esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 3 LoRa Technology Overview
### LoRaWAN
#### 3.2.2 Regulation  
LoRa operates in the band without an ISM license that is available worldwide. In Europe, the frequency bands licensed for this use are the EU433 (433.05-434.79 MHz) and the EU863-870 (863-870 MHz) bands. In the USA, it is the US902-928 (902-928 MHz) band. This band has the main advantage of being free to use without a license but, due to its great use, there is a lot of interference and, the transmission rate is low. These bands are regulated by different organizations. In Europe, it is regulated by the European Telecommunications Institute (ETSI). In the USA, by the Federal Communications Commission (FCC). Most countries copy the rules standardized by these agencies except for some countries such as Japan and South Korea that have their own agencies. In Spain, the Secretary of State for Telecommunications and the Information Society is responsible for applying the European standards set by the ETSI. Some of these rules are:  
* Using the EU863-870 frequency band. * For the uplink, the maximum transmission power allowed is 25 mW (14 dBm). * For the downlink (at 869.525 MHz), the maximum transmission power is limited to 0.5 W (27 dBm). * The duty cycle is 0.1% and 1%, depending on the channel. * The maximum antenna gain is set to +2.5 dBi. The duty cycle is a measure to reduce collisions in the band. LoRa bases its standard on a duty cycle of 1% in Europe [31]. This means that both end devices and gateways can only transmit 1% of the time (e.g., if there is a 500 ms ToA, a message can be sent again in \(99\times 500=49\),500 ms, 49.5 s). Figure 4 shows the value of Time on the Air (ToA) in ms for a bandwidth of 125 kHz, at the 863-870 MHz frequency band, using different payloads. It is worth mentioning that service operators may also have their restrictions of use. Figure 4: ToA (in ms) for different SF using a bandwidth of 125 kHz. To compare LoRa with the rest of the IoT technologies, a comparison with the protocols Wifi [32], ZigBee [32], SigFox [33] and NB-IoT [33] is performed and showed in Table 1.  
Some IoT applications [34], such as the one presented in this paper, have specific requirements such as long-range, low bit rate, and low power consumption. Solutions based on short-range technologies (e.g., Zigbee) or personal areas are not adequate for these types of long-range environments, which is why the Zigbee and Wifi standards are discarded for our purpose. If we focus on the LPWAN standards that do meet these requirements, we see that NB-IoT depends either on already deployed 3 GPP networks or on its high-cost network deployment. It also has a more limited scope than SigFox and LoRa. As for technical specifications, SigFox and LoRa are similar. However, LoRa has the advantage of having an open protocol and, it allows the deployment of its own low-cost network unlike SigFox, in which we depend on the coverage offered by the operator. This is why LoRa was chosen as the protocol to develop the project. Figure 5 shows a comparison of the main features of the most used wireless technologies [32; 33; 35; 36; 37].
## 3 Methodology
### Network Integration  
The DOA estimation model proposed in this study consists of the SAM adaptive filter, and LSTM and CNN networks. Specifically, SAM is used to adaptively filter the frequency components of colored noise, which can be seen as a preprocessing module of the signal. Then, LSTM and CNN construct a double-branch model to learn the spatial and temporal features, and a fully connected module is applied to fuse the output of LSTM and CNN. Finally, the DOA results are output by softmax. The key benefits of this model encompass the following: (1) The SAM filter module can adaptively learn features of colored noise, and filter unimportant frequency components, which improves the noise suppression ability of the model. (2) Acoustic vector arrays capture data with temporal attributes, and by leveraging LSTM, which excels in processing time series data, the accuracy of the DOA estimation model can be bolstered. (3) The input for CNN consists of the covariance matrix of the received signal, with the data dimension solely determined by the number of array elements, enabling DOA estimation irrespective of the snapshot count. (4) Integrating LSTM with CNN and concurrently extracting both temporal and spatial information from the signal data can further improve the performance of the model. The overall framework diagram of the model is shown in Figure 6.
## 3 Candidate Generation in Parallel Decoding of Rate-1 Nodes  
In this section, we show that it is possible to exclude paths in Rate-1 nodes of Fast-SSCL decoders. For that purpose, we incorporate the order of the list of incoming candidates and the indices of the least reliable LLRs, i.e., the positions where bit flips are carried out. Therefore, we exploit the relations between the \(L_{\text{out,p}}\) possible paths. In contrast to [11], we consider both the information about the order of the input paths and the order of sorted LLR magnitudes simultaneously and in relation to each other. We reduce the number of considered candidate paths to decrease the computational complexity of the Rate-1 node itself and the subsequent sorter unit. The theorems and their proofs in Section 3.1 are based on considerations about the relations between the PMs of candidates with different numbers of flipped bits. In Section 3.2, the findings of these considerations are combined with the knowledge about the indices of the bits that have to be flipped, to describe a Partial Order (PO) of the candidates. This PO is used to generate only the candidate paths that need to be considered in Rate-1 nodes to preserve error-correction performance of conventional SCL decoding. Furthermore, threshold-based path pruning of [20] is combined with PO-based candidate generation in Section 3.3 denoted by PO with threshold \(S\) (POS) candidate generation. This threshold is extended in Section 3.4 resulting in Extended POS (ExPOS) candidate generation to further reduce the number of generated candidates and enable more efficient implementations with negligible error-correction performance loss.  
Throughout this section, we often use the wording \"path \(a\)\" or \"candidate \(b\)\" to abbreviate the phrases \"the path with index \(a\)\" or \"the candidate with index \(b\)\".  
We use \(j\in[0,2^{P}-1]\) to index all \(2^{P}\) possible candidates originating from input path \(l\). The \(P\) bits of the binary expansion of \(j\) are used inside the _Candidate Generator_ (Figure 2) to construct the flipping patterns for the generation of the candidate partial sums \(\beta^{l,j}\). Each 1 in the binary expansion of \(j\) stands for a flipped bit position. Specifically, a less significant 1 in the binary expansion of \(j\) is mapped to a less reliable bit location in \(\boldsymbol{a}^{v,l}\) known from the result of the _LLR Sorter_ (Figure 2). Thus, \(j\) indicates the positions of flipped bits in \(\beta^{l,j}\). The Hamming weight \(\text{hw}(j)\), defined as the number of ones in the binary expansion of \(j\), gives us the number of flipped bits in candidate \(j\).
## 4 Experimental Result
### Quantitative Analysis
#### 4.3.2 Token-Level Evaluation  
ShARe/CLEF: The ShARe/CLEF dataset consists of 299 de-identified clinical free-text notes derived from the MIMIC-II database [25]. The task assigned to the models is identifying entity names within the text that map to specific UMLS (Unified Medical Language System) codes. This task is designed to evaluate the model's capacity to capture the meaning of each word in a sentence. The F1 score was used to compare the baseline and proposed models' entity extraction performance.  
i2b2-2010: This dataset focuses on shared NLP tasks designed to improve language understanding within the clinical domain [26]. The task requires models to predict relationships between medical problems, tests, and treatments within sentences. Similar to the ShARe/CLEF evaluation, the F1 score was used to measure and compare the performance of baseline and proposed models with extraction tasks.
# Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities
## 2 Significance of Energy Efficiency in IoT Use Cases
### Case Study: Smart Home  
IoT is a technological phenomenon that connects the physical and the computing worlds, while enabling ubiquitous communication at any time using various services for smart home applications. For domestic purposes such as heating control, security, lighting, etc., the smart home concept provides efficient management mechanisms, with increasing usage of smart devices. The smart home control system based on Coordinator-based ZigBee networking (CoZNET) mitigates the interference effect and minimizes the energy consumption of domestic appliances [13]. Moreover the workings of a smart home system based on CoZNET is divided into three sections, namely Smart Interference Control System (SICS), Smart Energy Control System (SECS) and Smart Management Control System (SMCS). SICS enables the smoother communication with lesser packet loss, while mitigating interference effects using multi-attribute decision making-based channel assignment mechanism in the entire smart home. SECS further reduces energy consumption in the smart home, with the usage of a light control system and a household appliance control system, then a number of facilities are provided to users for exploiting the control of the entire smart home system, while implementing a fundamental unit called SMCS.  
Figure 2 shows the smart home scenario including various domestic appliances such as an air conditioner, refrigerator, dishwasher, washing machine, lights, television, etc. Among these appliances, some appliances consume more electricity, namely the refrigerator, air conditioner, etc. and some consume less electricity such as the lights, television, etc. Moreover electricity consumption for a single appliance also varies based on the execution cycle phase considered. For example, in a washing machine, the electricity consumption of a drying cycle is more than the electricity consumption of a washing cycle. Therefore it is challenging to manage the usage of these domestic appliances in a smart home system efficiently, while minimizing the electricity consumption. Therefore, the issue of energy efficiency plays a significant role to make a smart home system efficient.
# Molecular Basis of Irisin Regulating the Effects of Exercise on Insulin Resistance
## 1 Introduction  
Insulin resistance (IR) is recognized as a hallmark of metabolic dysfunction, and many individuals develop cell dysfunction and type 2 diabetes mellitus (T2 DM) eventually [1]. T2 DM has become more common in developed and developing countries over the last few decades, now affecting about 8% of the world's population [2]. It is clear that T2 DM is triggered by a combination of IR in the adipose, liver, and skeletal muscle [3], involving a variety of risk factors, including hyperlipidemia, obesity, inflammation, and physical inactivity [1].  
An important goal in preventive strategies for T2 DM is to reverse those adverse factors and then improve IR. Among the intervention regimens, regular exercise can increase energy consumption, muscle mass, and blood circulation between tissues, and reduce fat mass and inflammations, which ultimately lead to improvement of insulin sensitivity [4; 5; 6]. However, understanding the mechanism that mediates the influence of exercise on IR has been challenging.  
Skeletal muscle has already been recognized as a secretory tissue for a long time [7]. Exercise stimulates the skeletal muscle to produce a large number of myokines [8], such as myostatin, fibroblast growth factor-2, L-Lactate, kynureine, interleukin-6 (IL-6), etc.; they either exert autocrine and paracrine effects or enter the circulation and act as endocranial regulators affecting the physiological function of organs and tissues [9; 10; 11; 12]. As a newly identified myokine, irisin is provoked by exercise, mainly secreted from muscle, andsecondarily secreted by adipose and other tissues in small amounts, to regulate energy consumption [10; 13; 14]. Given the close relationship between irisin and exercise and its essential role in regulating energy metabolism, irisin is expected to become a potential candidate for modulating the benefits of exercise on insulin action. This review aimed to best our understandings of the signaling pathways that regulate the interaction between exercise, irisin, and insulin action, seeking potential strategies for T2 DM prevention.
## 3 Platform Design Criteria
### Sensor Node Design Criteria
#### 3.1.1 Sensor Node Energy Supply  
Energy supply can be an important cost of the sensor nodes. Its role is to provide energy for node operation between maintenance (e.g., for battery replacement or node cleaning), with the continuity necessary for the expected node quality of service (QoS).  
An important decision is whether to use a regenerative supply. Energy harvesting can be effective when ambient energy is available, such as vibration, fluid flow, temperature differences and light [75]. Although their cost and QoS are difficult to estimate in the general case, combined energy harvesting can increase the availability [76]. These supplies may add to node or platform cost, for instance by increasing the physical dimensions of the node, by additional casing requirements (e.g., a waterproof transparent window for light harvesting), by additional restrictions for field placement for effective ambient energy harvesting or by increasing the complexity of the deployment procedure.  
Primary batteries, on the other hand, store all of the energy needed for the entire node lifespan or the period between scheduled maintenance. The minimum battery capacity can be obtained by multiplying the node average energy consumption and the expected lifespan. Battery cost is roughly proportional to its capacity, but the scheduled maintenance for replacement of depleted batteries can be a major contributor to the overall solution cost.
# Discrete-Time Sliding Mode Filter with Adaptive Gain
## 2 Overview of Related Works
### First-Order Adaptive Windowing  
In a paper [23], [PERSON] et al. proposed a discrete-time differentiator, of which the full name is \"first-order adaptive windowing (FOAW)\". The basic principle of FOAW is that the window size is chosen as small for a rapid motion to instantly reflect fast varying signals, while it is chosen as large for a slow motion to obtain a smooth signal. Specifically, the complete algorithm of FOAW is as follows:  
```
1:FOR \(n\in\{1,\cdots,\max(k+1,N_{\max})\}\)
2:\(v:=\left(u(k)-u(k-n)\right)/\left(nT\right)\)
3:FOR \(i\in\{1,\cdots,n\}\)
4:\(e:=u(k-i)-u(k)+iTv\)
5:IF \(|e|>R\) RETURN\(x_{2}(k):=v\)
6:END FOR
7:END FOR
8:RETURN\(x_{2}(k):=v\),
```  
**Algorithm 2** FOAW  
**Algorithm 3** FOAW  
**In FOAW, the current and previous input values are used for windowing, as shown in Figure 4. Specifically, the algorithm checks whether all of the points \(\{u(k),u(k-1),\ldots,u(k-n-1),u(k-n)\}\) are inside the region constructed by the two end points \(u(k)\) and \(u(k-n)\), and the parameter \(R\), as shown in Figure 5. If it is the case, the window size \(n\) is further increased for providing a smoother signal. The increase of \(n\) is continued until at least one point of previous input values lies outside the region, as also illustrated in Figure 5, or \(n\) reaches its maximum value \(N_{\max}\). Then, the last value of \(v\) obtained by the finite-time differentiation is provided as the output \(x_{2}(k)\). In such a way, FOAW optimizes the tradeoff between the output smoothness and the suppression of delay.**  
Figure 4: Block diagram of first-order adaptive windowing (FOAW).  
Figure 5: Windowing in FOAW.
# A Scalable Spatial-Temporal Correlated Non-Stationary Channel Fading Generation Method
## 4 Measurement Results and Analysis
### Hardware Resource Consumption  
To validate the effectiveness of the spatially correlated fading generation method in resource conservation, we implemented a matrix computation module and function computation module on the FPGA chip XC7 VX690T using both time-division multiplexing and the improved CORDIC scheme. Additionally, we implemented the same modules based on parallel processing and the LUT approach. The hardware resource utilization for both sets of approaches is presented in Table 2. From the table, it can be observed that the resource utilization based on our proposed method is 4.98%, which is lower than the 11.85% when combining parallel processing with the LUT approach. Our proposed method utilizes a complex multiplication optimization algorithm to reduce the number of multipliers, resulting in a decrease in DSP resource utilization from 12.81% to 1.58%. Furthermore, the improved CORDIC algorithm saves memory resource usage, reducing block RAM resource utilization from 13.33% to 4.55%. The discussion above confirms the effectiveness of the spatially correlated fading generation method in conserving hardware resources.
## 4 Model and Notations  
Job characteristics are modeled as follows:  
Let \(N\) be the number of jobs to be scheduled. Each job \(i(i=1\ldots N)\) is composed of a set of operations \(J_{i}\) that must be processed according the defined processing route. Let M be the number of all available material resources \"machines\". For each operation \(j\), let \(m_{j}\subset M\) be the set of operations that can perform \(j\in\mathrm{ji}\) and \(p_{ik}\) be the corresponding processing times.  
To transition from executing operation \(\mathrm{O}_{ij}\) to operation \(\mathrm{O}_{ij^{\prime}}\) on machine k, a setup time \(\mathrm{st}_{\mathrm{ijij}\times k}\) must be incurred. In our problem, the setup time for a job is dependent on the previous job that was processed on the same machine and thus on the job processing sequence. For each machine \(K(k=1\ldots m)\), let \(l_{k}\) be the number of unavailability periodsand \(\left[v_{k}^{l},v_{k}^{l}\right]\) the time window of unavailability of material resource \(k\in m\). The processing of each job on the latter can only be preempted by this interval \(\left[v_{k}^{l},v_{k}^{-l}\right]\) and resumed once the machine becomes available. Let \(d_{i}\) denote the due date and specify the time limit by which job \(i\in N\) should be completed. The number of jobs, their respective processing times, and due dates are predetermined and known beforehand. Each machine has a capacity and can only process one job at a time. A machine can only process one operation at a time. The processing of the latter can be interrupted by an unavailability period. Setups cannot be interrupted by an unavailability period and should occur when the machine is available during the setup interval, and, once completed, the processing of the associated operation should start. There is no limit on the capacity of the intermediate stock (buffer) between the production stages. Finally, the objective is to minimize the total tardiness.  
The notation used in this mathematical modelling is summarized in Tables 2 and 3:
## 4 Shear Strength Prediction Using Neural Hybrids of SFLA-ANN and WDO-ANN  
### Data Division and Preprocessing  
Like other simulation methods, the development of intelligent models requires two sets of data, namely, training and testing samples [47]. The majority of data are specified to pattern analysis and training the models. The efficiency of the learning process is then evaluated by applying the network on unknown data (i.e., the testing samples). In other words, in this phase, the network predicts the shear strength for unseen soil conditions. The quality of testing results indicates the generalization capability of the model [48; 49; 50].  
As mentioned, the dataset consisted of the information of 315 soil specimens. Out of those, 252 samples (i.e., 80% of the data) were selected for training the ANN, SFLA-ANN, and WDO-ANN models. Accordingly, the remaining 63 samples (i.e., 20% of the data) were devoted to the testing process. It is worth noting that the division of data was done based on a random selection process.  
Figure 4: The relationship between shear strength and the input variables; (**a**): depth of sample, (**b**): percentage of sand, (**c**): percentage of silt, (**d**): percentage of clay, (**e**): percentage of moisture content, (**f**): wet density, (**g**): liquid limit, (**h**): plastic limit, (**i**): plastic index, and (**j**): liquidity index.
## 3 Throughput Performance Limits of Multimode Underwater Acoustic Networks  
In Section 2.2, the communication capability of each link is assumed to be equal, resulting in a normalized throughput upper limit of the network packet of N/2. However, these conclusions may be problematic when considering the varying data transmission capabilities between different network links. For instance, in the scheduling result of an isosceles triangle network described in Section 2.2, where an MFSK communication system is employed at the physical layer with a communication rate of 800 bps, the upper limit of network throughput reaches 1200 bps. However, because the distance between the nodes \(l_{3}\) is relatively close and the propagation loss is small, it can adopt a high-rate communication mode, such as PSK, whose communication rate can reach 1800 bps. The propagation loss \(l_{1}\) and \(l_{2}\) is large, and the MFSK with a low communication rate can still be used to transmit data. At this time, the optimal scheduling result of the three-node network is given according to Formula (5), and its throughput limit is 1/2 \(\times\) 00 + 1/2 \(\times\) 800 + 1/2 \(\times\) 1800 = 1700 bps. However, if only \(l_{3}\) (node 1 and node 2) is used to transmit a PSK signal, its network throughput is 2/2 \(\times\) 1800 = 1800 bps, which is greater than the maximum throughput of 1700 bps for the three-node network. For multimode networks, the traditional throughput upper limit analysis results have problems and are no longer applicable.  
The [PERSON] decision process [23; 24; 25] can be described by a quintuple: \((S,A,P,R,\gamma)\). The specific meanings are shown in Table 1.  
The policy \(\pi\) is a mapping function from state to action \(\pi\): \(S\to A\). That is, given the current state \(s\), the next action \(a=\pi(s)\) is determined according to the policy \(\pi\). For every fixed strategy \(\pi\), the value function \(V_{\pi}(s)\) satisfies the Bellman equation:  
\[\begin{split} V_{\pi}(s)&=E\big{[}R_{t+1}+\gamma R_ {t+2}+\gamma^{2}R_{t+3}+\ldots\big{|}s_{t}=s\big{]}\\ V_{\pi}(s)&=\sum\limits_{a}\pi(a|s)\bigg{(}R(s,a)+ \gamma\sum\limits_{s^{\prime}\in S}P_{s\pi(s)}(s^{\prime})V_{\pi}(s^{\prime}) \bigg{)}\end{split} \tag{10}\]  
The action value function can be defined considering the value effect of the action \(a\) taken  
\[\begin{split} q_{\pi}(s,a)&=E\big{[}R_{t+1}+\gamma R _{t+2}+\gamma^{2}R_{t+3}+\ldots\big{|}s_{0}=s,A_{t}=a\big{]}\\ q_{\pi}(s,a)&=R(s)+\gamma\sum\limits_{s^{\prime} \in S}P_{s\pi(s)}(s^{\prime})q_{\pi}(s^{\prime},a^{\prime})\end{split} \tag{11}\]  
The purpose of solving the Markov decision process is to find an optimal action strategy \(\pi\) in the current state \(s\), and the corresponding value function of the optimal action strategy is the largest.  
In this paper, the throughput performance limit problem of multimode networks is modeled as a Markov decision process, and the greedy algorithm and dynamic programming algorithm are combined to iteratively solve the network throughput and optimal scheduling results. The following is a detailed introduction.
## 3 Experiments and Results
### Experimental Results
#### 3.2.1 Cooling Effect Produced by Fuel Cells
Fuel stick temperature drop test 1—captured by thermal imaging device 1. Figure 9: (**a**) Temperature of fuel stick before connecting to the fuel cell stack. ; (**b**) temperature of fuel stick after connection to the fuel cell stack for an extended period (\(\sim\)20 min), with an initial sudden release of hydrogen from the fuel stick. Fuel stick temperature drop test 2—captured by thermal imaging device 2.