esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Layered Parallel Architecture and Application for Large Matrix LDPC Decoder
## 3 Design and Implementation
### Decoding Early Termination  
To end the decoding as early as possible after successful decoding, researchers have proposed various mechanisms for early termination (ET) decoding iterations [16; 17].  
Among them, full parity check is the most obvious. Hard-decision aid (HDA) [17] takes advantage of the fact that the hard-decision result remains relatively stable when the decoding converges, with simple hardware implementation. Row parity check processes layer by layer, which may cause an error floor [18].  
Figure 4a gives the comparison of the average decoding cycles (ADC) of the 3 methods. SNR is the multiple signal-to-noise ratio points tested, and ADC is the average number of decoder iterations corresponding to the early termination decoding method, which corresponds to the average decoding time in the circuit. As can be seen, the average time for full parity is the smallest, the row parity check is slightly larger, and HDA consumes the most time.  
Due to the BER performance indicator for 50G-PON (less than \(10^{-12}\)), in this paper, we decide to use full parity check. However, the traditional full parallel parity-check method requires the construction of a large parity tree structure, which consumes a lot of circuit area resources. In this paper, a serial full parity-check method is proposed to reduce the complexity by using the cyclic shift characteristic of the parity matrix.  
As shown in Figure 4b, before the decoder begins decoding, all the parity-check result registers are reset to 0. After the decoding starts, the new hard-decision message and the  
Figure 4: (**a**) Average decoding cycles comparison. (**b**) Serial full parity-check circuit.  
old hard-decision message pass through an XOR (exclusive OR) gate to obtain the changed value of the decision message. Then, according to the matrix information stored in ROM, the shifter is controlled to shift the updated decision message, and the updated decision message is XOR with the original value of the parity-check result registers to update the value of the parity-check result registers. The value of the parity-check result registers reflects the check result of the corresponding row, and its value is updated synchronously every cycle. When the values of the parity-check result registers are all 0, it means that the check is successful but considering the conditions have been met at the beginning of decoding; so, only when the number of iterations of the decoder is greater than 1, the judgment is made.
# Distribution of Volatile Compounds in Different Fruit Structures in Four Tomato Cultivars
## 2 Results and Discussion
### Differences in the Volatile Profiles among the Cultivars
#### 2.4.1 The Volatile Profile of Pericarp (PE)  
Among the four varieties (\"FL 47\", \"Tygress\", \"Tasti-Lee\", and \"Cherokee Purple\"), the proportion of total volatile concentrations in PE were 28.34%, 30.81%, 28.42%, and 29.99%, respectively, which showed the highest concentration of the volatile compounds compared to other tissues in all four varieties (Table 5). The concentrations of aldehydes in PE of \"Tasti-Lee\", \"Tygress\", \"FL 47\", and \"Cherokee Purple\" tomato fruits were 30.56%, 32.40%, 30.59%, and 32.94%, respectively, higher than those in other inner tissues. The aldehydes/alcohols ratio in PE was higher than the inner tissues in SC and LS (Table 4). Aldehydes and their corresponding alcohols were important tomato flavor volatiles. Alcohols could be oxidized to aldehydes, and accomplished the alcohol-to-aldehyde conversion. The high ratio of aldehydes/alcohols in PE may be due to its higher oxygen concentrations in external tissues.  
In addition, PE showed higher levels of aromatic proportion in the \"Tygress\", \"Tasti-Lee\", and \"Cherokee Purple\" types, which suggested that PE provided a more powerful aroma than the other tissues (Figure 2). The herbaceous series showed a higher intensity in PE, which is due to the high OAVs of aldehydes, especially cis-3-hexenal, hexanal, and trans-2-hexenal. These three compounds  
Figure 2: Aromatic proportion and aromatic series per tissue in tomatoes. (**a**) “Tasti-Lee”, (**b**) “Tygress”, (**c**) “FL47”, and (**d**) “Cherokee Purple”.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Concentration (mg L\({}^{-1}\))**} \\ \hline  & **Pericarp (PE)** & \
\begin{tabular}{c} **Septa and** \\ **Columella (SC)** \\ \end{tabular} &
\begin{tabular}{c} **Locular Gel and** \\ **Seeds (LS)** \\ \end{tabular} & **Stem End (SE)** \\ \hline FL47 & 28.425 \(\pm\) 4.551  & 24.464 \(\pm\) 3.277  & 27.895 \(\pm\) 1.840  \\ Tygress & 29.781 \(\pm\) 7.373  & 26.333 \(\pm\) 3.031  & 24.234 \(\pm\) 4.493  & 16.300 \(\pm\) 6.411  \\ Tasti-Lee & 21.317 \(\pm\) 5.934  & 18.624 \(\pm\) 2.550  & 18.486 \(\pm\) 10.662  & 16.567 \(\pm\) 6.529  \\ Cherokee Purple & 24.795 \(\pm\) 4.904  & 19.860 \(\pm\) 6.902  & 21.414 \(\pm\) 8.067  & 16.608 \(\pm\) 5.219  \\ \hline \hline \end{tabular}
\end{table}
Table 5: Volatile total concentrations per tissue.  
were C6-aldehydes which imparted \"green\", \"leafy\", \"grassy\", \"tallow\", and \"fatty\" notes in tomato fruit. Based on their biosynthetic pathways, they were generated from C18 fatty acid, which were acted upon by TomLox C and 13-hydroperoxide lyase (13-HPL). Firstly, 13-hydroperoxides (13-HPOs) were produced by the act of TomLox C, and then they were cleaved by 13-hydroperoxide lyase (13-HPL), which is a key enzyme for C6-aldehydes synthesis to release C6-aldehydes, both hexanal and cis-3-hexenal. The latter could further be converted into trans-2-hexenal [30].
## 3 Design of UI System for the WalkON Suit
### Input Interface for the User of the WalkON Suit  
Since the advanced methods such as voice recognition or EEG analysis are not yet fully reliable, a physical interface such as tact button switches may still be the most intuitive and reliable means for an input interface. Complete paraplegics wearing powered exoskeletons should hold the crutches for stabilizing and maintaining the body balance. Therefore, the crutches of the WalkON Suit are designed not only to meet the ergonomic conditions, but also for the user to access and operate the tact switches easily while holding the crutches.  
The number of tact switches to be installed on the crutch handle should be determined considering both the complexity of operation and the possibility of maloperation. (1) If the number of tact switches  
Figure 5: Configuration of the UI of the WalkON Suit. The UI includes (**a**) two tact switches and (**b**) an OLED display installed on a crutch, (**c**) see-through display glasses, (**d**) a multi-color LED on a backpack and a buzzer.  
is less than that of the motion modes, which are the different leg motions that a powered exoskeleton can realize according to the user's command, it is mandatory for the user to select the desired mode by pressing the button(s) appropriate times. This may cause an operation error due to the user's mistake. (2) If the number of tact switches matches that of the motion modes, the operation strategy becomes simple. The powered exoskeleton introduced in [32] used four tact switches to control four motion modes. In this case, however, too many buttons may be necessary, which increases the risk of maloperation.  
Since the paraplegic user should hold the crutch handle tightly while operating the powered exoskeleton, the workspace of the thumb is the only area where tact switches can be placed. Therefore, the number of tact switches of the crutch handle of the WalkON Suit was selected as two; Button 1 and Button 2, as shown in Figure 5. The main function of Button 1 is action or select, and that of Button 2 is the termination of the current mode. The action and select are distinguished by the duration of pressing Button 1, i.e., the user can initiate an action by pressing Button 1 for a short duration, and select any desired mode by pressing Button 1 for a longer duration.  
The two tact switches are used to manage the following four motion modes.
## 2 Background Knowledge
### Rules with Certainty Factors  
Given that in many situations, things are not always certain, there is a need to represent that uncertainty. Certainty may refer to a rule itself or to evidence statements. Rules provided by experts may not be 100% certain. Certainty Factors (CFs), introduced in the expert system MYCIN [14], are an old, empirical, but widely used method of dealing with uncertainty in rule-based systems.  
CFs can take values in the interval [\(-\)1, 1], where \"\(-\)1\" means \"totally uncertain\", \"1\" means \"totally certain\", and \"0\" means \"undefined\" (this is an impractical case). Usually, CFs have positive values. The above rule is presented below, with a certainty factor of 0.7 (CFR = 0.7):  
R: **if** fish-weight > 15 **and** mouth-lower-jaw is deformed **and** anorexia is yes  
**then** fish-disease is ceratotho (0.7)  
This means that when the rule is fired, the fact that \"fish disease is ceratotho\" is derived with a certainty of 0.7. When using CFs, more than one rule with the same hypotheses but different evidence statements may be used. In such a case, if we have, let say, two rules, R1 and R2, with certainties CFR1 and CFR2, the common hypothesis is derived with a certainty CFR12 calculated by the following formula (which can be used for more rules consecutively), given that CFR1 and CFR2 are positive:  
\[\text{CF}_{\text{R1R2}}=\text{CF}_{\text{R1}}+\text{CF}_{\text{R2}}\text{ (1 }-\text{CF}_{\text{R1}})\]  
In cases where the truth of the evidence is not totally certain, the certainty of the hypothesis is reduced. For example, in the above rule, if we have the following CFs for the three antecedents: CF\({}_{1}\) = 0.6, CF\({}_{2}\) = 0.8, and CF\({}_{3}\) = 1.0, the CF of the hypothesis will be calculated as follows:  
\[\text{CF}=\text{CF}_{\text{e}}\ \times\text{CF}_{\text{R1}},\text{CF}_{\text{e}}= \text{min}\ (\text{CF}_{1},\text{CF}_{2},\text{CF}_{3})\]  
given that we have only the \"and\" connective in the evidence statements. So, CF = min (0.6, 0.8, 1.0) \(\times\) 0.8 = 0.6 \(\times\) 0.8 = 0.48 (<0.7).  
In practice, the CFs of elicited rules are given by the expert(s) during the design phase, whereas the CFs of the evidence statements are given by the user(s) of the system during its running phase. For an extensive treatment of rule-based reasoning, see [5] (ch. 8) or [13] (ch. 3).  
In most diagnostic systems that use CFs, especially those dealing with fish diseases, the CFs of the evidence statements are given by the experts during the design of the system as representing the importance of the corresponding evidence (symptom) in deriving the hypothesis and are treated as normal CFs [15; 16]. This is not absolutely correct, because the semantics of a CF are not related to its importance but to the uncertainty of the symptom. Therefore, in such cases, they should be treated in a different way (see our approach in Section 4.3.3).
# Smart Healthcare Network Management: A Comprehensive Review
## 5 Reference Architecture for Smart Healthcare System Network Management
The \"smart\" nature of these systems is primarily attributed to their autonomous operation, which relies on closed-loop control and networking capabilities. Managing large-scale smart healthcare systems is a complex task that demands meticulous coordination and systematic oversight. A structured management approach emphasizes the management process itself rather than just the outcomes. To enhance efficiency, we propose a management system reference architecture, which aims to define the key components of smart healthcare system network management, their interrelationships, and their roles. This framework ensures that operational processes remain cost-effective, precise, and well organized while maintaining an optimal inventory. The architecture must accommodate the diverse restoration requirements of biomedical sensors, as determined by healthcare professionals. The key architectural requirements include the following:  
* Provide reactive and proactive fault management. * Enable objects to cooperate in providing the desired service. * Provide a reliable end-to-end connection and service. * Implement high-level asset management. * Provide scalability to accommodate new patients and services. * Support patient mobility. * Enforce security measures at all levels. * Identify network management protocols. Figure 5 shows a network management reference architecture for a smart healthcare system. The architecture consists of five layers, and each layer consists of different components. The architecture considers the stakeholders' requirements and policies. It consolidates existing industry frameworks and standards to maximize systems' interoperability. The system architecture should also consider requirements from external regulators for compliance. Figure 5: Smart healthcare system management reference architecture.
# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
## 3 Proposed Method  
### Problem Statements  
Consider a set of users \(U\), where each user \(u\) possesses a historical interaction record \(H^{u}=\big{\{}\ S_{1}^{u},\ S_{2}^{u},\ \ldots,\ S_{t}^{u}\big{\}}\). These records consist of \(t\) sessions, each ordered chronologically by the time of interaction. Within each session, denoted as \(S_{j}^{u}=\left\{i_{1}^{u_{j}},\ i_{2}^{u_{j}},\ j\ \ \ldots,\ i_{v}^{u_{j}}\right\}\), there are \(v\) items with which the user has interacted, also arranged in the order of interaction within that session. The objective is to predict a set of items \(\{i_{\text{\emph{r}1}},\ i_{\text{\emph{r}2}},\ldots,i_{\text{\emph{r}k}}\}\) that the user is most likely to interact with next. These predicted items are typically ranked in descending order of interaction probability. Table 1 presents the primary symbols utilized in this paper. It also briefly introduces additional terms such as \(R_{i_{\text{\emph{r}i}},i_{\text{\emph{r}i}}}\), \(C_{i_{\text{\emph{r}i}},i_  
Specifically, the embedding module first generates a random number table, tailored to the size of the input data. It then converts the item ID into an embedded representation, which becomes the input for the model.  
The context generation module generates potential contextual information that is considered for recommendations. As the GRU (Inner) processes the embedded representation sequentially, a predicted representation is generated one-by-one, based on the latent features learned previously, and thus, it is closely related to the interactive Item. The prediction representation generated by the GRU (Inner) is treated as this potential context information.  
The representation fusion module performs feature fusion to generate the preference representation. Feature fusion involves extracting session features by merging item embeddings with latent-contextual representation. The basic LCII performs feature fusion only and uses the result directly as the preference representation. Note that Section 3.2.3 will describe two strategies using additional 'Term Fusion' to generate the preference representation. The first strategy, named LCII-Pre, performs 'Term Fusion' before feature fusion. The second strategy, named LCII-Post, performs 'Term Fusion' after feature fusion. The representation fusion of LCII-Pre is shown in Figure 3, while that of LCII-Post is shown in Figure 4.  
Subsequently, the sequence processing module uses the GRU (Outer) for learning and prediction. The GRU (Outer) accepts the first prediction representation from the GRU (Inner) as the initial state, receives sequentially the preference representation, and produces the final prediction. The predictions are fed into the prediction module. The prediction module ultimately produces the user's recommendation list and provides the prediction evaluation.  
Figure 2: LCII Architecture.
## 5 Results and Discussion
### Comparisons Regarding the Factors Which Affect Drivers and Barriers of IS Adoption
#### 5.2.2 Profitability of IS Adoption (RQ6)  
Based on the analysis of the exploratory question in the first part of the questionnaire, two prominent different groups of companies were initially identified in the sample that has stated that they use IS, as shown in Figure 6: Those which consider that the IS adoption leads to high level of profitability (IS1_H = n1 = 36) and those that obtained relatively satisfying level of profitability due to IS adoption (IS1_RS = n2 = 40). Through this research there is an effort to assess whether there are statistically significant differences between the two groups regarding the reasons that motivate them to adopt IS (Table 4) and also the barriers that would prevent them to adopt IS (Table 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Average Rating**} \\ \cline{2-4}
**Drivers** & **High Level of** & **Profitable in Relatively** & \(p\)**-Value** \\  & **Profitability** & **Satisfying Level** & \\ \hline Competitive Advantage & 4.28 & 3.70 & 0.003 \\ Customers’ Satisfaction & 4.31 & 4.18 & 0.596 \\ Reduce production cost & 4.44 & 4.23 & 0.264 \\ Reduce errors/mistakes & 4.75 & 4.42 & 0.024 \\ Production Scheduling/Planning & 4.58 & 4.33 & 0.042 \\ Maintain appropriate inventory & 4.53 & 4.30 & 0.173 \\ Select appropriate Suppliers & 4.06 & 3.36 & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Drivers for IS adoption—level of profitability.  
Figure 6: Profile of the respondents they have adopted IS with respect to the level of profitability.  
In regards with the driving factors of IS adoption the results show that IS1_H differ significantly from IS1_RS companies only with respect of the four following motivators: Competitive advantage (\(p\)-value = 0.003), reduce errors (\(p\)-value = 0.024), production planning (\(p\)-value = 0.042), and select appropriate suppliers (\(p\)-value = 0.005). There is no difference between the two groups in regards to the rest motivating factors regarding the drivers of IS adoption. That is also the conclusion about these two groups regarding the barriers of IS adoption since \(p\)-value is greater than 0.05 in each one of them, which means that the respondents of these two groups are thinking alike.
## 1 Introduction  
Environmental processes and climatic conditions can be extracted from magnetic minerals that are present in sediments ([1, 2, 3] and reference therein). With the available modern magnetic instruments, it is possible to detect the presence of trace minerals throughout their magnetic properties, using the dependence to magnetic fields (coercivity) and temperature (Curie point). The key in such paleoclimatic and paleoenvironmental reconstructions is based on three main aspects: composition, concentration, and granulometry of the magnetic minerals.  
Caves provide ideal environments for sediment deposition as well as for human activity to furnish protection. Added to UNESCO's World Heritage List in 2000, the caves of the Sierra de Atapuerca site provide unique testimony of the presence and evolution of the earliest hominids in Europe. The so-called Atapuerca archaeological site includes, among others, the Trinchera del Ferrocarril (Railway Trench), which provides a sign of continuous human occupation from the Early and Middle Pleistocene.  
Previous studies based on palynomorphs and faunal remains from the Sierra de Atapuerca caves have attempted to decipher the climatic conditions that were favourable to human presence [4, 5, 6], but there is still a lot of uncertainty.  
For this reason, we carried out a first environmental reconstruction of the upper sedimentary succession of Gran Dolina cave [7] based on the magnetic properties of the sediments. Therefore, encouraged by our initial results, we extended the study to the uppermost stratigraphic units in Gran Dolina using a similar approach. In this paper, after a description of the regional setting and the basics of the archaeological site, we describe the magnetic parameters that we use in order to characterize the composition, concentration, and grain size of the magnetic minerals present in the cave sediments and then finally present a coherent interpretation of the environmental conditions and possible correlation to the Marine Isotope Stage (MIS).
# Improved Direct Linear Transformation for Parameter Decoupling in Camera Calibration
## 2 Parameter Coupling Analysis
### Error Coupling Analysis
Figure 2 shows the coupling between the optical center point of the image and the translation vector. When the optical center point of image \(O_{c}\) is offset, the origin of the camera coordinate system shifts. To continue fitting the pinhole model, the space point, \(P_{i}\), under the camera coordinate system will produce an offset. The space point under the world coordinate system is fixed so that the relationship between the world coordinate system and camera coordinate system changes. Figure 3 shows the coupling between the camera focal distance and the translation vector. The camera focal distance is calibrated based on the relative distance between multiple space points. When the camera focal distance changes, that is the origin of the camera coordinate system, \(O_{c}\), moves to \(O^{{}^{\prime}}\), the world coordinate system moves along the direction of the optical axis of the camera because the distance is fixed between \(P_{1}\) and \(P_{2}\). When the Euler angles are zero, the value of the optical axis direction is \(t_{z}\). In summary, under the influence of image noise and distortion, when the size of the templates, number of feature points and relative distance between the template and camera are certain, coupling errors occur in the camera's intrinsic and extrinsic parameters that affect the calibration parameter accuracy. Therefore, to improve the accuracy of the calibration parameters, it is necessary to remove the parameter coupling errors. Figure 2: Relationship between the principal point and translation.