esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference-Aware Opportunistic Dynamic Energy Saving Mechanism for Wi-Fi Enabled IoTs
## 2 Related Work  
For improving energy efficiency, unscheduled automatic power save delivery (U-APSD) was defined in the IEEE standard 802.11e [8], and it is also known as Wi-Fi Multi-Media (WMM) power save. While the stations (STAs) using legacy PSM need to wait for the beacon frame to initiate a data download, WMM-PS STAs can initiate the download at any time, thus allowing more frequent data transmission for applications that require them. Further, power save multi-poll (PSMP) is a feature of 802.11n that extends the APSD mechanism by allowing the STAs to operate on a group schedule rather than individually. In 802.11n, spatial multiplexing power save (SMPS) was defined to reduce idle listening power using request to send (RTS) and clear to send (CTS) to enable multiple antennas [8]. The legacy PSM is mandatorily supported in all WLAN devices because IEEE 802.11 must be backward compatible with legacy devices and access categories while WMM-PS, PSMP, and SMPS are optionally operated to improve power saving efficiency for specific uses, such as multiple-input multiple-output (MIMO), group schedule, and timing-sensitive applications. In the existing 802.11 WLAN standards, 802.11 legacy power saving mode (PSM) is operated by traffic existence only without considering network conditions, and so it incurs significant energy efficiency loss in a dynamic channel accessed network.  
There have been several studies on the improvement of energy efficiency in the idle listening period. In [10], the authors present an adaptive clock rate during the idle listening period; this method reduces energy consumption by employing dummy bits or preambles. Combined with a carrier sensing indicator, the gated clock technique can be used for power saving to improve the dynamic energy efficiency at the receiver [11]. These techniques are very effective methods for reducing power consumption in the idle listening time. However, they cannot be used for data processing operation. The authors of [12] presented a performance characterization of 802.11 ac in a testbed, and they showed that wider bandwidth improves throughput performance at the cost of higher power consumption.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 2 Background and Related Works  
### Navigational Aids for People with VI  
Navigation is the ability to plan and execute a route to a desired destination. It is essential to have a spatial representation of users' surroundings (i.e., digital maps, cognitive maps [18], building layouts), direction information, and continuous updates of their location in that representation (localization) [19]. Over the last 70 years, researchers have proposed many prototypes to aid PVI in both outdoor and indoor navigation. In this section, we only review a subset of such prototypes that are widely used and run on smartphones (for a chronological review, see [PERSON] and [PERSON] [20]).  
Smartphone apps for outdoor navigation rely on GPS sensors for localization and commercial map services (e.g., Google Maps, OpenStreetMap [21]) for wayfinding. Examples include BlindSquare [22], SeeingEyeGPS [23], Soundscape [24], and Autour [25]. These apps are feasible for navigating large distances for PVI by providing spatial descriptions and turn-by-turn directions through audio. However, they are not reliable in the last few meters [26] due to a wide margin of error in GPS accuracy (\(\pm\)5 m [27]).  
The weaker GPS signal strength indoors is also a barrier to indoor navigation. To overcome this limitation, researchers have fused available smartphone sensors as alternatives for indoor navigation, such as motion sensors, Bluetooth [28], Infrared [29], NFC [30], RFID [31], sonar [32], beacon [33], and camera. The lack of sufficiently detailed indoor map data is the other challenge [34; 35]. To mitigate this challenge, researchers have proposed constructing indoor maps by understanding the semantic features of the environment (for a complete list, see [PERSON] and [PERSON] [36]). Unfortunately, these solutions require additional deployment and maintenance effort to augment the physical environment [37], as well as a significant bootstrapping cost for setting up databases of floor plans [38] and structural landmarks [39; 40]. Some solutions also require users to carry specialized devices (e.g., an IR tag reader [29]). For these reasons, no single indoor navigation system is widely deployed.
## 2 Related Works
### Summary  
The cited research neglected the reconstruction of spaces across floors in a multistory building. Although a few works [26; 43] reconstructed the planes of stairs, the reconstruction of a complete connected area across floors in a multistory building was not mentioned.  
The above methods showed that the reconstruction of 3D indoor models in a multistory building has been far from satisfactory, and that the prominent deficiency of these methods lies in the reconstruction of long corridors and connected areas across floors. Labeling the partitioned cells on a long corridor is difficult to perform when using an energy minimization approach, which is resolved by a graph-cut operation. Because of the presence of decomposed cells, over-segmentation occurs for extended wall lines, particularly in long corridor areas. To resolve this issue, research has focused on the implicit assumption that each room, including long corridors and other large rooms, are scanned from exactly one position [21; 40]. However, these approaches failed in most situations that are commonly found in the real world. In this study, a comprehensive segmentation method is proposed to overcome this prominent deficiency, and room-space segmentation is separated from geometric space partitioning. Long corridor reconstruction is resolved by overlapping segmented rooms that are created by the morphological erosion method with space-partitioned cells. Such an integration of vector and raster preserves the integrity of room-space and the geometric regularity of rooms.  
In addition, watertight models can be reconstructed in this paper without a priori knowledge on viewpoints, and this method is not restricted to the strong Manhattan world assumption, thus making the reconstruction model more reliable and faithful. Several assumptions are followed in the proposed method.  
(1) The ceilings and floors are horizontal, and the vertical planes are parallel to the gravity vector [1; 42].  
(2) Each step in the stair area shares the same height, width and length [26].  
(3) The door is located between a pair of parallel walls.  
(4) The input point cloud should be relatively homogeneous in terms of density; otherwise, the method will break during line fitting and room-space segmentation.
## 4 Data Preparation for Neural Networks
### Data Standardization and Transformation  
Input and output variables involved in the data acquisition process are, generally, physical quantities of different types. For this reason, they may have very different magnitudes, which could affect the artificial neural network negatively. When the data used for training a network have significantly different magnitudes, the convergence and learning process can be slowed down significantly. In some cases, this can cause the network to not effectively perform the learning process [56]. This is why the quantities of interest for the model should be preprocessed in order to obtain magnitudes all within the same range. Normalization and standardization are two widely used preprocessing methods to achieve that. In this paper, a process of standardization has been performed. This process, for each predictor individually, involves scaling data so that the mean results in zero (centering), and the standard deviation results in unity and retains the shape properties of the original dataset, that is, the same skewness and kurtosis [57].  
The standardization related to a generic observation \(x_{i}\) is defined as in Equation (7).  
\[z_{i}=\frac{x_{i}-\mu}{\sigma}. \tag{7}\]  
where \(\mu\) is the mean, and can be defined as in Equation (8), and \(\sigma\) is the standard deviation, and is defined in Equation (9).  
\[\mu=\frac{1}{n}\cdot\sum_{i=1}^{n}x_{i} \tag{8}\]  
\[\sigma=\sqrt{\frac{1}{n-1}\cdot\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{ 2}}. \tag{9}\]  
It is crucial to emphasize that to prevent data leakage, the process of standardization should only be performed on the training subset. Afterward, the same values of mean and standard deviation should be used to standardize the channels associated with the validation and test subsets.
## 3 Materials and Methods
### FE-SEM Analysis of Argon-Ion Polishing Section  
To analyze the mineral composition of black shale, FE-SEM analysis was performed on argon-ion polished sections. The polished sections were prepared by cutting the shale samples into 10 mm \(\times\) 10 mm \(\times\) 5 mm slices, which were then subjected to argon-ion polishing. The polished sections were coated with a thin layer of carbon to enhance conductivity.  
The FE-SEM analysis was conducted using a Hitachi FE-SEM (Tokyo, Japan) equipped with a secondary electron probe and X-ray energy dispersive spectrometer (EDS). The operating conditions were as follows: acceleration voltage of 15 kV, working distance of 10 mm, spot size of 300 nm, and maximum resolution of 9 nm per image. The EDS analysis had a resolution of 133 eV and a detection limit of 0.1 wt% for elements.  
For calibration, certified reference materials with known compositions, including pure quartz, feldspar, and calcite, were used to ensure accurate elemental identification and quantification. The analysis focused on determining the mineral composition, content, morphology, sorting, roundness, and grading of grains to identify their origin.
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 5 Discussion: Application, Challenges and Future Perspectives
### The Future of Building Energy Harvesting  
Our discussions in Section 4 indicate that energy harvesting is expected to play an increasingly important role in powering various sensing and monitoring devices within the buildings of the future. Although these technologies have significantly lower power densities at present (Table 5), especially when compared to those for batteries (Table 4), in most cases, these power densities (in the mW range), matched with efficient energy accumulation mechanisms, can be sufficient for powering various sensor devices, recharging batteries, and powering other electronic appliances within buildings. Despite these limitations at present, the technology provides some vital benefits such as self-sustainability, prolonged lifetimes, ease of installation, and a multitude of available ambient sources of energy. Distinctive approaches to energy harvesting, such as hybrid energy harvesters (that utilize multiple sources of power) [145], harvesting energy from water and soil pH difference [146; 147], tree trunks and leaves [148; 149], and using graphene-based materials and structures [150], have also been explored in recent years, indicating the future potential for less conventional energy harvesting sources. Demonstration projects for building-integrated renewable technologies (e.g., SPECIFIC's 'Active Classroom' and 'Active Office' [12] (Figure 1a) which utilize a wide range of sensor, communication, battery and renewable energy technologies will also continue to play a primary role in proving reliability and capability in use, validating theories and models, and ensuring that the building sensors and controls are usable and not overly complicated for all technical and non-technical stakeholders involved.
## 3 Results
### Prediction of Membrane Wetting with Sodium Dodecyl Sulfate
#### 3.2.2 Contact Angle  
Figure 3 shows the contact angle of the three different membranes as a function of the sodium dodecyl sulfate concentration. The PTFE and PES membrane show contact angles above 90\({}^{\circ}\) for all SDS concentrations used in this study, whereas the contact angle with PE decreases below 90\({}^{\circ}\) for SDS  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Membrane** & **\(\delta\) (\(\mu\)m)** & **\(\varepsilon\) (\%)** & \(d_{\text{mean}}\) (\(\mu\)m)** & \(d_{\text{max}}\) (\(\mu\)m) & \(\theta_{\text{water}}\) (\({}^{\circ}\)) & \(\theta_{\text{hexadecane}}\) (\({}^{\circ}\)) & **LEP (bar)** \\ \hline PE & 99 & 76\% & 0.30 & 0.43 & 138 & 0 & 3.9 \\ PES & 81 & 58\% & 0.51 & 0.59 & 132 & 92 & 4 \\ PTFE & 77 & 83\% & 0.17 & 0.19 & 120 & 0 & 10.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the membranes used in this study.  
Figure 2: Surface tension as function of sodium dodecyl sulfate (SDS) concentration in an aqueous solution of 35 g/L NaCl and critical surface tension for the membrane surface used in this study.  
concentrations above 50 mg/L. Based on these results, wetting is only expected for the PE membrane at SDS concentrations higher than 50 mg/L, which confirms the observations in Section 3.2.1.
## 3 Materials and Methods
### Features
#### 3.3.5 Flow Direction  
**Feature feasibility:** Flow direction indicates which system initiates the connection. For typical operation, the flow is initiated by one system, requiring a service, after which the amount of data sent from one system to the other is likely to be predictable, especially when the service in question is known. Variance in this behavior indicates abnormal behavior.  
**Our data:** In our samples, the usage of a multicast protocol makes it impossible to group messages to \"conversations\" as with unicast protocols. The destination data is a multicast address, not an IP address of a system.
## 6 Conclusions  
This study proposes an efficient and easy-to-use feature for the thickness assessment of ferromagnetic plates covered with claddings and insulations. The feature is obtained by decoupling the analytical solution, thus is only sensitive to the plate thickness, unaffected by other interference factors. Firstly, the insulated ferromagnetic plate is modeled as a four-layered structure, its solution provides a basis for the theoretical analysis. Secondly, by analyzing the electromagnetic wave propagation in the four-layered structure through reflection and transmission theory, the cladding-induced interference is successfully decoupled from the PECT signal. In addition, by using the first integral mean value theorem, the inversely proportional relationship between the PECT signal and the insulation thickness as well as the sensor lift-off is deduced. Thirdly, the similarity measurement based feature is proposed for thickness assessment. A 16 Mn steel step plate is used as an example to study the performance of the feature. Results show the proposed feature is sensitive only to the plate thickness, and it is independent of the interference factors, including the cladding material and thickness, the sensor lift-off, and the insulation thickness.  
The feature proposed in this paper is based on the similarity measurement, which effectively suppresses some interference factors. However, to eliminate the influence of environmental noises, new features shall be found based on fuzzy similarity measures to further improve the thickness assessment accuracy [27; 28]. The method proposed in this paper will contribute for PECT application in ferromagnetic plate detection. Further study will include the studies for other materials and defects assessment.
## 4 Discussion  
### Key Unigenes to Code Enzymes in Gleditsioside Biosynthesis  
MVA in the cytosol and MEP/DOXP in plastids are the two main processes for saponin biosynthesis, of which IPP is one of the most significant intermediate products [43; 44]. In the gleditsioside synthesis pathway, HMGCR is a key rate-limiting enzyme of MVA, as has been proved by previous reports [45], and the gene encoded by HMGCR (_CL5845.Contig1_) showed high expression in _G. sinensis_ (Figure 10). Additionally, a previous study reported that one TSAR1/2 belonging to bHLH could activate HMGCR and all precursor genes in the MVA pathway to promote saponin biosynthesis in _Medicago truncatula_[46]. In our investigation, _CL5845.Contig1_ was up-regulated in each period compared with GS1, indicating it might be a key gene to code the HMGCR enzyme, which affects gleditsioside content in _G. sinensis_. DXPS was considered to be the most important rate-limiting enzyme in the MEP pathway, and the gene activity decreased significantly with time [47]. In addition, the expression changes of DXPS (CL13160.Contig1) and ISPG (CL4067.Contig3 and CL4067.Contig4) showed a downward trend in the MEP/DOXP pathway. Moreover, the key IDI (CL1651.Contig1) enzyme, responsible for the mutual transformation of IPP and DMAPP, was gradually up-regulated in the MVA pathway. The expression of this enzyme might significantly affect gleditsioside synthesis, as reported in previous research [18].  
Gleditsioside is mainly oleanane-type triterpenoids [48; 49]. From the annotation of unigenes in KEGG, CYP93E1, which belongs to the cytochrome P450 family, could act as the enzyme to synthesize oleanane-type triterpenoid (Table S3). Comparing the last two steps in gleditsioside biosynthesis, most of the LUPs, as shown by the high expression of Unigene6088 (LUP4), were up-regulated. In contrast, CYP93E1 (CL11248.Contig4) was up-regulated in the early stage and down-regulated in the late stage (Figure 10). The results indicate that during the later stages, especially in GS6, the synthesis of gleditsioside is likely linked to the high expression of these genes. Conversely, the gradual reduction in expression of specific genes throughout this process may contribute to the inhibition of gleditsioside synthesis, thereby maintaining the overall production of gleditsioside at a balanced level [50].  
Figure 9: Functional validation of three key genes. (**A**) Gene expression profiles of _hm_ (HMGCR), _lup_ (LUP4), and _cyp_ (CYP93E1) genes at different stages. Data are means (\(\pm\) SD) (n = 3). Different letters above the bars indicate significant differences (_p_ < 0.05) as determined by one-way analysis of variance (ANOVA) and post hoc Tukey HSD analysis. (**B**) Gleditsioside content determination of wild-type pods and pods overexpressing _hm_ (HMGCR), _lup_ (LUP4), and _cyp_ (CYP93E1) genes in _G. sinensis_. Data are means (\(\pm\) SD) (n = 3). Asterisks denote significant differences: ***, \(p\) < 0.001 (Student’s _t_-test).
###### Abstract  
Due to the increase in the length of the mining face, the pressure characteristics and spatial distribution in fully-mechanized mining faces are different from those in typical mining faces, which leads to great challenges in roof management and the intelligent control of ultra-long mining faces. Taking the ultra-long mining face of a medium-thick coal seam in the northern Shaanxi mining area as an example and using field monitoring data for the working resistance of the hydraulic supports, a non-linear prediction method was used to extract the features of the dynamic data sequence of the working resistance of the hydraulic supports, and a deep learning method was used to establish a pressure prediction model for ultra-long mining faces based on the adaptive graph convolutional recurrent network (AGCRN) algorithm. In the proposed model, the supports in the fully mechanized mining face were regarded as the logic nodes of a topological structure, while the time-series resistance data for the supports were regarded as data nodes on a graph. The AGCRN model was used to determine the spatiotemporal relationship between the working resistance data of adjacent hydraulic supports, thereby improving the accuracy of the proposed model. The MAE and MAPE were employed as performance evaluation indices. When the node-embedding dimension was set to 10 and the time window was set to 16, the corresponding MAE and MAPE values of the prediction model were the minimum values. Compared with the reference models (i.e., the BP, GRU, and DCRNN models), the MAE and MAPE of the AGCRN model were 38.75% and 23.49% lower, respectively, indicating that the AGCRN model effectively demonstrates high accuracy in predicting the working resistance of supports. The AGCRN model was applied in the prediction of the working resistance of the supports of the ultra-long fully mechanized mining face. The results revealed that the working resistance of the supports in the lower and upper areas was relatively small along the strike, whereas the working resistance of the supports in the middle area was large, exhibiting a zoning pattern of \"low-high-low\" in terms of the average working resistance. In conclusion, the proposed model provides data references for the state of the hydraulic supports, pressure identification, and intelligent control of the ultra-long mining faces of the medium-thick coal seams in northern Shaanxi.  
ultra-long mining face; support resistance; deep learning; pressure prediction 2023
## Appendix A Estimating \((\lambda,\mathbf{Q},\mathbf{\Theta},\mathbf{\Sigma})\)
In general, \(\alpha_{\mathbf{\Theta}_{j}}\) can be expressed as:  
\[\alpha_{\mathbf{\Theta}_{j}}=\frac{\pi(\mathbf{\Theta}_{j}^{*}|\mathbf{y}, \mathbf{\Theta}_{-j},\mathbf{\Sigma},\mathbf{Q},\lambda)\cdot q(\mathbf{ \Theta}_{j}^{*},\mathbf{\Theta}_{j})}{\pi(\mathbf{\Theta}_{j}|\mathbf{y}, \mathbf{\Theta}_{-j},\mathbf{\Sigma},\mathbf{Q},\lambda)\cdot q(\mathbf{ \Theta}_{j},\mathbf{\Theta}_{j}^{*})}. \tag{14}\]  
Note that the choice of the Metropolis kernel \(q\) is essentially arbitrary, and a symmetric \(q\) in the sense that \(q(\mathbf{\Theta}_{j},\mathbf{\Theta}_{j}^{*})=q(\mathbf{\Theta}_{j}^{*}, \mathbf{\Theta}_{j})\) is usually preferred. And in that case, the ratio \(q(\mathbf{\Theta}_{j}^{*},\mathbf{\Theta}_{j})/q(\mathbf{\Theta}_{j},\mathbf{ \Theta}_{j}^{*})\) is canceled in the above expression (14). Here, for the proposed density, we use a multivariate normal distribution centered at the current \(\mathbf{\Theta}\), with variance-covariance matrix given by an information-type matrix [49] whose inverse has \((u,v)\)th element:  
\[\sum_{i=1}^{n_{j}}\,\left(\frac{\partial g(\mathbf{t}\,|\,\mathbf{\Theta}_{j})} {\partial\mathbf{\Theta}_{j,u}}\right)^{\prime}\left(\frac{\partial g( \mathbf{t}\,|\,\mathbf{\Theta}_{j})}{\partial\mathbf{\Theta}_{j,v}}\right)+ \frac{1}{2}\frac{\partial}{\partial\mathbf{\Theta}_{j,u}}\frac{\partial}{ \partial\mathbf{\Theta}_{j,v}}(\mathbf{\Theta}_{j}-\eta)^{\prime}\mathbf{\Sigma }^{-1}(\mathbf{\Theta}_{j}-\eta). \tag{15}\]  
This expression combines the expected information (the first term) with the prior information (the second term) and offers an advantage of avoiding singular information matrices. Unfortunately, a tedious initial analysis has to be conducted to obtain estimates of \(\mathbf{\Theta}_{j}\) and \(\mathbf{\Sigma}\) from which to evaluate (15). So, the Metropolis algorithm described above can be carried out by using an arbitrary variance-covariance matrix. The posterior means of \(\mathbf{\Theta}_{j}\) and \(\mathbf{\Sigma}\) are then plugged into (15) from the subsequent analysis. \(Updating\)\(\mathbf{\Sigma}:\) We generate a new value of \(\mathbf{\Sigma}^{-1}\) directly from its full conditional posterior distribution. This is straightforward since it has an explicit expression for its full conditional posterior distribution.