esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of AgRED Performance in LR-WPAN Dense Ad-Hoc Networks +
## 1 Introduction  
Monitoring and controlling several aspects of large networks has seen a breakthrough with the arrival of IoT and Wireless Sensor Networks (WSN) [1]. Sensor devices that used to utilize IEEE 802.11 (Wi-Fi) in the unlicensed spectrum suffer from congestion due to high density and larger bandwidth as specified by the standard [2]. The information that needs to be communicated commonly consist of small data that do not require high bandwidth but connectivity to the gateway and co-existence of a large number of devices that IEEE 802.11 cannot provide [3]. Therefore, IEEE defines a new standard 802.15.4 [4] to support small sensor devices that use low communication range, smaller bandwidth and low energy, but with the ability to cease much denser networks in a smaller area. 802.15.4 is the base for several other LR-WPAN [5] technologies that build on it including Zigbee, Bluetooth low energy, DASH7, ISA100.11a, 6 LoPAN, and WirelessHart.  
Although the problem of co-existence and appropriate bandwidth are addressed in the standard, devices used under the IEEE 802.15.4 standard are small and have limited resources, and therefore suffer from packet drops due to queue overflows. AQM techniques can be used to optimize queues of small sensors as AQM techniques are very simple to implement and do not require much in the way of computational resources. AgRED [6], based on Random Early Detection (RED) [7], provides performance improvements in severely congested networks.  
The rest of this paper is organized as follows. Section 2 presents some of the work in the literature focusing on small sensor deployment. Section 3 presents the ways in which AgRED addresses the congestion in small nodes to optimize queue overflows. The resultsafter intensive simulation and analysis are presented in Section 4, while Section 5 presents conclusions and future work.
## 4 Proposed Scheme
### Extensions  
In this subsection we discuss several extensions of the proposed framework. We first consider \(k\)-maximum data retrieval problem as follows. Let us denote \(k\)-th largest data from the scores \(y_{1},\ldots,y_{n}\) by \(y_{[k]}\). The \(k\)-maximum data retrieval problem is for the sink to collect \(y_{[1]},y_{[2]},\ldots,y_{[k]}\). Similar overhearing and selective forwarding can be applied with a small change. The selective forwarding can be modified such that a node forwards only the data larger than the \(k\)-th largest data that it has received/forwarded. Note every sensor needs to maintain only \(k\) sets of variables to keep track of the history of \(k\) largest data since max is a divisible function. Similar modification can be made for the overhearing technique.  
Another extension is a scenario where the sensors generate data samples periodically. Assume the sensors generate data samples at every unit time. When the signal for data retrieval is broadcast to the network, the sensors report the most recent data samples, say the last \(b\) samples, to the sink. The time instants of data sample generations are called _timestamps_. Thus the data samples are periodically generated and stored over a sliding time window of the length of \(b\) timestamps where the timestamps are one unit time apart. The goal of sink is to retrieve \(b\) maximum scores, one for each timestamps. Specifically suppose the timestamps for the maximum data retrieval are denoted by \(t_{1},t_{2},\ldots,t_{b}\). Denote the score at sensor \(i\) for timestamp \(t_{j}\) by \(y_{i}(t_{j})\). The objective for the sink is to collect \(y^{*}(j)=\max_{i\in I}\left[y_{i}(t_{j})\right]\) for \(j=1,2,\ldots,b\). The latency is measured as the time when all of \(b\) maximum scores \(y^{*}(j)\), \(j=1,2,\ldots,b\) are retrieved at the sink. We assume a packet can contain only one data sample, and only one packet can be transmitted per frame. The timestamp is assumed to be included in a packet. In this application the sensors can apply rules for selective forwarding and overhearing separately to packets with different timestamps. A set of packets with the same timestamp can be treated as an individual _flow_, thus the proposed scheme can be applied separately to each flow. However the latency is affected by the parameter \(b\). Since initially \(b\) packets need to be transmitted by every node, when \(b\) is large the network may undergo congestion for a long period of time. We later study the impact of \(b\) on the performance using simulations.
# LPWAN-Based Vehicular Monitoring Platform with a Generic IP Network Interface
## 5 Test-Bench
### Equipment and Implementation
The other end of the LoRaWAN channel was a RisingHF RHF2S008 base-station gateway, using Semtech's SXI301 chip. In this case, an omnidirectional antenna of 5 dBi was used. The LoRa parameters were statically set with SF 12, CR 4/5, and BW 125 kHz; the adaptive data rate (ADR) feature of LoRaWAN was disabled for the testing activities. The rest of the configurations employed in the experimental tests are shown in Table 1. Regarding LoRaWAN security, its functions were kept untouched, and so end-to-end AES encryption was attained [26]. Please note that the encryption/decryption process was transparent for us, as it was automatically performed by the LoRaWAN modules in both the end-nodes and the gateway. Further, the joining procedure followed by the motes in order to gain connectivity through the gateway was the Over The Air Activation (OTAA), defined by the LoRaWAN specification. Packets received by the gateway are forwarded to the network server, which deals with LoRaWAN MAC encapsulation and sends the SCHC packet to the application gateway. This is in charge of decompressing and reassembling fragments, with the aim of finally obtaining the original IPv6 datagram to be forwarded to the data server (Figure 4). The data server receives the UDP data messages and feeds an InfluxDB database. This server also hosts a dashboard, implemented with Grafana [32], which takes data from the database to show all vehicle parameters in real-time. A set of plots, with their corresponding database queries, have been included in the interface to show, among others, the path followed by a vehicle, individual OBD parameters, or a mix of them, and alerts attending to pre-configured events or thresholds.
# A Survey on Hardware Accelerators for Large Language Models
## 6 In-Memory Hardware Accelerators
### iMCAT  
In 2021, [PERSON] et al. presented a novel in-memory architecture for the acceleration of Transformer networks for long sentences, called iMCAT [34]. The proposed framework uses a combination of Xbars and CAMs to accelerate Transformer networks. The acceleration of Transformer networks is achieved through a combination of several techniques. These include computing in-memory to minimize memory transfer overhead, caching reusable parameters to reduce the number of operations, exploiting available parallelism in the attention mechanism, and using locality-sensitive hashing to selectively filter sequence elements based on their importance.  
The performance evaluation shows that this approach achieves a 200\(\times\) speedup and 41\(\times\) energy improvement for a sequence length of 4098.
## 4 Experiments
### Object Detection
For ICRCAB, on the one hand, if the number of row vectors and column vectors is too large, there will be a huge memory overhead when establishing self-attention and it will gradually become CRCAB. On the other hand, if the number of row  
Figure 9: Improved Darknet53 based on CRCAB and ICRCAB. vectors and column vectors is too small, more information will be lost after compressing the features in the pooling layer. Finally, eight was chosen as the number of row and column vectors in ICRCAB. To increase the rigor of the experiment, each model was trained five times. Similar to classification, the evaluation measure was finally obtained by averaging; the IOU was 0.5 during the test. The experimental results are shown in Table 3.  
There was a slight decrease in detection accuracy for cars and people in some of our proposed network models, but our models showed enhanced adaptability for buildings and motorbikes. According to mAP evaluation, all our proposed network models performed better than EquirectYOLO and ShpereNetYOLO, so the experimental results of object detection demonstrated that our proposed model was effective. We selected EquirectYOLO, ShpereNetYOLO, ECDAYOLO, and CEDA-ICRCAYOLO as object detection examples. As shown in Figure 10, it can be seen that our models produced better detection results than EquirectYOLO and ShpereNetYOLO. Figure 10: Object detection instance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Building** & **Car** & **Motor** & **Person** & **mAP** \\ \hline EquirectYOLO & 83.66 & 90.75 & 43.25 & 79.61 & 74.32 \\ SphereNetYOLO & 83.88 & 91.61 & 43.86 & 78.77 & 74.53 \\ ECDAYOLO & **84.85** & 90.61 & **46.37** & 78.56 & **75.09** \\ CRCAYOLO & **86.87** & 91.43 & **48.46** & 79.02 & **76.45** \\ ICRCAYOLO & **85.75** & **91.67** & **53.31** & 78.78 & **77.38** \\ ECDA-CRCAYOLO & 82.92 & **92.03** & **52.48** & **80.23** & **76.92** \\ ECDA-ICRCAYOLO & **84.60** & 90.85 & **54.49** & **80.21** & **77.54** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results for the different models on ERP-Detect. It is of note that, although the number of images in ERP-Detect after removing repeated scenes was only 669, this did not affect the effectiveness of our model. In addition, compared with the baseline model, our proposed model had a small increase in parameters. The baseline model (EquirectYOLO) had 235 MB of parameters, while our largest model, ECDA-CRCAYOLO, had 266 MB of parameters, which was an increase of 31 MB of parameters; however, this is acceptable for deep learning. CRCAB itself had very few parameters because, in addition to the last convolutional layer that fuses two row-column attention mechanisms, we used a convolution kernel of shape \(3\times 1\) and \(1\times 3\) for row and column direction when building attention. The reason for the increase in the number of parameters by 31 MB was the number of channels in the input features, and that the training speed of our proposed model was not much different from the baseline model EquirectYOLO.
## 3 Results and Validation
### Parameterization Results
#### 3.3.6 Impact of the Piezo Actuator on Ice Protection  
In this section, the impacts of the piezoelectric both as ice protection system and in terms of the stress transmitted to the structure are preliminarily discussed. To this end, the model shown in Figure 11 was integrated with an ice accretion measuring 3 mm in thickness, with the same angular extension of the piezo actuator, covering the entire width of the arch and placed in its middle. The model is shown in Figure 29.  
The properties of the ice used in the simulation are summarized in Table 11.  
An FRF analysis was implemented to estimate the maximum stress level in the beam and the shear action at the interface with the ice within the frequency bandwidth. In Figure 30, in the graph on the top, the stress level (von Mises) produced in the beam by the piezo device excited at 10 V is plotted (blue line) and compared with the PVC yield stress (blue dashed line, 52 MPa, Table 1). As is evident, within the entire bandwidth the stress remains well below the stress limit. This is also confirmed by the safety factor (black line) plotted in the graph on the bottom (minimum value of 3.7). Finally, in the graph on the top, the shear at the interface with the ice (red line) was compared with the adhesion strength (red dashed line, 0.2 MPa). As is evident, a tension of 10 V is enough to cross the shear strength threshold for all peaks of the bandwidth, thereby confirming, even preliminarily, the effectiveness of the ice protection method and its compliance with the structural integrity of the beam.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Tensile modulus (GPa) & 9.1 \\ Density (kg/m\({}^{3}\)) & 917 \\ Adhesion strength on plastic material (MPa) & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Ice properties considered for the simulation [30; 31].  
Figure 29: Finite element model of the test specimen integrated with an ice accretion.
# Computational Evaluation of Shock Wave Interaction with a Cylindrical Water Column
## 1 Introduction
The governing equations consist of two continuity equations for the two different phases, the mixture momentum and energy equations, and the volume fraction advection equation, e.g., [8; 15]. As far as flow turbulence modeling is concerned, both under-resolved direct numerical simulation (DNS) and large eddy simulation (LES) approaches are commonly followed, e.g., [4]. The main goal of the present work was the computational evaluation of the initial stages in the interaction process between the air shock front and the cylindrical water column following a relatively light approach, which was the unsteady Reynolds-averaged Navier-Stokes (RANS) modeling. The mean turbulent flow in a virtual shock tube device was simulated by supplying the calculation with a suitable turbulence closure model. The interaction process was simulated in the shear-stripping regime that was at relatively high Weber and low Ohnesorge numbers. Numerical calculations were conducted by employing one of the CFD solvers that are commonly and successfully used for building virtual wind tunnels in industrial gas dynamics research, e.g., [16; 17]. The compressible flow governing equations were solved by means of a finite volume (FV)-based numerical technique, while the transient tracking of the air-water interface was approximated on the fixed FV mesh using the volume of fluid (VOF) method [18]. The present CFD modeling procedure was validated against reference data that were provided by both high-resolution numerical solutions and experiments. The rest of this manuscript is organized as follows. After describing the overall computational model in Section 2, the results of the numerical simulations are presented and discussed in Section 3. Finally, in Section 4, some concluding remarks are drawn.
## 2 Materials and Methods
### SDC Module  
Google Maps Service was used to identify each station to implement the SDC module. Subsequently, Selenium WebDriver was employed to retrieve data related to congestion levels at each hour. Google Maps Service provides real-time congestion levels and compares them to the estimated daily average, theoretically known to the public urban transport provider. This allows the proposed algorithm to adjust the number of buses in real time based on the varying congestion levels.  
To implement the SDC module, the following steps were taken:  
1. Station identification with Google Maps Service. Each public urban transport station was identified using Google Maps Service for the analyzed route;
2. Data retrieval using Selenium WebDriver to scrape data related to congestion levels in each hour of the day from Google Maps Service. Selenium WebDriver's automated browsing capabilities allowed for systematic data collection;
3. Real-time congestion levels were computed to compare current level congestion against the estimated daily averages. The estimated daily average congestion levels are known to the public urban transport provider and serve as a baseline for comparison;
4. Algorithm adjustment based on real-time data. By comparing real-time congestion data with historical daily averages, the algorithm can make informed decisions about adjusting the frequency and deployment of public urban transport vehicles.  
The SDC calculates the congestion factor (\(SDCF\)) across the route using relation (1). Google Maps Service describes various levels of traffic congestion and conditions, including light, moderate, heavier-than-usual, heavy, peak hours, and standstill. All these statuses are included in relation (1).  
\[SDCF=\frac{\sum_{i=1}^{n}cf_{i}}{n}, \tag{1}\]where:  
_SDCF_ ranges between 1 and 5 and is a generic mathematical tool for measuring the congestion levels on a route;  
_n_--the total number of stations along a route;  
_cf_--congestion factor corresponding to station \(i\), using the correlation from Table 1.  
The congestion factor is assigned numeric values based on the observed traffic status from Google Maps Service, ranging from 1 for light traffic to 6 for standstill conditions. This relationship is further integrated into a generic decision-making formula.
## 3 Satellite Selection Algorithm
### Quasi-Optimal Satellite Selection Algorithm  
The quasi-optimal satellite selection algorithm (QOSA) calculates the geometric redundancy of each satellite with other satellites through the spatial position information of the visible satellites. It then gradually removes the satellites with the largest redundancy and updates the sequence of visible satellites until the number of remaining satellites reaches the required minimum number [25]. The core idea of the algorithm is to establish the satellite redundancy equation, quantify the redundancy, and approximate the optimal satellite combination with a small amount of computation. The redundancy between two satellites can be expressed as the following cost function:  
\[J_{ij}=\cos 2\theta_{ij} \tag{21}\]  
where \(\theta_{ij}\) is the angle between the line-of-sight vectors \(los_{i}\) and \(los_{j}\) of the two satellites \(i\) and \(j\) and the receiver. When the two satellites are co-linear with the receiver, i.e., when \(\theta_{ij}\) is \(0^{\circ}\) or \(180^{\circ}\), the cost function is maximum and the two satellites have the maximum redundancy, whereas when the line-of-sight vectors of the two satellites are perpendicular, i.e., when \(\theta_{ij}\) is \(90^{\circ}\), the cost function is minimum. In order to select the satellite with maximum redundancy from multiple satellites, the cost function of a single satellite with respect to all the satellites should be established. The cost function of a single satellite i can be expressed as  
\[J_{i}=\sum_{j}\cos 2\theta_{ij} \tag{22}\]  
The process of the quasi-optimal satellite selection algorithm can be roughly described as follows: first, calculate the cost function \(J_{i}\) for each satellite, and then eliminate the satellites with the largest cost function; if the number of satellites is larger than the desired value, repeat the process until the number of satellites reaches the selection requirement.  
As explained at the end of the previous section, generally, the smaller the satellite navigation positioning error is, the smaller the GDOP value will be, and the more dispersed the satellite layout in space is. Due to the quasi-optimal satellite selection algorithm's practice of eliminating satellites with maximum redundancy, the final satellite selection result will tend to disperse the satellite layout in space. Although this algorithm is similar to a greedy algorithm, each round of the satellite selection algorithm only considers the current optimal solution and cannot guarantee that the final result is the global optimal solution, but compared with the large amount of computation that the optimal satellite selection algorithm needs to calculate the GDOP \(C_{m}^{k}=\frac{m!}{k!(m-k)!}\) times [26], the quasi-optimal satellite selection algorithm is more suitable for engineering applications.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 4 Sensitivity Analysis of Smoothing Probability
### Transition Parameter Variation
#### 4.1.1 Algorithm Implementation  
In this algorithm, \(e\) is the sequence of observation and \(\theta=\theta_{a}\). It solves the recursive backward probability sensitivity function Equation (10) using the backward matrices \(B^{k}\) for \(k=T,\cdots,t\). Once the transition matrix is decomposed into the transition matrices \(\tilde{A}\) independent of \(\theta_{a}\) and \(\widehat{A}\) dependent on \(\theta_{a}\), the coefficients of the sensitivity function are computed by filling the backward matrices \(B^{k}\) as follows. First, \(B^{T}\) is initialized as a vector of ones for \(n\) hidden states.  
\[B^{T}=\textit{ones}(n,1)\]  
The remaining matrices \(B^{k}\) of size \(n\times k,k=T-1\leq k\leq t\) are computed by filling them as follows: Once the observation matrix \(O^{k+1}\) is represented as a diagonal matrix, two temporary matrices \(\textit{Btmp}_{1}\) and \(\textit{Btmp}_{2}\) are used for computing the coefficients as previously explained. \(\textit{Btmp}_{1}\) is computedas the matrix multiplication of \(\widehat{A}\) that is independent of \(\theta_{a}\), \(O^{k+1}\) and \(B^{k+1}\) and a zero vector of size \(n\) is appended at its end. \(B^{k+1}\) represents the recursion in the backward probability. The same way \(Btmp_{2}\) is computed as the matrix multiplication of \(\widehat{A}\) that is dependent on \(\theta_{a}\), \(O^{k+1}\) and \(B^{k+1}\) and a zero vector of size \(n\) is appended to the front. Then \(B^{k}\) is set as the sum of \(Btmp_{1}\) and \(Btmp_{2}\),  
\[B^{k}=Btmp_{1}+Btmp_{2}\]
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 3 Nutritional and Biochemical Composition of Date Fruit and Date By-Products
### Bioactive Compounds--Phenolic Compounds and Antioxidant Activity  
The date fruit is a rich source of phenolic compounds that possess biological activity due to the presence of a benzene hydroxyl ring with a carboxyl group [58]. The date fruit contains a range of phenolic compounds, including phenolic acids, flavonoids, and high-molecular-weight compact polymers [3; 36]. There are four primary categories of phenolic compounds found in plants, namely phenolic acids, flavonoids, stilbene, and lignin. They can be categorized based on their dissimilarities in structure, the number of phenol rings, and the type of molecular linkage [59]. The polyphenols exhibit their antioxidant properties by eliminating free radicals and reactive oxygen species, thereby inhibiting the oxidative process, which may help prevent diseases associated with oxidative stress, and improving glycemic control, lowering blood pressure, and improving lipid profiles [27]. Phenolic acids and flavonoids are the main polyphenols present in the fruit pulp and seeds, and due to the higher concentration of phenolic compounds in the seeds, the antioxidant capacity of the seeds is often greater than that of the pulp. [PERSON] et al. [60] analyzed polyphenols in Khalas date seeds in three human-consumable forms: date seed powder, date seed pits bread, and date seed extract and identified 27, 29, and 15 compounds via HPLC-ESI-UV/MS/MS and confirmed by UPLC-QTOF MS/MS, respectively. These compounds included hydroxylenamic acids, hydroxybenzoic acids, hydroxyphenyl acetic acid, flavanols, flavonoids, and flavones. Flavan-3-ols were the most abundant polyphenols. The study revealed that polymeric proanthocyanidins are present in date seeds and can potentially undergo gut microbial metabolism. During the process of in vitro digestion, there was an increase in the number of polyphenols present in the intestinal phase, indicating a favorable level of bioavailability. Several key phenolic acids and flavones were actively transported across Caco-2 monolayers, suggesting significant bioavailability from date seeds, while some procyanidins remained undetected in transport, possibly due to their binding to carbohydrates and protein precipitation properties. Several studies have evaluated the antioxidant activity of date palm, and some of these studies are listed in Table 2. However, it is worth noting that the content of phenolic compounds in dates can differ based on the variety of dates.