esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Considered Architecture
### Advanced Resource Reservations with NSIS  
In our work, we deal with NSIS-MDP and NSIS-MIP: NSIS-MIP services are admitted into the system if a pre-reservation phase on the future visited cells obtains a positive answer from the system (passive reservations). NSIS-MDP services, instead, do not provide passive reservations and can reserve the bandwidth only on the active cell (where the call has originated). For both NSIS-MIP and MDP, adaptive bandwidth reservation is admitted [20; 31], so the assigned resources can change during the active session.  
Obviously, this kind of behavior during the Call Holding Time (CHT) can guarantee a flexible resource management (not possible for NSIS-MIG), with a huge increase of system utilization. [PERSON] et al. proposed the MRSVP [5] to support mobility for RSVP. The main advantage of the MRSVP is the ability to make active and passive reservations [5], as discussed in the following. Our attention is neither focused on the signaling performance of the employed protocol nor on the management of hand-off events in terms of protocol signaling activities, but it is important to consider how a user can make a passive reservation on a remote location [30].  
The most important concept in MRSVP is the set of _proxy agents_; there are two types of proxy agents:  
1. local proxy agents, generally identified as the active cell in which a mobile host is making the service request (hence, the active reservation); NSIS-MDP users make use of local proxy agents only (they request the service only on the current cells);
2. remote proxy agents, generally identified as the passive cells which will be probably visited by mobile hosts (hence, dealing with passive reservations); NSIS-MIP make use of remote proxy agents in order to manage their passive requests remotely.  
The scenario considered in this paper consists of the following elements: each cell is covered by an AP, which is connects to the Internet by a Switching Subnet (SS). The active reservation request (made by mobile hosts to the current cells) is routed to the sender node by appropriate NSIS messages. For the NSIS-MIP class, the Pre-RESERVE message is sent to the local AP (and to the remote ones through the SS); all the involved APs will answer with a positive acknowledgement (ack) if enough bandwidth is available.  
If the NSIS-MIP user will not receive all the positive acts (RESPONSE NSIS messages), then it will retry to connect later. If all the APs (active and passive) will have the possibility to accommodate the new request, the mobile NSIS-MIP will perform its reservation request by sending the RESERVE message (active-RESERVE for the local AP, passive-RESERVE for the remote APs). If the requesting node belongs to NSIS-MDP class, messages are exchanged only with the current active AP (for details see [39]).
## 4 Results
### Determination of Volume  
To determine the volume of the rock glacier, TLS observations were made in 2012, 2014, 2016, and 2019. The volumetric calculations obtained for 2012-2014 gave a loss of \(-625.95\) m\({}^{3}\); \(-1403.18\) m\({}^{3}\) for 2014-2016, and \(-2418.76\) m\({}^{3}\) for 2016-2019. The overall value for the seven years (2012-2019) was \(-4447.89\) m\({}^{3}\) (Figure 8) (Table 3).  
Figure 8: Comparison of digital elevation models: (**a**) Period 2012–2014. (**b**) Period 2014–2016. (**c**) Period 2016–2019. (**d**) Whole period (7 years) 2012–2019.  
These values are in accordance with the subsidence data from the steel rods in Table 1, in which greater displacements are observed in 2014-2016 and 2016-2019 than in 2012-2014. The reason for these lower values for 2012-2014 is that in 2013 the rock glacier was covered by snow and a period of just one year had to be considered, whereas 2014-2016 and 2016-2019 are periods of two \"snow-free\" years.  
The interpretation of Figure 8a is that the dynamic of the period 2012-2014 is generally homogeneous throughout the rock glacier with values of subsidence between \(-3\) cm and \(-50\) cm (soft orange colour). Additionally, Figure 8b,c for 2014-2016 and 2016-2019, indicate that for the rock glacier as a whole subsidence is greater than \(-50\) cm (dark orange and red colour); therefore, in these periods there was a great loss of volume, which is in accordance with the data in Table 1 and Figure 5. Lastly, Figure 8d indicates the loss of volume for the 7 year period (2012-2019).  
The uncertainty of the coordinates obtained using TLS is \(\pm 3\) cm, which is the error in the measurement itself using TLS and is backed up by the GNSS data. The yellow colour on the graphs indicates that there is no variation between measurements in the rock glacier and therefore this data is within the \(\pm 3\) cm of uncertainty.
## 2 ROI Extraction
### The PQFT Model  
Guo proposed the PQFT in the hyper-complex frequency domain [9]. The generalized coordinated color features are used to construct the quaternion. The hyper-complex quaternion matrix is represented as follows:  
\[\mathrm{q}(\mathrm{x},\mathrm{y})=f_{0}+f_{1}\mu_{1}+f_{2}\mu_{2}+f_{3}\mu_{3} \tag{1}\]  
where \(\mu_{1}\), \(\mu_{2}\), and \(\mu_{3}\) are the imaginary units, \(\mu_{1}^{2}=\mu_{2}^{2}=\mu_{3}^{2}=\mu_{1}\mu_{2}\mu_{3}=-1\), and \(f_{0}\) is the real part. If the value of the real part is 0, it is called a pure quaternion.  
By combining the multi-feature components of the color image, the hyper-complex quaternion is formed as follows:  
\[f_{0}=I(x,y,t)-I(x,y,t-1) \tag{2}\]  
\[f_{1}=(r+g+b)/3 \tag{3}\]  
\[f_{2}=RG=R-G \tag{4}\]  
\[f_{3}=BY=B-Y \tag{5}\]  
where \(R=r-(g+b)/2\), \(G=g-(r+b)/2\), \(B=b-(r+g)/2\), and \(Y=(r+g)/2-\left|r-g\right|/2-b\). r, g, and b are the three channels of the color image. \(f_{0}\) is the motion characteristic component and is used to express the brightness difference between two adjacent frames. \(I(x,y,t)\) and \(I(x,y,t-1)\) represent the current frame and the previous frame image in the video. \(f_{0}=0\) when it is used for a single-frame static image.  
The hyper-complex Fourier transform \(Q[u,v]\) of the quaternion \(q(x,y)\) of the input image is computed, and the polar coordinate transformation form is described as follows:  
\[Q[u,v]=\|Q[u,v]\|e_{\mu}\mathrm{I}^{\phi(\ u,v)} \tag{6}\]  
where \(\|.\|\) is the module of each element in the hyper- complex matrix, and \(Q[u,v]\) is the frequency domain expression of \(q(x,y)\).  
The amplitude spectrum \(\mathrm{A}(\ u,v\ )\), phase spectrum \(\mathrm{P}(\ u,v\ )\), and Eigen spectrum \(\chi(\ u,v\ )\) are calculated as follows:  
\[\mathrm{A}(u,v)\ =\!\!\|Q[u,v]\| \tag{7}\]  
\[\mathrm{P}(u,v)=\tan^{-1}\!\!\left(\frac{\|V(Q[u,v])\|}{\mathrm{S}(Q[u,v])}\right) \tag{8}\]  
\[\chi(u,v)=\frac{V(Q[u,v])}{\|V(Q[u,v])\|} \tag{9}\]where \(S\) and \(V\) represent the real and imaginary part of the \(Q[u,v]\), respectively.  
The phase spectrum is preserved, the inverse quaternion Fourier transform is carried out, and the saliency map of PQFT is obtained:  
\[S=g*\|Q^{-1}\big{\{}A(u,v)e^{XP(u,v)}\big{\}}\|^{2} \tag{10}\]  
where \(g\) represents the Gaussian filtering function, and \(Q^{-1}\) is the inverse quaternion Fourier transform. \(S\) is the saliency map.  
By the above calculation, the PQFT model can quickly achieve a saliency map of color video images and obtain good detection results. The PQFT is established by using two kinds of features (color and motion information) to construct the quaternion in the RGB or CIE Lab mode. However, the panchromatic remote sensing image has only one gray channel, and a single image has no motion information, which can result in a great reduction in the extraction accuracy for PQFT. The method adopts single-scale filtering and does not take into account the multi-scale phenomenon of different target sizes.
# Experimental Research on Bond Behavior between GFR Bars and Stirrups-Confined Concrete
## 3 Experimental Result  
In the pullout tests, the pullout load and the displacement values were used to calculate the corresponding average bond stress and slip. The average bond stress were calculated as the pullout force divided by the surface area of the bar that is embedded in the concrete with the assumption of uniform bond stress distribution along the embedment length:  
\[\tau=\frac{P}{\pi dl} \tag{1}\]  
where \(\tau\) is the average bond stress; \(P\) is the imposed force; \(d\) is bar diameter; and, \(l\) is the embedment length. Due to the low elastic modulus of the GFRP bar, a significant degree of elastic deformation occurs at its loading end, resulting in deviations in the calculation of slip at the loading end, which influence the accuracy of the \(\tau\)-\(s\) relationship [24]. Nevertheless, measuring of the slip at the free end enables the accurate reflection of the actual slippage of the GFRP bar in the concrete. Consequently, in this study, the slips corresponding to average bond stress are derived from the free of slip. Table 3 depicts the test results, in which \(\tau_{\text{max}}\) is representative of the maximum average bond stress (bond strength), and \(s_{\text{m}}\) is the free end slip corresponding to the maximum average bond stress; \(\tau^{*}\) is the average bond strength that is obtained by calculating mean value of bond strength for identical specimens. \(s^{*}\) is the average free end slip corresponding to \(\tau^{*}\); P represents pullout failure; and, S represents concrete splitting failure. Figures 5-9 shows the \(\tau\)-\(s\) curves of the different specimens. Due to the results of the identical specimens being basically the same, one curve was chosen for each specimen type in Figures 5-9. It is apparent that the \(\tau\)-\(s\) curves of the specimens exhibit distinctive features with different surface characteristics of the bar. Section 4 discusses this phenomenon.  
Figure 4: Pullout test setup.  
Figure 3: Pullout specimens.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Specimen** & \(\tau_{\text{max}}\) & \(\tau^{*}\) & \(s_{\text{m}}\) & \(s^{*}\) & **Failure** & **Specimen** & \(\tau_{\text{max}}\) & \(\tau^{*}\) & \(s_{\text{m}}\) & \(s^{*}\) & **Failure** \\  & **(MPa)** & **(MPa)** & **(mm)** & **(mm)** & **Mode \({}^{*}\) & **(MPa)** & **(mm)** & **(mm)** & **Mode \({}^{*}\)** \\ \hline R1-  
Figure 5: Representative \(\tau\)-\(s\) curves for R1 bars for the (**a**) without and (**b**) with stirrups.  
Figure 6: Representative \(\tau\)-\(s\) curves for R2 bars for the (**a**) without and (**b**) with stirrups.  
Figure 7: Representative \(\tau\)-\(s\) curves for R3 bars for the (**a**) without and (**b**) with stirrups.
## 3 Multi-Searcher Optimization
### Double Layer Searcher  
As shown in Figure 3, in the process of initialization, a series of searchers are randomly distributed within the search area by using Equation (36), which is the so-called global searcher (GS) in the MSO. Surrounded by each global searcher and centered around this searcher within a specified radius, some other searchers are randomly distributed, known as local searchers (LS), which are subject to the global searcher. These LS aims to perceive the surrounding dynamic change process in the vicinity of each GS and then perform a local search. When the searching process of the GS is finished, the LS will initiate a subsequent search. This feature can effectively improve the convergence speed and avoid a low-quality local optimum.  
The search radius of the GS depends on the distance from the global best (Gbest) and the level of each global searcher. The level will be equal to 0 if a global searcher is finding the minimum cost, and vice versa. Hence, the search radius of the GS is calculated as follows:  
\[r=\max\{(r_{\max}-r_{\min})\frac{k_{GS}}{n_{GS}-1}+r_{\min},dist(\text{GS}, \text{Gbest})\} \tag{37}\]  
where \(r_{\max}\) and \(r_{\min}\) are the pre-set maximum and minimum radii, \(k_{GS}\) represents the level of GS, \(n_{GS}\) is the number of global searchers, _G_best is described as the global optimal solution, and _dist_ (GS,Gbest) denotes the distance between the global searcher and the global best.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 3 Results  
### Experimental Datasets  
To generate synthetic load profiles with GANs, the dataset used for training the models should represent realistic and diverse electricity consumption patterns over time. A good dataset includes both temporal and contextual features to help the GAN learn patterns accurately. Granularity of 15 min, 30 min, or 1 h is common for load profiles. Ideally, at least one year of data should be available to capture seasonal-related characteristic changes in the profiles.  
Residential load profiles are rarely available in public domains due to numerous factors, including privacy concerns, data ownership restrictions, and challenges in data collection. Unlike industrial or aggregated grid-level data, which is often anonymized and released for planning purposes, residential consumption data can reveal highly sensitive personal information. Utilities and data providers are therefore reluctant to share such data. Additionally, residential datasets are typically collected through smart meters, which may not be uniformly deployed or accessible across regions, limiting the volume and variety of data available. Even when such data exist, it is often locked behind proprietary platforms or research agreements, making synthetic data generation a crucial alternative for  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Latent Dimension & 16 \\ Batch Size & 128 \\ Epochs & 1000 \\ Number of Critics & 10 \\ Lambda & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameters of the proposed Conv1D-WGAN-GP.  
researchers and model developers who need realistic, privacy-safe alternatives for training and evaluation.
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
###### Abstract  
The multipath transmission control protocol (MPTCP) is considered a promising wireless multihoming solution, and the 3 rd generation partnership project (3 GPP) includes it as a standard feature in the fifth-generation (5G) networks. Currently, ns-3 (Network Simulator-3) is widely used to evaluate the performance of wireless networks and protocols, including the emerging MPTCP protocol. This paper investigates the fidelity of the Linux kernel implementation of MPTCP in the ns-3 direct code execution module. The fidelity of MPTCP simulation is tested by comparing its performance with a real Linux stack implementation of MPTCP using a hardware testbed for two different setups. One setup emulates the existence of a bottleneck link between the sending and receiving networks, whereas the other setup does not have such a bottleneck. The fidelity of ns-3's simulation is tested for four congestion control algorithms, namely Cubic, linked-increases algorithm (LIA), opportunistic LIA (OLIA) and wVegas for relatively short and long data flows. It is found that the uplink MPTCP throughput performance exhibited by the ns-3 simulator matches the hardware testbed results only if the flows are long-lived and share no common bottleneck link. Likewise, the MPTCP throughput achieved during a downlink scenario using the ns-3 simulator and the hardware testbed are close to each other across all algorithms except wVegas regardless of the flow size if there is no bottleneck link. Moreover, it is observed that the impact of LTE handover on MPTCP throughput is less significant in the simulator than the real hardware testbed, and it is setup-dependent.  
multipath TCP; ns-3; simulation; direct code execution; congestion control; heterogeneous; wireless networks; Wi-Fi; LTE; 5G +
Footnote †: journal: Sensors
## 4 Results
### The Contribution of Each Sector  
Further statistical analysis was conducted on the sources of the increase of the total urban carbon emissions, which is the contribution of each urban subsystem to the total emissions. As shown in Figure 4, the proportion of carbon emissions of the residential sector before 1996 accounted for less than 6%, and the peak period of this proportion appeared from 2000-2008 and was between 6% and 10%. After 2005, the proportion began to decrease and went down to 6.73% in 2016. The proportion of the commercial sector decreased year by year, from 24.7% in 1950 to 6.8% in 1995, then increased gradually and kept floating between 10% and 20%. The industrial sector contributes the most to the total carbon emissions. Before 1996, the contribution rate of carbon emissions had been maintained at about 80%. From 1996 to 2000, the contribution rate of emissions decreased to about 50%. This state had been maintained until 2005. During 2006-2008, there was a new rise of 10% compared with the previous stage. After 2008, the industrial sector's contribution has continued to fall, and by the end of 2016, it had dropped from 50% to about 40%. The proportion of carbon emissions from transportation fluctuates greatly and is relatively unstable. Before 1986, the proportion was less than 10%, and it rose sharply from 1987 to 1996. In 2003, the transportation sector accounted for 27.2% of the total emissions. In 2008, it fell to 15.9%, and then rose directly to almost 40% of total emissions before slowly declining after 2013, as shown in Figure 7. The increase of traffic carbon emissions is mainly due to the traffic carbon emissions in the city, and the sum of passenger and freight carbon emissions accounts for only about 10% of the carbon emissions in this field. The increase of carbon emissions is linearly related to the ownership of cars, as shown in Figure 8.  
Figure 8: Growth trends of various transportation carbon emissions and car ownership changes.  
Figure 7: The structure of carbon emissions by sector from 1949 to 2016.
## 3 Experimental Results and Discussion
### Experiment on Data Set 2
As a result, in the homogeneous areas, the classification is enhanced, while some classification errors often happened on the edges of the regions. In contrast, CAMRF-FLI introduces the class spatial dependency information and the local similarity measure based fuzzy local information, thus, it can provide a good trade-off between oversmooth and spatial regularization and, hence, reduces the edge blurring artifacts and preserves small patches when performing spatial regularization.
## 2 System Model and Channel Modeling Methods
### System Model
#### 2.1.2 System Model for NLOS Scenarios  
The V2I-VLC system model for the NLOS scenario is shown in Figure 3. Taking the rays emitted from Tx1 as an example, two NLOS links are mainly considered for the proposed V2I-VLC scenario, namely, the NLOS link formed by the reflection of \(M\) (as shown by the red line in Figure 3), and the NLOS link formed by the reflection of \(RS\) (as shown by the yellow line in Figure 3).  
Now, consider the above two NLOS links separately. The schematic of the NLOS scenario formed by \(M\) reflection is shown in Figure 4 (taking the example that Tx1 is located to the left of the Rx axis). For the rays from Tx1, the NLOS propagation distances from \(V_{1}\) to \(M\) and from \(M\) to Rx are denoted as \(D_{NLOS,1}^{V_{1}-M}\) and \(D_{NLOS,1}^{M-PD}\). The rays from the headlights fall on \(M\) and are reflected. Denote the angles of incidence and reflection as \(\delta_{i,m}\) and \(\delta_{r,m}\), as shown in Figure 3. By the Law of Reflection, it follows that \(\delta_{i,m}=\delta_{r,m}=\delta\). Based on the reflection points, the longitudinal distances of light propagation from \(V_{1}\) to \(M\) and from \(M\) to Rx can be expressed as \(I_{V_{1}-M}\) and \(I_{M-PD}\). Also, it can be obtained that \(I_{V_{1}-PD}=I_{V_{1}-M}+I_{M-PD}\). In addition, the NLOS scenario formed by the rays emitted from Tx2 and reflected by \(M\) is similar to the situation of Tx1.  
The reflection phenomenon also exists when the headlights' rays fall on the RS. The schematic of the NLOS scenario formed by the reflection of the \(RS\) is shown in Figure 5 (taking the example that Tx1 is located to the right of the Rx axis). For the rays from Tx1, the NLOS propagation distances from \(V_{1}\) to \(RS\) and from \(RS\) to Rx are denoted as \(D_{NLOS,1}^{V_{1}-RS}\) and \(D_{NLOS,1}^{RS-PD}\). The rays from the headlights strike \(RS\) and are reflected. Denote the angles of incidence and reflection as \(\delta_{i,rs}\) and \(\delta_{r,rs}\); it follows that \(\delta_{i,rs}=\delta_{r,rs}\). Based on the reflection point on RS, the longitudinal distances of light propagation from \(V_{1}\) to \(RS\) and from \(RS\) to  
Figure 4: Schematic model of the V2I–VLC system in the NLOS scenario formed by \(M\) reflection.  
Figure 3: Schematic of V2I–VLC system model for NLOS scenario.  
Rx are denoted as \(I_{V_{1}-RS}\) and \(I_{RS-PD}\), respectively, and \(I_{V_{1}-PD}=I_{V_{1}-RS}+I_{RS-PD}\). In addition, the height of Rx from the ground is the same as the height of Tx, which is expressed as \(L_{PD}\). Similarly, the NLOS link from Tx2 can be studied.
## 3 Test Results
### Repeated Freezing and Thawing Resistance  
The relative dynamic moduli of the HFLMC mixtures are shown in Figure 8. All mixtures satisfied the target relative dynamic modulus of 80% after 300 cycles of freezing and thawing. However, plain mixtures showed a marked decrease in relative dynamic modulus after 90 cycles with further decreases up to 300 cycles. In contrast, the fiber-reinforced mixtures had relative dynamic moduli exceeding 95% or higher, indicating greater freeze-thaw resistance than the plain mixtures. Note that the single-type fiber systems also exhibited decreases in relative dynamic modulus, although the cycle at which the decreases began varied with the type of fiber: 120 cycles for PVA fiber, 180 cycles for nylon fiber, and 210 cycles for jute fiber. After 300 cycles, the relative dynamic moduli of mixtures containing PVA+nylon, PVA+jute, jute+nylon fiber, PVA, jute, and nylon fibers were 98.30%, 97.82%, 96.32%, 94.36%, 94.34%, and 94.26%, respectively, By comparison, the relative dynamic modulus of the plain mixture was 93.37%. The relative dynamic moduli of hybrid fiber-reinforced mixtures remained nearly constant. In particular, the PVA+nylon hybrid fiber-reinforced mixture exhibited the highest freeze-thaw resistance. This was attributed to the presence of latex in the mixture [29]. It is also likely that the fibers suppressed the formation and growth of microcracks induced by repeated freezing  
Figure 7: Mass loss of HFLMC mixtures.  
and thawing. Note also that the hybrid fiber mixtures were more resistive to freeze-thaw cycles than the plain mixtures.