esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to investigate the relationships between the mechanical properties of plant roots and the soil reinforcement characteristics of the dominant species in the dominant riparian plants under various flooding durations. The objective was to comprehensively evaluate the optimal flooding duration for each plant under various flooding durations. This research was conducted to provide a scientific basis for plant restoration efforts. The primary focus of the study was on common species found in the middle and lower reaches of the Yangtze River, including _Carex_, _Cynodon_, and _Eleusine_. These species were cultivated in a local field setting and subsequently subjected to flooding tests of varying durations. The diameter of the root system gradually increases with prolonged flooding duration, while other root morphologies exhibit a trend of initially increasing and then decreasing. The flooding environment significantly influences the relationship between root diameter and the mechanical properties of the roots. This condition adversely affects _Carex_, whereas it has a beneficial impact on _Cynodon_ and _Eleusine_. During the early stages of flooding, the shear strength of the plant root-soil complex increases; _Carex_ is optimally applied in the restoration and protection of areas subjected to three to four months of flooding, with its ornamental value being particularly pronounced. _Cynodon_ performs best in areas with up to six months of flooding, _Eleusine_ is especially effective in regions with less than two months of flooding.  
flooding duration; root morphology; root strength; shear strength Article
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 5 Privacy Preserving Federated Learning
### Homomorphic Key Generation and Distribution  
As shown in step 1 of Figure 2, individual public-private key pairs (\(pk_{i}\), \(sk_{i}\)) for \(i\in\{1,\cdots,N_{c}\}\) are generated at the KGC and are sent to clients for encryption and decryption through secure channels, where \(N_{c}\) is the number of clients participating in the training process of the proposed system. Before the KGC distributes the key pairs, it performs an authentication procedure for the clients and delivers the public-private key pairs to authenticated clients. The clients' public keys for encryption and a list of authenticated clients are transmitted to the CS and the CP, and the CS utilizes only local model parameters from authenticated clients. In addition, the partially private keys \([psk_{i}^{(1)},psk_{i}^{(2)},\cdots,psk_{i}^{(K)}]\) generated by the KGC are only sent to the CS and the CPs through secure channels for cooperative decryption, respectively.
# Two Deformation Behaviors in Resin-Impregnated Glass Fibers under Different Flow Rates
## 2 Background and Theory  
LCM processes are widely used to manufacture composite materials for automobile and ship applications in a cost-effective manner. RTM and vacuum-assisted RTM are representative LCM manufacturing processes. One of the main technologies in LCM is the impregnation of fiber reinforcements with a liquid resin. Resin flow in the LCM process can be regarded as a Newtonian flow in a porous medium, which can be modeled using [PERSON]'s law:  
\[u_{i}=-\frac{K_{ij}}{\mu}\frac{\partial p}{\partial x_{j}}, \tag{1}\]  
where \(u_{i}\), \(K\), \(\mu\), and \(p\) represent the volume-averaged resin velocity, permeability tensor of the reinforcement, resin viscosity, and fluid(resin) pressure, respectively. The permeability is a key parameter for determining resin flow in the porous media of reinforcement. For determining the permeability, measurements of the saturated and unsaturated permeability are used. The former measures the pressure difference between two distinct points at a given flow rate under steady-state flow conditions in a fully saturated reinforcement in uni-directional flow [22]:  
\[K_{sat}=\frac{\mu\frac{Q}{A}}{\left(-\frac{dP}{dx}\right)}=\mu\frac{Q}{A} \frac{L}{(P_{1}-P_{2})}, \tag{2}\]  
where \(K_{sat}\), \(L\), \(P_{1}\), and \(P_{2}\) are the saturated permeability, the distance between the two pressure sensors, and the fluid pressure values at the distinct points 1 and 2, respectively. The unsaturated permeability in uni-directional flow is determined by following the flow front over time as the reinforcement is impregnated [22]:  
\[K_{unsat}=\frac{\left(1-V_{f}\right)\mu}{2P_{1}t}\{L(t)\}^{2}, \tag{3}\]  
where \(K_{unsat}\), \(V_{f}\), \(t\), and \(L(t)\) are the unsaturated permeability, fiber volume fraction, time instant, and distance between the liquid inlet and the flow front as a function of time, respectively.  
It should be noted that for both of these permeability measurement methods, the pressure gradient is obtained assuming a linear pressure distribution, as expressed in the following relationship:  
\[\frac{dP}{dx}=-\frac{(P_{2}-P_{1})}{L}, \tag{4}\]  
Generally, the permeability value is assumed to have the same value for a given fiber reinforcement type, regardless of the measurement method. However, it has been reported that the saturated and unsaturated permeability values can differ for the same reinforcement [23; 24; 25; 26; 27]. In many cases, the saturated permeability values exceed the unsaturated permeability values [23; 24; 25; 26; 27; 28]. Many possible reasons for this discrepancy have been reported; capillary effects, void entrapment, and fiber deformation problems, etc. Among them, present study focused on the fiber deformation during the mold filling process as one of the contributors to the discrepancy. Therefore, the same value of saturated and unsaturated permeability is related to the assumption that fiber tows was not deformed during the resin impregnation.
###### Abstract  
(1) Background: The value of dual-energy CT angiography (DE-CTA) in the detection of peripheral arterial disease (PAD) has been widely recognized. We aim to evaluate the diagnostic accuracy of virtual non-contrast (VNC) imaging of DE-CTA compared to true non-contrast phase (TNC). (2) Methods: Our Internal Review Board (IRB) approved prospective study enrolled 40 patients (28 men, 12 women; median age 69 y, range 41-93 y) who underwent lower extremity DE-CTA for symptomatic PAD. Mean attenuation values of TNC and VNC were obtained by placing circular regions of interest (ROI) at five levels from the aortic to the popliteal arterial lumen, reported in Hounsfield units (HU), and compared using a two-sample \(t\)-test. The subjective quality of VNC images was assessed by two independent radiologists with 10 and 4 years of CTA-imaging experience according to a 4-point scale and verified by the intra-class correlation coefficient (ICC). Dose Length Product (DLP) values of each DE-CTA examination were also considered. (3) Results: Except for the external iliac artery, VNC attenuation values were significantly lower than the corresponding TNC values at all levels, with a mean difference ranging from 14.1 and 8.7 HU. At qualitative analysis, VNC images were considered excellent to diagnose in 40%, good in 50%, and sufficient in 10% of cases. No cases of non-diagnostic VNC imaging were reported. Avoiding the TNC phase, a mean reduction in DLP of 54% for each DE-CTA was estimated. (4) Conclusions: TNC and VNC images showed comparable reliability and diagnostic accuracy in the detection of PAD. VNC may be considered a promising substitute for TNC from the perspectives of dose reduction and workflow optimization.  
computed tomography angiography; peripheral arterial disease; dual-energy computed tomography; digital scanned projection radiography; dual energy; image reconstructions +
Footnote †: journal: Applied Sciences
# Impact of Putting on Traffic Safety: A Synthesis of Research Findings
## 3 Results and Discussion
### Field Observation Studies  
As shown in Figure 4, the analysis reveals significant disagreement regarding the effect of rutting on the accident rate. Only six studies reported a significant impact of rutting on reducing traffic safety [11; 14; 15; 22; 41; 42]. However, the majority of studies showed no or minimal impact, or even a reduction in accident rate, due to increased rutting [8; 9; 10; 18; 20; 21; 43]. Some of these studies reported a meaningful relationship between rutting and accident rates under special circumstances such as rainy weather and nighttime [19; 31]. Thus, field observation-based studies are particularly useful to understand the impact of various factors in real-world streets.  
Multiple studies concluded no statistically significant relationship between rutting and accident rate. [PERSON] et al. [13] and [PERSON] and [PERSON] [44] concluded that no statistically significant relationship existed between rut depth and crash rate. [PERSON] and [PERSON] [43] reported a fluctuating effect of rut depth on the accident rate. [PERSON] et al. [18] did not find a clear trend between rut depth and accident frequency. [PERSON] et al. [45] found no significant effect of rutting on traffic safety when dealing with data without distinguishing between dry and wet pavement. [PERSON] et al. [9] found no significant relationship between rut depth and accident rate, possibly due to the low rut depth values (average 1.2 mm and max 7.6 mm) in their data. [PERSON] et al. [31] did not find a statistically significant relationship except for data models at night and during rainy weather.  
Multiple studies have reported a positive impact of rutting on traffic safety. [PERSON] et al. [10] found that the crash rate was reduced from 20 to about 11 crashes per 100 MVkm when the rut depth increased from 0 to 20 mm. [PERSON] et al. [46] found an increase in accident risk with rut depths of 6-10 mm but a decrease when depths reached 10-16 mm, possibly due to drivers reducing speed and increasing awareness.  
Some studies reported a negative impact of rutting on traffic safety. Data presented by [PERSON] et al. [47] showed a clear effect of rut depth on crash rate. [PERSON] and [PERSON] [21] noted an increase in accident risk by about 25% when the rut depth increased from about 3 mm to about 25 mm. [PERSON] et al. [41] found that roads with rut depth exceeding 15 mm had an accident rate increase from about 32 to about 57 accidents per 100 MVkm. [PERSON] and [PERSON] [23] reported a statistical relationship between rut depth and accident rate for rut depth classes of 0-3 mm, 3-5 mm, and greater than 5 mm. However, these rut depth values are small and typically considered acceptable by highway agencies. [PERSON] et al. [8] found that most accidents occurred on roads with lower rut depths. However, the significance of this conclusion is diminished by the fact that most roads exhibited low rut depths.
###### Abstract  
Enterprise networking optimization has become crucial recently due to increasing demand for a secure, adaptable, reliable, and interoperable network infrastructure. Novel techniques to optimize network security and toimprove scalability and efficiency are constantly being developed by network enablers, particularly in more challenging multi-cloud and edge scenarios. This paper, therefore, presents a comprehensive review of the traditional and most recent developments in enterprise networking. We structure the paper with particular emphasis on the adoption of state of-the-art technologies, such as software-defined wide area network(SD-WAN), secure access service edge (SASE) architecture, and network automation, driven by artificial intelligence (AI). The review also identifies various challenges associated with the adoption of the aforementioned technologies. These include operational complexity, cybersecurity threats, and trade-offs between cost-effectiveness and high performance requirements. Furthermore, the paper examines how different organizations are addressing a plethora of challenges by exploiting these technological innovations to drive robust and agile business interconnectivity. The review is concluded with an outline of possible solutions and future prospects, capable of promoting digital transformation and enhancing seamless connectivity within the enterprise networking environment.  
Keywords:cybersecurity; cloud computing; edge computing; enterprise networking; SD-WAN
_applied_  
_sciences_  
_Article_  
**Numerical Simulation and Field Test of the Interaction between Existing Station and Enclosure in Open Excavation and Adding Stories Construction**  
**[PERSON] 1, [PERSON] 1, [PERSON] 1,**2**, [PERSON] 2 and [PERSON] 2  
1 School of Civil Engineering, Shandong Jianzhu University, Jinan 250101, China  
2 China Railway Fourteenth Bureau Group Co, Ltd., Jinan 250101, China  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +86-186-6010-9753  
**Abstract: When constructing open excavation and adding stories on the upper part of an existing station structure, the new foundation pit contacts with the existing station at zero distance; subsequently, the construction has a greater impact than that of other close constructions. To explore the cross-impact of new and existing structures on open excavation and story-adding above the existing station, in this study, through numerical simulation and field measurement, we analyze the deformation law of the existing station and foundation pit retaining structure in the excavation and story-adding construction above the existing station. The following conclusions are obtained. The excavation of the upper foundation pit leads to the overall uplift of the existing station, and the sidewall exhibits an internal extrusion trend, which is caused by an increase in the horizontal load on the station caused by the unloading of the vault and expansion of the formation plastic zone. Affected by the existing station structure, some foundation pit retaining structures are end-suspended piles; the bottoms of the end-suspended pile slips are evident. This study offers control measures for the primary support connection between the pile bottom and initial retention and protection of the existing station, deep hole grouting reinforcement of the stratum behind the pile, and reserving earth sperm at the pit bottom to reduce the deformation of the end-suspended pile. The research results can provide a reference for constructing the added story of a subway transfer station.**  
**Keywords: existing station; foundation pit by open excavation; adding stories construction; retaining structure; reinforcement measure**
## 4 Discussion
### Moment-Rotation Envelope Curves
#### 4.2.4 Effect of the Steel Beam Section on Joint Performance  
As shown in Figure 14c, the flexural resistance and initial stiffness of specimen SJ-3 were 50.9% and 62.4% higher than those of SJ-6, respectively. Moreover, the flexural resistance and initial stiffness of specimen SJ-4 were 22% and 28% higher than those of SJ-7, respectively. Therefore, an increase in the beam section size increased the initial stiffness and flexural resistance of the joint, and this effect was especially pronounced for the joints with thin end-plates. This was due to that the thick end-plate directly affected the failure mode of the joint and had a greater influence on the joint performance than the steel beam section.
## 3 Performance Analysis
### Capacity Analysis with Adaptive Transmission
#### 3.2.3 Channel Inversion with Fixed Rate (CIFR)  
The CIFR technique alters the transmission power at node \(S\) to sustain a constant SNR level of the system, with the fixed rate modulation and the fixed-code design. The average capacity of the CIFR technique employed in this scheme can be written as [2] (Equation (28)):  
\[\overline{C}_{CIFR}=\frac{B}{2}\log_{2}\bigg{[}1+\bigg{(}\int_{0}^{\infty} \frac{1}{\gamma}f_{\gamma_{t}}(\gamma)d\gamma\bigg{)}^{-1}\bigg{]}. \tag{17}\]  
Plugging Equation (6) into Equation (17), we have:  
\[\overline{C}_{CIFR}= \frac{\log_{2}}{2}\sum_{i=0}^{\infty}\sum_{j=0}^{\infty}\sum_{n=0 }^{N_{1}\mu_{1}+i-1N_{2}\mu_{2}+j-1}\xi_{1}\xi_{2}B\Delta\Pi\Psi\] \[\times\Bigg{[}1+\bigg{\{}\int_{0}^{\infty}\frac{(\psi_{1}+\psi_{2 })\gamma^{n+l}}{e^{(\psi_{1}+\psi_{2})\gamma}}d\gamma-\int_{0}^{\infty}\frac{ (n+l)\gamma^{n+l-1}}{e^{(\psi_{1}+\psi_{2})\gamma}}d\gamma\bigg{\}}^{-1}\Bigg{]}. \tag{18}\]  
Now, solving the integral, with the help of [46] (Equation (3.351.3)), we obtain the average capacity as:  
\[\overline{C}_{CIFR}= \frac{\log_{2}}{2}\sum_{i=0}^{\infty}\sum_{j=0}^{\infty}\sum_{n=0 }^{N_{1}\mu_{1}+i-1N_{2}\mu_{2}+j-1}\xi_{1}\xi_{2}B\Delta\Pi\Psi\Bigg{[}1+ \bigg{\{}\frac{(\psi_{1}+\psi_{2})^{n+l}}{\Gamma(n+l+1)-(n+l)\Gamma(n+l)}\bigg{\}} \Bigg{]}. \tag{19}\]  
In CIFR, the degradation of the transmission capacity occurs because it maintains a fixed rate, regardless of the channel condition. A modified channel inversion technique, called truncated channel inversion with fixed rate, is used to improve this capacity loss. In TIFR, the fading channel is inverted when the total SNR from node \(S\) to node \(D\) rises above the predetermined threshold SNR \(\gamma_{o}\). The channel capacity using the TIFR scheme is derived by [2]:  
\[\overline{C}_{TIFR}=\frac{B}{2}\log_{2}\bigg{[}1+\frac{1}{\int_{\gamma_{o}}^{ \infty}\frac{1}{\gamma}f_{\gamma_{t}}(\gamma)d\gamma}\bigg{]}\big{(}1-P_{out} \big{)}. \tag{20}\]The exact analytical expression for the channel capacity using this technique can be derived by inserting Equation (6) into Equation (21), which gives:  
\[\overline{C}_{TIFR}= \frac{\log_{2}}{2}\sum_{i=0}^{\infty}\sum_{j=0}^{\infty}\sum_{n=0}^{ N_{1}\mu_{1}+i-1N_{2}\mu_{2}+j-1}\xi_{1}\xi_{2}B\Delta\Pi\Psi\] \[\times\left[1+\bigg{\{}\int_{\gamma_{o}}^{\infty}\frac{(\psi_{1}+ \psi_{2})\gamma^{n+l-1}}{e^{(\psi_{1}+\psi_{2})\gamma}}d\gamma-\int_{\gamma_{o} }^{\infty}\frac{(n+l)\gamma^{n+l-2}}{e^{(\psi_{1}+\psi_{2})\gamma}}d\gamma \bigg{\}}^{-1}\right]\big{(}1-P_{out}\big{)}. \tag{21}\]  
Now, solving the integral, with the aid of [46] (Equation (3.351.2)), we obtain:  
\[\overline{C}_{TIFR}= \frac{\log_{2}}{2}\sum_{i=0}^{\infty}\sum_{j=0}^{\infty}\sum_{n=0 }^{N_{1}\mu_{1}+i-1N_{2}\mu_{2}+j-1}\xi_{1}\xi_{2}B\Delta\Pi\Psi\] \[\times\left[1+\bigg{\{}\frac{\Gamma\big{(}n+l,(\psi_{1}+\psi_{2}) \gamma_{o}\big{)}-(n+l)\Gamma\big{(}n+l-1,(\psi_{1}+\psi_{2})\gamma_{o}\big{)} }{(\psi_{1}+\psi_{2})^{n+l-1}}\bigg{\}}^{-1}\right](1-P_{out}\big{)}. \tag{22}\]
## 6 Results and Discussion  
### Mel-Spectrogram and Raw Waveforms  
Table 2 shows that the sample-level raw waveform model achieves results comparable to the frame-level mel-spectrogram model. Specifically, we found that using a smaller hop size (81 samples \(\approx 4\) ms) worked better than those of conventional approaches (about 20 ms) in the frame-level mel-spectrogram model. However, if the hop size is less than 4 ms, the performance degraded. An interesting finding from the result of the frame-level raw waveform model is that when the filter length is larger than the stride, the accuracy is slightly lower than the models with the same filter length and stride. We interpret that this result is due to the learning ability of the phase variance. As the filter size decreases, the extent of phase variance that the filters should learn is reduced.
## 7 Conclusions  
In this paper, a mutation-based ICS time-series anomaly generation method is proposed. This technique first utilizes the encoder of the powerful generation model, AVRAE, to map observations into latent space. Then, it applies carefully designed mutation operations to alter the latent representation. The decoder of AVRAE reconstructs the observations from the mutated latent representations. To design appropriate mutation operations, commonly used ICS datasets were analyzed to derive the types of anomalies they contain. Based on this analysis, mutation operations were proposed to generate point anomalies, contextual anomalies, and collective anomalies. Evaluations using the HAI and SWaT datasets confirmed that the proposed method could generate ICS time-series anomalies that are both visually and statistically plausible.  
Furthermore, by using the augmented ICS dataset with synthetic anomalies, various one-class classification models and binary classification models were compared. The results empirically demonstrated that a robust binary classifier could be trained using the augmented dataset. This study presents an effective means to augment ICS datasets, which are often plagued by imbalance issues. The proposed technique is transferable to other time-series datasets with similar characteristics, although its application to sequential datasets of a different nature, such as text, requires further research. Additionally, the method's dependence on the performance of the underlying generation model and its inability to generate scenario-based anomalies were identified as limitations. Consequently, future research will naturally extend to applying the proposed method to various datasets and exploring more powerful anomaly detection models.  
Conceptualization, [PERSON]; methodology, [PERSON]; software, S.J.; validation, K.K.; formal analysis, [PERSON]; investigation, K.K.; resources, [PERSON]; data curation, D.M.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, D.M.; funding acquisition, D.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government (MSIT) (No. 2022-0-00961).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ICS & Industrial control systems \\ i.d & Independently and identically distributed \\ ELBO & Evident lower bound \\ GPMM & Generalized probabilistic monitoring model \\ EM & Expectation-Maximization \\ SDDA & Sparse distribution dissimilarity analytics \\ VAE & Variational autoencoder \\ RNN & Recurrent neural network \\ LSTM & Long short-term memory \\ \end{tabular}
#### 4.1.2 Hydrochemical Parameters  
A total of 42 production wells in the research area were sampled for water analysis. The anion and cation concentrations were analyzed, and relevant parameters were calculated. (Table 2). TDS, sodium chloride coefficient, desulfurization coefficient, etc. are typical hydrochemical parameters used to reflect the characteristics of the groundwater environment. Upon analysis, it was discovered that the dominant ions in water are Na\({}^{+}\), Cl\({}^{-}\), and HCO\({}_{3}\)\({}^{-}\) with relatively high concentrations, indicating a Na-HCO\({}_{3}\) water type. The TDS of the water sample varies between 970.00 and 5334.00 mg/L, with an average of 2156.81 mg/L. The sodium chloride coefficient (rNa\({}^{+}\)/rCl\({}^{-}\)) of the water sample ranges from 0.16 to 6.85, with an average of 2.92; the desulfurization coefficient (rSO\({}_{4}\)\({}^{2-}\)\(\times\) 100/rCl\({}^{-}\)) ranges from 0.12 to 13.75, with an average of 2.03. According to [PERSON] et al. [7], groundwater with a sodium chloride coefficient lower than 10, a desulfurization coefficient lower than 1, and a TDS exceeding 1500 mg/L indicates a reducing environment with  
Figure 3: Correlation of average water production with average gas production.  
Figure 2: Contour map of average water production of No. 15 coal.  
weak groundwater activity [11; 29]. Finally, the water dynamics in the Shouyang Block are characterized by weak groundwater runoff or retention in most areas.