esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Using the classification approach described earlier, and based on spectral analysis results, a map of the different agricultural species was produced for the study area (Figure 9). Results generally showed that the classification using both classifiers was satisfactory, with the exception of some confusion between a few classes, which is likely due to the spectral similarity of the crops.  
Previous investigations using geospatial techniques have shown a strong efficiency in land use/land cover change monitoring [6; 11], crop mapping [3; 18], and identification of agricultural systems [8]. [PERSON] et al. [3] carried out an insightful study in the plains of Tadla, Morocco. The objectives included mapping different agricultural species using high-resolution satellite data and machine learning approaches and comparing the different used approaches. Their study showed an overall accuracy of 89.26%, 85.27%, and 57.17%, respectively, for random forest, support vector machine, and spectral angle mapper, with a Kappa index of 0.85, 0.80, and 0.4, respectively. Comparing our result with this one, our study also showed a high overall accuracy of 89.76% for SVM and 84.03% for RF, and a Kappa index of 0.79 and 0.68, respectively.  
The CROPWAT model was used to determine water demand for the different agricultural species in the region. Three parameters were calculated, namely, the crops' potential evaporation, the effective rain, and the irrigation requirements. The results obtained by [40] showed that the irrigation requirements varied according to the location, whereas the required water quantity per palm varied between 115 and 200 liters per day. Comparing our result with this one, our study showed that olive trees, fruit trees, and other crops followed by cereals are the most water demanding, with needs exceeding 500 mm. The water demands of cereals and vegetables are lower than that of other classes, with amounts of 300 mm and 150 mm, respectively.  
As explained, water consumption is increasing in the Saiss plain. This is mainly due to excessive exploitation. According to the 1939-2002 groundwater data record, there is a constant deficit of approximately 100 Mm\({}^{3}\)/year, with an estimated inflow of 242 Mm\({}^{3}\)/year and an estimated outflow of 342 Mm\({}^{3}\)/year. While the output includes abstraction with 260 Mm\({}^{3}\)/year, and rivers and springs with 82 Mm\({}^{3}\)/year, 22% of the water balance is dedicated to human drinking water supplies and 78% to private irrigation [56].
## 1 Introduction
(2023) [10] used the landslide data of the Mw 5.7 Yiliang earthquake, selecting nine influencing factors to develop a coseismic landslide probability model using Bayesian probability method and logistic regression model. The model was applied to predict landslides for the Ludian earthquake, demonstrating a good fit and confirming the similarity in landslide characteristics between the Yiliang and Ludian earthquakes. [PERSON], [PERSON] et al. (2016) [11] analyzed the differences between the landslides triggered by the Ludian earthquake and the landslide triggered by the Jinggu earthquake, and the result revealed that these differences mainly resulted from four factors: earthquake intensity, population density, building seismic resistance, and geological environment. Such studies often focused on specific rainfall or seismic events in affected regions, with few studies conducted on the developmental characteristics and spatial distribution patterns of landslide disasters in the Zhaotong area. Moreover, the selection of influencing factors for landslide disasters is greatly influenced by the geological environment, climatic factors, and human engineering activities in the study area, which limits the spatial distribution of landslide disasters and the analysis of influencing factors in this region. In this study, landslide data provided by the Bureau of Natural Resources and Planning, combined with visual interpretation of high-resolution satellite images from Google Earth platform, were utilized to obtain information on 161 landslides and 3646 potential geological disasters in the Zhaotong area. Six influencing factors were selected for investigation: elevation, slope angle, slope orientation, geological unit, distance to rivers, and rainfall. This study not only analyzes the correlation between landslide occurrences and such potential factors based on statistical data, but also further examines the relationship between landslide occurrences and population distribution. The main objective of this work is to figure out how the potential factors exert influence on the spatial distribution of landslides, which can provide essential data and scientific support for landslide prevention and mitigation planning in the Zhaotong area.
## 1 Introduction
SATLD assigns the corresponding ground station to each downlink task firstly, and then determines the specific start time of each downlink task. The SATLD algorithm proposed in this paper is a method that balances profit and efficiency. First, the problem of resource allocation in the MGSS problem is solved. Second, according to the characteristics that some specific tasks need to be transmitted to the ground station in a short time, a method for determining the exact start time of the task considering the waiting time is given. The remainder of this paper is organized as follows. In Section 2, a mathematical model of the MGSS problem is proposed. Section 3 presents the main scheme of the SATLD. Experimental results are presented in Section 4 to verify the efficiency of the model and the effectiveness of the proposed algorithm. The last section offers conclusions and directions for future research.
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
## 3 Cybersecurity in the IEC61850 Standard
### IEC 62351-6: Security for GOOSE  
The challenges of securing GOOSE and SV messaging, by verifying message authentication, integrity, and encryption, are much more difficult to achieve than in other systems, since these protocols differ greatly in network latency compliance requirements.  
In 2007, WG15 developed the IEC62351 standard that addresses cybersecurity issues of power system communication standards under the jurisdiction of TC57, such as IEC61850. IEC62351-1 stipulates that GOOSE and SV messages must have two mandatory security measures, the authenticity and integrity of the messages. While for confidentiality, IEC62351-1 stipulates that encryption algorithms cannot be applied due to the difficulty that IEDs would have in processing said algorithms due to their limited ability to perform computing functions. With these security measures, it is possible to counteract unauthorized modification of data, its manipulation and reproduction, and to avoid MiTM and DoS attacks where, for example, malware could simulate a false IED. To achieve these security requirements in GOOSE and SV messages, IEC62351 has developed two recommendations, IEC62351-6:2007 (currently obsolete) and IEC62351-6:2020. To ensure the authenticity and integrity of the messages, the use of different MAC (Message Authentication Code) algorithms is suggested. Table 1 shows the algorithms proposed by IEC62351-6:2020 to secure the GOOSE and SV protocols [10].  
The MAC implementation requires a key pre-shared by both the publisher and the subscribing IEDs. HMAC can provide message authentication using a shared secret instead of using digital signatures with asymmetric cryptography, such as the algorithms proposed by IEC62351:2007. HMAC uses two hash calculation steps. The secret key is first used to derive two keys: internal and external. The first step of the algorithm produces an internal hash derived from the message and the internal key. The second pass produces the final HMAC code derived from the result of the internal hash and the foreign key. Therefore, the algorithm provides better immunity against length extension attacks, so the security of HMAC is guaranteed by the secure distribution of the symmetric key and its unpredictability [24].  
Table 2 shows the computational times required by the IEDs and the end-to-end communication time of the different MAC algorithms proposed in IEC62351-6:2020 for the exchange of GOOSE messages with their secure extended frame. As can be seen in Table 2, the end-to-end message exchange, in any of the proposed algorithms, meets the requirement of the IEC61850-5 standard that says that GOOSE messages cannot take more than 3 ms. Therefore, the proposed MAC algorithms to ensure integrity and authenticity can be successfully applied for both GOOSE and SV messaging.
# Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
###### Abstract  
The classic Hodgkin-Huxley model is widely used for understanding the electrophysiological dynamics of a single neuron. While applying a low-amplitude constant current to the system results in a single voltage spike, it is possible to produce multiple voltage spikes by applying time-varying currents, which may not be experimentally measurable. The aim of this work is to estimate time-varying applied currents of different deterministic forms given noisy voltage data. In particular, we utilize an augmented ensemble Kalman filter with parameter tracking to estimate four different time-varying applied current parameters and associated Hodgkin-Huxley model states, along with uncertainty bounds in each case. We test the efficiency of the parameter tracking algorithm in this setting by analyzing the effects of changing the standard deviation of the parameter drift and the frequency of data available on the resulting time-varying applied current estimates and related uncertainty.  
inverse problems; time-varying parameter estimation; ensemble Kalman filter; Hodgkin-Huxley; neuron dynamics Article
## 3 Z-Score Method Using the Proposed Scale Estimator
### Proposed \(S_{n}\) Scale Estimator for Outlier Detection  
The proposed \(S_{n}\) scale estimator as an alternative to MAD scale estimators has several advantages, one of which is lower gross error sensitivity. This is because the \(S_{n}\) scale estimator considers the relationship that is the distance between the RSS observations, making it less sensitive to outliers [29]. Furthermore, the \(S_{n}\) scale estimator has a higher Gaussian efficiency of 58% and is very effective at detecting outliers in both symmetric and asymmetrically distributed observations, which has been extensively validated theoretically and empirically in earlier research works [29; 31; 37].  
Mathematically, the \(S_{n}\) scale estimator is presented in Equation (8) [29].  
\[S_{n}=c\times median_{m}\{median_{n}\{\left|\mathit{rss}(m)-\mathit{rss}(n) \right|\}\} \tag{8}\]  
where \(\mathsf{m}\in[1,2,\ldots,N]\), \(\mathsf{n}\in[1,2,\ldots,N]\), \(m\
eq n\), \(c=1.1926\)  
The Z-score value based on the \(S_{n}\) scale estimator is presented in Equation (9).  
\[Z_{S_{n}}(\mathit{rss}(n))=\frac{\left|\mathit{rss}(n)-\mathrm{MED}_{\mathit{ rss}_{i}}\right|}{\alpha\times S_{n}} \tag{9}\]  
For each \(\mathit{rss}(\mathbf{n})\) in Equation (1), the Z-score value is calculated using Equation (9) with the scale estimator obtained using Equation (8) and it is an outlier if it satisfies Equation (10).  
\[\left|Z_{S_{n}}(\mathit{rss}(n))\right|>\left|\gamma\right| \tag{10}\]  
where \"\(\gamma\)\" is called the rejection criterion above which an RSS observation is considered an outlier.  
The next step after detecting outliers is to decide what to do with them. The approach to dealing with outliers is presented in the section that follows.
## 3 Results
### Biological Similarity Evaluation  
To evaluate the biological similarity performance of GBNSS, we use two existing measures: the Simpson overlap of Gene Ontology and functional coherence (FC).  
As depicted in Figure 6, the Simpson overlap coefficient was used to evaluate the performance of GOSim across four different datasets. Comparative analysis revealed that GBNSS surpassed NSSRF2, NSSRF3, NSSRF4, and PathEmb on all four datasets. Strikingly, within the top decile of ranked similarities on the Mus Musculus dataset, the Simpson overlap coefficient achieved by GBNSS was marginally less than 0.9, demonstrating a high level of concurrence between the predicted functional similarities.  
Functional coherence [39] evaluates biological relevance by analyzing the semantic relationships of GO terms associated with the relevant protein-coding genes. The similarity between two biological networks is computed as  
\[FC(u,v)=\frac{|S_{u}\cap S_{v}|}{|S_{u}\cup S_{v}|}, \tag{6}\]  
where \(S_{u}\) and \(S_{v}\) are the GO term set of network \(u\) and network \(v\). The higher the functional coherence, the more similar the two networks are. The corresponding biological performance is shown in Figure 7. The FC of GBNSS on Homo Sapiens, Mus Musculus, and Saccharomyces cerevisiae are larger than those of NSSRF and PathEmb. As a result,  
Figure 4: The topological performance is examined by Simpson overlap on the Bos Taurus and Saccharomyces cerevisiae datasets.  
Figure 5: The topological performance is examined by Simpson overlap on the Homo Sapiens and Mus Musculus datasets.  
GBNSS consistently outperforms the other methods, showing that it can produce more biologically relevant outcomes in the context of biological network similarity searches.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 4 Reverse Packet Elimination
### Path Considerations  
RPE assumes a routing protocol capable of providing upward routes for data and replicated data, as well as downward routes for RPE-cancelling packets. The RPL routing protocol which is part of the 6 TiSCH suite offers both these capabilities. Since the downward RPE-cancelling packet always originates at the sink node, both storing and non-storing RPL mode may be utilized with RPE.  
The RPL preferred parent selection needs to be adapted such that the source node can employ both Path A and Path B to forward traffic. Such a mechanism is implemented in [18]. Selection of different parents, namely Preferred Parent and Alternative Parent, is in itself a field to be studied, and is not treated further here. An example of work in this direction can be found [23].  
RPE requires routing and accompanying schedules to yield disjoint paths through the network. Investigation into this assumption, such as, e.g., the requirement on network density, is deferred to future work. As described above, for our evaluation we assume a centralized PCE as envisioned in, e.g., 6 TiSCH to provide this capability. This centralized strategy seems to be an intuitive solution for these cases since it has an end-to-end awareness such that disjoint paths can be guaranteed. The 6 TiSCH hop-by-hop scheduling model [5] may also be feasible since it has the same end-to-end property.
## 4 Results and Discussion  
This section presents an experimental evaluation of the proposed system, aimed at optimizing the in-memory execution of data-intensive workflows. We experimentally assessed the effectiveness of the IIWM using Apache Spark 3.0.1 as a testbed. In particular, we generated two synthetic workflows for analyzing different scenarios, by assessing also the benefits coming from the use of the IIWM using a real data mining workflow as a case study.  
In order to provide significant results, each experiment was executed ten times, and the average metrics with standard deviations are reported. In particular, for each experiment, we evaluated the accuracy of the regression model in predicting memory occupancy and execution time.  
We evaluated the ability of the IIWM to improve application performance taking into account two different aspects:  
* Execution time: Let \(m_{1}\) and \(m_{2}\) be the makespan for two different executions. If \(m_{2}<m_{1}\), we can compute the improvement on makespan (\(m_{imp}\)) and application performance (\(p_{imp}\)) as follows: \[m_{imp}=\frac{m_{1}-m_{2}}{m_{1}}\times 100\%\hskip 28.452756 ptp_{imp}=\frac{m_{1} }{m_{2}}\]* Disk usage: We used the on-disk usage metric, which measures the amount of disk usage, jointly considering the volume and the duration of disk writes. Formally, given a sequence of disk writes \(w_{1},...,w_{k}\), let \(\tau^{{}^{\prime}}_{i}\), \(\tau^{{}^{\prime\prime}}_{i}\in\mathbb{T}\) be the start and end time of the \(w_{i}\) write, respectively. Let also \(W:\mathbb{T}\rightarrow\mathbb{R}\) be a function representing the amount of megabytes written to disk over time \(\mathbb{T}\). We define on-disk usage as: \[\text{on-disk usage}=\sum_{i=1}^{k}\frac{1}{\tau^{{}^{\prime\prime}}_{i}- \tau^{{}^{\prime}}_{i}}\int_{\tau^{{}^{\prime}}_{i}}^{\tau^{{}^{\prime\prime}} _{i}}W(\tau)d\tau\]  
Specifically, for each workflow, we reported: (i) a comparison between full-parallel and the IIWM in terms of disk usage over time; (ii) a detailed description of the scheduling plan generated by both strategies; (iii) the average improvement on makespan and application performance with the IIWM; (iv) statistics about the use of disk, such as the time spent for I/O operations and the on-disk usage metric; (v) the execution of the workflow by varying the amount of available memory, in order to show the benefits of the proposed scheduler in different limited memory scenarios.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 3 LP-WAN Solutions for IIoT Services
### Standardization Bodies' Efforts  
Besides the platforms reviewed above, there are different solutions proposed by well-recognized standardization bodies that are currently under study. For example, the IEEE has proposed the P802.11 ah [31] and 802.15.4k [32] standards. The former presents a series of modifications at the 802.11 PHY and MAC layers aimed at adapting them to sub-GHz bands (excluding TV white space). Using the well-studied Orthogonal Frequency Division Multiplexing (OFDM), it is intended to reach a minimum data rate of 100 kbps and a transmission range up to 1 km [33]. In this standard, the co-existence with other technologies, such as all those based on the IEEE 802.15.4 PHY-layer specifications, is being considered. In turn, the IEEE 802.15.4k standard presents MAC and PHY layer specifications to facilitate Low Energy Critical Infrastructure Monitoring (LECIM) applications. This standard defines two PHY modes: Direct-Sequence Spread Spectrum (DSSS) and Frequency Shift Keying (FSK). The former permits links of up to 20 km in line of sight (5 km in non-line of sight) with data rates of up to 125 kbps. The proposed architecture is a point-to-multipoint network by means of a star topology composed by two types of nodes, namely a PAN (Personal Area Network) coordinator and the edge-devices. The communication between the collector and the sensors is asymmetric, aimed at limiting the \"listening\" time of the battery-powered sensors. This standard permits employing both sub-GHz and 2.4 GHz bands using Binary Phase Shift Keying (BPSK) and OQPSK modulations. In turn, the 3 GPP group (3 rd Generation Partnership Project) is working on the development of the LTE-MTC (Long Term Evolution-Machine-Type Communications) standard [34]. In the LTE Release 12, the Cat 0 speed of 1 Mbps was defined, but in order to reduce the chipset's complexity and power consumption, there is a plan to define an even lower speed of about 200 kbps (referred to as Cat M) in the next release, Release 13. Although the standard is still being developed, it has been decided to make use of 1.4 MHz channels within the cellular band (450 MHz) in order to provide bi-directional connectivity between edge-nodes and the base station. Finally, aimed at presenting a comprehensive comparison among all the reviewed LP-WAN platforms, Table 1 shows their most relevant characteristics. Please note that the presented values have been extracted from the platform's specification sheets and some of them could be provisional figures due to the ongoing evolution of the different solutions.
## 4 Conclusions  
In this study, hydrogen production from catalytic steam reforming of phenol was performed over Ni/ZrO\({}_{2}\) (Ni-Co ratio = 1) and Co/ZrO\({}_{2}\) (Ni-Co ratio = 0) catalysts. In order to investigate the promising catalyst activity towards phenol conversion (\(Y_{1}\)) and hydrogen yield (\(Y_{2}\)), as well as reduce the total number of experiments, a statistical design of experiment (DOE) using the 2\({}^{5}\) full factorial method (five variables, each variable at two levels) was performed. The five variables were temperature (\(A\)), liquid feed flow rate (\(B\)), catalyst weight (\(C\)), Ni to Co ratio (\(D\)), and phenol concentration (\(E\)). Thirty-two sets of experiments were performed to explore the effects of the reaction variables and their interaction on the phenol conversion and the hydrogen yield. The catalyst performance testing shows that 800 \({}^{\circ}\)C, 0.16 mL/min feed flow rate, 0.3 g of catalyst, 0 ratio of Ni-Co (Co/ZrO\({}_{2}\)), and 10 wt % of phenol in the feed gave a phenol conversion of 96.8% and a hydrogen yield of 72.4%. The statistical DOE indicated that within the limits that were being examined, all the variables had a significance level of 5% (\(p<0.05\)). In addition, the interaction among \(A\) versus \(E\), \(B\) versus \(E\), \(C\) versus \(E\), and \(D\) versus \(E\) were found to have a significant effect on \(Y_{1}\) and \(Y_{2}\) responses. This was proven by Pareto charts and Normal plots. The analysis of the regression coefficient results for the reduced regression model indicates that the model can fit the experimental data for both \(Y_{1}\) and \(Y_{2}\) responses with correlation coefficients (\(R^{2}\)) of 0.968 and 0.936, respectively. The parity charts of predicted versus observed responses also confirm the fit of the experimental data with the regression model. The \(p\)-value of the reduced model was zero and that model is considered to be statistically significant. The surface and contour plots show that the \(E\) variable is more significant with higher levels of \(A\) and \(C\) and lower levels of \(B\) and \(D\). The response optimizer tool indicated that the optimum conditions for the reduced model were 800 \({}^{\circ}\)C, 0.16 mL/min, 0.3 g catalyst, 0 Ni-Co ratio, and 10 wt % of phenol concentration to obtain 94.98% of \(Y_{1}\) and 67.4% of \(Y_{2}\), respectively.  
The authors acknowledge the financial support given for this work by Universiti Teknologi Malaysia (UTM) under the Research University Grant (10H19 and 13H36), and The Malaysian Ministry of Higher Education under FRGS 4F478 and 4F403.  
[PERSON] drafting the whole work, [PERSON] is the main supervisor who supervise the whole study (including design of the work, analysis, and interpretation of data), [PERSON] is the Co-supervisor who help to supervise the whole study (including design of the work, analysis, and interpretation of data), [PERSON] contributed on optimization part, [PERSON] contributed on analysis and interpretation of data, [PERSON] contributed on XRD and TPR characterization.  
The authors declare no conflict of interest.