esa-sceva/satcom-chunk-collection huggingface dataset:
# A Convex Optimization Algorithm for Electricity Pricing of Charging Stations
## 2 The Model of Electricity Pricing of Charging Stations
The optimization problem of electric vehicle users is transformed into a series of Karush-Kuhn-Tucker (KKT) optimality conditions. \[\min f_{\text{EV},n}=\max(-f_{\text{EV},n})=\max(\lambda^{n}E_{\text{ev}}^{n }+\sum\limits_{t\in T}\mu_{\text{ub},t}^{n}\overline{p}_{c}), \tag{9}\]\[ \begin{array}{l}\sum\limits_{t\in T}p_{c,t}^{n}-F_{\mathrm{ev}}^{n}=0,\forall n \in N\\ 0\leq p_{c,t}^{n}\leq\overline{p}_{\mathrm{c}}\\ \frac{\partial\overline{p}_{c,t}^{n}}{\partial p_{c,t}^{n}}=\pi_{\mathrm{c},t}- \lambda^{n}-\mu_{\mathrm{lb},t}^{n}-\mu_{\mathrm{ub},t}^{n}-\mu_{\mathrm{a},t }^{n}=0,\forall t\in T,\forall n\in N\\ 0\leq\mu_{\mathrm{lb},t}^{n}\pm p_{c,t}^{n},\forall t\in T,\forall n\in N\\ 0\geq\mu_{\mathrm{ub},t}^{n}\pm(p_{c,t}^{n}-\overline{p}_{\mathrm{c}}),\forall t \in T,\forall n\in N\end{array}  \tag{10}\]  
However, Equation (10) includes some complementary constraints, which leads to the nonlinearity of the model. A big-M method is applied to relax the complementary constraints into mixed integer constraints, as Equation (11). At the same time, the main problem can be expressed as Equation (12) according to duality theory. \[ \begin{array}{l}0\leq\mu_{\mathrm{lb},t}^{n}\leq MX_{t}^{n},\forall t\in T,\forall n\in N\\ 0\leq p_{c,t}^{n}\leq M(1-X_{t}^{n}),\forall t\in T,\forall n\in N\\ 0\leq\overline{p}_{\mathrm{c}}-p_{\mathrm{c},t}^{n}\leq MY_{t}^{n},\forall t \in T,\forall n\in N\\ M(Y_{t}^{n}-1)\leq\mu_{\mathrm{ub},t}^{n}\leq 0,\forall t\in T,\forall n\in N \end{array}  \tag{11}\]  
\[\max\limits_{\pi_{c,t}}f_{\mathrm{CSs}}=\sum\limits_{n\in N}\left(\lambda^{n} E_{\mathrm{ev}}^{n}+\sum\limits_{t\in T}\mu_{\mathrm{ub},t}^{n}\overline{p}_{ \mathrm{c}}\right)-\sum\limits_{t\in T}\pi_{b,t}P_{b,t}, \tag{12}\]  
where \(M\) is a given big value; \(X_{t}^{n}\) and \(Y_{t}^{n}\) are binary variables for relaxing Equation (10). So far, the charging station power planning model is a MINLP with integer variables and non-linear expressions. Additionally, the quadratic equality constraints in Equation (2) make the model non-convex. At the same time, the benefits of DSO and CSs should be considered, which lead to multiple solutions of the model. In short, the problem of power planning for charging stations is a NP-hard problem and existing methods have difficulty in solving this problem.
## 1 Introduction
In addition, there are numerous problems associated with the chemistry of these systems, such as problems with rheology in pastes, mortars and concrete; problems related to the supply of raw materials; implementation problems in civil works due to the use of activator solutions; and the need to use curing temperatures in some systems (activation of fly ash). For all this, the need arises to search for alternative cementing materials, both to Portland cement and alkaline-activated systems. In this sense, hybrid cements are a good option [19]. These systems are characterized by the use of a high content of supplementary cementitious materials (SCM), mainly residues between 70 and 80 wt %, so that the use of Portland cement (between 20 and 30 wt %) can be minimized. However, although the cement content is low in these systems, it is necessary to incorporate an alkaline activator in their composition, generally in solid form (Na\({}_{2}\)SO\({}_{4}\) is the most used). From a microstructural point of view, the compatibility of the gels formed during the hydration processes of hybrid cements (C-S-H, C-A-S-H and N-A-S-H gels) possesses important consequences from a technological and application point of view, making it a priority line of research worldwide. On the other hand, it should be noted that there are numerous studies related to the durability of these systems to chemical agents, freeze-thaw, high temperatures, and so on, but there is limited information available on durability to wear in these alkaline-activated and/or hybrid systems. These cementitious materials could be used as floors, tiles, pavements or runways so they can withstand abrasion, scraping and percussion of cars and heavy trucks or even airplanes; therefore, it is crucial to understand their durability to wear. However, although it is one of the most important characteristics to study in construction materials, there are few studies that analyze wear resistance and wear mechanisms in alternative cementitious materials [20; 21]. To determine the effectiveness of the proposed industrial residues as cementitious materials, it is necessary to carry out this study and evaluate their durability to wear since some of these systems are gaining a high degree of acceptance in various applications. Therefore, the aim of the present work is to study new materials (hybrid mortars) through the use of different wastes (slag, fly ash and ceramic wastes) to evaluate their use as pozzolanic materials and their durability to abrasion. This study is mainly based on Bohme tests that consist of sliding the mortar against corundum abrasive materials placed on a cast iron wheel under a load.
# Parallel Load-Bearing and Damping System Design and Test for Satellite Vibration Suppression
## 6 Lightweight Support Design  
To avoid the interference between the dampers and the PAF, supports are necessary. The damping force is transferred through the supports, so they are required to withstand the peak damping force. Because the weight of the dampers' main parts is heavy (3.75 kg \(\times\) 8 = 30 kg), supports should be as light as possible. In summary, the supports' structure should be designed and optimized according to the PAF's structure and damping force.  
As shown in Chapter 5, the max damping force is known to be 31.128 kN. The supports' design load is 32 kN, which is applied on the lugs. Therefore, the boundary condition is that six degrees of freedom of all bolt holes are fixed, and the concentrated force is 32 kN. There are two load cases; the only difference is the direction of the damping force, which can be \(\pm\)32 kN.  
Figure 8: Damping rod’s coefficient–velocity curve.  
Figure 7: Damping rod’s force–velocity curve.  
The original designs of upper and lower supports are shown in Figure 9. First, topology optimization was performed by _Altair Inspire_ software. With the draw direction constraints, the optimization results were as shown in Figure 10.  
According to the topology optimization results, the geometric model was reconstructed by _CATIA_. The middle plane of the earpiece and the base was extracted to build the corresponding FE model, which is shown in Figure 11. This FE model was used to perform sizing optimization. Another engineering constraint is that the thickness of the lugs cannot be less than 8 mm. The sizing optimization problem and results are shown in Table 5. The base thicknesses were added, because the strength of the structure near the bolt hole exceeds the stress constraint. The results were rounded for technical requirements.  
After sizing optimization, the following minute adjustments were made. At first, a three-dimensional geometric model was created based on the sizing optimization results. Then, some parts of materials of the model were removed based on the former topology optimization results. To further reduce the supports' weights, some low stress elements were removed according to FE static analysis results. Finally, the weight was controlled to less than 0.5 kg for each support, and the final structures are shown in Figure 12.  
Figure 11: Sizing optimization FE model: (**a**) upper support; (**b**) lower support.  
Figure 9: Supports’ original design: (**a**) upper support; (**b**) lower support.  
One damping rod and two supports were assembled as a set of dampers. The structure of the damper is shown in Figure 12(a). The assembly model of PAF with the damping system is shown in Figure 12(b).
## 3 Results and Discussion
### Generated Aerosols Conductance Measurements
[8]) at 60% RH (the DRH of AN) due to the 76% AN in the WI-mix solution; this sharp transition was followed by a second increase in conductance (0.37 \(\upmu\)S RH\({}^{-1}\)) till 79% RH (DRH of AS) due to the presence of 24% AS within the WI-mix solution. It noteworthy that the second conductance increase of WI-mix was smoother than the first one and was finally followed by a conductance decrease till 90% RH. As demonstrated in previous studies [8, 98, 99] the hygroscopic growth after the first DRH (i.e., that of AN) causes the dilution of the solution formed on the filter smoothing the second step of conductance increase (that of AS); moreover, at higher RH the continuous water condensation diluted the ion concentrations that at the end led to an electrical conductance decrease. The aforementioned behavior was also observed analyzing the SU-mix sample: a first electrical signal (1.41 \(\upmu\)S RH\({}^{-1}\)) occurred at 60% RH in keeping with the low AN content (14%) and a second strongest conductance increase (3.08 \(\upmu\)S RH\({}^{-1}\)) reached its maximum at 79% RH in agreement with the 86% AS within the SU-mix. Finally, as happened for the WI-mix, the continuous water condensation led to a dilution of ions resulting in an electrical conductance decrease till 90% RH. Due to EC emissions, e.g., from traffic [62], and its conductive properties [100] the EC presence becomes particularly crucial in any atmosphere, not only related to the Po Valley one described in the previous sections. Thus, as the aim of the present work is to investigate the synergy between EC and inorganic ions in promoting conductance a correlation with chemistry was checked. Particularly, the conductance value at the DRH overcoming was related with the product (i.e., synergic effect)  
Figure 7: **(a)** Scheme showing the phenomenology behind the results of the conductance measurements obtained for pure saline samples (**b**) and for mixed saline and soot samples. The side bars represent a pair of electrodes of a filter housing in the AEC and the area between them is an exemplification of the laboratory generated aerosol samples deposited on a PTFE filter, before and after the deliquescence of the saline components. Gold lines represents the occurring bridging in panel B.  
between the number of equivalent of inorganic ions and the mass of EC on filters (both ambient and laboratory generated: Figure S8a,b, Supplementary Materials). Note that the mass of EC cannot be transformed into moles as EC is not a well-defined molecule characterized by its own molecular weight. Results highlighted a high level of correlation (R\({}^{2}\) > 0.9); despite this, for a prognostic model, the amount of water molecules [18] and the cake structure [89; 90; 91] should be quantitatively accounted for in future works.
## 4 Discussion
### The 2018 Drought  
Our measure of drought, SPEI, by focusing on climatic water balance largely ignores soil conditions. Our P-model simulations, likewise, have no explicit soil moisture term. How important might that omission be for our results? For a Mediterranean evergreen broadleaf forest, [PERSON] et al. [52] found consistent anomalies when comparing eddy covariance GPP estimates with simulations generated by a remote sensing product (MODIS17A); those discrepancies were most pronounced during summer months of the ten-year study. The authors found that relative water content, a measure of soil water limitation, could explain ca. 75% of the GPP anomalies. Soil moisture and VPD effects can, however, be challenging to separate because low soil moisture is often accompanied by high VPD [53]. After controlling for covariation in greenness, VPD and other factors [PERSON] et al. (2019) concluded that soil moisture deficits were a globally important constraint on GPP inducing a global reduction of ca. 15% and amplifying the effects of extreme weather events. By contrast, [PERSON] et al. [30], also studying the European drought of 2018, found that when soils are wet, moderate drying could have a positive effect on GPP; and that the relative importance, for GPP, of VPD and soil moisture depends on the prevailing soil water conditions, with soil moisture dominating in the driest soils.  
It therefore seems likely that even if canopy temperatures are based on LST (or alternatively, for climate modelling applications, calculated using the canopy energy balance), it will be necessary to formulate responses of stomatal behaviour and photosynthetic capacity to low levels of soil moisture.
## 4 Evaluating the Effect of the Maze Meltdown
### Effect on Toll Bridges  
Traffic volumes observed at four toll bridges (see Figure 6) were evaluated together with the travel times (see Figure 7) provided by www.511.org[26]. The solid grey line in Figure 7a represents average travel times observed along the SFOBB, while the solid black line represents the travel time reported on the day after the Maze Meltdown. It is notable that travel times on 30 April (when BART use was free as declared by the governor) remained much lower than the average weekday travel times. This marked reduction in travel times was accompanied by a reduction in traffic using the SFOBB, as shown in the box plot in Figure 6a.  
On 1 May, the travel times from 6:00 a.m. to 8:10 a.m. were comparable with other weekday travel times, but it remained much lower during the remainder of the day. Traffic volume recorded at the SFOBB on 1 May is shown in Figure 6a. Figure 7c-e together show the average weekday travel times observed during N1, R1, R2, and N2. Notice that the travel time returned to the N1 state  
Figure 5: **(a)** Distribution of the average ridership of stations with \(p\)-values <0.05 during R1; **(b)** distribution of the average ridership of stations with \(p\)-values between 0.05 and 0.1 during R1; **(c)** distribution of the average ridership of stations with \(p\)-values <0.05 during R2; **(d)** distribution of the average ridership of stations with \(p\)-values between 0.05 and 0.1 during R2; **(e)** distribution of the average ridership of stations with \(p\)-values <0.05 during N2; and **(f)** distribution of the average ridership of stations with \(p\)-values between 0.05 and 0.1 during N2.  
gradually as the connectors were sequentially reopened. The corresponding traffic volumes are shown in Figure 6a.  
Similar travel time patterns were observed at Richmond San Rafael Bridge (RSRB) (see Figure 1a) on 30 April, 30 May, and during R1, R2, and N2. The travel times along Dumbarton Bridge (DB) appeared to be unaffected by the collapse of the connectors. Travel times on the San Mateo Bridge (SMB) for 30 April and 30 May were unavailable, however, travel times during R1, R2, and N2 were comparable to N1 level. The corresponding traffic volumes at those bridges with respect to different time periods are shown in Figure 6.  
Based on the estimated changes in toll bridge traffic on the affected bridges, and the toll price, which was $4 in 2007, the estimated reductions in toll revenues are estimated to be $136,750 and $94,000 for 30 April and 1 May, respectively, based on the estimated value of time for Bay Area commuters [27].  
Figure 6: (**a**) Box plot of the weekday AADT at the SFOBB toll plaza and the AADT observed after Maze Meltdown; (**b**) box plot of the weekday AADT at the SMB toll plaza and the AADT observed after Maze Meltdown; (**c**) box plot of the weekday AADT at the RSRB toll plaza and the AADT observed after Maze Meltdown; and (**d**) box plot of the weekday AADT at the DB toll plaza and the AADT observed after Maze Meltdown.
# Simulation-Based Shading Loss Analysis of a Shingled String for High-Density Photovoltaic Modules
## 3 Results and Discussion
### Shading Characteristics of Conventional Module  
Figure 14 shows an image of the conventional PV module that was used for the measurement of the shading characteristics. In this case, the bottom shading was measured by generating artificial shading of 5 cm, 10 cm, and the size of one cell (156.75 mm).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **No Shading** & **5\% Shading** & **10\% Shading** & **20\% Shading** \\ \hline Size (cm2) & 235.05 & 235.05 & 235.05 & 235.05 \\ \(\mathrm{I_{sc}}\) (A) & 1.88 & 1.79 & 1.70 & 1.50 \\ \(\mathrm{V_{oc}}\) (V) & 3.34 & 3.34 & 3.33 & 3.31 \\ FF (\%) & 78.35 & 78.44 & 78.51 & 78.60 \\ Eff (\%) & 21.00 & 20.04 & 18.94 & 16.69 \\ \(\mathrm{P_{m}}\) (W) & 4.93 & 4.71 & 4.45 & 3.92 \\ Power ratio (\%) & 100 & 95.53 & 90.26 & 79.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Dependence of the simulated shading characteristics on the horizontal shading ratio of a 5-cell shingled string.  
Figure 13: Power rate of shingled string according to horizontal and vertical shading.  
Table 6 presents the measured shading characteristics of conventional PV modules. In the case of a conventional PV module, one solar cell of each string is located at the bottom. Therefore, when bottom shading occurs, bypassing is not performed and it can be seen that the overall output of the module is reduced. This clearly indicates that conventional PV modules are more vulnerable to bottom shading than shingled PV modules.
## 4 Methodology
### Federated Learning Setup  
The federated learning configuration used in this work is illustrated in Figure 2, which shows how smart home edge nodes collaborate with a central coordinator over 5G links to train a global anomaly detection model. We implement a standard FedAvg protocol [20] with the following settings:  
* Federated Rounds: 100.
* Clients per Round: 5-10 (simulating active smart home nodes).
* Data Distribution: Non-IID across clients (each device sees distinct behavior profiles).
* Model Updates: Weights transmitted securely after local epochs (typically 2-5 per round).
* Communication: Simulated via 5G emulated links with latency and bandwidth constraints.
* Hardware Specs: Simulated edge devices: 2-4 core CPUs, 2 GB RAM; Aggregator: 8-core CPU, 16 GB RAM.  
Optional enhancements include gradient noise (DP) and compressed updates for bandwidth efficiency.
## 4 Experiment Results and Discussion
### Performance Evaluation and Discussion
#### 4.4.4 Defense Mechanism Suggestions for Future Works  
This section explores additional defense mechanisms that have the potential to improve security and privacy, building on the previously introduced and tested mechanisms.  
Future research could explore the potential of implementing role-based access control (RBAC) in the FL ecosystem. RBAC could play a crucial role in managing and controlling access to the various components and processes involved in FL. By employing RBAC, it would be feasible to regulate and grant access to clients, so unauthorized clients could not join the system, thus mitigating the risk of insider attacks. Additionally, RBAC could prevent clients with limited authorization from sending their local updates to the server or receiving the updated global model from the server. This would ensure the traceability and trustworthiness of client gradients in the aggregation process, thereby enhancing the security and privacy of the FL ecosystem.  
Another potential strategy is to adopt a trustworthy aggregation protocol. To facilitate trustworthy aggregation, a filtering layer can be added before the aggregation process to check for anomalous behavior in the gradients. Specifically, the filtering layer can evaluate each client's gradients against an unseen dataset that is statistically representative of the application's needs. If the filtering layer detects malicious gradients, the federated server can exclude or delete them from the aggregation process. For example, gradients from local models like those in Figure 12c,d could be excluded from the aggregation process. If the malicious gradients are not filtered out, they will not only affect the global model but will also prevent other clients from training their local models to their maximum accuracy. This approach would be optimal when combined with our proposed methodology, resulting in a final global model that is both robust and highly accurate.
## 4 Results and Discussions
### Effect of Pile Inclinations
#### 4.1.2 Bending Moment  
Figure 11 demonstrates the non-dimensional bending moment profiles of piles under different pile inclinations. Likewise, almost all the non-dimensional bending moments of the battered piles (\(\beta\
eq 0^{\circ}\)) are less than those of the vertical piles (\(\beta=0^{\circ}\)). The variation of pile inclination from \(0^{\circ}\) to \(-10^{\circ}\) and \(-20^{\circ}\) would reduce the maximum non-dimensional bending moments by 50.8% and 71.8%, respectively, in the event of \(p=34.375\) kPa, and reduce the maximum non-dimensional bending moment by 41.1% and 52.3%, respectively, in the event of \(p=84.375\) kPa. The change of pile inclination from \(0^{\circ}\) to \(+10^{\circ}\) and \(+20^{\circ}\) would reduce the maximum non-dimensional bending moment by 14.0% and 44.9%, respectively, in the case of \(p=34.375\) kPa, and reduce the maximum non-dimensional bending moment by 9.1% and 44.5%, respectively, in the case of \(p=84.375\) kPa. Compared with the vertical piles, these observations have also proved the outstanding performance of the battered piles, especially the negative battered piles, in resisting lateral soil movement for similar aforementioned reasons.  
Comparing Figure 11a,b, there seem to be some differences in the shape of the bending moment profiles in the two loading stages, but the tendencies of the curves are not wholly changed. For instance, the bending moments in both cases increase first and then decrease along the depth direction. The apparent discrepancy is mainly caused by the varied location  
Figure 11: Non-dimensional bending moment profiles under different inclinations: (**a**) \(p=34.375\) kPa; (**b**) \(p=84.375\) kPa.  
of the maximum bending moment. The depth of the maximum bending moment moves down from the middle and upper part of the pile to the middle part when the surcharge increases from \(p=34.375\) to \(84.375\) kPa. The change can be more evidently observed in the following section, _Effect of Surcharge Magnitudes_. It implies that the lateral movement caused by the embankment surcharge is relatively small and can be resisted only by the reaction of shallow foundation soils when \(p=34.375\) kPa. However, with the gradual increase of surcharge, the reaction of the deeper foundation soil layer will be further mobilized.