esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
_Orchidaceae_ is a family of rare plants and their presence in metal-rich biotopes has ecological and conservation importance [38]. Orchids colonizing mining heaps rely on the fungal mycelium to obtain the necessary nutrients since their root system is not well developed. They belong to metal excluders, and mycorrhizal fungi seem to play an important role in filtering out the heavy metals [38; 39; 40]. Despite these extreme conditions, the environment is sufficiently tolerable to the population of orchids, soil filamentous fungi and bacteria. In orchidaceous mycorrhizala, fungi have the ability to support the composition of plants in stressed ecological niches and to increase the plant's condition [41]. The properties of orchidaceous mycorrhizala in mycoheterotrophic plants that allow colonization under stress conditions are changes in root morphology, orchid metabolism, such as enzymatic activities, secretion of organic acids with low molecular weight and phenols, and associations with orchideous mycorrhizal fungus adapted to the environmental conditions in which orchids occur [42; 43]. To identify fungal and bacterial communities in the roots of orchids and surrounding soil in heavy metal contaminated areas we used the marker gene sequencing approach which has been the most commonly used method to analyze these communities. It is simple, fast, cost-effective, and well-tested approach [44; 45] in comparison to whole-genome sequencing [45; 46]. A total of 171 species of fungi were detected from five samples of orchids, belonging to seven phyla, _Ascomycota, Basidiobolomycota, Basidiobolomycota, Chytridiomycota, Monoblepharomycota, Mortierellomycota_, and _Mucoromycota_ (Table 1). The highest biodiversity of fungi was situated in the roots of _E. pontica_ ([PERSON]), and the surrounding soil of _E. atrorubens_ ([PERSON]). Moreover, the most abundant phylum of fungi was _Basidiomycota_ in both soil and the roots of orchids. Furthermore, the most abundant species of fungi in the roots of orchids was _Cortinarius saturatus_, _Mycena_ sp., _Mycena citronmarginata_, _Mycenella bryophila_, _Colletotrichum capsici_, _Trichophaea pseudogregaria_, _Phacidium_ sp., _Russula solaris_, _Mortierella_ sp., _Amphiema byssoides_, _Mycena sanguinolenta_, _Amphiema_ sp., and _Piloderma byssium_ (Figure 2). Further, in the surrounding soil, it was _Cortinarius saturatus_, _Cortinarius scandens_, _Helvellosebacina concescens_, _Mortierella_ sp., _Cortinarius_ sp., _Helvellosebacina sp., _Cuirophyllus virgineus_, _Tetracladium_ sp., _Colletotrichum capsici_, _Cortinarius vernus_, _Penicillium_ sp., _Saitozyma podzolica_, and _Solicoccozyma terricola_ (Figure 3). In addition, we observed that the biodiversity in sampled pairs were consistently higher in soil samples, both in the number of discovered species (median 2.7x higher) and the evenness (median 2.66x higher of Faith's phylogenetic diversity). Decreased diversity could be due to the specific environment in the roots of the orchids.
## 3 Results and Discussion
### Frontier MOs and Electron Mobility
At the same time, we also found a negative correlation between electron affinity and electron mobility, that is, the higher the electron affinity, the lower the electron mobility, and the better the electrical resistance of insulation materials. This is consistent with the research results of [PERSON] and co-worker [18], but we found an interesting phenomenon in Table 4. We can see that the research objects without benzene ring structures strictly follow the rule that the greater the electron affinity energy, the smaller the electron mobility. However, after the introduction of a benzene ring, it changed. The larger the electron affinity energy, the greater the electron mobility. EA(\(a\))\({}_{\text{Ebb}}\) (\(-\)1.51 eV) \(>\) EA(\(a\))\({}_{\text{Eba}}\) (\(-\)1.55 eV), \(\mu_{\text{Ebb}}\) (9.85 \(\times\) 10\({}^{-16}\) m\({}^{2}\)V\({}^{-1}\)s\({}^{-1}\)) \(>\)\(\mu_{\text{Eba}}\) (2.23 \(\times\) 10\({}^{-19}\) m\({}^{2}\)V\({}^{-1}\)s\({}^{-1}\)); this might due to the introduction of strong electron-donating groups in Eba, which increases the electron cloud density on the benzene ring, weakens its ability to accept electrons, and results in a relative decrease in EA(\(a\)) value. Meanwhile, the introduction of strong electron-donating carriers increases the depth and density of traps compared to Ebb and slows down the electron transport rate. In addition, the introduction of electron-withdrawing groups not only reduces the electron density on the benzene ring but also increases the EA(\(a\)) value, making it more capable of accepting high-energy hot electrons. It can also increase the depth and density of traps in the structure, thereby increasing the electrical resistance. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Molecular Formula** & \(\mathbf{\lambda_{e}}\) & \(\mathbf{E_{\text{g}}}\) & **EA(\(a\))** & **IP(\(a\))** & **EA(\(v\))** & **IP(\(v\))** \\ \hline \(\mathbf{\lambda_{\text{e}}}\) & 0.16 & 13.77 & \(-\)3.14 & 10.13 (10.08) [29] & \(-\)3.21 & 11.04 \\ \(\mathbf{\lambda_{\text{OH}}}\) & 0.65 & 4.47 (4.60) [32] & 0.16 & 7.78 & \(-\)0.16 & 8.10 \\ \(\mathbf{\lambda_{\text{C}}}\) & 0.19 & 10.94 & \(-\)2.91 & 7.27 & \(-\)3.01 & 8.04 \\ \(\mathbf{\lambda_{\text{H}_{2}}}\) & 0.22 & 11.56 & \(-\)2.82 & 8.27 & \(-\)2.93 & 9.11 \\ \(\mathbf{\lambda_{\text{C}}}\) & 0.52 & 10.36 & \(-\)1.51 & 8.47 & \(-\)1.75 & 8.67 \\ \hline \end{tabular}
\end{table} 
Table 3: The \(\lambda_{\text{e}}\), \(E_{\text{g}}\), EA and IP of studied molecules calculated as well as the corresponding experimental data in the bracket (in eV).
# Analysis Strategies for MHz XPCS at the European XFEL
###### Abstract  
The nanometer length-scale holds precious information on several dynamical processes that develop from picoseconds to seconds. In the past decades, X-ray scattering techniques have been developed to probe the dynamics at such length-scales on either ultrafast (sub-nanosecond) or slow ((milli-)second) time scales. With the start of operation of the European XFEL, thanks to the MHz repetition rate of its X-ray pulses, even the intermediate \(\mu\)s range have become accessible. Measuring dynamics on such fast timescales requires the development of new technologies such as the Adaptive Gain Integrating Pixel Detector (AGIPD). \(\mu\)s-XPCS is a promising technique to answer many scientific questions regarding microscopic structural dynamics, especially for soft condensed matter systems. However, obtaining reliable results with complex detectors at free-electron laser facilities is challenging and requires more sophisticated analysis methods compared to experiments at storage rings. Here, we discuss challenges and possible solutions to perform XPCS experiments with the AGIPD at European XFEL; in particular, at the Materials Imaging and Dynamics (MID) instrument. We present our data analysis pipeline and benchmark the results obtained at the MID instrument with a well-known sample composed by silica nanoparticles dispersed in water.  
XPCS; European XFEL; data analysis; dynamics; diffusion Article  
Analysis Strategies for MHz XPCS at the European XFEL  
1 Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany; [EMAIL_ADDRESS] (LL.); [EMAIL_ADDRESS] (A.J.)  
AlbaNova University Center, Department of Physics, Stockholm University, SE-10691 Stockholm, Sweden; [EMAIL_ADDRESS]  
The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany; [EMAIL_ADDRESS] (G.G.); [EMAIL_ADDRESS] (F.L.)  
European X-ray Free-Electron Laser Facility, Holzkoppel 4, 22869 Schenefeld, Germany; [EMAIL_ADDRESS] (J.M.); [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (A.M.)  
Center for Molecular Water Science CMWS, Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany;  
22 AlbaNova University Center, Department of Physics, Stockholm University, SE-10691 Stockholm, Sweden; [EMAIL_ADDRESS]  
The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany; [EMAIL_ADDRESS] (G.G.); [EMAIL_ADDRESS] (F.L.)  
European X-ray Free-Electron Laser Facility, Holzkoppel 4, 22869 Schenefeld, Germany; [EMAIL_ADDRESS] (J.M.); [PERSON]S] (M.S.); [EMAIL_ADDRESS] (A.M.)  
Center for Molecular Water Science CMWS, Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany;  
22607 Hamburg, Germany; [EMAIL_ADDRESS] (F.L.); [EMAIL_ADDRESS] (M.R.)  
These authors contributed equally to this work.  
Current address: Blue Yonder GmbH, Oberbaumbrcke 1, 20457 Hamburg, Germany.
## 2 System Model  
### Heterogeneous Network  
Consider the downlink of a two-tier HetNet composed of a macro base station (MBS), \(J\) small BSs (SBS), and \(K\) UEs, with \(\mathcal{J}=\{1,2,\dots,J\}\) and \(\mathcal{K}=\{1,2,\dots,K\}\) being the sets of SBSs and UEs, respectively. The system network is depicted in Figure 1. In this paper, we assume there is no cross-tier interference in the network, which can be achieved by using different frequency bands for transmissions in the two tiers (e.g., sub-6 GHz in tier 1 and millimeter wave bands in tier 2).  
The MBS is equipped with an antenna array of size \(N_{T}\), which is assumed to be larger than the the number of SBS \(J\), i.e., and \(N_{T}>J\). Orthogonal frequency division multiple access (OFDMA) is utilized for the downlink communication between SBSs and UEs, with a total number of subchannels \(N_{sub}\).
## 4 Experimental Discovery and Analytics
### Implementation of the Framework  
We try to construct the discovery framework and its related algorithms to mine a work transference network from an event log dataset. The essential point of the framework starts from mining all temporal workcases as well as all temporal work transferences, each of which can be found from a corresponding event trace based on the completed timestamps of its events in the temporal order of precedence. Figure 4 illustrates the algorithmic mining procedure. In the framework, there are two new concepts, namely, work transference pair-groups and a work transference lineal set, essential to implementing the WTNM discovery. A work transference pair-group is formed through the pairing of the immediate predecessor-successor (the completion time precedence; every event in an event trace has the timestamp property as its completed times of the work item) of the performers in the temporal order of the work transference, and by adding its related activity on the edge of the corresponding pair. This step is implemented by combining a TWC and a TWT for the workflow instance. For the temporal work transference, \(TWT_{1}=\{p_{1},p_{2},p_{3},p_{1},p_{4}\}\) and the temporal workcase, \(TWC_{1}=\{\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4},\alpha_{6}\}\) in Figure 4, they are combined into the work transference pair-group, \(WTPG_{1}=\{((p_{start},p_{1}),\alpha_{1}),((p_{1},p_{2}),\alpha_{2}),((p_{2},p _{3}),\alpha_{3}),((p_{3},p_{1}),\alpha_{4}),((p_{1},p_{4}),\alpha_{6}),((p_{4 },p_{end}),\emptyset)\}\).  
By amalgamating all work transference pair-groups, we can make a work transference lineal set that serves as a building block of a WTNM. For example, by connecting all lineal sets in Figure 4, we can confirm the constructed \(WTNM\) that includes the formal discovery results of Definition 6, such as \(\sigma_{i}(p_{4})=\{p_{1},p_{7}\},\sigma_{o}(p_{5})=\{p_{6},p_{7}\}\), \(\psi_{i}(p_{3},p_{1})=\{\{\alpha_{1},18\},\{\alpha_{2},1008\}\}\), and \(\psi_{o}(p_{1},p_{5})=\{\{\alpha_{7},55\}\}\).  
The details of the algorithmic mining procedure are not presented herein. However, based on these concepts and their related algorithms, we successfully implemented a work transference network mining system that can discover a WTNM from an event log dataset formatted in the XES standard. Figure 5 shows a captured screen of the implemented mining system. On the screen, dashboard and visualization panels of the system can be seen. The main items, such as choose log and analyze, and the boxes on the dashboard panel indicate that the 2018 BPI Challenge Dataset was chosen in this experiment, and the results of 43,809 event traces recorded by enacting 41 different types of activities conducted by 165 workflow performers in all. The graphical model shown in the visualization panel is a work transference network discovered from Figure 4: Concrete discovery framework.  
Figure 5: Captured screen of the work transference network mining system.
## 3 Experiments and Results  
This section provides an overview of the datasets used in the experiments. Subsequently, the training details of the DCE and AAG modules are presented. To assess the efficacy of the proposed intelligent design approach based on textual descriptions, we systematically conducted a sequence of experiments. Specifically, for the DCE module, ablation studies showed that data augmentation and the semantic re-extraction layer enhanced the accuracy of architectural design semantic extraction, resulting in a more precise textual encoder for semantic extraction. In the case of the AAG module, qualitative experiments confirmed that our method effectively generates architectural sketches consistent with the provided textual semantics. By leveraging a pre-trained diffusion model to render the generated sketches, we produced realistic high-rise building renderings that better capture the underlying semantic nuances compared to direct diffusion model generation. Furthermore, comparisons with existing real-life buildings were conducted to demonstrate the effectiveness of the proposed method.
# Design of Static Random-Access Memory Cell for Fault Tolerant Digital System
## 3 Radiation-Hardened SRAM Cell Design
### Radiation-Hardened-by-Design 12 T SRAM Cell (RHBD-12 T)  
Figure 6 shows a schematic of the radiation-hardened-by-design 12 T SRAM cell (RHBD-12 T) proposed in [18]. The key structural feature of RHBD-12 T is that no nFET diffusion nodes are connected to Q and \(QB\) by using pFET access transistors. Thus, 1 to  
0 upset is not generated at the Q or \(QB\) nodes, and only 0 to 1 upset occurs, which is recoverable. For example, when the datum \"1\" is stored by Q = S1 = VDD and \(QB\) = S0 = 0 V, as shown in Figure 6, \(QB\) can be temporarily raised by SEU, which turns on N4. However, P8 is in the off state; thus, no other storage nodes are affected. The raised \(QB\) is pulled down back to its original state by the P7-N2 path.  
Unlike Q or \(QB\), the S0 or S1 node can suffer from both 0 to 1 and 1 to 0 node upsets. However, these upsets can be recovered as follows. First, if 1 to 0 upset occurs at the node S1 for the data \"1\" state, P8 is turned on, while N3 is turned off. Although P8 is turned on, Q is not pulled down because N4 is still off. Thus, \(QB\) is kept low and P1 stays turned on, and S1 is recovered to VDD. Second, if 0 to 1 upset occurs at the node S0 for the data \"1\" state, N1 is temporarily turned on to pull down the node S1, which turns on P8. Again, however, N4 remains off, the high Q is unchanged, and \(QB\) can remain low. Thus, the node S1 is pulled back up to VDD, leading to the pulling down of S0 back to its original value 0 V.  
The shortcoming of RHBD-12 T is the large read access time due to the use of the pFET access transistor and pull-down transistor. For reading data \"1\", the pre-charged BLB is discharged by the cell current flowing through P5-P7-N2. However, unlike other memory cell structures, the magnitude of VGS in access transistor P5 and pull-down transistor P7 is decreased as BLB is discharged, which severely slows down the read operation. In addition, the use of pFET transistors significantly degrades the write speed and stability.
## 6 Simulation and Evaluations
### Performance Metrics  
Sailing efficiency, in terms of the time to reach the destination, is an important performance metric of the motion control algorithms. We evaluated the difference of sailing efficiency of the greedy algorithm using metric function (10), denoted as greedy, and metric function (11) denoted as greedy + risk, respectively. The sensing noises were considered by enlarging the ship radius \(r\).  
Collision avoidance is also an important performance metric. The VO-based greedy velocity scheduling algorithm may fall into local optima when it reaches a position that is surrounded by moving ships. In this case, collisions will happen, because the ship cannot find a feasible way to avoid collisions. The greedy + risk algorithm avoids collisions by considering the risks of falling into local optima. Several parameters may impact the probability of collisions happening.  
1. the density of surrounding ships, denoted by \(d\);
2. the velocities of other ships, denoted by \(v_{s}\);  
We evaluate the collision avoidance performances of greedy, greedy + risk, and dynamic programming by varying the various parameters.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Machine Vision Systems for Bottling Quality Inspection  
The application of AI in the bottling and quality control stages of winemaking is pivotal in ensuring product consistency, safety, and efficiency. By implementing AI-driven technologies, winemakers can maintain high standards and reduce variability across batches, thereby enhancing the overall quality and marketability of their products.  
Machine vision systems, powered by AI, are revolutionizing the bottling process by providing advanced quality inspection capabilities. These systems utilize high-resolution cameras and sophisticated image processing algorithms to detect imperfections in bottles, labels, and packaging materials in real-time. Recent advancements in machine vision technology have enabled the development of highly accurate and efficient inspection systems, which are crucial for maintaining the high standards expected in the winemaking industry.
## 4 The IEEE 802.15.5 WPAN Mesh Standard
### Synchronous Communications: SES
In this Figure, after the failed transmission attempt by sender B through the _contention-based method_, this node decides to transmit its message to the destination by using the _reservation-based method_. Unlike method (1), we consider that sender B is out of the coverage area of the destination, while node A is in coverage within the destination and sender B, what allows to retransmit messages from sender B to the destination. Under these conditions, sender B initiates the process to allocate dedicated slots within the _inactive duration_ by broadcasting a _reservation request_ message. The request message contains, among other fields of information, the addresses of the destination and the next intermediate node in the dedicated path (in this example, sender A). In particular, it should be remarked that this request message is subject to the transmission rules of the _active duration_ and, as it was mentioned, it employs the unslotted CSMA-CA algorithm to avoid collisions. In conformity with the reservation process, when node A receives the request message, it first checks the destination field included in its header and, since node A is not the destination, this node inspects the message header again to find out the next node. Due to the fact that sender B selected node A as next intermediate node (other neighbor nodes receive the same request, but discard this message), node A continues broadcasting the reservation request towards the destination. In this case, the _reservation request_ message from node A has a double function: it forwards the request message to the destination, and, simultaneously, it sends sender B a confirmation of the dedicated link between both nodes. Now, upon the reception of the request by the destination, this node sends node A confirmation of the link by unicasting a _reservation reply_ message. At this point, a dedicated path is established between sender B and the destination. So, when the _active duration_ expires, sender B starts transmitting its data message within the first slot (slot 0) of the _inactive duration_. Then, once the data has been received by node A, it waits for the following slot (slot 1) to continue dispatching the data message to the destination. The transmission process finishes when node A receives the _acknowledgement_ message sent by the destination.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
###### Abstract  
Forest fire monitoring is very much needed for protecting the forest from any kind of disaster or anomaly leading to the destruction of the forest. Now, with the advent of Internet of Things (IoT), a good amount of research has been done on energy consumption, coverage, and other issues. These works did not focus on forest fire management. The IoT-enabled environment is made up of low power lossy networks (LLNs). For improving the performance of routing protocol in forest fire management, energy-efficient routing protocol for low power lossy networks (E-RPL) was developed where residual power was used as an objective function towards calculating the rank of the parent node to form the destination-oriented directed acyclic graph (DODAG). The challenge in E-RPL is the scalability of the network resulting in a long end-to-end delay and less packet delivery. Additionally, the energy of sensor nodes increased with different transmission range. So, for obviating the above-mentioned drawbacks in E-RPL, compressed data aggregation and energy-based RPL routing (CAA-ERPL) is proposed. The CAA-ERPL is compared with E-RPL, and the performance is analyzed resulting in reduced packet transfer delay, less energy consumption, and increased packet delivery ratio for 10, 20, 30, 40, and 50 nodes. This has been evaluated using a Contiki Cooja simulator.  
forest monitoring; Internet of Things; fog; LLN; aggregator +
Sensors