esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Impact of AGBCPT under Different Stock Trends  
To show the effectiveness of the proposed approach on different trends, we conducted experiments using different stock trends as the testing datasets, including upward-trend, correction-trend, and downward-trend datasets. The buy-and-hold method (BAH) was used in comparison with AGBCPT. BAH is executed as the following steps. It buys all of the stocks on the first trading day and sells them all on the last trading day, after which the profit of the transactions is calculated. As shown in Figure 11, the datasets used in this experiment were selected correspond to Taiwan stock market trends.  
The trends were chosen as the following testing intervals: (1) 2020 was selected as the upward-trend dataset, (2) 2012 is selected was the correction-trend dataset, and (3) 2015 was selected as the downward-trend dataset. According to the trend periods, three training and testing periods are shown in Table 10.  
Figure 11: Taiwan stock market trends.  
Figure 10: Profit of different parameter settings on one-year dataset (2012).  
From Figure 12, in the three training periods, we observe that both methods yield positive profits. The profits of the three trends with AGBCPT are 50.05%, 58.32%, and 26.39%, which are all better than that with BAH. Based on the trained results, their profits on the testing datasets are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Market Trend** & **Training Period** & **Testing Period** \\ \hline Upward trend & 2016–2019 & 2020 \\ Correction trend & 2009–2011 & 2012 \\ Downward trend & 2011–2014 & 2015 \\ \hline \hline \end{tabular} The training results of the AGBCPT and BAH methods are shown in Figure 12.  
\end{table}
Table 10: Training and testing periods of three trends.  
Figure 12: AGBCPT and BAH training results.  
Figure 13: AGBCPT and BAH testing results.  
From Figure 13, we observe that in the testing phase, the results of the upward-trend dataset show that the 9.86% profit of AGBCPT is better than that of BAH (5.98%). In the correction-trend dataset, the results show similar profits for both AGBCPT and BAH: the profit of AGBCPT is 6.25%, and that of BAH is 6.37%. For the downward-trend dataset, AGBCPT yields no profit in the testing period (0%). However, compared with the BAH, AGBCPT is better than BAH because the profit of BAH is \(-\)17.01%. This shows that AGBCPT reduces risk on a downward-trend dataset.
## 3 Problem Formulation
### Definition of Cost Functions, Energy Loss Function
#### 3.1.1 Operating Cost  
As for the operating cost, we mainly consider the operating cost of the GB and combined heat and gas, which can be defined as Equations (1) and (2), respectively:  
\[C_{GB}^{O}=\sum_{i=1}^{N_{GP}}\left(a_{i}\times H_{GBi}^{2}+b_{i}\times H_{GBi}+c _{i}\right), \tag{1}\]  
\[C_{CHP}^{O}=\sum_{i=1}^{N_{CHP}}\left(\begin{array}{c}a_{i}\times P_{CHPi}^{2 }+b_{i}\times P_{CHPi}+c_{i}+d_{i}\times H_{CHPi}^{2}\\ +e_{i}\times H_{CHPi}+f_{i}\times H_{CHPi}\times P_{CHPi}\end{array}\right), \tag{2}\]  
where \(H_{GB}\) and \(H_{CHP}\) are the heat produced by gas boiler and combined heat and gas, respectively, \(P_{CHP}\) is the electrical power produced by CHP.  
As for renewable energy resources such as photovoltaic and wind turbine, regardless of installation cost, the operating cost of renewable energy resources can be defined as follows:  
\[C_{RES}^{0}=pr_{RES}P_{RES}. \tag{3}\]  
where \(pr_{RES}\) is the operating cost per kilowatt, and \(P_{RES}\) is the output power of renewable energy sources. As photovoltaic is the renewable energy resource used in this paper, the operating cost of PV is \(pr_{RES}\)= 0.7 yuan/kWh.  
According to Equations (1)-(3), the operating cost function can be defined as:  
\[C_{cost}^{O}=k_{1}C_{GB}^{O}+k_{2}C_{CHP}^{O}+k_{3}C_{RES}^{O}. \tag{4}\]  
where \(k_{1}\),\(k_{2}\), and \(k_{3}\) are parameters to balance the order of magnitude among operating costs.  
Under general conditions, the main grid is used as a supplementary device. However, when the whole system is under an extremely heavy load, the total output power of devices in the system fails to meet the load demand of users, and electricity has to be purchased from the main grid. Therefore, the cost of buying electricity should be added to the cost function. The electricity purchasing cost can be defined as:  
\[C_{grid}^{O}=P_{grid}\times bid_{grid}. \tag{5}\]  
where \(P_{grid}\) is the power purchased from the main grid, and \(bid_{grid}\) is the electricity price.  
Thus, the cost function of a multi-energy system can be rewritten as:  
\[C_{cost}^{O}=k_{1}C_{GB}^{O}+k_{2}C_{CHP}^{O}+k_{3}C_{grid}^{O}. \tag{6}\]
# A Dynamic Risk Assessment and Mitigation Model
## 4 Methodology
### Mitigation Suggestion  
After the successful estimation of DRA, our methodology determines whether the identified vulnerabilities (CVEs) are listed in the CISA KEV Catalog. This process provides two critical insights. First, it confirms that the specified vulnerability has been previously exploited, highlighting a credible risk of exploitation within the environment where it is detected. Second, it identifies whether a patch is available to mitigate the vulnerability or whether the affected asset has reached end-of-life status, in which case it should be removed from the network. Armed with this information, organizations can take immediate and informed actions, such as applying patches, to address these vulnerabilities effectively and reduce their exposure to potential threats.  
Additionally, our model correlates CVE-IDs with CAPEC-IDs through CWE-IDs, offering detailed mitigation suggestions for each identified CVE. The CAPEC framework provides specific mitigation actions that can be implemented to reduce the assessed risk. By correlating each CVE with its corresponding CAPEC, our model delivers tailored, vulnerability-specific guidance to inform actionable risk reduction strategies.  
It is acknowledged that each organization operates in a unique environment, and not all CAPEC-recommended actions may be applicable. However, by presenting the full range of potential mitigation actions, the methodology equips organizations with comprehensive information to evaluate and implement measures suited to their context.  
All provided information--including whether a vulnerability is listed in the CISA KEY Catalog and the associated mitigation suggestions--is specific to each CVE and tailored to every risk scenario. As a result, organizations gain detailed, scenario-specific guidance to address and mitigate the risks associated with each vulnerability.
## 2 Case Study  
The Urbas landslide is located in the Potoska Planina area, NW Slovenia, on the southern slopes of the Belscica and Stol peaks in the Karavanke mountain range (Figure 1). The landslide covers an area of 177,000 m\({}^{2}\)[26], with steep slopes (generally from 30 to 70\({}^{\circ}\)) at altitudes of 1200 m to 1350 m (Figure 1). The volume of the sliding mass was estimated at 1,578,700 m\({}^{3}\)[26]. It is a source area for debris flow and poses a risk to the settlement of Koroska Bela [29]. The sliding mass consists of tectonically deformed and weathered Upper Carboniferous-to-Permanic claystone, siltstone, sandstone, conglomerate, and carbonate rocks covered by talus material from upper-lying Triassic-to-Lower Jurassic carbonate rocks [26; 32; 33]. The landslide is a deep-seated rotational slide with a main sliding surface at a depth of approx. 15 m, with surface displacements of 6 cm in the upper part and 40 cm in the lower part [28].  
The average annual precipitation in the area is 1950 mm, with peaks in autumn and spring [34]. Snow cover is present for some 110 days per year [35; 36], and the average annual temperature is 4-6 \({}^{\circ}\)C [37].  
The groundwater in the landslide is recharged by the infiltration of precipitation and subsurface inflow from the karst-fissured aquifers in the carbonate rocks that form the upper part of the landslide recharge area [30]. Below the steep limestone ridge, scree and rockfall (talus) material accumulates and covers the upper part of the landslide [38]. The groundwater level and thickness of the saturated zone in this material depends primarily on the morphology of the upper boundary of the low-permeability Paleozoic clastic and carbonate rocks. The main surface water spring is Urbas, located in the middle part of the landslide. The observed discharge of the spring is between 0.7 L/s and 20 L/s [25]. After heavy rainfall, these values are likely to be exceeded. The water from the spring is used for the water supply of the mountain huts in the vicinity. Some additional springs and marshes are linked to contacts between the low-permeability landslide body and the overlying limestone debris. The precipitation distribution in the region is influenced by elevation and slope exposure in the Julian Alps and Karavanks. The estimated average recharge altitude of springs originating at the landslide ranges from approximately 1700 to 1800 m a.s.l., with mean residence times ranging from 2 to 5 months [30].  
Figure 1: Geological map of the study area with landslide boundary (modified after [26]), tracer injection and sampling points.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 4 Discussion  
### Irrigation and Cluster Effect on Tree Performance  
Increasing volumes of irrigation increased yield and TCSA of olive trees, in agreement with the large literature on the subject [5; 7; 8; 9; 48]. Differences between irrigation treatments were significant regardless of the different soil types in the orchard, as expected. Previous studies on field crops also reported that, although soil properties were major sources of plant variability, water management and fertilization had greater impact on vegetative activity than soil features [34; 65]. Precision management of olive orchards aims at optimizing the water use efficiency in terms of fruit (WUE\({}_{\text{f}}\)) and oil (WUE\({}_{\text{o}}\)) yield per liter of consumed water. We determined the impact of the three irrigation regimes on tree productivity in the two clusters identified by the combined use of soil proximal and vegetative remote sensing. The values of WUE\({}_{\text{o}}\) measured in our work were much higher than those measured by [PERSON] et al. [9] in a similar experiment carried out in Portugal (WUE\({}_{\text{o}}\) of 0.19, 0.16 and 0.07 g L\({}^{-1}\) for T2-100% EIC, T1-30% EIC and T0-rainfed, respectively), but similar to those reported by [PERSON] et al. [8] (WUE\({}_{\text{o}}\) of 0.24 and 0.30 for full and sustained deficit irrigation, respectively). We showed differences in either WUE\({}_{\text{f}}\) or WUE\({}_{\text{o}}\) within the orchard. The higher tree productivity measured in Cluster 1 also resulted in a higher WUE\({}_{\text{f}}\) (0.90) and WUE\({}_{\text{o}}\) (0.32) compared to Cluster 2 (0.67 and 0.27 for WUE\({}_{\text{f}}\) and WUE\({}_{\text{o}}\), respectively). In particular, in Cluster 1 the WUE\({}_{\text{f}}\) and WUE\({}_{\text{o}}\) of FI trees was 0.98 and 0.39, respectively, whereas in Cluster 2 the trees subjected to the same irrigation regime showed values of 0.90 and 0.36 for the same parameters, respectively. Differences in WUE\({}_{\text{f}}\) and WUE\({}_{\text{o}}\) in the two clusters were more evident under deficit irrigation (WUE\({}_{\text{f}}\) and WUE\({}_{\text{o}}\) of 1.06 and 0.38, respectively, in Cluster 1, and 0.72 and 0.31 in Cluster 2), and rainfed conditions (WUE\({}_{\text{f}}\) and WUE\({}_{\text{o}}\) of 0.66 and 0.21 g L\({}^{-1}\), respectively, in Cluster 1, and 0.40 and 0.15 g L\({}^{-1}\) in Cluster 2), indicating the increasing impact of the cluster characteristics on tree productivity as irrigation volumes decreased. In a recent study carried out in a high density olive orchard the two management zones identified using a similar approach (combining proximal and remote sensing) showed significant differences in fruit yield only in one out of three years [46]. The authors reported that the lack of differences in those two years was due to a different irrigation management (timing and amount of the applied water) in the two zones.
# Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification
## 6 Results and Discussion
### Evaluation of the Surrogate DI Model  
In this section, we present the evaluation of the proposed DL model based on the numerical test data of 95 delamination cases representing the frames of the full wavefield propagation, which was not shown in the proposed DL model during training. The proposed DL model was evaluated using numerical test data to demonstrate the capability to predict the interaction of Lamb waves with the delamination of various locations, shapes, and sizes.  
Three different representative cases were selected from the numerical dataset to show the performance of the developed DL model. Figures 7-9 show three different frames from three selected numerical test cases. The frames on the left column represent the labels to which the prediction of the proposed DL model is compared. The frames on the right column represent the prediction using the DL model. Particular frames were selected to show the interaction of the propagating Lamb waves with the delamination, namely the 10 th, 20 th, and 30 th frames after the interaction with the delamination. These frame numbers were easily extracted knowing the A0 mode velocity and the modelled delamination location.  
As can be seen in the first scenario (Figure 7), the delamination occurred at the upper-left side of the plate; in the second scenario (Figure 8), the delamination occurred at the top left of the plate, whereas in the third scenario, (Figure 9) the delamination occurred at the top centre of the plate.  
In all the presented cases (Figures 7-9), the change in the wave velocity due to delamination was well reproduced by the DL model. The wave reflections from the delamination were very well predicted in Figure 8, whereas in some cases and frames, the reflection pattern differed between the label and the DL prediction--compare Figure 9e,f. It should be underlined that these reflections were of much smaller amplitudes than the main wavefront, and the proposed DL model was not able to correctly reproduce all the detailed intricacies of the Lamb wave reflections. A more complex DL model could be required to further improve the results. Nevertheless, the testing results are satisfactory and very promising.  
From all three scenarios, it can be confirmed that the proposed DL-based surrogate model has reconstructed the full wavefield containing delamination with minimal error. Furthermore, the PSNR and Pearson CC values of all three of these scenarios are shown in Table 1. The mean PSNR value was 21.8 dB, and the mean Pearson CC value was 0.98 on all of the test data. These values confirm that the predictions from the proposed DL model are accurate.  
Figure 7: First scenario: comparison of predicted frames with the label frames at 10 th, 20 th, and 30 th frames after the interaction with delamination.  
Figure 8: Second scenario: comparison of predicted frames with the label frames at 10 th, 20 th, and 30 th frames after the interaction with delamination.
## 3 State-of-the-Art
The challenge is, then, to make \"Internet-of-Things\" accessible to the underprivileged [14] by leveraging the LoRaWAN protocol. The concept that allows extending the coverage or the feasibility of a city-wide coverage network based on autonomous LoRaWAN Base Stations in which only the Master Base Station needs to beconnected to the Internet is what distinguishes our proposal. Most of the LoRaWAN gateways encountered on the market are designed to reach TTN or other servers by means of the Internet. An open-source LoRaWAN Gateway described in [15], collects the LoRa packets from the end-nodes and transports them to the network server over IP using the MQTT protocol. This research is therefore a first step towards building sustainable infrastructure to extend coverage to places with limited connectivity like it's the case in many regions of Africa. [PERSON] [16] states that \"in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g., network, storage, etc.)\". He also states that \"The Fog (Edge) computing paradigm has been proposed to address this weakness by pushing to the edges processes of knowledge discovery using data analytic. However, edge devices have limited computational capabilities\". \"We agree with him, and we also believe that an autonomous base station, despite its limited resources, must be able to first process the data locally, make it available to the users in real time, before possibly forwarding it to the core network for wide scale management\". This study is therefore oriented in this direction and will allow the implementation of the minimal and cheaper infrastructure for the feasibility of the smart city in developing countries. This infrastructure must allow citizens to install their end nodes and then access their own information. Data can also be shareable. We propose, then, a multi-services edge gateway for communities with three variants: a standard box always connected to the Internet, a box connected intermittently over a delay tolerant network and a standalone box catering to the essential needs of isolated networks. In all these three cases, community members can use their smartphones or tablets to access the data of their interest, as well as additional content, through the WiFi Access Point embedded in the Base Station.
# Minimization of _n_th Order Rate Matching in Satellite Networks with One to Many Pairings
## Appendix A  
In the \(u\) expression below (2), the terms are constant and positive instead of \((e^{T}y_{i})^{1-m}\) (setting that \(\sum\limits_{o=1}^{N}x_{io}=e^{T}y_{i}\)), which includes the variables. It is easy to prove in (5) and (6) that the aforementioned factor in the \(u\) expression is convex. We apply the rule about the composition with an affine mapping [27]. This rule states that for scalar functions \(g\), \(h\) and affine function \(\mathbf{A}v+b\), with \(\mathbf{A}\) being a matrix and \(v\), \(b\) being vectors, all with appropriate shapes, then the \(g(v)=h(\mathbf{A}v+b)\) is convex (concave) if \(h\) is convex (concave). Applying this rule, we set \(h(z)=z^{1-m}\) and \(g(y_{i})=(e^{T}y_{i})^{1-m}=h(e^{T}y_{i})\). It is easily provable that \(\mathrm{d}^{2}\mathrm{h}/\mathrm{d}x^{2}\) is positive, because \(m\geq 2\) in (5), (6) and \(z\geq 1\) (resulting in \(1\leq e^{\mathsf{T}}y_{i}\) in \(S_{2}\)). To conclude, considering the aforementioned rule, \(h\) and consequently \((e^{T}y_{i})^{1-m}\) are convex, and the convexity of terms in the expression of \(u\) is only related with the sign either of \((-1)^{m}\) in (5) or \((-1)^{m+1}\) in (6).
## 2 Experiment
### Test Programs
#### 2.3.2 Central Pull-Out Test  
The pull-out test loading device was an MTS E45.305 electronic universal testing machine with a test system consisting of a mainframe, controller and test software. The maximum force rating of the test machine was 300 kN, the loading rate range was 0.001 to 250 mm/min, the deformation measurement range was 0.2% to 100% FS and the positional resolution was 1.7 \(\times\) 10\({}^{-5}\) mm. The specimen could be loaded using force, displacement or strain control. The test was performed by clamping the bar in the upper end of the fixture and then applying tension by precisely controlling the position of the center beam electronically. The high-temperature furnace was moved to the loading position of the testing machine using a moving bracket for the central pull-out test at high temperatures. The loading equipment is shown in Figure 4. The pull-out test was fully auto-controlled by the test system. The specimen was loaded at a rate of 98 N/s at the start stage. After 80% of the ultimate load, the specimen was converted to displacement control at a rate of 0.005 mm/s.  
Figure 4: Loading device.  
Figure 3: Diagram of the electric heating high-temperature furnace and electric furnace controller.  
The test machine's TestSuite software recorded the external load in real time. The average bond stress (\(\tau\)) between rebar and concrete was assumed to be uniform along the anchorage length and was obtained using Equation (1). The \(S_{F}\) was the slip at the free end, which was measured by the pull-wire displacement gauge.  
\[\tau=\frac{P}{\pi dl_{a}} \tag{1}\]  
where \(P\) is the tension force applied at the loaded end (kN); \(d\) is the diameter of the rebar (14 mm); \(l_{a}\) is the anchorage length of the rebar in concrete (70 mm).
## 3 Precise Positioning Using Uncombined PPP (UPPP) Algorithm
### Analysis of GNSS Observations from Different Logging Apps  
In this section, we first thoroughly investigate the inconsistency between the pseudorange, carrier-phase and Doppler observations in Section 4.1.1. Such misbehavior has been recently reported in [39]. Another issue which will be covered in Section 4.1.2 is the presence of some carrier-phase observations without changes over time. The results of these two subsections clearly indicate the importance of analyzing GNSS logger outputs before using them.
# Using Graph Neural Networks for Social Recommendations
## 3 Methodology
### RelationalNet Algorithm Architecture
#### 3.2.2. Embedding Layer [13]  
The utilization of embedding layers is commonplace in the field of Natural Language Processing (NLP), as evidenced by numerous works such as [4; 25; 26]. The embedding layer is part of the hidden layers in a deep neural network, taking high-dimension input and outputs into a lower dimension. RSs utilize this technique to represent users and items with respective free vector encodings.  
* Let \(P\in R^{m\times D}\) represent the free latent embedding matrices of users with \(D\)-dimensions. The embedding layer executes an index selection operation to generate the unrestrained latent vector of the user \(p_{a}\) for user \(a\) at \(a\)th row. Figure 3 shows that step.  
Figure 2: Graphs used in the RelationalNet model.  
* Similarly, let \(Q\in R^{n\times D}\) represent the free latent embeddings matrices of items with \(D\)-dimensions. The embedding layer executes an index selection operation to generate the unrestrained latent vector of the item \(q_{b}\) for item \(b\) at the \(b\)th row.