esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results
### Number of Executors  
In this experiment, we compare the number of executors obtained by all algorithms. A utilization factor of 60% is used and 10 callback sets are randomly generated with a cardinality of 100 and a deadline on request.  
We then successively run all algorithms on all workloads and determine the maximum number of executors among the 10 sets of callbacks.  
Figure 6: Success ratio vs. deadline.  
As shown in Figure 7, the proposed algorithm outperforms RMS, LL, and GBFS in terms of the number of executors. For both RMS and GBFS, the algorithms try to regroup callbacks with the same period to the same executor, while LL regroups periods to the same executor. The proposed algorithm builds executors from callbacks with arbitrary periods. This leads to a minimal number of executors with low variance while guaranteeing system schedulability even in very stringent deadline situations. On the other hand, in RMS and GBFS, the variance is greater and the number of executors may be twice the number of distinct periods. This may have a major influence on the system stack memory. For example, suppose that all callbacks consume the same stack memory size of 512 bytes. The stack of an executor is the maximum size of all callbacks assigned to it. Therefore, the stack memory used to implement MPS for a deadline interval [0.2, 1] is 20 executors*512 bytes = 10 kb. However, in the proposed algorithm, it is only 2 executors*512 bytes = 1 kb.  
Let us also note that the RMS and GBFS algorithms do not succeed in scheduling the system, since the deadline becomes tight where the proposed algorithm still finds a schedulable system.
## 2 Materials and Methods
### CMP and SER Evaluation  
A coupon CMP (POLI-300, G&P Technology, Busan, Korea) equipment with a commercial pad (IC 1010/Suba IV, Dupont, DE, USA) was used for the CMP evaluation. Detailed CMP experimental conditions are shown in Table S1. Static etch rate (SER) experiments used to verify the dissolution rate of tungsten were performed. To measure the SER, tungsten wafer samples cut into 2 \(\times\) 2 cm\({}^{2}\) in each experimental composition were immersed 3 min in the sufficient amount of slurry solution. The thickness of tungsten films was measured using a four-point probe (FFP) (CMT-SR5000, Changmin Tech, Seongnam, Korea). The specific resistance (\(\rho\)) of tungsten films was estimated to 5.6 \(\times\) 10\({}^{-8}\)\(\Omega\cdot\)m, and the thickness was calculated from dividing by the sheet resistance estimated through FFP in a constant condition as shown in the following equation.  
\[\text{Thickness (cm)}=\frac{Specific\ resistance\ (\text{ohm}\cdot\text{cm})}{ Sheet\ resistance\ (\text{ohm}/\text{sq})} \tag{1}\]  
The CMP evaluation to obtain material removal rate of tungsten films was repeated three times, and the average numbers are reported here.
## 2 Related Work and State-Of-The-Art Technologies
### Volume  
Of course, for some special use cases, large chunks of data also need to be exchanged between tasks that could not easily be transferred via the Redis message queues. As presented in [5] and Figure 3, for such cases Docker containers could mount--as already mentioned--file system volumes that are provided by a distributed file system and shared by all containers which utilize these to solve the storage and transfer large batches of data as files. Since Kubernetes supports multiple distributed file systems for providing mounted volumes for different purposes ([[https://kubernetes.io/docs/concepts/storage/volumes/](https://kubernetes.io/docs/concepts/storage/volumes/)]([https://kubernetes.io/docs/concepts/storage/volumes/](https://kubernetes.io/docs/concepts/storage/volumes/))), different volume types are available for the framework to improve the speed of reading and writing large files. The efficiency of reading and writing large files directly from such a volume is much higher than the transmission of large files with Redis. Especially, when Redis has not allocated enough memory. The I/O adapters automatically detect if there are large files to transfer. In that case, the adapters will only transmit the unique IDs and not the original entire file. According to the IDs, the next processor in the workflow and its Docker container can use its I/O adapter to read the data of the large file from the volume directly and efficiently.
## 4 Mathematical Formulation
### Constraints
#### 4.1.5 Vehicle Configuration  
The proposed model allows for overtaking, which may lead to an unequal turnover time and departure headway for the same train type. It is impossible to obtain an accurate value of the vehicle number by using the ratio of the average turnover time to the average departure headway. Therefore, we propose a new method for calculating the vehicle number, as shown in (19)-(23). When the train \(i\in\mathcal{I}_{k}\) belonging to type \(k\in\mathcal{K}\) starts operation at the origin station \(s_{i}^{(o)}\), the number of remaining available vehicles belonging to type \(k\in\mathcal{K}\) shall not be less than 0, i.e., (20)-(22), where \(N_{k}^{(d)}(t)\) and \(N_{k}^{(d)}(t)\) are the cumulative used and cumulative returned vehicles of type \(k\in\mathcal{K}\) at time \(t\geq T^{(b)}\), respectively. The turnover time \(r_{i}\) of train \(i\in\mathcal{I}\) is given by (23). Note that, when \(r_{i}\) is too large, only calculating the trains in the planned period \([T^{(b)},T^{(e)}]\) may not ensure train turnover in the previous and posterior periods. Therefore, we take the planned period \([T^{(b)},T^{(e)}]\) as the cycle and consider the situation of \(M\) cycles, where \(M\) equals the ratio of the operation duration to the plan duration.  
\[N^{(veth)}=\text{argmin}\sum_{k\in\mathcal{K}}N_{k}^{(veth)} \tag{19}\]  
\[N_{k}^{(veth)}-m_{k}\Big{|}N_{k}^{(d)}(t)\Big{|}+m_{k}\Big{|}N_{k}^{(d)}(t) \Big{|}\geq 0,\forall k\in\mathcal{K},t\geq T^{(b)} \tag{20}\]  
\[N_{k}^{(d)}(t)=\{i_{j}|a_{s_{j}^{(o)},j_{j}}+j(T^{(e)}-T^{(b)})\leq t,\forall i _{j}\in\mathcal{I}_{k},j\leq M\},\forall k\in\mathcal{K},t\geq T^{(b)} \tag{21}\]  
\[N_{k}^{(a)}(t)=\{i_{j}|a_{s_{j}^{(o)},j_{j}}+r_{ij}+j(T^{(e)}-T^{(b)})\leq t, \forall i_{j}\in\mathcal{I}_{k},j\leq M\},\forall k\in\mathcal{K},t\geq T^{(b)} \tag{22}\]  
\[r_{i}=2(a_{s_{i}^{(o)},j}-a_{s_{j}^{(o)},j}+h_{r}),\forall i\in\mathcal{I} \tag{23}\]
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 4. Valorization Approaches of Date Processing Waste
### Water Desalination and Purification
Crucially, the adsorbent product demonstrated a remarkable remediation efficiency of 69.4% when applied to the treatment of actual agricultural runoff contaminated with 2,4-DPA herbicide, underscoring its readiness and potential for real-world applications. This study effectively valorized date palm leaf waste as a valuable adsorbent through a simple and cost-effective method for addressing the issue of agricultural wastewater pollution caused by harmful herbicides. The process of pyrolysis was employed to create biochar from date seed biomass, and the current study reveals that the porous activated carbon known as DSAC-1:1.5 is a practical and high-performing electrode material suitable for capacitive deionization in desalination applications [82]. According to the study by [PERSON] et al. [82], an optimal weight ratio of 1:1.5 (biochar: KOH) resulted in mesoporous date seed biochar-activated carbon with a uniform structure. It displayed amorphous and graphitic carbon formation due to KOH activation, offering a mixed micro- and mesoporous nature. Electrochemical tests revealed a specific capacitance of 400 F g\({}^{-1}\) for DSAC-1:1.5, making it suitable for NaCl desalination, achieving 22.2 mg gm\({}^{-1}\) electrosorption capacity and 86.4% charge efficiency. The electrode proved reusable for six cycles without performance decline. This low-cost date seed biochar-activated carbon outperforms existing carbon-based electrodes in desalination applications. While the use of date processing waste in water desalination shows promise, several challenges must be addressed. One such challenge is the variability of the chemical composition of date processing waste, which can affect the effectiveness of the pretreatment and the performance of the desalination membranes [117]. This challenge can be overcome through proper selection and processing of the raw materials to ensure consistency in the quality of the bioactive compounds [117]. However, most current research in this area has only been conducted on a laboratory scale, and more research is required to determine the feasibility of large-scale production and commercialization of these products [118; 119]. Finally, the regulatory and environmental impacts of using date processing waste in desalination processes need to be further studied to ensure no unintended negative consequences.
# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 4 Algorithm Simulation and Analysis
### Comparison of Algorithm Complexity  
The proposed LEAD-RP protocol achieves the shortest computation time among the evaluated schemes primarily due to its lightweight cluster head selection mechanism and reliance solely on local node information. Unlike HEED, which requires multiple rounds of neighbor information exchange and iterative convergence, resulting in a typical complexity of \(\mathcal{O}(N\log N)\), and LEACH, which incurs additional communication overhead for global cluster reformation, LEAD-RP computes a single cost function based on the residual energy and average neighbor distance to select cluster heads in a single step. This design yields a near-linear complexity of \(\mathcal{O}(N)\) per round, and the enhanced cluster stability reduces the need for frequent re-clustering. Consequently, LEAD-RP maintains significantly lower per-round computational overhead, particularly as the network scales from \(N=100\) to \(N=300\) nodes, as demonstrated in Figure 8.
# Hardware Platform-Aware Binarized Neural Network Model Optimization
## 2 Background and Related Work
### Binarized Neural Networks (BNNs)  
Over the recent years, Binarized Neural Networks (BNNs) have been highly favored by researchers due to their hardware-friendly characteristics. With only two states for their weights and activations, BNNs significantly boost the target hardware by reducing their memory consumption and computation effort. In particular, as in [9], the authors introduced an Xnor-Nets, which quantizes the weights and activations to +1 or \(-1\) in the following equation:  
\[x^{b}=Sign(x)=\begin{cases}+1,\text{if }x\geq 0\\ -1,\text{ otherwise}\end{cases} \tag{1}\]  
where \(x^{b}\) is the output of the function after binarizing. Based on this characteristic, in BNNs, the multiplication operations of both convolution and fully connected layers can be simplified to simple XNOR operations, while the accumulation operation is replaced by the popcount operation without accuracy degradation, as shown in Figure 1. On the other hand, with only one-bit representation, memory used for parameters and output activation storage reduces by almost \(32\times\) compared to \(32\) bit-full precision weights and activation. Consequently, power consumption for memory access considerably reduces with BNN models rather than conventional convolution or fully connected layers. These mentioned advantages from BNNs are the reason leading to an increase in preference of researchers towards BNNs with resource-constrained hardware.  
In the scope of this paper, the deployment of BNNs in resource-constrained hardware is studied. More specifically, the implementation for training our BNNs has followed the method in [10], where the input pixels of the first layer are not binarized. Accordingly, an architecture search algorithm that can estimate the hardware performance at the design time is performed to explore BNNs explicitly optimized for the target hardware. The design strategies for our BNNs are explained in detail in the following sections. Moreover, the architecture search algorithm and hardware cost estimation at the design time is also described.
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## 1 Introduction
[27] proposed a U-Net model that incorporates skip connections into the ED model to enhance the accuracy in predicting laminar flow results. On the basis of the aforementioned studies, methods have been introduced to predict solutions for Reynolds-averaged Navier-Stokes (RANS) flows around a cylinder [11] or over airfoil shapes [28; 29]. [PERSON] et al. [30] further utilized parameterized boundary information as the input domain through an FC layer. Nonetheless, most of the datasets used by these models are external or the internal flow fields share similarities with the external flow. Notably, these datasets contain an arbitrarily shaped object that is positioned near the center of the channel, with the walls at the top and bottom ends of the input domain remaining parallel. Therefore, in this study, we created a novel dataset containing various channel shapes and evaluated the training feasibility of the baseline models. Throughout different experiments, it was demonstrated that the baseline models could not properly learn internal flows, such as pipe flows. To solve this problem, we introduced a stream-wise bidirectional (SB)-LSTM module to learn the lateral connections for each pixel. The SB-LSTM module can be easily attached to existing models, thereby effectively increasing the internal flow learning ability. The proposed SB-LSTM module reduced the mean relative error (MRE) of the x-component of velocity, y-component of velocity, and pressure during evaluations by at least 2.7%, 4.7%, and 15%, respectively. To the best of our knowledge, the proposed approach is the first method to predict a flow in arbitrarily shaped channels with flow separation and corner vortices and ensures high performance. The contributions of this study are summarized as follows. * An SB-LSTM module is proposed to capture important physical properties of internal flows, whereby model performance is significantly improved by simply connecting the module in the latent space. Qualitative and quantitative evaluations conducted on various channel structures demonstrate the excellent performance of the proposed method compared with that of baseline models.
## 3 Data and Methods
### Methods  
Based on comprehensive consideration of field outcrop sections and previous researchers' findings, paleogeomorphology was reconstructed by integrating seismic and well logging data and using erosion volume calculation. The specific steps are as follows:  
First, based on well logging and seismic data, the erosion points were identified from seismic reflection characteristics. These points were combined to generate the erosion surface. The uneroded side of the surface indicates the remaining strata, while the other side corresponds to the eroded areas. The thickness of the remaining strata was calculated by measuring the difference between the top and bottom surfaces of the uneroded layers.  
Next, the eroded strata thickness was estimated through linear extrapolation the spatial trends of the uneroded strata. This method was applied to each layer to reconstruct the original stratigraphic morphology before erosion.  
Figure 1: (**a**) Schematic diagram showing the location of the Ordos Basin. (**b**) Distribution of structural units in the Ordos Basin.  
Finally, starting with the oldest strata at the base, the residual thickness was added to the eroded thickness for each layer. This step-by-step calculation enabled the reconstruction of the spatial configuration of each stratigraphic top surface, ultimately resulting in the paleogeomorphological form.  
As seismic data are based on time-domain, a time-depth conversion was applied using well and logging data to obtain depth-domain thickness maps. This process was conducted using the Geoscope software (3.3).  
Furthermore, well logging data were used to analyze the geometry and stacking patterns of the strata. Combined with previous researchers, the geological significance of the hydrocarbon of the Yan'an Formation was assessed.
# SEM Studies Assessing the Efficacy of Laser Treatment for Primary Teetis: A Systematic Review
## 2 Materials and Methods
### Eligibility Criteria  
The review includes articles that examine the efficacy of laser treatment of primary teeth assessed with SEM published in English. Studies were considered acceptable for inclusion in the review if they met the following criteria:  
* Changes in the specified surfaces of human teeth were evaluated using an optical microscope and/or scanning electron microscope (SEM) and/or profilometers and/or micro-computed tomography (Micro-CT).
* Studies involving the use of lasers to irradiate tooth structures.
* In vitro studies
* Studies in English
* Non-randomized controlled clinical trials (NRS); and
* Randomized controlled clinical trials (RCT).  
The reviewers excluded research that met the following standards:  
* Non-English papers
* Clinical reports
* Opinions
* Editorial papers
* Review articles
* No full-text accessible
* Duplicated publications  
There were no restrictions on the year of publication.
## 3 Measurement Campaign
### Estimation of Point Target's Phase Center  
The DLR calibration site, located in Southern Germany, has already been used for geometric, radiometric and polarimetric calibration of a number of space-borne SAR missions like TerraSAR-X, TanDEM-X, S-1A and S-1B [19]. For the current study, the three remotely controlled and configurable corner reflectors were used as reference targets. For a given overpass, each of these targets can be automatically aligned using an individual schedule configured from a remote station. For a planned acquisition, the exact overpass time as well as alignment angles in azimuth and elevation are determined using spacecraft predicted orbit data.  
The exact phase center position (latitude, longitude, height) for each corner reflector is calculated from a model considering the corner reflector's geometry and the individual adjusted configuration for each acquisition. The geometric accuracy of the model has been validated for all three corner reflectors by evaluating a number of individual measured GPS target positions at each site. The geometric deviations are converted into a local coordinate system (\(x\), \(y\), \(z\)); the accuracy results for each target are summarized in Table 2.  
Low deviations in the order of a centimeter have been determined between the measured and predicted phase center positions for all three dimensions (dx, dy and dz column in Table 2). This confirms that no significant bias remains in a specific direction. The absolute position error (\(\sigma_{s}\)) has been estimated by calculating the mean square root for each target.  
\begin{table}
\begin{tabular}{c c c} \hline
**Satellite** & **S-1A** & **S-1B** \\ \hline
**Observation Period** & **August-October 2015** & **December 2016–February 2017** \\ \hline  & S1-ASC & S1-DES \\  & S1-DES & S2-ASC \\ Acquired Beams/ & S3-ASC & S3-DES \\ Orbit Direction & S3-DES & S4-ASC \\  & S6-ASC & S5-DES \\  & S6-DES & S6-ASC \\ \hline \end{tabular}
\end{table}
Table 1: Stripmap mode observation period over the DLR calibration site.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Target** & **Mean dx (cm)** & **Mean dy (cm)** & **Mean dz (cm)** & \(\sigma_{s}\) **(cm)** \\ \hline CR-1 & 0.5 & \(-0.7\) & \(-0.5\) & 2.5 \\ CR-2 & 1.0 & 0.2 & \(-0.5\) & 2.4 \\ CR-3 & 1.4 & \(-0.6\) & \(-0.7\) & 2.9 \\ \hline \end{tabular}
\end{table}
Table 2: Geometric accuracy for DLR’s three remote controlled corner reflectors (CR) for each dimension (x, y, z) and the absolute position error \(\sigma_{s}\).  
position error is below 3 cm for all targets. The orbit accuracy for the precise orbit determination has been verified to fulfill the requirement of 5 cm in 3D [20]. Note that the absolute target position is finally converted to the same reference frame, which is also used for Sentinel-1 by the orbit data (ITRF 2008).
###### Abstract  
In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods.  
massive heterogeneous IoT networks; distributed channel selection; reinforcement learning; multi-armed bandit +
Footnote †: journal: Applied  
0000-0001-9599-2232-2333][PERSON]  
1 National Institute of Information and Communications Technology, Tokyo 184-8795, Japan; [EMAIL_ADDRESS] (S.H.); [EMAIL_ADDRESS] (Y.W.); [EMAIL_ADDRESS] (Y.S.)  
2 Department of Electrical Engineering, Tokyo University of Science, Tokyo 125-8585, Japan; [EMAIL_ADDRESS] (R.K.); [EMAIL_ADDRESS] (S.-J.K.); [EMAIL_ADDRESS] (M.H.)