing
## 2 Related Works
However, these authors only considered offloading problems within a single time slot, and dynamic offloading strategies for satellite-ground networks under multiple time slots have not been investigated. Furthermore, [PERSON] et al. [5] proposed to jointly study the collaborative computation and resource allocation among LEO satellite networks. In the paper, the user tasks are selected for Local computing or offloaded to the satellite for collaborative computing. Specifically, the arrival of tasks was modeled as a Poisson process, and the task completion rate was maximized under the delay constraint. However, the author only considered the metric of task completion rate and did not consider the impact on task processing delay and energy consumption. Besides that, the DQN algorithm uses an experience replay mechanism. The interaction of the algorithm with the environment requires a large amount of memory resources, which is not friendly to satellites with limited resources [16; 17]. To address the shortcomings of the scenarios and algorithms in the aforementioned studies, this paper proposes a multi-task, multi-time slots, multi-satellites offloading scenario, and suggests a succinct and effective algorithm as the solution.
# Current Progress Regarding _Cordyceps militaris_, Its Metabolite Function, and Its Production
## 2 Biological Compounds of _C. militaris_
### Ergothioneine  
Ergothioneine, with a molecular formula of C\({}_{9}\)H\({}_{15}\)N\({}_{3}\)O\({}_{2}\)S and a molecular weight of 229.3 g/mol, is a water-soluble thiol compound. It is classified as a non-protein amino acid and is naturally synthesized by certain bacteria, plants, and fungi. Notably, mammals do not produce ergothioneine endogenously, underscoring its importance as an essential dietary nutrient. It has gained attention due to its identification as a biogenic key substrate of the organic cation transporter 1 (OCTN1), which has a protective role in monocytes and is associated with autoimmune disorders like rheumatoid arthritis and [PERSON]'s disease [93]. _Cordyceps militaris_ is a rich source of ergothioneine, as its fruiting bodies contain a concentration of 782.3 mg/kg dry weight (DW), and that of mycelium ranged up to 130.6 mg/kg DW [19]. Another study estimated a concentration of 409.8 mg/kg DW in its fruiting bodies [17].  
Ergothioneine exhibits potent antioxidant properties, as it can scavenge hydroxyl radicals and inhibit their generation from hydrogen peroxide in a copper or iron ion-dependent manner. Additionally, it acts as an inhibitor of the copper ion-dependent oxidation of oxyhemoglobin, and of arachidonic acid peroxidation mediated by mixtures of myoglobin (or hemoglobin) and hydrogen peroxide. Furthermore, ergothioneine can protect alpha 1-antiproteinase from inactivation by hypochlorous acid due to its ability to effectively scaveng this reactive molecule [33].  
Anti-inflammatory properties, including the modulation of interleukin (IL)-6, were attributed to ergothioneine. Furthermore, it may mitigate peroxyacetyl nitrate (PA)-induced cell death by diminishing the activity of the mitogen-activated protein kinase (MAPK) cascade [35].  
The antiaging effect of ergothioneine is based on its ability to inhibit ultraviolet A (UVA)-induced activator protein (AP)-1 translocation, which in turn inhibits collagenolytic matrix metalloproteinase-1 activation and type I procollagen degradation [37].  
Ergothioneine, along with its precursor, trimethyl histidine, and trehalose are considered anti-stress compounds that are maintained at high levels during starvation-mediated fasting [38].  
Through activation of Nrf2 and FoxO3, ergothioneine exhibited cytoprotective effects, and FoxO3 appeared to play a role in erythroid proliferation and differentiation [94].  
The radioprotective effect of ergothioneine is attributed to the reduction of ergothioneine disulfide, which is formed by irradiation-generated hydrogen peroxide, to ergothioneine in a neutral solution. Subsequently, the regenerated ergothioneine can again react with hydrogen peroxide [40].
# IoT-Based Implementation of Field Area Network Using Smart Grid Communication Infrastructure
## 2 Hierarchy-Based Smart Grid Communication Infrastructure for Smart Farming Applications
### Field Area Network  
A field area network is applicable for agriculture automation. As shown in Figure 2, it consists of various sensor nodes, a smart meter, renewable energy resources and data acquisition, and a control system for complete monitoring and the control of a smart farm. Smart meters receive commands from the central power grid and control various appliances based on the received commands. Smart farming necessitates various information such as weather conditions, temperature, soil humidity and fertility, source, and load conditions for the operation of various ancillaries, metering data, the time of day usage, auxiliary power, etc. Wireless sensor networks are used for sensing, measurement, and communication of these parameters. A communication backbone can facilitate a complete automation of pumping, harvesting, spraying, and fertilizer spreading. Smart farming includes sensing, measurement, data acquisition, monitoring, control, and the remote access of field area network parameters. A system for remote monitoring and the control of a plug-in hybrid electric vehicle (PHEV) can also be designed for smart farming electric vehicles. A PHEV consists of a gasoline or diesel engine with an electric motor and a rechargeable battery that can be recharged from an electrical power outlet [16]. A field area network is connected to the cloud for web-based monitoring and the control of various parameters. Seamless remote data acquisition can create an interactive system between researchers, experts, and  
Figure 1: Hierarchy-based smart grid communication infrastructure for smart farming applications.  
various stakeholders. Precision farming is based on site-specific conditions. The field area network ranges for the coverage area of a few meters. IEEE 802.15.1 (Bluetooth), IEEE 802.15.4 (Zigbee), IEEE 802.3 (Ethernet), IEEE 802.11 (WLAN/Wi-Fi), and narrowband power line communication (PLC) standards, among others, can be used for field area networks [26; 27; 28; 29; 30; 31].
## 1 Introduction  
### Background and Motivations  
The vehicular delay-tolerant network (VDTN) is a novel transmission architecture designed to address issues, such as the potential unavailability of an end-to-end path between the source and destination in vehicular communication environments [1, 2, 3, 4]. In VDTNs, intelligent connected vehicles (ICVs) can store, carry, and forward data asynchronously [5]. Communication is possible even when there is no continuous network connection. This transmission architecture is particularly suitable for scenarios where infrastructure is inadequate, such as highways or remote areas [6]. In this situation, by adopting \"store-carry-forward\" mechanisms, vehicular networks can effectively reduce link interruptions, thereby enhancing the efficiency of information exchange between ICVs [7; 8; 9].  
The application domains of VDTNs are diverse, including autonomous driving, geographic information retrieval, and emergency response services. In VDTNs, road side units (RSUs) typically serve as local central nodes, responsible for data forwarding and information caching [10]. However, because the RSUs are fixed, they will face serious challenges in actual vehicular networks. Especially in environments with high mobility, fixed RSUs can lead to reduced delivery ratio and increased delivery delay. Additionally, in rural road scenarios, the coverage of fixed RSUs is limited, which affects transmission performance [11; 12; 13].  
Facing the above challenges, the introduction of unmanned aerial vehicles (UAVs) as replacements for traditional RSUs has emerged as a potential solution [14; 15; 16]. UAVs are highly mobile and can be deployed flexibly, enabling them to cover areas that are difficult for fixed RSUs to reach. Therefore, the limitations of RSUs can be addressed to provide wider network coverage. In addition, the coverage of blind spots can be reduced. In this situation, UAVs not only enhance network flexibility but also facilitate rapid deployment in emergency scenarios, positioning them as a critical technique for optimizing the performance of VDTNs [17; 18; 19]. Furthermore, using UAVs in VDTNs can promote vehicular applications in complex scenarios and provide feasible ideas and technical support for the development of future intelligent transportation systems.
## 4 Experiments and Result  
The operation of the proposed system has been verified through demonstration tests and an aircraft maintenance simulation was developed, as shown in Figure 4. This simulation was proposed as educational material for aircraft maintenance procedures, equipment, and terminology. We selected an unfamiliar topic to examine the educational effects on the subjects. The aircraft model is the KT-100, an introductory aircraft modified from Korea's first civilian aircraft, the KC-100. The simulation contains the maintenance process of the KT-100 based on our system. Therefore, multiple users can access the process based on a single aircraft and multi-party interaction is possible. The actions of all users are shared, and thus the maintenance process can be carried out together. In this experiment, an expert user and a beginner user learned the training content together. The purpose of this study was to confirm the educational qualities and presence offered by our system compared with those offered by video education.
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 5 Conclusions and Future Work  
In this paper, BESDC is proposed to reduce the energy consumption for extending the IoT lifetime with a given bounded error. Two-tier BEA is applied to assign the proper bounded error to EDs and SNs. Then, EB-RLE and EB-HC are used to improve temporal and data correlation (codebook) compression in sensor data within given bounded error. The spatial correlation compression is applied for SN to compress the bounded error sensor data difference among neighboring sensed data received from different EDs. Thus, the total size of transmission data in sensor-tier devices and fog-tier machines can be minimized to achieve energy and storage saving within the given bounded errors.  
The experiments for four different environmental datasets in Taiwan, including forest temperature, PM2.5 and CO of air quality and seismic wave, demonstrate that our BESDC can reduce transmission data by around 28% to 68% and save around 20% to 72% energy consumption for ED when assigning 0.25% to 1% of error tolerance, respectively. For SN using the same bounded error range, spatial correlation compression can reduce transmission data by around 34% to 73% and save around 45% to 78% energy consumption, given that the SN connects to two neighboring EDs. Moreover, for SN with two faraway EDs, the transmission data can be reduced by around 28% to 72% and the energy consumption can be saved from 38% to 77%.  
Since the proposed BESDC scheme is implemented between perception and network layers of IoT, we believe it can help other energy-efficient IoT schemes applying higher layer approaches, such as network topologies and routing protocols (e.g., [25; 46; 26]), to reduce their payload overhead for further enhancing their energy-efficient IoT services, without affecting the QoS2/QoD required.  
The future work will consider improving the proposed BESDC for cost-effective prediction of the lost sensor data via learning from the data received previously. We will also consider applying the smart contract technique in fast big sensor data query [40; 41] with different bounded errors for users on differentiated service levels, since the smart contract using blockchain technology can preserve the resilience and transparency for big sensor data access at different levels of privacy controls.  
Conceptualization, R-I.C.; methodology, R-I.C.; software, Y-H.C., L.-C.W.; validation, R-I.C., C.-H.W.; formal analysis, R-I.C., Y-H.C., C.-H.W.; investigation, Y-H.C., L.-C.W., C.-H.W.; data curation, R-I.C., Y-H.C.; writing--original draft preparation, Y-H.C., L.-C.W., C.-H.W.; writing--review and editing, R-I.C., Y.-H.C., C.-H.W.; visualization, Y-H.C., L.-C.W., C.-H.W.; supervision, R-I.C.; project administration, R.-I.C.; funding acquisition, R.-I.C. All authors have read and agreed to the published version of the manuscript. This research was funded by MOST Taiwan, grant numbers 108-2410-H-002-230-MY2 and 109-3116-F-002-002-CC1 and the APC was funded by 108-2410-H-002-230-MY2 and 109-3116-F-002-002-CC1. The authors declare no conflict of interest.
# Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence
## 5 Experiments
### Sentence-to-Video Retrieval Results  
We summarize the results of the sentence-to-video retrieval task on the MSR-VTT dataset in Table 1. We compared the proposed method to the existing methods [28; 32; 38; 55]. The proposed method obtained 7.1 (dual) at R@1, 21.2 (triple) at R@5, 32.4 at R@10 (triple), and 29 at MR (triple). These are competitive with [32; 55], which are the state of the art.  
VSE and VSE++ adopt a triplet ranking loss, and VSE++ incorporates hard-negative samples into the loss to facilitate practical training [56]. We adopted this strategy. The results show that VSE++ performs significantly better than VSE at every R@_k_. Although the single-space model and VSE++ adopted similar loss functions, we found slight improvements in performance. However, the dual-space model achieves much better performance compared to VSE++. The results demonstrate the importance of using the sequential visual information of videos for learning an embedding space.  
Multi-Cues [32] calculates two similarities in separated embedding spaces and then averages them to produce a final similarity. The proposed method has higher performance compared to Multi-Cues. The similarity aggregation strategy is the main difference between the proposed method and Multi-Cues. Note that the average suffers from aligning videos and sentences due to their variations. Thus, some videos need global visual features, and some need sequential visual features. The proposed method has a flexible strategy for merging similarities. The experimental results show that the proposed strategy is more useful for measuring similarity than a naive merging approach with equal importance to each embedding space, such as average.  
The Cat-Feats [55] embeds videos and sentences by concatenating feature vectors extracted by three embedding modules: CNN, bi-directional GRU, and max pooling. Cat-Feats is slightly better than the proposed method at R@1 and R@5, e.g., 7.7 and 7.1 for Cat-Feats and dual-space at R@1, respectively. However, the proposed method (triple-space) outperforms Cat-Feat at R@10 and median rank, such as 32 and 29 by Cat-Feat and the triple, respectively. These results imply that the proposed method and Cat-Feats can assist each other. There is a possibility to improve performance by incorporating the feature concatenation mechanism used in Cat-Feat into the proposed method.  
Finally, the proposed method with triple-space achieves better results than single- and dual-space at three metrics: R@5, 10, and median rank. Therefore, The results show that integrating multiple similarities can lead to a better, reliable retrieval performance.
## 2 Materials and Methods
### Classifier Comparison
#### 2.4.1 Minimum Distance Classifier  
The minimum distance method (MDM) calculates the Euclidean distance \(d(x,y)\) between the spectral signatures given in the training data set and the spectral signatures of the image pixels. The spectral distance is calculated through the following expression:  
\[d(x,y)=\sqrt{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}} \tag{4}\]  
where \(x\) is the spectral signature vector of an image, \(y\) is the spectral signature vector of the training area and \(n\) is the number of bands of the image. Once the spectral distance is computed for every pixel, the class with the closest spectral signature to the training set is assigned according to the following discrimination function [56]:  
\[xeC_{k}\Longleftrightarrow d(x,y_{k})<d(x,y_{j}) \tag{5}\]  
where \(C_{k}\) is the land cover macro-class or class, \(y_{k}\) is the spectral signature of class \(k\) and \(y_{j}\) is the spectral signature of class \(j\). This equation is valid when \(k\
eq j\).