esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Third, the above studies only train and validate the proposed methods on the instances of a certain scale, and they do not really apply them to the large-scale problems. Therefore, it is necessary to further explore the improvement of DRL to overcome these deficiencies and efficiently solve the SAOSSP. In this study, we propose a deep reinforcement learning algorithm with a local attention mechanism (DRLLA) to solve the SAOSP in a non-iterative manner. The primary contributions are summarized as follows:  
1. A neural network framework based on an encoder-decoder structure is designed to construct a high-quality scheduling solution in an auto-regression manner. Furthermore, a local attention mechanism is proposed to guide the network model to focus on the more promising tasks in the solution construction process by narrowing the candidate task range into a local one, which greatly improves the solution quality. 2. An adaptive learning rate strategy is developed to guide the actor-critic training algorithm to train the proposed network model. In the training process, the adaptive learning rate strategy appropriately reduces or increases the learning rate of the training algorithm according to the reward curve trend, obtaining better training effectiveness. 3. Extensive experiments are conducted to verify the proposed algorithm with our created SAOSSP datasets, which contain numerous various-scale instances and can be publicly available for related studies. The experimental results validate that the proposed algorithm has superior performance in solution quality, generalization performance, and computational efficiency over the comparison algorithms. The remainder of this paper is organized as follows. In Section 2, we describe the SAOSSP in detail and build the corresponding mathematical model with complicated constraints and an optimization objective. Section 3 presents a neural network with a local attention mechanism for the SAOSSP and an actor-critic training algorithm with an adaptive learning rate for the training of the proposed network. Section 4 presents the experimental results, and Section 5 presents the conclusions of the study with a summary and directions for future work.
## 5 Results and Discussion
### Anomaly Extraction  
Rocks and minerals containing groups such as Fe\({}^{2+}\) or Fe\({}^{3+}\) have characteristic spectral band shapes, which are mainly reflected in the visible bands. Fe\({}^{3+}\) has strong absorption in the spectral range of 0.85 \(\upmu\)m-0.94 \(\upmu\)m and appears as an absorption valley in the OLI 2 and OLI 5 bands. In remote-sensing images, it is shown by low brightness values. OLI 4 is the iron ion reflection band, which is shown as relatively bright in the OLI 4 image. The sign of the feature vector should be opposite to that of the coefficients of OLI 2 and OLI 5.  
Because the visible light band is more sensitive to iron oxide, OLI 2, OLI 4, OLI 5, and OLI 6 are selected for principal component transformation to extract iron anomaly information, and OLI 7 is excluded to eliminate interference from hydroxyl or carbonate ion information. The criterion for judging the principal component representing the iron anomaly material is that the feature vector constituting this principal component should have an OLI 4 coefficient opposite in sign to the coefficients of OLI 2 and OLI 5, and generally has the same sign as the coefficients of OLI 6. Such a principal component is called an iron abnormal principal component. According to previous research, anomalies often appeared in the fourth principal component. The statistical analysis of the image feature vectors in the study area is shown in Table 3.  
From Table 3, PC1 contains 88.96% of the information in the entire map, mainly reflecting brightness and terrain information, while PC4 only contains 0.39% of the information, but it represents abnormal information in the map. In order to obtain more accurate extraction results, the interference factors of mountain shadows and water bodies are eliminated using a mask file, and the fourth principal component where anomalies are concentrated is subjected to density segmentation. The result shows that the distribution of anomalies  
Figure 11: Lithological mapping using SVM classification based on Landsat OLI data.  
matches well with the exposed mantle peridotite, indicating good indicative significance (Figure 12).
# Spectrum Modeling of Out-of-Band Intermodulation for Dual-Band RF Amplifiers in OFDM Modulation
## 4 Conclusions  
In this article, we expanded our dual-band spectrum modeling, from in-band IM and CM previously, to out-of-band intermodulation, which will be used to evaluate the interference to adjacent channels. The dual-band spectrum model could help spectrum planners and regulators to identify where the interferences will be.  
Conceptualization, Q.W. and X.L.; Data curation, X.Y. and S.Y.; Formal analysis, X.Y.; Investigation, X.Y., S.Y. and F.L.; Methodology, Q.W. and F.L.; Project administration, F.L.; Resources, X.Y., S.Y. and F.L.; Software, X.Y. and S.Y.; Supervision, F.L.; Validation, X.L. and F.L.; Visualization, X.Y. and S.Y.; Writing-original draft, S.Y.; Writing-review and editing, X.Y., S.Y. and F.L. All authors have read and agreed to the published version of the manuscript.  
Publication of this article in an open access journal was funded by the Portland State University Library's Open Access Fund.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
5G Fifth Generation  
IP3 third order intercept point  
CR Cognitive Radio  
4G Fourth Generation  
LTE Long Term Evolution  
Figure 4: The red line is the theoretically predicted PSD, and the blue line is the experimental result.
## 5 Discussion
### Client Selection Criteria  
In the context of FedRecI2C, client selection in practical application scenarios should be grounded in factors such as the client's CPU capacity, network bandwidth and historical activity records [30]. In distributed settings, it is feasible to integrate methods from the relevant literature, e.g., [5; 30], with FedRecI2C. For a client to partake in an FL training round, specific conditions must be met. First, it must lie within the server's reachable range. Second, during the training period, it is ideal for the client to be idle, connected to a power source, and have access to an available wireless network [42]. Given these requirements, available or communication-constrained clients are randomly and uniformly distributed  
Figure 8: Impact of early-stop on PFedMF-I2 CL. (**a**) MovieLens-100K. (**b**) MovieLens-1M. (**c**) Lastfm-2K. (**d**) Foursquare. \"es = 100” means early-stop I2 CL when communication round = 100 and so forth.  
within the system [43]. Hence, the client selection process should not only consider the client's resource status but also incorporate an element of randomness.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 2 Vehicular Named Data Networking (VNDN)
### Content Store (CS)  
A Content Store (CS) is one of the important data structures used in VNDN-based communication. CS temporarily stores the data packets when it receives from the producers or intermediate vehicles. In VNDN-based communication, data packets are meaningful and need to be stored in a cache, i.e., CS, which can be forwarded to the consumer upon request. In order to maintain the updated version, the most recently used data packet can replace the old one. The vehicle checks the data packet in its CS when an interest packet is received.
# Experimental Study on the Flexural Behavior of Alkali Activated Fly Ash Mortar Beams
## 2 Experimental Procedure
### Materials  
Apart from the especial size 4 mm bars, which were courtesy of a factory nearby [21], the rest of the reinforcing bars were bought in the market. All of the bars were tested according to standard NP EN 10002-1 [22]. In these tests, four samples of each type of steel, about 40 cm long, were used. Table 2 shows the averages values of yield strength of reinforcement (fsy), ultimate strength of reinforcement (fsu), and strain of reinforcement steel at maximum load (\(\epsilon\)su).  
The granulometry of the coarse aggregates to be used in the mixtures was conditioned by the size of the specimens to be cast, especially the cover. It was found that the size of the aggregates would not be appropriate and they were not used. A sand was used.  
For the mortar production, Normal Portland Type I CEM I 42.5 R cement (Compressive Strength = 42.5 MPa) was used, from CIMPOR. According to the producing company, this material consists essentially of CaO (61.5%) and SiO\({}_{2}\) (21%).  
The fly ash came from the Sines thermal power plant and resulted from the burning of coal. The ashes are certified by the National Laboratory of Civil Engineering (LNEC); thus, fulfilling the compliance and performance requirements defined in Annex ZA of standard NP EN450-1 [23]. Its composition essentially contains calcium oxide, CaO (71%) and potassium oxide, K\({}_{2}\)O (16%), according to tests carried out at the Department of Earth Sciences of the University of Coimbra. Moreover, for these ashes, the high specific surface leads to great reactivity. An activator needs to be used; it must be dosed and concentrated by taking into account the binder, namely its chemical composition, and the degree of fineness that influence the activation reaction. In this work, it was decided to use an activator composed of sodium hydroxide (NaOH) and sodium silicate (NaSiO3), in the proportion of 1:2, respectively. The NaOH was obtained by mixing caustic soda with water in the appropriate proportions so that the 12.5 M molal concentration of NaOH could be achieved following the indications given by [PERSON] [24]. This decision also took into account the experience of other works carried out at the host laboratory regarding workability, and also the limits of the molar  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Diameter [mm]** & **fsy [MPa]** & **fsu [MPa]** & **fsu/fsy** & \(\epsilon\)**su [\%]** \\ \hline
4 & 524 & 665 & 1.27 & 5.0 \\ \hline
6 & 572 & 823 & 1.44 & 5.0 \\ \hline
8 & 614 & 706 & 1.15 & 11.1 \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical characteristics of steel bars.  
Figure 1: Beams before casting.  
concentration of NaOH so that the alkaline reaction occurs in full: 12 M according to [PERSON] [25] and 20 M according to [PERSON] [26]. The second component of the activator, sodium silicate (\"D40\") was purchased from the company \"Quimitecnica\".
###### Abstract  
The modular multilevel converter with embedded supercapacitor energy storage system (MMC-SCES) is a promising solution to the integration challenges posed by large-scale renewable energy. However, inconsistencies in supercapacitor characteristics across energy storage submodules (ESMs) can lead to state-of-charge (SOC) imbalances, reducing overall energy storage utilization. To address this challenge, this paper proposes an enhanced SOC balancing control strategy that leverages the inherent correlation between SOC and submodule capacitor voltage. The strategy simultaneously regulates both energy storage power and ESM capacitor voltage to maintain balance. A two-terminal transmission system with MMC-SCES is built in PSCAD/EMTDC. The results demonstrated that the proposed strategy achieved SOC balancing with only six centralized energy storage controllers, while the SOC balancing of the ESMs remained independent of the startup time. The implementation of the reduced switching frequency voltage balancing algorithm reduced the average switching frequency by 94.54% while maintaining the maximum SOC difference below 0.50%. Moreover, the adaptive coefficients improved the balancing speed by approximately 15% and reduced the initial circulating current by approximately 25%.  
supercapacitor energy storage; bidirectional DC/DC converter; modular multilevel converter; state-of-charge balancing; centralized control; adaptive optimization Article
## 6 Results and Discussion
### Temporal Efficiency of Different Models  
As shown in Figure 8, when the number of training times reaches approximately 400, the loss function value of the CAE part of the CAE_CNN model is less than 0.05. The loss function value of the CNN part of the CAE_CNN model occurs during a descending state in the training process and a total of 22,000 training times is required for the CAE_CNN model. However, the loss function value of the CAE_SMX model vacillates between 0.2 and 0.4 before the number of training times reaches 8,340, and there is no decreasing trend until the number of training runs reaches 40,000. The loss function value of the Best_CNN model also occurs during a descending state in the training process; however, it fluctuates when the number of training runs is between approximately 36,000 and 37,000, and a total of 40,000 training times is required. These results show that the CAE_CNN model is much more efficient than the CAE_SMX model and Best_CNN model.  
In general, the CAE_CNN model can significantly improve the classification accuracy and temporal efficiency of the classification process of object-oriented remote sensing images compared to the pure CAE model and the pure CNN model.
## 3 Proposed Scheduling Algorithm
### End-to-End Reliability in WSNs  
The most widely used method to provide the end-to-end reliability in IWSNs is retransmitting packets when packet loss happens. There have been many studies to determine the required number of time slots to satisfy a given end-to-end reliability requirement and schedule the time slots [10; 11; 12]. Two types of time slots are used to provide the reliability: dedicated time slot and shared time slot. In a dedicated time slot scheme, a fixed number of time slots are dedicated to transmit a packet, which needs a larger number of time slots compared with the shared time slot scheme. In the shared time slot scheme, we can reduce the number of time slots by taking the advantage of statistical multiplexing of independently transmitted packets when multiple packets share a link. We considered the shared time slot scheme in this paper.  
When the target end-to-end PRR (e2 ePRR) is given to a IWSN, the number of time slots that needs to be assigned to a packet transmitted by a node depends on e2 ePRR, the number of links in the routing path, and the PRRs of the links in the path [10; 11; 12]. To determine the number of time slots for a packet transmitted by a node with k hops away from the sink, we first determine the required per-hop link PRR (\(PH_{k}\)) to satisfy the given e2 ePRR as follows:  
\[PH_{k}=e2 ePRR^{1/k} \tag{1}\]  
When multiple packets are sharing a link, we took the average of the per-hop link PRRs of the packets as the required per-hop link PRR (\(PA_{k}\)). Let \(S_{k}\) be a set of \((p_{i},h_{i})\) sharing the transmission link of node \(k\), \(k\), \(P_{k}\)\(>\). Then, \(PA_{k}\) of the link \(<\)_k_, \(P_{k}\)\(>\) is calculated as:  
\[PA_{k}=(\sum_{(p_{i},h_{i})\in S_{k}}PH_{h_{i}})/|S_{k}| \tag{2}\]  
The number of time slots to be assigned to node \(k\) transmitting its packet through link \(<\)_k_, \(P_{k}\)\(>\), \(TS_{k}\), is calculated as:  
\[TS_{k}=\min_{n}\bigg{\{}PA_{k}\leq 1-\sum_{j=0}^{|S_{k}|-1}\left(\begin{array} []{c}n-|S_{k}|+j\\ j\end{array}\right)R_{k}\big{/}(1-R_{k})^{n-|S_{k}|+1}\bigg{\}} \tag{3}\]  
In the example network of Figure 1, let us assume that all nodes have one packet to transmit in each slot frame and e2 ePRR = 0.99 for all packets. Then,  
* \(PH_{1}\) = 0.99, \(PH_{2}\) = 0.99499, \(PH_{3}\) = 0.99666;
* \(S_{1}\) = \(\{\)(p\({}_{1}\), 1), (p\({}_{2}\), 2), (p\({}_{4}\), 2), (p\({}_{3}\), 3)\(\}\); and  
Figure 1: An example network graph.  
\(\bullet\)\(PA_{1}=\sum(\mathrm{PH}_{1}+2*\mathrm{PH}_{2}+\mathrm{PH}_{3})/4)\) = 0.99416.  
Thus, \(TS_{1}\) = 7 by Equation (3), and the number of time slots that needs to be assigned to node 1 is 7 to satisfy the target end-to-end PRR. The PA and TS value for all nodes in Figure 1 is shown in Figure 2a.
## 4 Conclusions  
Principal component analysis allowed to better understand the relationships between the composition of the olive oils and the storage conditions. Eleven variables were identified (oxidized phenolic compounds, non-oxidized phenolic compounds, total phenolic compounds, elenolic acid, \(\alpha\)-tocopherol, 3,4-DHPEA-EDA oxidized, _p-HPEA-EDA_ oxidized, _3,4-DHPEA-EA-AH_ oxidized, _3,4-DHPEA-EDA_, _p-HPEA-EDA_, and _3,4-DHPEA-EA-AH_) that allowed for characterization of the samples according to their storage conditions. The EVOOs stored at lower temperatures under conditions C1, C2, and C3 differed from the oils stored at higher temperatures (conditions C4 and C5). The higher temperature conditions were associated with increased levels over time of compounds derived from the hydrolysis and oxidation of secoiridoid derivatives. EVOOs stored under lower temperature conditions (C1, C2, and C3) were associated with a higher quantity of secoiridoid derivatives and non-oxidized phenolic compounds, indicating that lower storage temperatures limited the oxidation of the oil components. The oils stored in condition C2 were inversely related to the content of \(\alpha\)-tocopherol, highlighting the antioxidant action of \(\alpha\)-tocopherol in response to photooxidation.  
Utilizing a regression on partial least squares, a model was built that described the formation of (_E_)-2-nonenal as a function of the 11 independent variables described in the PCA, indicating (_E_)-2-nonenal as a marker of EVOO quality over time. Linear regressions of (_E_)-2-nonenal were determined as a function of time, with significance for conditions C4 and C5. Therefore, early oxidation in EVOO could be monitored by measuring (_E_)-2-nonenal in the oil.  
Through an analytical focus, this study provides scientific evidence that storage conditions are highly relevant to ensure the quality of EVOO. As such, storage conditions are an important factor to consider when exporting oils to destination markets that have higher average room temperatures than those recommended for EVOO storage. Lack of consideration for storage conditions could result in the appearance of sensory defects in the oil and thus in a loss of EVOO category.  
Conceptualization, N.R. and A.S.; methodology, N.R. and B.S.; software, E.F.; validation, B.S., L.C. and N.R.; formal analysis, L.C. and A.S.; investigation, N.R. and B.S.; resources, R.V., N.R. and A.S.; data curation, N.R., E.F. and A.S; writing--original draft preparation, N.R., E.F. and A.S; writing--review and editing, N.R., R.V and A.H.M.; visualization, R.V. and N.R.; supervision, N.R. and R.V.; project administration, N.R.; funding acquisition, N.R. and A.S. All authors have read and agreed to the published version of the manuscript.  
\"This research was funded by PROYECTO ENLACE PEEL, FCQYF, UNIVERSIDAD DE CHILE,\" and \"PROYECTO FONDECYT, grant number 1191682\".  
Not applicable.  
The authors declare no conflict of interest.
Effectiveness, Safety and Patients' Satisfaction of Nabiximabs (Sativex(r)) on Multiple Sclerosis Spasticity and Related Symptoms in a Swiss Multicenter Study  
[PERSON]  
1 Multiple Sclerosis Center (MSC), Department of Neurology, Neurocenter of Southern Switzerland, 6900 Lugano, Switzerland; [EMAIL_ADDRESS] (R.S.); [EMAIL_ADDRESS] (G.C.R.); [EMAIL_ADDRESS] (G.D.); claudio.gobbi@eoc.  
spasticity involving one third of patients after 10 years from diagnosis [1; 2; 3]. Spasticity and associated symptoms, such as spasms, pain, sleep disturbances, and bladder dysfunction, interfere with patients' mobility and daily activities, impairing autonomy and quality of life [4; 5], and increasing healthcare burden [3].  
MS spasticity management varies across countries, often involving a multidisciplinary approach [6]. First line treatments are physiotherapy and pharmacotherapy including baclofen, tizanidine, dantrolene, gabapentin, clonazepam, and botulin toxin A, although the quality of evidence supporting the use of these drugs is low or even lacking [7]. Additionally, these treatments are not effective or poorly tolerated in a significant proportion of patients, resulting in inadequate symptom management [8]. Another treatment option is represented by the intrathecal baclofen subcutaneous pump, limited however by surgical/post-surgical risks and the need to turn off the pump for performing an MRI [9].  
Nabiximabs is a complex botanical mixture containing balanced quantities of \(\Delta\)-9-tetrahydrocannabinol (THC) and cannabinoid (CBD), along with other cannabinoid and non-cannabinoid components [10; 11; 12].  
A number of randomized controlled trials [13; 14; 15] exploring the efficacy and safety of nabiximabs mainly as add-on therapy in MS patients have reported a significant reduction in spasticity compared with placebo, a rapid occurrence of treatment response (within the firsts 4 weeks), a persistent efficacy over time without need to increase the dosage, and a low drug discontinuation due to side effects or inefficacy (10%) [13].  
In 2013, nabiximabs oro-mucosal spray [Saitvex(r), Almirall, Alte Winterthurerstrasse 14, 8304 Wallisellen (CH), Switzerland] was approved in Switzerland as treatment for MS patients with moderate to severe spasticity inadequately responsive to other antispastic medications [10; 11].  
Given the absence of a national Swiss registry on the use of nabiximabs, we conducted a post-marketing survey in Swiss MS patients to collect data on real-world effectiveness, tolerability and satisfaction of nabiximabs used as add on treatment or monotherapy for spasticity, and to identify clinical factors which may predict treatment response.
# A Four-Stage Method for Active Control with Online Feedback Path Modelling Using Control Signal
## 4 Conclusions  
A systematic four-stage method is proposed for feedforward active control systems to perform online feedback path modelling with the control signal. The proposed method avoids the interference between the control operation and feedback path modelling by appropriate toggling of the switches used for managing the control flow. Furthermore, adaptive decorrelation filters are employed to improve the feedback path modelling performance, which in turn improves the active control performance. Simulation results show that the proposed method outperforms [PERSON]'s method in terms of faster convergence and lower residual noise, which uses auxiliary white noise for modelling the feedback path. However, the proposed method is effective for broadband control only due to the fact that the feedback path modelling performance is highly reliant on the frequency content of the control signal. Future works include investigating a proper howling detection technique that can provide high sensitivity for stability detection and a less distorted reference signal for effective control operation, and by applying the variable-tap-length algorithm to find the optimal number of decorrelation filter coefficients necessary to pre-whiten complicated signals.  
Conceptualization, S.P. and X.Q.; Methodology, S.P. and X.Q.; Validation, S.P.; Investigation, S.P.; Writing--original draft preparation, S.P.; Writing--review and editing, X.Q. and J.J.; Supervision, X.Q. and J.J.; Project administration, X.Q.; Funding acquisition, X.Q. This research was funded by the Australian Research Council's Linkage Project, grant number LP160100616. The authors declare no conflict of interest.