esa-sceva/satcom-chunk-collection huggingface dataset:
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 4 Methodology
### XXE Detector  
The key to detecting XXE attacks lies in the use and definition relationships of XML entities. Following the XML rule that _an entity must be defined before it is used_, we propose an algorithm (Algorithm 2) to construct the UD chain of XML entities and detect an XXE attack. For XXE detection, we only need to focus on whether the constructed UD chain is complete, and the behavior of its nodes, eliminating the need for machine learning models. In detail, we check all packets in period T, extract XML and DTD data from different packets  
Figure 7: Packet processing result of Figure 4.  
and restore them, then look for the use and definition of XML entities to construct the UD chain. Based on the integrity of the chain and the behavior (such as network accessing, file reading, and code execution) of the chain's tail node, XXE attacks are detected.  
```
Input: All packets in period T Output: XXE detection results, where true means there is a XXE attack
1 use_def \(\leftarrow\) init();
2 entityMap \(\leftarrow\) init();
3 results \(\leftarrow\) init();
4 foreachpacket in packetsdo
5 packetInfo \(\leftarrow\) parsePacket(packet);
6 ifpacketInfo.containsXML()then
7 relatedPackets \(\leftarrow\) findPacketsWithTime(packet, packets, T);
8 payloads \(\leftarrow\) extractPayloadsInPackets(relatedPackets);
9 entities, values \(\leftarrow\) extractEntitiesAndValues(payloads);
10 entityMap.addAll(entities, values);
11 entityUseList \(\leftarrow\) extractEntityUseList(payloads);
12 foreachentityUse in entityUseListdo
13 use_def.put(entityUse, null);
14
15 end if
16 entityDefList \(\leftarrow\) extractEntityDefList(payloads);
17 foreachentityDef in entityDefListdo
18 ifuse_def.contains(entityDef)then
19 use_def.set(entityDef, entityDef);
20
21 end if
22
23 end if
24 integrity \(\leftarrow\) true;
25 foreachud in use_defdo
26 ifuse_def.getValue(ud) is nullthen
27 results.put(packet, false);
28 integrity \(\leftarrow\) false;
29 break;
30
31 end if
32
33 end if
34
35 end if
36
37 end if
38
39 end if
40
41 end for
```  
**Algorithm 2**XXE detection.
## 1 Introduction
This motivates the need for a QGCT-driven approach. Hence, in order to explore the correlation between factors such as meteorological environment and power load changes as well as to achieve accurate load forecasting, a load forecasting method based on QGCT and BiLSTM has been proposed in this paper. The contributions of this paper are summarized as follows:  
(1) The key factors selection method for load change based on quantile regression and Granger causality is proposed, which breaks through the limitation that the mean Granger causality test only focuses on a certain part of the conditional distribution to analyze each interval more carefully and flexibly and to identify the key factors that are more capable of influencing the change of load. (2) The load forecasting model based on key influencing factors and BiLSTM is constructed, which the selected environmental factors with significant relationship to load changes are used as input features to improve the accuracy of load forecasting. (3) The sensitivity analysis of load forecasting to environmental factors based on single-factor perturbation at seasonal scales is proposed to highlight the role of geographic-climatic coupling mechanisms on the interpretability of load forecasting models. The rest of this paper is organized as follows: the causality test for load influencing factors is introduced in Section 2; the load forecasting based on BiLSTM and the leading influencing factors is introduced in Section 3; the case studies based on actual data collected from Fujian province, China are given in Section 4; the conclusion is given in Section 5.
## 3 Congestion Prediction Model
### Preliminaries
#### 3.1.2 Transportation Demand Analysis Techniques  
Traffic demand analysis is the process of looking at the relevance between traffic demand and socio-economic activities, the analyzing the factors that determine traffic demand and its impact on changes in traffic. Traffic demand analysis is distinguished from traffic prediction, which has a primary goal of producing traffic forecasts for individual links. Whether the results of traffic demand analysis can be used to predict future traffic depends on the ability to understand the impact of factors that affect predictive self-traffic and the ability to predict the various explanatory variables used in transportation demand models. Transportation demand modeling is important for the forecasting process, but there are limits to modeling as a means of prediction. Traffic demand analysis can be used as a model for short-term prediction of traffic volume, but the usefulness of transportation demand models for traffic prediction becomes increasingly limited as the forecast period lengthens [30].  
The development of models that can meet the ultimate goal of transportation planning is required. These include a direct demand model for inter-city travel demand analysis, four-step transportation demand forecasting methods for the target area (which is divided into multiple zones for analysis), and a probability choice model to select the best alternative and to maximize the utility among all possible alternatives.
## 4 Discussion
### The Effects of the Variations among the ET Products on Water Resources Management  
ET is a significant climate variable that uniquely links the water cycle. It can help understand both sides of the water supply-demand equation [5; 36]. The model calibration, using ET products combined with gaged runoff data, have effectively improved the performances of the hydrological models; moreover, ET products are also attractively used to estimate the runoff in the ungauged basins [5; 13; 14]. Thus, the value variations of ET could have a large influence on the other water balance components. Compared with the gaged runoff data and the condition of the total water storage changes estimated with the GRACE satellites from 2002 to 2014 [37], the runoff and water storage change in the MRC provided by the AWAP are reasonable respectively. The results in this study show the obvious influences of different ET products on the estimation of runoff and water storage undoubtedly creates a challenge for the calibration of hydrological models with such large variations of water balance components [13; 14; 19]. More importantly, such inaccurate information would seriously influence the precision and reliability of the decision-making on regional water resources allocation and regulation, which may artificially aggravate the unbalance of water allocation between different reaches in catchments, and between human resource use and ecosystem protection. In addition, such large variations of ET cast a shadow over the objective assessment of the effects of land use and cover change and climate change on basin water cycles, which are attracting increasing global attention [38; 39].  
As the predominant variable for water management in agricultural food production, the atmospheric demand for ET is a reference to irrigation [40; 41]. In the MRC, the uncertainty of water allocation and low water availability were the main impediments to irrigated farms including dairy, broadacre and horticultural industries [42]. It was found in this study that the estimations of annual irrigation with these five ET products range from 49.1 mm using TerraClimate ET to 154.6 mm using GLDAS ET. Such a large range of estimated irrigation could lead to over-irrigation with water loss via infiltration of groundwater or it could result in unnecessarily returning water to rivers for environmental protection at the expense of necessary irrigation, resulting in lower crop yields due to crops suffering the effects of insufficient water, especially during the growth stage of the crop.
# Exploring ISAC: Information-Theoretic Insights
## 3 Mono-Static ISAC with Sensing Distortion
### Log-Loss Distortion  
The work in [58] considered a related setup, where distortion is measured in terms of log-loss distortion. The goal of sensing is thus rather to obtain a soft estimate, i.e., probability distribution \(Q_{\hat{\mathcal{S}}|X^{n}Z^{n}}(\cdot|x^{n},z^{n})\) for the state estimate, instead of a state sequence \(\hat{\mathcal{S}}^{n}\). The model described in the previous section can be adapted to account for a log-loss distortion constraint simply, where in particular the achievability criterion (4b) has to be replaced by the following requirement:  
\[\varlimsup_{n\to\infty}\frac{1}{n}\sum_{i=1}^{n}\mathbb{E}\left[\log\frac{1}{ Q_{\hat{\mathcal{S}}^{n}|X^{n}Z^{n}}(S^{n}|X^{n},Z^{n})}\right]\leq\mathbb{D}. \tag{15}\]  
In analogy to Lemma 1, it can be argued that the optimal log-loss estimator \(Q_{\hat{\mathcal{S}}^{n}|X^{n}Z^{n}}\) is in product form \(Q_{\hat{\mathcal{S}}|XZ}^{\otimes n}\) and given by the posterior probability in (6)  
\[Q_{\hat{\mathcal{S}}|XZ}^{*}(s|x,z)=P_{S|XZ}(s|x,z)=\frac{P_{S}(s)P_{Z|SX}(z|s,x)}{\sum_{\hat{s}\in\mathcal{S}}P_{S}(\hat{s})P_{Z|SX}(z|s,x)}, \tag{16}\]  
which is solely determined by the channel law and the state distribution but not by the utilized coding scheme.  
We can thus conclude that the equivalent of the capacity-distortion-cost tradeoff for log-loss distortion is:  
**Theorem 2**.: \[\mathsf{C}_{\mathrm{LogLoss}}(\mathbb{D},\mathbb{B})=\max_{\begin{subarray}{ c}P_{X}\in\mathcal{P}_{\mathbb{B}}:\\ H(S|XZ)\leq\mathbb{D}\end{subarray}}\;I(X;Y),\] (17)  
_where the entropy and the mutual information are calculated according to the joint law \(P_{SXYZ}=P_{S}P_{X}P_{YZ|XS}\)._  
Note once more that any kind of receiver-side information can be incorporated in the received signal \(Y\), and thus can be treated within the exposed framework. Note further that [58] considers a slightly different approach where the distortion constraint needs to be  
Figure 3: Capacity–distortion tradeoff of fading AWGN channel with \(\mathbb{B}=10\) dB and \(\sigma_{\mathrm{fb}}^{2}=1\) (Rate is measured in _nats_).  
satisfied for any realization of the message \(W=w\). Interestingly, the two models lead to the same capacity-distortion-cost tradeoff and under both models, the optimal estimator is the posterior estimator.
# Sea Purslane as an Emerging Food Crop: Nutritional and Biological Studies
## 3 Results and Discussion
### Mineral and Heavy Metal Composition
Iodine is a vital nutrient for human health, since it regulates thyroid function. Low iodine intake is responsible for thyroid disorders [51]. The iodine content (0.05 mg/100 g of dry matter) presented in sea purslane leaves was higher than in cereals and grains (ranging from 0.0016 to 0.039 g/100 g), meat (0.0034-0.034 g/100 g), dairy products (0.047-0.069 g/100 g), fresh fruit (0.00018 g/100 g), fresh vegetables (0.0036 g/100 g), leafy vegetables (salad) (0.0236 g/100 g), mushrooms (0.021 g/100 g), or nuts (0.0218 g/100 g), which are all expressed in dry matter [52]. Although foods of marine environment such as marine fish and seaweed are the major suppliers of iodine [53], _H. portuloides_ represents an important source of iodine. The oral intake of iodine recognized for adequate nutrition in human adolescents and adults is 150 mg per day [52]. The ingestion of 100 g of fresh leaves corresponds to 7.3% of the daily values recommended for iodine [43]. In general, _H. portulacoides_ leaves are a good source of minerals such as Ca, Mg, Mn, and Cu. It should be noted that 100 g of fresh leaves provide values of 24% and 28%, respectively, for calcium and magnesium, 74% for manganese, and 17% for copper. Cadmium, lead, and mercury, considered human carcinogens [55], were detected in _H. portulacoides_. According to the Commission Regulation (EC) n\({}^{\circ}\) 466/2001 of 8 March 2001, the maximum content of cadmium in leafy vegetables, fresh herbs, celery, and all cultured mushrooms is 0.2 mg/kg (20 \(\mu\)g/100 g of fresh weight), and the maximum lead content in Brassica, leafy vegetables, fresh herbs, and all mushrooms is 0.3 mg/kg (30 \(\mu\)g/100 g of fresh weight) [56]. Thus, the concentrations of toxic metals were below the legislated values and there was a much lower concentration of lead than in _Sarccornia pernius alpin_ and _Salicornia marinism_ from Castro Marim (Algarve, south of Portugal) (Table 3) [4]. The different amounts of these contaminants are dependent on the level of contamination of sediments in salt marshes, since halophytes have the ability to accumulate metals such as Zn, Cr, Pb, Ni, and Cd, among others. However, metal concentrations found in the above-ground tissues of halophyte plants such as _Salicornia fruticosa_ and _Salicornia martima_ from Tagus and Guadiana estuaries (Portugal) were up to four orders of magnitude lower than in below-ground parts, confirming metal retention in their roots and a residual upward translocation [54; 57; 58]. Mercury is a dangerous pollutant due to its high toxicity, making it a major threat to coastal ecosystems [24]. As other metals, the mobility of mercury is greater in the roots, and only a small part is translocated to the above-ground parts of the plant [10]. Hence,
# The Role of AI in Warehouse Digital Twins: Literature Review +
## 4 Analysis Framework
### Data  
This section provides an in-depth exploration of data's role in replicating warehouses in the virtual space. It is undeniable that the application of AI and DTs is heavily reliant on data. Almost every application of AI and DTs can be traced back to data as the starting point. Notably, the use of data in warehousing is diverse and varies depending on several factors, such as availability, source, and application. Three primary data types are collected in warehouses from different sources, including manufacturing information systems, IoT devices, and manual data entry [14]. These data sources include:  
* Environmental data (ED): such as temperature, humidity, and light intensity, could be crucial in decision-making processes or to represent and supervise the physical process accurately. Depending on the type of goods stored in the warehouse, these data may provide valuable insights into the most suitable storage conditions.
* Product data (PD): which entails information on inventory levels and storage locations is another key data type. Technologies such as radio frequency identification (RFID) can monitor storage locations and quantities, linking this information to the warehouse management system (WMS) and DTs for effective replenishment and stockkeeping.
* Handler data (HD) is the third type of data collected in warehouses, providing crucial information on workers and equipment, including their real-time locations. This data may be collected from workers' handheld devices, allowing for location tracking, and measuring other physical variables. It can also refer to equipment and automation data such as conveyors and AGVs.  
It is also essential to consider data sources to judge the level of DT interoperability and connectivity to the physical world. Data sources refer to the different systems and technologies used to collect and manage data within the warehouse. They may include WMS, IoT devices, and other digital technologies. When evaluating the DT interoperability and connectivity level, it is crucial to consider the compatibility of these data sources. For instance, if a virtual warehouse utilizes a WMS to manage inventory and orders, the DTsused should be able to interact with the WMS seamlessly to facilitate real-time data sharing, if not being integrated into it.
# A Rectangular Notch-Band UWB Antenna with Controllable Notched Bandwidth and Centre Frequency
## 2 Antenna Design
### Design of the UWB Antenna  
The design of the UWB antenna evolved from the simulation of a conventional rectangular patch antenna (Antenna-1) that is fed with a coplanar waveguide in CST Microwave Studio. Because conventional monopoles have a narrow bandwidth, the lower ends of Antenna-1 were truncated in a semicircle with radius \(R\) to achieve UWB characteristics. Figure 3 shows the evolution of the UWB patch antenna design, which is designated as Antenna-2.  
The optimized parameters of the Antenna-1: \(A\) = 16 mm, \(L\) = 24 mm, \(h\) = 1.52 mm, \(g\) = 1 mm, \(Lg\) = 7 mm, \(Lp\) =12 mm, \(Sw\) = 3.2 mm, and \(Wd\) = 0.6 mm. The optimized parameters of the Antenna-2: \(A\) = 16 mm, \(L\) = 24 mm, \(h\) = 1.52 mm, \(g\) = 1 mm, \(Lg\) = 7 mm, \(Lp\) =12 mm, \(Sw\) = 3.2 mm, \(Wd\) =0.6 mm, and \(r\) = 0.3 mm.  
Figure 3: The design evolution of the ultra-wideband patch antenna.  
Figure 2: Geometry of proposed antenna: (**a**) front view (**b**) side view, and (**c**) back view.  
The impedance characteristics of both antennas in terms of \(|\text{S}_{11}|\) are shown in Figure 4. The Antenna-1 showed an impedance bandwidth ranging from 3.1-7.8 GHz for \(|\text{S}_{11}|<-10\) dB, which is dramatically increased from 3.1 to 12.5 GHz in Antenna-2. It is worth noting that the impedance bandwidth of the antenna covered the entire bandwidth required for UWB technology, which is proposed by the Federal Communication Commission (FCC).
## 3 DSP Algorithm  
### Definitions  
**Definition 1** (Link weight).: _The weight of a link in a network is a non-negative number assigned to the link to determine how good the link is._  
The link weight can be calculated based on metrics such as the hop count, bandwidth, delay, load, throughput, bit error rate (BER), reliability, and maximum transmission unit (MTU). The weight of the link between two nodes \(n_{i}\) and \(n_{j}\) is denoted by \(weight\left(n_{i},n_{j}\right)\).  
**Definition 2** (Link table).: _The link table of a network is a table that contains the weights of all links in the network._  
**Definition 3** (Adjacent node).: _One node is an adjacent node of another node if it is directly connected to the node._  
**Definition 4** (Path distance).: _The distance of a path is the sum of the weights of its links. The shorter the distance, the better the path._  
The distance of path \(p=\{n_{0},n_{1},\ldots,n_{k}\}\) is determined as follows:  
\[p.distance=\sum_{i=1}^{k}weight(n_{i-1},n_{i}). \tag{1}\]  
where \(p.distance\) is the distance of path \(p\), \(n_{i}\) is the ith node in the path, and \(weight\)\((n_{i-1},n_{i})\) is the weight of the link between two nodes \(n_{i-1}\) and \(n_{i-1}\).  
**Definition 5** (Dual separate paths).: _Two paths between a source and a destination are called dual separate paths if they are node-disjoint paths._
## Materials and Methods
### HPLC Analysis  
#### Determination of EA and ETs  
The HPLC determination of EA and ETs was realized using an Agilent 1100 LC-DAD (Agilent Technologies, Palo Alto, USA) [4]. A C-18 Kromasil 100(Akzo-Nobel, Amsterdam, NL) column (250 \(\times\) 4 mm, 5 \(\upmu\)m), thermostat-controlled at 30 \({}^{\circ}\)C, was used for the chromatographic separations. Gradient elution of acetic acid/methanol/water (2:400:598) (solvent A) and methanol (solvent B), with a 1 mL/min flow rate was employed as follows: 100% A at 0 min, 0% A at 20 min, 100% A reset in 10 min and held for 5 min. The injection volume was 10 \(\upmu\)L, and the detection wavelengths were set between 220 and 600 nm. Free EA in the extracts was quantified at 245 nm by external standard, using an appropriate 4-point calibration line (y = 251.58\(\times\) - 204.41; R\({}^{2}\) > 0.999; range of linearity 2.5-200 \(\upmu\)g/mL; LOD = 0.78 \(\upmu\)g/mL; LOQ = 2.36 \(\upmu\)g/mL). The total ET content was determined after the preliminary acid hydrolysis (performed in replicate) of extracts in order to hydrolyze ETs in EA [4; 30]. A quantity of  
\begin{table}
\begin{tabular}{c|c c c c c} \hline \hline  & **Cultivar** & \
\begin{tabular}{c} **Extraction** \\ **Method** \\ \end{tabular} & **pH** & **TSS (\({}^{\circ}\)Brix)** &
\begin{tabular}{c} _TAC (mg_ \\ _Cyanidin-3-O-Glucoside_ \\ **g DW)** \\ \end{tabular} \\ \hline AK\_D & Akko & Decoction & 3.92 \(\pm\) 0.04 & 3.10 \(\pm\) 0.10 & 0.14 \(\pm\) 0.01 \\ AK\_PUAE & Akko & PUAE & 4.04 \(\pm\) 0.03 & 1.95 \(\pm\) 0.05 & 0.06 \(\pm\) 0 \\ WO\_D & Wonderful & Decoction & 3.56 \(\pm\) 0.02 & 2.05 \(\pm\) 0.05 & 0.08 \(\pm\) 0 \\ WO\_PUAE & Wonderful & PUAE & 3.65 \(\pm\) 0.03 & 1.80 \(\pm\) 0 & 0.05 \(\pm\) 0.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: pH, TSS, and TAC values of Akko and Wonderful pomeranate peel aqueous extracts obtained by the traditional (D) and innovative extraction technique (PUAE). Results are reported as mean \(\pm\) SE.  
2 mL of each extract, added to 2 mL HCl 2 M, was heated at 100 \({}^{\circ}\)C for 1 h in a boiling water bath. After the cooling time, 1 mL of NaOH 2M and 6 mL of methanol were added. The final solution was vortexed and filtered before the injection. Two replicated determinations of EA and ETs were performed for each sample analyzed and data were reported as mean values \(\pm\) SE. The EA chromatogram of one pomegranate peel aqueous extract is reported in the Supplementary Materials Figure S1.
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 4 Simulation Results and Evaluation
### Performance Results
Results show that for both scenarios, the method was used less than 65% of the time, where in 55% of the cases, the method was able to remove the abnormal measurement. About 7% of the time where the method has been used, the method was not able to remove the faulty BS but managed to remove those whose multipath was large in the majority of cases. The rest of the time, it was not able to perform the monitoring and hence exclude the faulty BS. This is because the method could remove one BS at a time, and therefore, during 43% of the time that there is more than one biased measurement, even if it manages to remove one, the rest of malicious BS will still contribute to the degradation of the accuracy. Finally, the increase in \(\mathrm{P_{pfa}}\) increased the percentage of failure of the method, and this will therefore affect in the performance of positioning. For example, when \(\mathrm{P_{pfa}}=0.5\), the percentage of the time where the method does not manage to exclude a BS increased almost double compared with the one when \(\mathrm{P_{pfa}}=0.1\). All in all, in conditions of a wide bandwidth, the performance is better compared to a narrower bandwidth. Figure 5: Results for 20 MHz bandwidth with 50 ns sync. err. Figure 6: Results for 50 MHz bandwidth with 50 ns sync. err. Figure 8: Results for 50 MHz bandwidth with perfect sync. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Scenario 1 (\%)** & **Scenario 2 (\%)** \\ \hline \multirow{2}{*}{**UTDoA measurements**} & Sync. err. & 81.86 \\  & No sync. err. & 18.14 \\ \hline \multirow{3}{*}{**BS for UTDoA positioning**} & One BS with sync. err. & 38.75 \\  & More than one BS with & 43.11 \\ \cline{1-1}  & sync. err. & 18.14 \\ \hline \multirow{2}{*}{**FDE performance**} & BS exclusion & 55.46 \\  & BS no exclusion & 6.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of BS exclusion mechanism in positioning accuracy. Figure 7: Results for 20 MHz bandwidth with perfect sync.